Copyright © 2019 Long Yin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
While achieving desired performance, there exist still many challenges in current cellular networks to support the multimedia content dissemination services. The conventional multimedia transmission schemes tend to serve all multicast group members with the data rate supported by the receiving user with the worst channel condition. The recent work discusses how to provide satisfactory quality of service (QoS) for all receiving users with different quality of experience (QoE) requirements, but the energy efficiency improvement of multimedia content dissemination is not its focus. In this paper, we address it based on adaptive clustering and device-to-device (D2D) multicast and propose an energy-efficient multimedia content dissemination scheme under a consistent QoE constraint. Our scheme extends the recent work with the proposed K-means-based D2D clustering method and the proposed game-based incentive mechanism, which can improve energy efficiency of multimedia content dissemination on the premise of ensuring the desired QoE for most multicast group members. In the proposed scheme, we jointly consider the cellular multicast, intracluster D2D multicast, and intercluster D2D multicast for designing the energy-efficient multimedia content dissemination scheme. In particular, we formulate the energy-efficient multicast transmission problem as a Stackelberg game model, where the macro base station (MBS) is the leader and the candidate D2D cluster heads (DCHs) are the followers. Also, the MBS acts as the buyer who buys the power from the candidate DCHs for intracluster and intercluster D2D multicast communications, and the candidate DCHs act as the sellers who earn reward by helping the MBS with D2D multicast communications. Through analyzing the above game model, we derive the Stackelberg equilibrium as the optimal allocation for cellular multicast power, intracluster D2D multicast power, and intercluster D2D multicast power, which can maximize the MBS's utility function. Finally, the proposed scheme is verified through the simulation experiments designed in this paper.
With the rapid evolution of mobile communication technologies and the proliferation of smart devices (e.g., phones, tablets, and laptops) and applications (e.g., WeChat, Facebook, and Dropbox), mobile users conveniently generate and disseminate various types of contents. It is noticeable that more than 70 percent of mobile data traffic will come from videos in the near future according to the prediction in .
In terms of efficiently disseminating the same multimedia content to multiple terminal users, the multicast is an effective way since it can optimize network resource utilization. The multimedia broadcast multicast service (MBMS) is incorporated into Long-Term Evolution (LTE) advanced networks by the 3rd Generation Partnership Project (3GPP) . Subsequently, 3GPP Release 9 issues the MBMS standard on LTE, which is called evolved MBMS (eMBMS) that is a key component in coping with the rapid growth in mobile multimedia traffic.
In the eMBMS network architecture, there are some standardized entities which support the dissemination of multimedia content, for example, a broadcast multicast service center (BMSC), a MBMS gateway (MBMSGW), multiple e-UTRAN NodeBs (eNodeBs), a mobility management entity (MME), and a multicell/multicast coordination entity (MCE). As described in , MME and MCE are control entities, where the former is involved in authentication and authorization for users, MBMS session control signaling, and mobility management, while the latter is responsible for both setting up MBMS radio bearers and coordinating the usage of same resources and transmission parameters across cells.
The requested multimedia content is firstly received by the BMSC from the content provider and then is provided to the MBMSGW attached to it. The MBMSGW is responsible for delivering this multimedia content to different eNodeBs. Finally, by wireless multicast transmission, the requested multimedia content is disseminated by eNodeBs to the associated mobile users that have subscribed for it.
However, as stated in the literature , the current multicast networks lack flexibility, openness, adaptability, and scalability. Therefore, to meet user plurality of multicast services, Bukhari et al.  explored how to design a new multicast architecture for the mobile packet core network. Furthermore, the wireless transmission power consumption of multimedia content dissemination is a cost that content providers cannot ignore. Even small transmission power savings are significant for content providers due to the huge capacity of multimedia content.
It is generally accepted that the ultradense network (UDN) can make each user enjoy a very high data rate with good quality of service (QoS) [4, 5]. This is because that the UDN is formed by deploying many small base stations within the traditional macro cell coverage area. The smaller cell size leads to the higher data rate, lower power consumption, and lower delay due to the closer distance between any wireless multicast receiver and its associated eNodeB. Clearly, this benefit depends on the deployment of more small base stations, and it in turn increases the deployment and maintenance costs of operators .
According to the Cisco Visual Networking Index (Cisco VNI) , Internet peak traffic grows faster than average traffic, where the former will increase by a factor of 4.6 from 2016 to 2021, while the latter will increase by a factor of 3.2. Since the gap appears to be a growing trend, mobile network operators are reluctant to ultradensely deploy small base stations or wireless access points in an everything, anytime, and anywhere mode, especially when they predict that the average utilization rate of equipment is very low.
In places such as villages and suburbs, even including some public areas (e.g., urban leisure square, sports venues, and expo centers), small base stations or wireless access points are rarely deployed in an ultradense mode, where a large crowd may be formed due to some spontaneous events (e.g., rural folk sports events, festival celebration activities, and large-scale commodity fairs). As a result, there exists a temporary weak cellular multicast coverage area, where the conventional multimedia transmission schemes will face some challenges.
For example, the conventional multimedia transmission schemes tend to serve all multicast group members with the data rate supported by the receiving user with the worst channel condition. Therefore, for a particular multicast service, a larger difference between channel conditions of multicast receivers will pay more unnecessary power consumption of multicast transmission.
The recent work  wants to guarantee satisfactory QoS for all receiving users with different quality of experience (QoE) requirements. However, it focuses on how to provide reliable multicast services with minimal network resources. In some cases, this goal is achieved at the expense of very low energy efficiency. For example, for some multicast group members at the cell edge area that can only receive multicast content in non-line-of-sight links, they will degrade the energy efficiency of the entire multicast group. Especially in the temporary weak cellular multicast coverage area mentioned above, it is more likely to occur.
In view of the generally accepted views, clustering is an effective way to strengthen coverage density, while device-to-device (D2D) communication has a particular advantage of improving coverage quality at the cell edge area. Therefore, in this paper, we address the above challenges based on adaptive clustering and D2D multicast, and the main contributions are as follows:(1)We propose an energy-efficient multimedia content dissemination scheme under a consistent QoE. Compared to the recent work, by the proposed K-means-based D2D clustering method and the proposed game-based incentive mechanism, our scheme can improve energy efficiency of multimedia content dissemination on the premise of ensuring the desired QoE for most multicast group members.(2)In the proposed scheme, we jointly consider the cellular multicast, intracluster D2D multicast, and intercluster D2D multicast for designing the energy-efficient multimedia content dissemination scheme, which provides a flexible approach for any content provider to find an appropriate way to disseminate a given multimedia content at the most reasonable energy cost.(3)In particular, we formulate the energy-efficient multicast transmission problem as a Stackelberg game model, where the macro base station (MBS) is the leader and the candidate D2D cluster heads (DCHs) are the followers. Also, the MBS acts as the buyer who buys the power from the candidate DCHs for intracluster and intercluster D2D multicast communications, and the candidate DCHs act as the sellers who earn reward by helping the MBS with D2D multicast communications.(4)Through analyzing the proposed game model, we derive the Stackelberg equilibrium as the optimal allocation for cellular multicast power, intracluster D2D multicast power, and intercluster D2D multicast power, which can maximize the MBS's utility function.
We organize the remainder of this paper as follows: the related works are summarized in Section 2. Then, the system model and problem formulation are described in Sections 3 and 4, respectively. Moreover, the algorithm description for the formulated problem is given in Section 5. Finally, simulations are shown in Section 6, and conclusions are drawn in Section 7.
The multicast D2D communication overlaying/underlaying on cellular networks and the corresponding spatial modeling have been accomplished comprehensively. Zhou et al.  analyzed the relationship between the number of D2D relays and the minimal time-frequency resource on retransmissions and derived the optimal number of D2D relays for performing multicast retransmissions.
Trestian et al.  proposed an energy-efficient cluster-based multicast scheme for multimedia content dissemination in an LTE D2D network to improve the network performance (e.g., energy efficiency and battery lifetime). Wu et al.  investigated the energy-efficient uplink resource-sharing problem in the mobile multimedia D2D communications with multiple potential D2D pairs and cellular users, while Wang et al.  explored a low-complexity distributed game-theoretic source selection and power control scheme to improve the multimedia transmission quality with latency constraints.
For some multicast data dissemination services (e.g., local file transfer, local advertisement, and data sharing for group driving), Kim et al.  believed that the multicast over clustered D2D networks is a suitable technology for this communication scenario. Also, in the defined device-to-device cluster (D2DC) multicast, common multicast data are delivered from a cluster head to multiple devices through D2D underlaying cellular networks, where each cluster disseminates distinct data internally.
Zhan et al.  believed that D2D-based cooperation has many advantages of improving multimedia content dissemination services in a scenario such as offloading the video traffic of the base station, where the mobile users within proximity to each other form a content-sharing group, since they are interested in the same multimedia content at the same time.
In [15–19], the authors thought that the group-aware mobile social video streaming sharing problem has aroused great interest in the academic and industry community. For example, live sports content should be delivered to a group of supporters' devices at the same time. Again, a group of friends may need to play an online game at the same time, and thus, they need to receive the same multimedia content in terms of this game at the same time. If these users are in proximity to each other, they may use D2D connections (e.g., WiFi or Bluetooth) to get the mutual interested content in a cooperative or opportunistic way .
Yap et al.  thought that, by using multiple radio interfaces simultaneously for cooperative multimedia delivery, the limitation of a single radio interface on a smart terminal can be overcome. Le et al.  implemented the cooperative multimedia delivery system by leveraging cellular connection and D2D links simultaneously to effectively transfer videos.
Jameel et al.  suggested that multimedia content dissemination services (e.g., Google Chromecast, mobile gaming, live sports program, IP video to TV (IPTV), high-definition (HD) movies, and video conferencing) can be facilitated by forming a D2D multicast group within a cluster. Here, the main goal of the authors is to make a device with good Internet connectivity act as a hotspot to offload/cache data during peak hours and then rebroadcast them to other devices by using direct links.
As stated in , it is generally difficult for a multicast source to send multimedia content at the same rate, which is suitable for all multicast receivers, due to very different channel conditions for each receiver. Meanwhile, they also thought that this problem can be mitigated by allowing a receiver with good channel condition to rebroadcast the correctly received content to other receivers via D2D links.
Non-cooperative game theory is widely used in the design of radio resource allocation schemes. For example, Zhou et al.  focused on the trade-off of energy efficiency and spectral efficiency, while Guan et al.  modeled the interaction relationship between end users and edge servers in an edge computing environment as a non-cooperative game process. Also, the studies in [26–30] considered energy efficiency of the D2D communication mode coexisting with the cellular environment. In the above multicast content transmission schemes, most of them also took non-cooperative game theory as a powerful tool for ensuring stable multicast content dissemination.
However, some works considered cooperative game theory to solve the vehicular content dissemination problem. For example, Wang et al.  proposed a dynamic vehicular content-sharing scheme by employing a coalition formation game, while Zhou et al.  proposed a dependable content dissemination scheme by combining the vehicle trajectory prediction with the resource allocation based on a coalition formation game. Since these works focus on the vehicular content-sharing scenario, the same content (e.g., traffic congestion and road condition information) needs to be simultaneously disseminated to all target vehicular users in a timely manner. Therefore, the desired goal is to obtain a required content in a dependable way with possible minimum network delay.
Due to the fast-varying channel quality and vehicular network topology , it is a challenging task to achieve dependable vehicular content dissemination. Based on data analysis , vehicle trajectory prediction will be helpful to design a relatively stable content dissemination scheme. The enrichment of data collection approaches [35, 36] will lay a solid foundation for the practical application of the schemes similar to the one in Zhou et al.'s study .
As can be seen from the above overview, the existing multicast transmission schemes are mainly built from the perspective of final consumers of multimedia content dissemination services. Therefore, the most prior one is to ensure QoE of consumers, while the consideration of network resources required for multicast transmission follows it. In fact, this kind of approaches is not suitable for multimedia content providers. If the energy efficiency of multimedia content dissemination process can be greatly improved, multimedia content providers are even willing to sacrifice QoE of a small number of consumers to maximize their benefits.
Feng et al.  categorized content sharing into two cases (i.e., local-direct sharing and local-request sharing). In the former case, the shared content is stored on a local device and thus is able to be shared directly with other local devices. In the latter case, the shared content is usually stored on a remote server and thus is requested by a target receiver and then delivered from this remote server to all the target receivers in a content-sharing group. In this paper, we focus on the latter case. Moreover, from the perspective of multimedia content providers, we will explore how to minimize energy consumption as much as possible on the premise of satisfying QoE for most final consumers.
In this paper, we consider a multimedia content dissemination system with one multimedia content provider (MCP) and many multimedia content users (MCUs) or consumers, where the servers of the MCP are usually located in the cloud infrastructure and the MCUs are randomly distributed in a macro cell, as shown in Figure 1. In such a macro cell, a variety of small base stations may be deployed in some areas, but there may also be weak coverage areas. If an MCU is close to a small base station, it will be associated with this small base station through which the multimedia content is disseminated to it (i.e., a small cell user). Otherwise, the multimedia content is disseminated to it (i.e., a macro cell user) through the macro base station.
Figure 1: General heterogeneous cellular network architecture.
As mentioned above, when a base station offers a multicast service, it tends to ensure the QoE of MCU with worst channel condition in its coverage area. Usually, the larger coverage area will lead to the higher energy consumption for the corresponding base station. The introduction of clustering and D2D communication to assist the multicast service of the base station is beneficial to reduce the multicast transmission power of the base station, where the base station only needs to ensure the QoE of D2D cluster heads (DCHs) (especially those with the worst channel conditions) in its coverage area, and then these DCHs rebroadcast to the MCUs associated by them in a D2D communication mode.
Furthermore, when the D2D mode is applied to intercluster multicast transmission, the multicast transmission power of the base station will be further reduced. Especially in a large area with weak coverage, as shown in Figure 1, the effect of power reduction for the base station is more significant. As shown in Figure 2, under the control of the base station, K DCHs are selected from the MCUs and the corresponding K D2D multicast clusters are determined by the K-means-based clustering algorithm proposed in this paper, where an MCU is associated with a DCH based on the channel quality between them.
Figure 2: Coexistence scenario for cellular multicast, intracluster D2D multicast, and intercluster D2D multicast. (a) Hybrid multicast mode. (b) Intracluster D2D multicast. (c) Intercluster D2D multicast.
After the DCHs are determined, the base station only needs to ensure the DCHs' QoE (i.e., the receiving quality of the video stream), while each DCH will ensure its associated cluster members' QoE. Therefore, the number of objects served directly by the base station is reduced, so it has the ability to formulate more targeted service strategies for these specific service objects.
The base station may choose a policy to ensure all the DCHs' QoE. This goal may be achieved at a larger power cost, especially when the DCHs are distributed in a large area. As an alternative, the base station may take measures to ensure the QoE of the DCHs that are closer to it, and in turn, these DCHs ensure the QoE of the DCHs that are further away from the base station in a rebroadcasting mode, where the base station may save transmission power, while the DCHs performing the rebroadcasting operation will consume more energy.
Due to the selfishness and rationality of DCHs, the base station has to pay the DCHs for their rebroadcasting services in order to motivate them to provide continuous good services. That is, the power consumed by the DCHs to provide rebroadcasting services is ultimately compensated by the base station. Therefore, when a base station disseminates multimedia video streams, what is the best way to save power? This is exactly the topic of this paper.
It is necessary to expound the relevant basic theory before putting forward the concrete scheme. According to the Shannon capacity formula, when a DCH (e.g., i) directly receives the multimedia content from the base station, the data rate per unit of spectrum for DCH i is expressed as follows:where ri is the data rate per unit of spectrum for DCH i, while γi is the signal-to-interference-plus-noise ratio (SINR) for DCH i denoted as follows:where pc is the transmission power for the base station, is the channel attenuation coefficient of the link from the base station to DCH i, Ni is the noise power perceived by DCH i, and Fcell,i is the interference power perceived by DCH i over a cellular channel and is estimated by the following formula:where is the channel attenuation coefficient of the link from the cellular interfering source k to the interfered DCH i, pk is the transmission power of the interfering source k, and Icell,i is the set of cellular interfering sources of DCH i.
Similarly, for an intercluster multicast transmitter DCH i, if the receiving DCH j has the worst channel condition among all the intercluster multicast receivers, the cell transmission power actually adopted by the D2D transmitter DCH i should not be less than the result computed by the following equation:where is the minimum transmission power for the transmitter DCH i in an in-band D2D channel for intercluster multicast, which can meet the QoE of the specified multicast receiving group; is the channel attenuation coefficient of the link from DCH i to DCH j; Nj is the noise power perceived by DCH j; and Fin,j is the interference power perceived by DCH j over an in-band D2D channel, which is estimated by the following formula:where is the channel attenuation coefficient of the link from the in-band D2D interfering source k to the interfered DCH j, pk is the transmission power of the in-band D2D interfering source k, and Iin,j is the set of in-band D2D interfering sources of DCH j.
Also, for an intracluster multicast transmitter DCH i, if the MCU u has the worst channel condition among all the intracluster multicast receivers, the D2D transmission power actually adopted by the transmitter DCH i should not be less than the result computed by the following equation:where is the minimum transmission power for the transmitter DCH i in an out-band D2D channel for intracluster multicast, which can meet the QoE of the specified multicast receiving group; is the channel attenuation coefficient of the link from DCH i to MCU u; Nu is the noise power perceived by MCU u; and Fout,u is the interference power perceived by MCU u over an out-band D2D channel, which is estimated by the following formula:where is the channel attenuation coefficient of the link from the out-band D2D interfering source k to the interfered MCU u, pk is the transmission power of the out-band D2D interfering source k, and Iout,u is the set of out-band D2D interfering sources of MCU u.
Similar to the work in , we also suppose that the MCP has the information of all the MCUs, including the channel gain (or channel attenuation) information and the location information. The utility function of the MCP can be written as follows:where μmcp is the utility function of the MCP, and its physical meaning is the successfully delivered data rate per used power; η denotes the benefit coefficient which the MCP gives the base station and each DCH, where the base station and each DCH will actually benefit when η is more than 1; Nd is the set of DCHs to which the MCP expects to provide D2D multicast services, while is the number of members in Nd; Nt is the set of all MCUs that are expected to be served by the MCP, while is the number of members in Nt; φt is the ratio of the number of MCUs that are successfully served by the MCP to the number of MCUs that are expected to be served; and φout,k is the ratio of the number of MCUs that are successfully served by DCH k to the number of MCUs that are expected to be served in the cluster with DCH k as its cluster head. can be estimated by the following formula:where Nc is the set of all DCHs that are expected to be served by the MCP, while is the number of members in Nc, and φc is the ratio of the number of DCHs that are successfully served by the MCP to the number of DCHs that are expected to be served, which is expressed as follows:where is the set of all DCHs that are successfully served by the MCP, while is the number of members in . The following formula can be used to compute the value of φt:where is the set of all MCUs that are successfully served by the MCP, while is the number of members in . The following formula can be used to compute the value of φout,k:where is the set of all MCUs that are successfully served by DCH k, while is the number of members in , and is the set of all MCUs in the cluster with DCH k as its cluster head, while is the number of members in .
Our objective in this paper is to maximize the utility function of the MCP defined in (10) while satisfying the QoE constraints. Thus, the optimization problem can be formulated as follows:where pmin and pmax denote the minimal and maximal transmit power for the base station, respectively. The optimization problem in (15) is to find the best value of . Here, we firstly derive the optimal power by taking advantage of the Stackelberg game theory, where the game players are the MCP (i.e., the leader) and the DCHs (i.e., the followers). That is, the MCP will buy the transmission powers from the DCHs for the D2D multicast service and the DCHs are the sellers who earn reward by offering the transmission powers to the MCP for the purpose of enabling the D2D multicast service.
In order to maximize the utility function of the MCP, besides buying the multicast transmission service from the base station, the MCP may hire a set of DCHs to help transmit data both in an intracluster multicast manner and an intercluster multicast manner. Such a D2D multicast mode may have some benefits for the MCP, which are determined by the serving rate of the multimedia content dissemination system (i.e., the ratio of the number of MCUs that are successfully served by the MCP to the number of MCUs that are expected to be served). Here, we denote the gain as .
Also, besides paying the base station a multicast transmission service fee, the MCP should pay the DCHs a certain reward if it decides to use a set of DCHs to aid multicast content dissemination, which is denoted as . Thus, the utility of MCP for enabling the D2D multicast service can be shown in (10).
The objective of the MCP is to ensure all the DCHs' QoE in its serving area with the minimal power consumption, that is, for any given price η, in order to maximize its utility shown in (15), how much it should buy from the base station and also how much it should buy from the DCHs.
By selling power to the MCP, the utility function of the selected DCH k can be written as follows:where αk and βk are binary decision variables (i.e., their values are 0 or 1), in which αk determines whether the MCP pays the DCH k for intercluster D2D multicast transmission or not and βk determines whether the MCP pays the DCH k for intracluster D2D multicast transmission or not. That is, if αk or βk is 1, the MCP pays the DCH k.
As mentioned above, cellular multicast power only covers DCHs. Therefore, any DCH (e.g., k), which is covered by the base station but has the most distance to the base station, must send the same content again to its intracluster members, and thus, the parameter βk needs to be set to 1. If there is still any DCH uncovered by the base station, the DCH k should perform intercluster D2D multicast as long as any uncovered DCH is located in its intercluster D2D multicast coverage area, where the parameter αk needs to be set to 1.
From the previous sections, we can know that the MCP decides the price (i.e., the benefit coefficient η > 1) for buying the power, and then each DCH would set the amount as much as possible to increase its utility function. On the contrary, if the amount of the power from a DCH is too much, then the MCP would give up this DCH at the expense of increased base station transmission power. From formula (17), it is obvious that providing more power for D2D multicast services will bring more revenue to a DCH. However, once such a DCH is abandoned by the MCP, it will not get any benefit. Therefore, a DCH will not be greedy to increase investment. That is, there is a cap on the amount of power available for sale, since selling more power does not necessarily yield better returns.
For a given application, the MCP can assume that , , and are fixed. Also, each DCH (e.g., k) will report its two power values (e.g., and ) to the MCP. Therefore, in (10), the MCP can assume that it only changes the values of , , and . To simplify the expression, we approximately convert the expression for (i.e., formula (5)) as follows:where d is the effective coverage distance of the base station when it adopts as its transmission power and denotes the nonlinear increasing function with respect to d. Similarly, we approximately convert the expression for (i.e., formulas (11) and (12)) as follows:where denotes the linear decreasing function with respect to d. Also, we approximately convert the expression for φt (i.e., formula (13)) as follows:where denotes the linear increasing function with respect to d. Therefore, formula (10) can approximately be written as follows:where and . From (22), we can observe that μmcp is a continuous function with respect to d. Moreover, the first-order and second-order derivatives in terms of formula (22) can be written aswhere the difference between and is very small since they have the same order of magnitude. Also, since is approximated as a constant and is not a constant, we can make by letting d take the value that is big enough. Based on the above analysis, the numerator in (24) is positive, while the denominator is negative. Therefore, we have , which means that is a concave function with respect to d, and thus, there exists an optimal value in terms of d to maximize .
The value of (25) (or (26)) is always more than 0 when αk = 1 (or βk = 1), and thus, DCH k has an incentive to use its maximum transmission power to maximize its utility. However, as mentioned above, the value of αk (or βk) is determined by the MCP. If the MCP believes that any power value provided by DCH k is not conducive to the improvement of the D2D multicast service, it will set αk (or βk) as 0, and thus, DCH k will get nothing. Therefore, it is reasonable for DCH k to provide the power that would effectively help the MCP improve multicast services.
As mentioned before, there are inevitably some weak coverage areas in cellular networks, in which a sudden and unpredictable traffic demand is also possible to occur due to the unplanned appearance of the large number of mobile devices. When this happens, cellular spectrum resources will become scarcer in such weak coverage areas. Also, energy consumption increases sharply, since a large number of mobile devices away from access points require higher transmission power to ensure receiving quality. Especially for high-capacity multimedia content dissemination services, a long-distance transmission is a huge obstacle to the improvement of energy efficiency. Based on the idea of K-means algorithm, a new clustering algorithm is proposed as a possible solution, where the determination of K is critical for the improvement of energy efficiency.
On the one hand, if K is large enough, the number of clusters is large enough, and thus, it is beneficial to shorten the average transmission distance. On the other hand, if more clusters are formed, more cluster heads are needed to undertake the rebroadcast task and thus may offset the benefit in terms of energy efficiency from a short-distance transmission. Relatively accurate prediction of the K value may depend on big data analysis, while this paper only explores the impact of different K on energy efficiency. The details of the proposed K-means-based D2D clustering algorithm are shown in Algorithm 1.
Algorithm 1: The K-means-based D2D clustering algorithm.
The goal of Algorithm 1 is to lay a network foundation for the MCP to customize the transmission scheme for energy-efficient multimedia content dissemination, so it should be dutifully executed by the MCP. Also, the MCP can be relatively easy to obtain the input parameters (i.e., the number of clusters K, the number of served MCUs T, and the location information set of served MCUs Nt) of this algorithm. Through Algorithm 1, the position coordinates of K cluster heads and the association relation of T MCUs with K cluster heads can be easily obtained, which are stored in the DCH set and the assignment relation matrix of MCUs MTK, respectively.
Different from the classical K-means clustering metric, we set the channel gain (e.g., the channel gain from i to t) as a cluster metric so that each MCU is associated with a cluster head from which it gets the best transmission service.
Through the loop body in lines 2 to 10 of Algorithm 1, the desired coordinate points for K cluster heads will be obtained, but they do not necessarily correspond to the actual coordinates of MCUs. The execution of lines 11∼22 is needed for making up for this deficiency. A desired MCU acting as a DCH should both be closer to the desired coordinate point and have more energy reserve. To achieve a better trade-off between the two, for each desired coordinate point (for convenience, let us call it a reference point), we take this reference point as the center to form a circular region with x% of the clustering radius as its radius, where the MCU with the best given measurement value is selected as the D2D cluster head (i.e., DCH). If the parameter x is smaller, the deviation between DCH and the corresponding reference point is smaller. Also, the smaller parameter x leads to the smaller number of DCH candidates, and thus, it is less likely to get a desired DCH. On the contrary, if the parameter x is larger, the opposite case may occur. The given measurement value mentioned above can be estimated by the following formula:where τi, ei, and pi are the continuous multicast capability, initial energy reserve level, and multicast transmission power of the candidate DCH i, respectively. The value of pi should ensure that the MCU with the worst channel condition in the same cluster can obtain multicast services that meet the QoE threshold from the candidate DCH i.
After clustering, we need to determine the multicast transmission power of each DCH. In this paper, the WiFi channels in the 2.4 G frequency band are used by DCHs to perform intracluster D2D multicast transmission services, where there are only 3 non-overlapping channels, and thus, the cochannel is inevitable. Moreover, from formulas (8) and (9), we also see that the multicast transmission power of each DCH and those of its adjacent DCHs will affect each other. Therefore, we cannot directly solve it by simply using formulas (8) and (9).
For an individual DCH, it tends to minimize its cost due to its selfish and rational behavior. At the same time, it is inevitable to be affected by the behaviors of the other competitive individuals. Therefore, it is a sensible approach to design an iterative algorithm to solve this problem step by step. As described in Algorithm 2, firstly, each DCH (e.g., k) assumes that there is no cochannel interference (lines 2-3) and then obtains the multicast transmission power according to formula (8) (line 4), which should be of the minimum cost for meeting QoE for all the MCUs in the intracluster.
Algorithm 2: The iterative algorithm for intracluster D2D multicast transmission powers.
Then, the MCU k will notify the other MCHs of its cost (i.e., multicast transmission power) (lines 6-7), and also it receives the multicast transmission powers of the other MCHs (lines 12–14). After this, the MCU k will find that it should increase its cost to meet QoE for all the MCUs in the intracluster since the cochannel interference cannot be ignored.
Moreover, from formulas (8) and (9), we know that the cost function of each DCH is a nondecreasing function, where it will theoretically reach the upper limit that each DCH can pay, namely, the maximum transmission power. Therefore, a reasonable termination condition (line 5, where ε is a very small positive number) is used to avoid the cost of each DCH reaching its upper bound as much as possible.
Different from out-band D2D multicast communication in the intracluster, D2D multicast in the intercluster (or among the DCHs) will reuse the in-band frequency band. Therefore, the cochannel interference is relatively easy to control. For example, when the base station adopts a given downlink cellular channel for multicast transmission, any receiving DCH can execute multicast forward with the corresponding uplink cellular channel.
For the same multicast content dissemination, this pair of cellular channels will be interleaved on the relaying path from the base station to the final target DCH, which is not used by other multicast content distribution services, and thus, the cochannel interference will not occur. Therefore, in this paper, we will ignore the cochannel interference in formulas (5) and (6) to simplify the calculation process of cellular multicast power and intercluster D2D multicast power.
On the basis of this section, the MCP wants to search for a desired transmission scheme for multicast content dissemination. Firstly, the MCP offers a real-time power price to all the DCHs and then procures powers from the appropriate DCHs (e.g., the DCHs offering the best energy efficiency under the constraint of QoE) and builds the energy-efficient multicast content dissemination scheme.
Although each DCH wants to provide as much power as possible to earn more revenue, it must consider the payment costs that the MCP can bear to avoid being eliminated by the MCP. The Stackelberg game model  is applicable in the above scenario, in which the MCP acts as the leader and the DCHs act as the followers. Firstly, the MCP acts after considering the behaviors of the DCHs, and then the DCHs act in response to the MCP's action.
The description of pseudocode for the leader decision process is shown in Algorithm 3, where the goal is to determine the coverage which the base station's multicast power should reach and the coverage of multicast services that the DCH is responsible for. In Algorithm 3, the MCP will broadcast η, Nk, and MTK to all the DCHs after invoking Algorithm 1 to get the DCH set Nk and the assignment relation matrix of MCUs MTK (lines 1-2). Then, the distances from the base station to all the DCHs and the corresponding transmission powers are calculated and then stored to the set Dk which is initialized as empty in advance (lines 3–8), which will be used later.
Algorithm 3: The base station multicast power assignment and selection of D2D multicast relays.
After receiving the payment promised by the MCP, each DCH will return a willingly provided intracluster D2D multicast power and an intercluster D2D multicast power. Therefore, after receiving the feedbacks from all the DCHs, the MCP will start the process of searching the multicast transmission scheme with optimal energy efficiency (lines 9–26), where the base station adopts a search mode which gradually increases the coverage of cellular multicast transmission (lines 11–21). At each step of the search process, the MCP must figure out what it should pay the selected DCHs (lines 13–16), since they are willing to assist the base station in the multicast content dissemination. Also, to detect the effect of each step, the base station will broadcast the test packet at the multicast power determined by this step (line 17). After the feedback results are obtained, the utility value of the MCP is calculated according to formula (10). If the utility is improved, the result is for later use (line 20).
After the search process is completed, the MCP will determine the resulting DCHs based on the recorded results (lines 22–25). That is, the MCP will set the mark variable βk of each selected DCH (e.g., k) as 1 if it wants to pay for the intracluster D2D multicast service and then send to each selected DCH (line 23). Also, the MCP will set the mark variable αk of each selected DCH (e.g., k) as 1 if it wants to pay for the intercluster D2D multicast service and then send to each selected DCH (line 24).
The description of pseudocode for the follower decision process is shown in Algorithm 4, where the goal is to maximize multicast power while avoiding elimination by the MCP as much as possible. Therefore, after receiving η, Nk, and MTK from the MCP via the base station (line 1), a DCH (e.g., k) firstly calculates the distance from the base station to it and the corresponding transmission power (lines 3-4), the distances from the base station to the other DCHs and the corresponding transmission powers (lines 6-7), and the distances from it to the other DCHs and the corresponding transmission powers (lines 8-9). Then, the DCH k stores the intercluster D2D multicast powers (that meet the given conditions, i.e., and ) (line 10) in the set Ek which is initialized as empty in advance (line 2), which will be used later.
Algorithm 4: The decision for D2D multicast relaying powers.
The conditions in line 10 are to ensure that the intercluster D2D multicast links are always pointing away from the base station. Moreover, the DCH k only ensures that its multicast transmission meets the receiving quality of the several DCHs closest to it (line 12). After invoking Algorithm 2 to get the intracluster D2D multicast power, the DCH k sends it to the MCP via the base station along with the intercluster D2D multicast power (lines 14-15). The DCH k is selected as the transmitter of the intercluster D2D multicast service if "αk = 1" is received from the MCP via the base station, while it is selected as the transmitter of the intracluster D2D multicast service if "βk = 1" is received from the MCP via the base station (line 16). Otherwise, the DCH k will consider readjusting intracluster or intercluster D2D multicast power (line 17).
We define the simulation metrics from the perspective of a multicast receiver as follows:(1)Multicast spectrum efficiency: it is the data rate achieved by a multicast receiver per unit of spectrum resource. For a given multicast content dissemination service, multicast spectrum efficiency depends on the BER threshold that ensures a user's QoE, which is usually estimated according to formula (4). If resources are insufficient to guarantee a user's QoE, the actual value can be estimated by formulas (1)–(3). Average multicast spectrum efficiency is the average value of all the multicast receivers' multicast spectrum efficiency values.(2)Multicast energy efficiency: it is the ratio of a multicast receiver's multicast spectrum efficiency to the corresponding multicast source's transmission power. Average multicast energy efficiency is the average value of all the multicast receivers' multicast energy efficiency values.(3)Continuous multicast service time: it refers specifically to the amount of time during which a multicast source continues to disseminate multimedia content, which can be estimated according to formula (27). Average continuous multicast service time is the average value of all the multicast sources' continuous multicast service time values.
In our simulations, a single macro cell scenario is considered, in which many MCUs are randomly located in the circular area with a radius of 400 m and the macro base station is situated at its center. We compare the impact of different numbers of clusters K on the built multicast transmission scheme in terms of the above three metrics and use the OMNeT++ 4.1 network simulator  to carry out the simulation experiments. Unless otherwise stated, the common simulation parameters are listed in Table 1.
Firstly, we explore the variation trend of multicast transmission performance with the number of MCUs in the fixed area. In this group of simulations, the noise power is set to 1E − 11 (i.e., 10−11 W), while the BER threshold of the receiving end is set to 1E − 8 (i.e., 10−8). When the number of MCUs is changed from 1200 to 2000 with a step of 200, the three types of performance metrics of the built multicast transmission scheme are shown in Figures 3(a)–3(c), respectively.
Figure 3: The variation trend of multicast transmission performance with the number of MCUs in the fixed area. (a) Average spectrum efficiency versus the number of MCUs in a fixed region. (b) Average energy efficiency versus the number of MCUs in a fixed region. (c) Average continuous service time versus the number of MCUs in a fixed region.
From Figure 3(a), we can see that, with the increasing number of clusters (e.g., K is increased from 30 to 60), average spectral efficiency basically shows a trend of decline. This is because some DCHs may waive QoE guarantees for the few multicast receivers on the purpose of saving more energy. This possibility increases as the number of clusters increases. Moreover, as shown in Figure 3(a), when the density of MCUs (i.e., the number of MCUs in the fixed area) is greater, this situation is more likely to happen.
Figure 3(b) shows that average energy efficiency has a trend of increase with the number of clusters, but the higher density of MCUs inhibits energy efficiency. The main reason is that more clusters are helpful to shorten the average communication distance and thus reduce the transmission power. On the contrary, when the density of MCUs is greater, the low energy efficiency clusters are more likely to happen, which is an inhibiting factor of energy efficiency improvement.
Average continuous service time depends on both the energy reserve level of the DCHs and the multicast transmission powers adopted by the DCHs. As the number of clusters increases, although it is possible to reduce the transmission power, it is also possible to reduce the number of candidate DCHs, and thus, the energy reserve of the selected DCH does not necessarily have an advantage. Therefore, as shown in Figure 3(c), when the density of MCUs is relatively small, the average continuous service time does not show a strict increasing or decreasing trend as the number of clusters increases. However, when the density of MCUs is large enough (i.e., 2000), it shows a strict increasing trend. This is because in the case of more DCH clusters, the large density is helpful to maintain more DCH candidates and thus to select the DCHs with more energy reserve levels.
Then, we explore the variation trend of multicast transmission performance with the noise power. In this group of simulations, the number of MCUs in the fixed area is set to 1400, while the BER threshold of the receiving end is set to 1E − 8. When the noise power is changed from 5E − 12 to 5E − 11 with a variable step length, the three types of performance metrics of the built multicast transmission scheme are shown in Figures 4(a)–4(c), respectively. From Figure 4(a), we can see that, with the increasing noise power, the change in average spectral efficiency is very small. This is attributed to the fact that the multicast transmitters pay more power values to ensure the QoE of the receivers, which can also be verified by the results in Figure 4(b).
Figure 4: The variation trend of multicast transmission performance with the noise power. (a) Average spectrum efficiency versus the noise power. (b) Average energy efficiency versus the noise power. (c) Average continuous service time versus the noise power.
In addition, under the parameter configuration of this group of simulations, as we can see from Figure 4(a), average spectral efficiency is better when the number of clusters is 50. However, under the same parameter configuration, average energy efficiency is better when the number of clusters is 60, which is shown in Figure 4(b). This shows that the diversity of factors affecting these two performance metrics cannot achieve a consistent desired result only through adjusting the number of clusters.
Figure 4(c) shows that average continuous service time has a trend of decline with the increase of noise power. This is because with the increasing noise power, the multicast transmitters must use greater powers to ensure the QoE of the multicast receivers, which can also be explained by the results in Figure 4(b). As mentioned above, more clusters can be helpful to shorten average communication distance and thus reduce average transmission power. However, as we can see from Figure 4(c), this advantage becomes less obvious with the increase of noise power.
Finally, we explore the variation trend of multicast transmission performance with the value of BER. In this group of simulations, the number of MCUs in the fixed area is set to 1400, while the noise power is set to 1E − 11 W. When the BER threshold of any receiving end is changed from 1E − 10 to 1E − 6 with a 10-fold step, the three types of performance metrics of the built multicast transmission scheme are shown in Figures 5(a)–5(c), respectively.
Figure 5: The variation trend of multicast transmission performance with the value of BER. (a) Average spectrum efficiency versus the value of BER. (b) Average energy efficiency versus the value of BER. (c) Average continuous service time versus the value of BER.
From Figure 5(a), we can see that, with the increasing value of BER, average spectrum efficiency shows a strict downward trend. The decrease of average spectrum efficiency can be explained as follows. The value of BER reflects the QoE requirement of users, where the larger value of BER means the lower QoE requirement of users. According to formula (4), the spectrum efficiency is reduced in theory, which is consistent with the trend shown in Figure 5(a).
Based on the reduction of the QoE requirement of users, much smaller transmission power can be spent to meet such a requirement. Therefore, as shown in Figure 5(b), average energy efficiency increases as the value of BER increases. Meanwhile, as the number of clusters increases, average communication distance becomes shorter, and thus, average energy efficiency has a more significant trend of increase. From Figure 5(c), we can see that the change of average continuous service time shows a certain increasing trend with the value of BER, while it shows a certain randomness from the perspective of the number of clusters. The reason behind the former phenomenon is obviously a reduction in power consumption, while the latter one is that, under different BER values, the number of clusters to obtain the desired energy efficiency is different.
In this paper, we propose an energy-efficient multimedia content dissemination scheme, including the K-means-based D2D clustering algorithm, the iterative algorithm for intracluster D2D multicast transmission power determination, the game algorithm for base station multicast power assignment and D2D multicast relay selection, and the game algorithm for D2D multicast relaying power decision. Different from the existing multicast transmission schemes, which are mainly built from the perspective of final consumers of multimedia content dissemination services, our scheme is built from the perspective of multimedia content providers, which does not require ensuring the QoE for all multicast receivers. That is, if the energy efficiency of the multimedia content dissemination process can be greatly improved, it is worth sacrificing the QoE of a small number of consumers. Therefore, it is more suitable for multimedia content providers to maximize their benefits.
We analyze the transmission performance of such a content dissemination system under different simulation parameters. In fact, some parameters are restricted by the application environment (e.g., environmental noise), while some parameters are restricted by the quality demand of user experience (e.g., BER). Facing different values of such parameters that cannot be controlled by the system, the multicast content dissemination system should adaptively adjust the controllable parameters (e.g., the number of clusters) so that the multicast transmission cost is always relatively small. Although some research results have been achieved in this paper, how to be more intelligent will remain in our future research plan.
The simulation data used to support the findings of this study are available from the corresponding author upon request.
This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61572528, 61873352, and 61272494.
Ericsson, Ericsson Mobility Report: On the Pulse of the Networked Society, Ericsson, Stockholm, Sweden, 2016.
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R. Trestian, Q. T. Vien, H. X. Nguyen, and O. Gemikonakli, "Eco-M: energy efficient cluster-oriented multimedia delivery in a LTE D2D environment," in Proceedings of IEEE International Conference on Communications (ICC), pp. 55–61, June 2015.
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The OMNeT++ Discrete Event Simulation System, Version 4.1, http://www.omnetpp.org.


As the (ridiculously) short winter in Texas appears to be giving way to spring, it is that time of year when parents start signing their kids up for sports teams. Since we live in a large metropolitan area, we have the luxury of choosing almost any sport at any time of year. Football, baseball, soccer, and basketball are virtually year-round activities here. At least once a year, my wife and I have a discussion about which activities to place our children in and for how long.
For many people, sports are an unhealthy obsession, but sports can actually be good for the development of character—if done the right way. Here are a few thoughts on the character that sports can build and some caveats for parents to go along with them.
While there are individual sports, we mostly think of team sports when we consider signing up our kids for a season. The beauty of such team sports is that they can counter the oppressive culture of individualism we often find in our society. Rather than spending time mindlessly staring at a screen with no consciousness of the world around them, children who play team sports get the opportunity to interact with other children in a (typically) competitive environment where they have to work together to accomplish a goal.
The level of teamwork varies with the sport, but one thing is consistent—the team is essential. No single child can compete against an entire team and be successful. For that matter, not even LeBron James could take the basketball court by himself and compete with even a decent college-level team.
Learning teamwork is important for our children because the "adult world" is full of interaction with others and collaboration to accomplish goals. From marriage to the workplace to church, we constantly interact with people for the sake of pursuing common goals.
As a leader, Moses learned this lesson from his father-in-law in Exodus 18. Jethro came to visit Moses in the wilderness and to bring his wife and sons back to him. While there, Jethro observed that Moses was handling all the disputes of the people by himself and advised him against such an approach. Jethro counseled him to "select out of all the people able men who fear God, men of truth, those who hate dishonest gain; and you shall place these over them as leaders of thousands, of hundreds, of fifties and of tens. Let them judge the people at all times; and let it be that every major dispute they will bring to you, but every minor dispute they themselves will judge. So it will be easier for you, and they will bear the burden with you. If you do this thing and God so commands you, then you will be able to endure, and all these people also will go to their place in peace" (Ex 18:21–23). Even the prophet Moses needed a team to accomplish the goal of judging the people and bringing them into the Promised Land.
Children's sports sometimes bring out the worst in parents. Many of us consider our child to be the next Peyton Manning, Derek Jeter, or Michael Jordan. However, our kids still need a team. They also need a team that respects them. When parents grandstand for their own child to be the focal point of the team, it actually does the child and the team a disservice. Camaraderie, selflessness, and unity are necessary for success on the field and in life. Even the most talented team can lose to another team that has learned to play together. Instead of promoting your own child, promote the team.
I'll never forget one of the coaches from my childhood. I didn't really like him very much. He made us run long distances. If you were last, you might have to run another lap. He constantly worked on drills to teach skills. All I wanted to do was score (which I rarely did). Then he expected us to go home and work more between practices. I didn't like what he required us to do. But he was teaching us the lesson of discipline.
Discipline is important in life, and sports have a built-in mechanism for teaching it—practice. Most kids are not blessed with the athletic ability to pick up the intricacies of a new game the first time they play. Yes, it may come easier to some than others, but even the best athletes practice (unless you're Allen Iverson).
The discipline children learn in playing sports can translate to the rest of life. Schoolwork requires discipline. Jobs require discipline. Spiritual growth requires discipline.
Thus, sports can help children learn the discipline necessary for life. Team sports in particular teach us that others are dependent upon us exercising our own discipline.
The danger for parents when it comes to discipline in sports is the tendency to fall into one of two extremes. We can demand perfection from our children that they simply cannot deliver or we can let them off the hook of practice and discipline. By always demanding absolute perfection, we may drive our children away from a desire to participate. By giving in to the temptation not to require practice, we may send our kids a message that practice and discipline are not necessary. Both extremes are problematic. Balance in this area is necessary.
One of my fondest sports memories came around the age of 9 or 10. I had been playing basketball in a church league for a few years already, but my church's team was not very good. In fact, we had not won a game in more than two full seasons. But this game was different. By some miracle, we had actually taken the lead at the end of the game and just needed to run out the last few seconds on the clock. We were inbounding the ball from under the opponent's basket and they were surrounding us to try to steal the ball. I broke open, caught the pass, and avoided the defense until time expired. We won! The taste of victory was sweet. It was even sweeter due to the lingering taste of defeat for more than two years.
Losing is a part of sports. Very few teams go undefeated. Learning to lose is an important lesson for life. It teaches perseverance. People who fail to learn how to pick themselves up after defeat tend to struggle mightily in life. At some point, we will be passed over for a promotion, or someone will critique what we say or write, or someone will tell us that our idea is a bad one. What we do when that happens says a lot about our perseverance.
Losing every game for two seasons was not fun, but most of our team stuck together. Over time we got better because we continued to discipline ourselves and trust one another. By the time we quit playing together in high school, we found ourselves in the city tournament.
We as parents do not like to see our kids lose. As a result, some parents refuse to allow their children to play on a team that loses. At the first sign of a season turning south, they will pull their child off the team in order to find a better one—often bad-mouthing the coach on the way out. Another possibility is to blame the losses on someone else and never acknowledge our child's role in the team's loss. That doesn't mean we need to be hyper-critical of our children's faults, but we need to teach them where they can improve. I'm sure I cost my basketball team a number of games due to my abysmal free throw shooting. But that is why I spent afternoons in the driveway shooting free throws—at my dad's suggestion. Failure is not fun, but it may be the best teacher.
This spring and summer, we will most likely have our kids in different sports and activities. I may even coach one of them. I hope they are successful, but I am more hopeful that they learn teamwork, discipline, and perseverance. Those are life skills they will take with them long after their days on the field or court are over.
*Thanks to my friend and former teammate Achaz Foster for the "throwback" photo of one of our soccer teams back in the day. Yes, I'm the chunky kids second from the left on the back row.


To determine an organisations' status quo within three separate, yet interdependent, fundamental elements of safety culture.
The People Driven Safety is based on organisational development (OD) principles and best-practice. The process engages all organisational levels, teams and individuals in a task, or sequence of tasks, aiming at the improvement of safe behaviours, individual development and enhancement of business competitiveness.
The Modes of Organisational Safety Readiness Inventory (MOSRI): ): which measures the level of organisational safety readiness in the organisation, thus the degree to which an organisation embraces safety as a core value at all levels in the organisation.
A favourable level of safety readiness would be a low score in the Reactive mode, a moderate score in the Compliance mode and a high score in the Internalisation mode.
The Safety Commitment Indicator: is the degree to which employees feel the organisation is committed to safety.
The PDS Safety Mindset Index: reflects the state of inspiration (that unlocks employee motivational drivers), as perceived by the workforce. It furthermore reflects the caring character and the trueness of the working relationships in the company and therefore reflects the state of the employee morale and loyalty.
The South African sample composed mostly of men (80.32%) with a modal age between 31 and 40 years that work in the mining & manufacturing industry and other at-risk environments (83.58%). The majority of the sample had between 6 and 10 years of experience in their job-roles and were in the A to B management level (58.91%) followed by C to D1 management levels (22.43%).
The sample was primarily involved in production (45.18%) and engineering (14.12%) activities. Consequently, the norm sample can be considered representative of workers in the manufacturing, agricultural & mining sectors.
The Prospect Screener allows organisations to process a large number of job applicants in a short space of time, allowing for the screening out of candidates that do not meet the minimum requirements.
Organisations spend a great deal of time and money on selection systems such as interviews, assessment instruments, and reference checking. It is important for organisations to ensure the cost-effectiveness of their processes, while retaining the highest standard of candidate. The Prospect Screener measures basic job capabilities in order to exclude those candidates that do not meet the basic requirements for selection. By measuring constructs such as basic vocabulary and arithmetic, detail-orientation, emotional stability, and dependability, the Prospect Screener acts as a starting point for further assessment.


text:
Solar's Share in India's Installed Power Capacity Rises to 11.2%
Conventional power projects still hold the largest share in the overall power mix, with 62.6%
Rahul Nair
India's installed renewable energy (including large hydro projects) capacity stood at 143.9 GW, accounting for a share of 37.4% in the overall power mix at the end of the second quarter (Q2) of 2021, according to preliminary data from the Central Electricity Authority (CEA), Ministry of New and Renewable Energy (MNRE) and Mercom's India Solar Project Tracker.
The share of renewable energy saw a marginal increase from the previous quarter (Q1 2021), when total renewable installations stood at 141.4 GW with a share of 36.94%. Meanwhile, at the end of Q2 2020, the installed renewable energy (including large hydro projects) capacity was 135.22 GW, accounting for 36.26% of the total power mix.
Based on the preliminary numbers, solar installations increased, accounting for 11.2% of the total installed capacity compared to 10.69% in Q1 2021. Wind installations also showed a marginal growth and accounted for 10.26% of the total capacity at the end of Q2 2021.
Meanwhile, biomass and small hydro accounted for 2.64% and 1.25%, respectively, in Q2 2021. Among renewables, solar accounted for nearly 30% of the total installed capacity at the end of June 2021.
According to Mercom India Solar Project Tracker, government agencies announced tenders for 8.5 GW of solar projects in the second quarter of the calendar year (CY) 2021, a 19% decrease compared to 10.6 GW in the previous quarter. However, the figures were up 60% compared to the same period last year, when only 5.3 GW of solar tenders were announced.
Energy from conventional sources
At the end of Q2 2021, conventional power sources had a cumulative installed capacity of nearly 240 GW, accounting for 62.59% of the total installed capacity – a small decrease from the last quarter's number of 63%. The segment included power generated from thermal-based sources, including coal
(52.49%), gas (6.48%), lignite (1.72%), and diesel (0.13%); nuclear power made up 1.76% of the total cumulative installed power capacity mix.
Coal continued to lead the way with nearly 202 GW of installations at the end of Q2 2021, a marginal decrease from 202.6 GW at the end of Q1 2021. The overall market share of coal dipped slightly and stood at 52.49%, along with the installed capacity of coal in Q2 2021. Large hydropower projects had the second largest market share with 12.04% of the total installed capacity.
According to the International Energy Agency (IEA), coal accounts for 70% of India's power generation while solar is less than 4%. However, solar energy will witness exponential growth and match coal's share in the Indian power generation mix by 2040 or earlier. The share of coal is expected to decline from 44% in 2019 to 34% in 2040.
Rahul is a staff reporter at Mercom India. Before entering the world of renewables, Rahul was head of the Gujarat bureau for The Quint. He has also worked for DNA Ahmedabad and Ahmedabad Mirror. Hailing from a banking and finance background, Rahul has also worked for JP Morgan Chase and State Bank of India. More articles from Rahul Nair.
CEACoalConventional EnergyGasLigniteMercom Research FocusMNRENuclearRenewable EnergySolar

redpajama_set_name: RedPajamaCommonCrawl


Variety of infographic about emails you can download free of charge. Please download these infographic about emails by using the download button, or right click selected image, then use Save Image menu.
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text:
Home Sports Soccer, baseball players named to postseason teams
Soccer, baseball players named to postseason teams
by KYLE PARMLEY, JIMMIE JOHNSON III
Photo by Todd Lester
Ethan Harradine, left, during a Class 7A semifinal playoff match against Vestavia Hills. Harradine led the team in scoring and broke the program's career scoring record, as he was named to the all-state team.
Four members of the Mountain Brook High School boys and girls soccer teams and two baseball players were recognized with postseason accolades.
On the Class 7A all-state soccer team, seniors Ethan Harradine and Eli Sellers each received first-team honors. Harradine led the team in scoring this season with 28 goals and will leave Mountain Brook as the program's career scoring leader with 60 goals.
Coach Joe Webb said Harradine made his presence felt on the attack even though he played midfield.
"Ethan was the conductor for our offense," Webb said. "He is a mercurial talent, which is common with a creative player like that."
Sellers was a standout defender who commanded a defensive unit that only allowed 22 goals this year. In addition to his on-field contributions, he also exhibited great leadership, serving as a team captain for four years.
Photo by Sarah Finnegan
Eli Sellers, right, is shown during a Class 7A first round playoff match against Hoover. Sellers was a leader on a defensive unit that allowed just 22 goals in 2018.
The boys team reached the 7A semifinals, where the Spartans fell to crosstown foe Vestavia Hills in Huntsville. Mountain Brook notched playoff victories over Hoover and Bob Jones to reach the Final Four.
On the girls team, junior midfielder Lamar Campbell and junior goalkeeper Sarah Francis Adair received first-team all-metro honors. Adair was also honorable mention on the all-state team, while Claire Brian and Emma Blakely were honorable mention all-metro.
All-state selections were made by coaches across the state and the all-metro teams were selected by Birmingham-area coaches.
From the baseball team, senior pitcher Aaron Shiflet and junior designated hitter Colton Yeager each were named to the second team of the 7A all-state squad, as voted on by the Alabama Sports Writers Association.
Shiflet was an outstanding reliever for the Spartans in the spring. In 26 innings of dominant work, he only allowed four unearned runs, saved five games and won three. He walked just five hitters and struck out 29 hitters on the year.
Shiflet plans on continuing his baseball career at Eckerd College in St. Petersburg, Florida.
Yeager makes much of his athletic contributions as a linebacker for the Spartans football team, but he made plenty of noise with his baseball bat as well. He hit for a .354 average with three home runs and 32 RBIs. He finished the year with 35 hits and 30 runs scored.
The Spartans baseball team had a successful season, winning Area 6 in dramatic fashion. Mountain Brook swept Spain Park in its final area series of the season and forced a one-game playoff against Oak Mountain.
In that tiebreaker, the Spartans won 2-1 in an extra-inning contest. Parker Garrison pitched all eight innings and Clay Stearns lifted Mountain Brook to the win with a walk-off homer. The Spartans fell to James Clemens in the first round of the state playoffs.
MBHS soccer MBHS Baseball Mountain Brook High School MBHS


Progression is defined as a deviation inside heritable features of just living microorganisms. Progress is in charge of the variety in all degrees of biological your life. History ends in the development of new totally different types and decrease of other folks. During the nineteenth century, Charles Darwin created the scientific reasoning behind history as outlined by alternative choices. Other theories blossomed inside twentieth century with popular evolution functionality including Darwinian's pure choices and traditional genes.
You have four concepts in the office in chats regarding advancement. They include alternative, inheritance, range and historic past. Difference refers to the dissimilarities of all varieties during a residents. Persons with a residents range between the other of their hereditary makeup subsequently ending up with totally different natural benefits. Inheritance http://nro11.neda.gov.ph/rdc/mfc/2015/10/essays/descriptive-vs-narrative-essay-writing-3/ defines completing examples of the hereditary products from parent around the young. Utilizing the assortment, a lot of people have handed down elements which make them greater survivors as opposed to the rest. Their offspring is very likely to exist and produce offspring compared to those without ever survival traits. The resultant will be the genes that have been more usual throughout the populace than others. In time, options brings about adjustments to inhabitants given that some versions recreate about others. Adjustments can take nights, ages, ages or numerous several years, only one product is certain, prosperous versions pile up in the long term (Mather, 2008).
An illustration of how VIST refers to advancement system is in terms of organisms along the lines of Aids malware, Hawaiian Berries Flies, Medium Flooring Finches, and Whales. Even as it required a whole most zillion many years to develop, it required the HIV virus a lesser time. Hawaiian Fresh fruit Flies change in whole body sizing, venture figure and wing trend. Environmental surroundings chooses the size and shape of Floor Finches. While in moistened years, the vegetation has very small soft plant seeds which may be beaked and thus quite likely to stay alive. In dry up years and years, the seeds are really difficult, considerable and beaked to ensure emergency. Now, enough time includes element in history. Regarding whales, the loss of hindlimbs, and evolves of forelimbs to flipper-like, the development of tailfin, exercise of nostrils toward back again of skull, and isolation of midst ear canal our bones is as a consequence of progress with time.
Mather, (2008) opines that purely natural choice mandates that there be genetic heterogeneity and not like genotypes make different approximate amounts of progeny. Both these elements are satisfied, and also offered computer data on history relationship the genetic theory of alternative and choice. Accordingly, types should deviate in a similar manner, but at some level than, choices and family unit the exact same group. Mather argues that the effective use of this structure causes the conclusion that varieties distinctions are polygenic meaning that they will depend on quantitative figures whoever differences are controlled by a lot of genetic aspects. This is in shut down agreement from what Darwin posits in Fisher that outward health conditions do quite tiny in progress besides leading mere variability. Fisher has it that this combination way of thinking of inheritance is far more impressive as compared to the mere factors of variation.
As a result, the factors which have been at enjoy in history feature variation, inheritance, selection and record. Even while inheritance works an essential function, conditions and time are really important.


Have fun using contrast fabrics and trim variations on 18″ dolls clothes. Pattern includes four dresses, two rompers, two cardigans & a headband. Dresses & rompers have hook and loop tape back closures for easy dressing.


Many sleepless nights have helped me push forward.
In the current updates I have improved the suspension system, which takes into consideration the amount of active coils in a spring to the resting length of the spring. Tyres have multi-point collision for a more accurate simulation. Wheels also now have camber, toe and castor configurations.
I have put together a video demonstrating the multi-point collision tyres.
There are several ways of simulating wheels on a vehicle in games, generally raycasting is used since it is more efficient, in this video I will show using the standard single ray cast for a wheel vs a custom method I have developed for Celerity, one of the common methods is shooting rays from the centre of the wheel, however my method works slightly different and reacts more realistically to it's environment.
If you'd like to stay updated, subscribe to my youtube channel and also keep checking back here!


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Jesuit Conference of the United States urging broader legislation
H.R. 4437: A Senseless Approach to Immigration Reform
WASHINGTON, December 12, 2005 – On Wednesday, the House of Representatives is expected to debate "The Border and Immigration Enforcement Act of 2005." Americans who favor comprehensive immigration reform that will better secure both our borders and basic human rights must act now and demand Congress fix the senseless solutions of H.R. 4437.
We have tried to find justice merely through enforcement. During the last ten years, the United States spent $25 billion and tripled the number of border patrol agents to address a growing immigration crisis. With the current situation worsening, no one tries to argue that the previous effort was successful. However, H.R. 4437 is saying to America we just need more of the same failed policies.
Justice can only be measured by the dignity afforded every man, woman and child, by the safeguards we as a society place on that dignity and by the economic opportunities we offer each family within our borders. The conscience of America – a nation built by dispersed peoples from around the world seeking a second chance – calls for a compassionate response, an alternative that protects workers from abuse and employers from sudden, drastic labor shortages.
Through criminalization, provisions to strip citizenship and restricting rights to judicial review, H.R. 4437 abandons compassion. By expanding the definition of smuggling to include anyone who aids or transports an undocumented immigrant, the legislation also asks Americans to ignore the humanitarian call of the Good Samaritan. It outlaws the very act of driving to the hospital with a man who may have been injured while laboring to keep fresh vegetables in our stores.
Were the United States to adopt the comprehensive reform advocated by the Catholic Campaign for Immigration Reform, we could better protect our country. Earned legalization is a practical response to the serious adverse effect of eliminating 11 million laborers from the U.S. economy, nearly five percent of the workforce, without any means of replacement. Inviting people out of the shadows of American life relieves our enforcement agencies of the burden of tracking millions of individuals and enables them to better focus on tracking down the small percentage of those entering America intent on smuggling people, drugs or weapons.
We welcome a continued discussion rooted in dignity and human rights and join with all who cherish humanity in calling for practical and comprehensive immigration reform that secures our borders and extends hope to those most in need. For the Society of Jesus, His words ring ever true, "As long as you did this for one of the least of my brothers or sisters, you did it for me."


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Q: Uploading File to Azure Blob Storage results in size 0 Able to successfully create a container and upload any file in a created container. Having an issue after downloading a file (any uploaded file) they are always zero. Here is my code using C# in a NET.Core application.
            // Get a reference to a blob
        BlobContainerClient container = new BlobContainerClient(connectionString: connectionString, blobContainerName: "test-container");
        await container.CreateIfNotExistsAsync();

        try
        {
            // Get a reference to a blob
            BlobClient blob = container.GetBlobClient(uploadFiles.First().FileName);

            string filePath = Path.GetFullPath(uploadFiles.First().FileName);

            // Upload file data
            await blob.UploadAsync(filePath, true);

        }
        catch (Exception e)
        {
            _ = e;
        }

enter image description here
Image is attached to show what is inside the Auzure portal storage account, the size there is ZERO ?!
I am following documentation from Microsoft and they are also not setting any size explicitly when uploading a file.

A: The following code can work:
using FileStream uploadFileStream = File.OpenRead(filePath);
await blob.UploadAsync(uploadFileStream, true);
uploadFileStream.Close();

redpajama_set_name: RedPajamaStackExchange


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This article is currently a featured article, as of December 1st, 2020.
The data file icon on the KopPad.
This article is about the Pikmin 3 item. For the persistence of a playthrough's progress, see saved game.
Data files are collectibles scattered around the surface of PNF-404 in Pikmin 3. They appear as glowing yellow SD cards that float in place. Data files may contain text and/or images, and there are 120 of them in all. There are 88 that must be physically picked up and 11 more received by doing certain optional tasks (such as collecting a spacesuit upgrade). The remaining 21 are automatically obtained during progress through the main story.
Once one has been collected, it disappears from its spot, and is added to the Exploration Notes menu on the KopPad. This app is unlocked after one data file is collected, in Pikmin 3, or upon finding the KopPad, in Pikmin 3 Deluxe. The app keeps a collection of all obtained data files. When the active leader touches a data file, a brief cutscene takes place, where the gameplay pauses, the leader is seen celebrating, and the data file is opened automatically. If, however, an inactive leader touches a data file, the object will still be collected, but the gameplay will not pause, nor will the file be opened – instead, the leader in question will announce they have found a data file. In the Hard and Ultra-Spicy difficulties in Pikmin 3 Deluxe, the cutscene does not take place for most data files, though some still have it.
Data files seem to be the medium in which Captain Olimar stores his notes in Hey! Pikmin. This is because the button that brings the player to the logs is a data file.
1.1 Pikmin Behavior
1.2 Controls
1.3 Indigenous Life
1.4 Area Hints
1.5 Olimar's Log
1.7 Olimar's Log Vol. 2
2 Purpose
Categories[edit]
Data files are organized in six categories, which can be found in the Exploration Notes menu. These categories are:
Pikmin Behavior[edit]
Main article: Pikmin Behavior.
This group (known as Pikminology in the European version) gives brief details about the various Pikmin types and their abilities. It also gives tips on what to do to rescue Pikmin in peril, and tips on using the Onion.
Main article: Controls.
This group consists of brief info on controls, such as using the KopPad, as well as upgrade information. Most of these files are obtained once a certain requirement is met.
Indigenous Life[edit]
Main article: Indigenous Life.
This group gives brief details about an enemy, such as its attacks and weak points. For example, one file in this group states that the Bulborb's eyes are its weak points.
Area Hints[edit]
Main article: Area Hints.
This group holds various logs that Olimar wrote. These logs provide a bit of a hint as of what the explorers must do to overcome challenges in the area. There are two per main area.
Olimar's Log[edit]
Main article: Olimar's Log.
This group contains logs that Olimar wrote on his journey on PNF-404. Some can be found in various areas, but most are found in the Formidable Oak, when Olimar was trying to escape the Plasm Wraith.
Main article: Secret Files.
This group holds nothing but pictures of Pikmin holding various numbers. There are two per area, and entering the code they form on a Wii U exclusive website allows the player to view video logs of Captain Olimar's journey during the story of Pikmin 3. These are exclusive to the original Wii U release of the game.
Olimar's Log Vol. 2[edit]
Main article: Olimar's Log Vol. 2.
This group contains more logs from Olimar, as well as other characters such as The President and Olimar's wife. These are exclusive to Pikmin 3 Deluxe, taking the place of the Secret Files.
Purpose[edit]
It is through data files that Alph, Brittany, and Charlie (and by extension, the player) gain understanding of the Pikmin and the world they come from. Data files, when collected, provide various hints and bits of information of various topics. Most of them are left behind by Captain Olimar, and besides leaving tips, they are used to keep an exploration log, and to try to contact anyone else who may be on the planet.
Some data files are not made by Captain Olimar however; at least not apparently. Some data files are obtained automatically by reaching certain points in the game, and merely explain some gameplay elements important exclusively to the player. In addition, a category of secret memos exists that is only useful outside of the game itself.
Data files do not alter gameplay when collected, with one exception in the original Pikmin 3: the data file found behind the crystal wall next to the landing side in the Garden of Hope unlocks the Go Here! mechanic.
Badges[edit]
In Pikmin 3 Deluxe, there are two badges the player can gain from the discovery of data files:
Intelligent Life: Retrieving 15 data files.
Hocotatian Hound: Retrieving all data files.
Intelligent Life. The badge shows a data file.
Hocotatian Hound. The badge shows a data file with a crown above it.
Alph when he first discovers a data file.
Brittany and some Yellow Pikmin coming across a data file.
A data file found in a body of water.
An example of the content of a data file.
French Note Note
German Daten Data
Italian Appunto Note
Spanish Archivo de datos Data file
Olimar's voyage log
Hey! Pikmin logs
Retrieved from "https://www.pikminwiki.com/index.php?title=Data_file&oldid=212322"
Tropical Wilds
Twilight River
Formidable Oak


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Princeton offering Black Lives Matter course with readings from avowed Marxist
Paul Sacca
David Ryder/Getty Images
Princeton University is offering a Black Lives Matter course, which will teach students about the "historical roots and growth of the Black Lives Matter social movement." Starting this fall semester, Princeton University students can enroll in the "#BlackLivesMatter" course, which will feature readings from a former Black Panther member and avowed Marxist, as reported by The College Fix.
The course description reads:
This seminar traces the historical roots and growth of the Black Lives Matter social movement in the United States and comparative global contexts. The movement and course are committed to resisting, unveiling, and undoing histories of state sanctioned violence against Black and Brown bodies. The course seeks to document the forms of dispossession that Black Americans face, and offers a critical examination of the prison industrial complex, police brutality, urban poverty, and white supremacy in the US.
The course includes readings from Angela Davis' book "Freedom is a Constant Struggle." Davis is an avowed Marxist, former Black Panther member, two-time vice-presidential candidate of the Communist Party USA, and was awarded the International Lenin Peace Prize by the Soviet Union.
The class at Princeton, which has a tuition of $56,010, will be taught by professor Hanna Garth, who is a sociocultural and medical anthropologist.
From Garth's website:
I am most broadly interested in the ways in which people struggle to overcome structural violence. My recent work is focused on the connections between food systems, structural inequalities, health, and wellbeing. This work has looked at the ways in which macro-level changes and shifts in local food distribution systems impact communities, families, and individuals. I have studied how food scarcity and reduced access to affordable food influence individual distress, and household and community dynamics. I have also studied the ways in which food justice organizations attempt to improve access to healthy food for low income communities.
The professor adds, "All of my research, teaching, and mentoring is designed around my commitment to feminist methodologies and critical race theory."
Garth has taught other classes, such as "Race and Racisms," "Postcolonial and Decolonial Theory," and "Theories of Social Justice."
Garth wrote a book titled "Black Food Matters: Racial Justice in the Wake of Food Justice," which "analyzes how Blackness is contested through food, differing ideas of what makes our sustenance 'healthy,' and Black individuals' own beliefs about what their cuisine should be."
"This comprehensive look at Black food culture and the various forms of violence that threaten the future of this cuisine centers Blackness in a field that has too often framed Black issues through a white-centric lens, offering new ways to think about access, privilege, equity, and justice," the overview of the book states.
Garth and Princeton administrators did not respond to requests for comments about the Black Lives Matter course from The College Fix.
In 2020, Princeton University offered a course titled: "Sociology 102: Police Violence, #BlackLivesMatter and the Covid-19 Pandemic." The course will "introduce students to the concept of race and discipline of sociology."
"Students will learn to study systematically how human groups interact with one another and how social networks and a variety of institutions help shape those interactions and outcomes," the course description reads. "How are these interactions and outcomes categorized and understood? Where do different people fit into the social categories we use to make sense of our societies, and why? And how are different actors able to transform those spaces in which to fit?"
In March, Princeton University welcomed Black Lives Matter Global Network co-founder Alicia Garza to hold a lecture titled: "The Purpose of Power."
In a 2015 interview, Black Lives Matter Global Network co-founder Patrisse Cullors said that she and Garza are "trained Marxists."
"Myself and Alicia in particular are trained organizers. We are trained Marxists," Cullors said in the interview. "We are super-versed on, sort of, ideological theories."
The Black Lives Matter Global Network website previously declared that the organization was against the nuclear family.
"We disrupt the Western-prescribed nuclear family structure requirement by supporting each other as extended families and 'villages' that collectively care for one another, especially our children, to the degree that mothers, parents, and children are comfortable," the BLM organization stated in its "About" section before deleting it last September.
Beauty company claims dictionary examples of the word 'beauty' are 'limiting and exclusive,' calls for change
Police say man accused of torturing women is using dating apps to find more victims and people to help him evade cops


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What is Jane Harman's Net Worth?
Top 10 Hollywood's Highest-Paid Celebrity Stylists
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Top 10 Most Popular Facebook Accounts in the World
This list contains the top 10 most popular Facebook accounts worldwide.
Celebrities and social media are now bonded in a unique way. Celebrities use social media platforms like Instagram, Twitter, and Facebook to promote special events, upcoming movies, or albums.
Facebook is a highly rated social media platform with a large user base. Every celebrity will have an account on this social media platform. Celebrities hold the record for most-followed accounts on social media sites. This is what our last post Top 10 Most Following Twitter Accounts in the Globe is all about.
Do you know who is the most liked Facebook Account? You are in the right place if you don't know who is the most popular Facebook Account. This post will tell you the top 10 most popular Facebook accounts. Let's now get to the details.
10 Taylor Swift (72 million followers on Facebook)
Taylor Swift is an American singer-songwriter. She was born in Reading, Pennsylvania on 13th December 1989. She is a well-known celebrity on Facebook and is considered one of the top contemporary recording artists in the world.
The American singer of the highest caliber has two records among the top 10 most viewed YouTube videos of all time. She is also ranked among the top 10 most liked Facebook accounts, which speaks volumes about her popularity around the globe. Her Facebook page has 72 million fans.
The most famous Facebook celebrity posts photos of her stage shows and albums on her social media accounts. She has an enormous fan base around the globe. Although she's not as active on social media, people still give her likes and emotions for old videos such as photos.


As we are getting closer to our big day - in exactly ten days I will be saying 'I Do' to the most gorgeous man I've ever met wearing the most stunning dress I've ever worn - making 10,000 decisions is growing out to making 100,000. Luckily I'm still enjoying being engaged and with that every little part of the wedding preparations. I'm trying hard to make sure every little detail is there for our most beautiful day but in the mean time I'm trying even harder not to turn into a perfectionist planner. After all it will be us and our guest who will make this day one of the most memorable moments of our life.
And when thinking about the 23rd of May becoming a memory to last a lifetime I've been on the lookout for the perfect wedding-day perfume. A fragrance can trigger so many memories and so I wanted something classic, elegant and sophisticated, sure to stay part of my favorites through all ages. Miss Dior was the answer. The Miss Dior Eau de Parfum and brand new Hair Mist couldn't be more wedding-worthy. The romantic and playful appeal will be the ideal extension of my wedding dress and the floral and feminine essences are screaming spring wedding to me. Bridal fragrance, check!


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Fuel consumption and emissions
中文(简体)Simpl. Chinese
中文 (繁體) Trad. Chinese
Porsche at the Los Angeles Auto Show 2017
World premiere of four sports cars, US debut of the new Cayenne
The most powerful Sport Turismo is a plug-in hybrid
Tailored for design and sportiness – the new Porsche 718 GTS models
Less is more – the new Porsche 911 Carrera T
The new Porsche Cayenne
Photos 718 Cayman GTS
Porsche Press Kits
Porsche Press Database
The Porsche newsroom
Exclusively for Journalists & the Online Community
Fuel consumption and emissions *
718 Boxster GTS: combined fuel consumption 9.2-8.5 l/100 km; CO2 emissions 210-195 g/km
718 Cayman GTS: combined fuel consumption 9.2-8.5 l/100 km; CO2 emissions 210-194 g/km
911 Carrera T: combined fuel consumption 9.5–8.5 l/100 km; CO2 emissions 215–193 g/km
Cayenne: combined fuel consumption 9.2-9.1 l/100 km; CO2 emissions 207-201 g/km
Cayenne S: combined fuel consumption 9.4–9.2 l/100 km; CO2 emissions 213–209 g/km
Cayenne Turbo: combined fuel consumption 11.4-11.3 l/100 km; CO2 emissions 261-258 g/km
Panamera Turbo S E-Hybrid Sport Turismo: combined fuel consumption 3.3 l/100 km; combined energy consumption 16.1 kWh/100 km; CO2 emissions 76 g/km
Range depending on the tyre set used
© 2020 Dr. Ing. h.c. F. Porsche AG. Imprint and legal notice. Privacy Policy and cookies. Fuel consumption and emissions.
* The latest Porsche models are designed to operate on fuels with an ethanol content of up to 10%. Data determined for standard specification and in the NEDC (New European Driving Cycle) in accordance with the Euro 5 and Euro 6 (715/2007/EC and 692/2008/EC) measurement method. The figures do not refer to an individual vehicle nor do they constitute part of the offer. They are intended solely as a means of comparing different types of vehicle. You can obtain further information about individual vehicles from your Porsche Centre.
Consumption figures were obtained on the basis of standard equipment. Special equipment may affect consumption and performance.