You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks 
Chapter Title:  D. Some Open Issues and Potential Directions 
1. Abstract of Cited Paper (BIB001):  Big data are widely recognized as being one of the most powerful drivers to promote productivity, improve efficiency, and support innovation. It is highly expected to explore the power of big data and turn big data into big values. To answer the interesting question whether there are inherent correlations between the two tendencies of big data and green challenges, a recent study has investigated the issues on greening the whole life cycle of big data systems. This paper would like to discover the relations between the trend of big data era and that of the new generation green revolution through a comprehensive and panoramic literature survey in big data technologies toward various green objectives and a discussion on relevant challenges and future directions. 
2. Abstract of Cited Paper (BIB002):  We introduce a new and increasingly relevant setting for distributed optimization in machine learning, where the data defining the optimization are unevenly distributed over an extremely large number of nodes. The goal is to train a high-quality centralized model. We refer to this setting as Federated Optimization. In this setting, communication efficiency is of the utmost importance and minimizing the number of rounds of communication is the principal goal. ::: A motivating example arises when we keep the training data locally on users' mobile devices instead of logging it to a data center for training. In federated optimziation, the devices are used as compute nodes performing computation on their local data in order to update a global model. We suppose that we have extremely large number of devices in the network --- as many as the number of users of a given service, each of which has only a tiny fraction of the total data available. In particular, we expect the number of data points available locally to be much smaller than the number of devices. Additionally, since different users generate data with different patterns, it is reasonable to assume that no device has a representative sample of the overall distribution. ::: We show that existing algorithms are not suitable for this setting, and propose a new algorithm which shows encouraging experimental results for sparse convex problems. This work also sets a path for future research needed in the context of \federated optimization. 
3. Abstract of Cited Paper (BIB003):  While Bitcoin (Peer-to-Peer Electronic Cash) [Nak]solved the double spend problem and provided work withtimestamps on a public ledger, it has not to date extendedthe functionality of a blockchain beyond a transparent andpublic payment system. Satoshi Nakamoto's original referenceclient had a decentralized marketplace service which was latertaken out due to a lack of resources [Deva]. We continued withNakamoto's vision by creating a set of commercial-grade ser-vices supporting a wide variety of business use cases, includinga fully developed blockchain-based decentralized marketplace,secure data storage and transfer, and unique user aliases thatlink the owner to all services controlled by that alias. 
4. Abstract of Cited Paper (BIB004):  Today the cloud plays a central role in storing, processing, and distributing data. Despite contributing to the rapid development of IoT applications, the current IoT cloud-centric architecture has led into a myriad of isolated data silos that hinders the full potential of holistic data-driven analytics within the IoT. In this paper, we present a blockchain-based design for the IoT that brings a distributed access control and data management. We depart from the current trust model that delegates access control of our data to a centralized trusted authority and instead empower the users with data ownership. Our design is tailored for IoT data streams and enables secure data sharing. We enable a secure and resilient access control management, by utilizing the blockchain as an auditable and distributed access control layer to the storage layer. We facilitate the storage of time-series IoT data at the edge of the network via a locality-aware decentralized storage system that is managed with the blockchain technology. Our system is agnostic of the physical storage nodes and supports as well utilization of cloud storage resources as storage nodes. 
5. Abstract of Cited Paper (BIB005):  Permissionless blockchains allow the execution of arbitrary programs (called smart contracts), enabling mutually untrusted entities to interact without relying on trusted third parties. Despite their potential, repeated security concerns have shaken the trust in handling billions of USD by smart contracts. ::: To address this problem, we present Securify, a security analyzer for Ethereum smart contracts that is scalable, fully automated, and able to prove contract behaviors as safe/unsafe with respect to a given property. Securify's analysis consists of two steps. First, it symbolically analyzes the contract's dependency graph to extract precise semantic information from the code. Then, it checks compliance and violation patterns that capture sufficient conditions for proving if a property holds or not. To enable extensibility, all patterns are specified in a designated domain-specific language. ::: Securify is publicly released, it has analyzed >18K contracts submitted by its users, and is regularly used to conduct security audits by experts. We present an extensive evaluation of Securify over real-world Ethereum smart contracts and demonstrate that it can effectively prove the correctness of smart contracts and discover critical violations. 
6. Abstract of Cited Paper (BIB006):  In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature. 
7. Abstract of Cited Paper (BIB007):  In this letter, we propose a block-chained federated learning (BlockFL) architecture, where mobile devices' local learning model updates are exchanged and verified by leveraging blockchain. This enables on-device machine learning without any central coordination, even when each device lacks its own training data samples. We investigate the end-to-end learning completion latency of BlockFL, thereby yielding the optimal block generation rate as well as important insights in terms of network scalability and robustness. 
8. Abstract of Cited Paper (BIB008):  Information from surveillance video is essential for situational awareness (SAW). Nowadays, a prohibitively large amount of surveillance data is being generated continuously by ubiquitously distributed video sensors. It is very challenging to immediately identify the objects of interest or zoom in suspicious actions from thousands of video frames. Making the big data indexable is critical to tackle this problem. It is ideal to generate pattern indexes in a real-time, on-site manner on the video streaming instead of depending on the batch processing at the cloud centers. The modern edge-fog-cloud computing paradigm allows implementation of time sensitive tasks at the edge of the network. The on-site edge devices collect the information sensed in format of frames and extracts useful features. The near-site fog nodes conduct the contextualization and classification of the features. The remote cloud center is in charge of more data intensive and computing intensive tasks. However, exchanging the index information among devices in different layers raises security concerns where an adversary can capture or tamper with features to mislead the surveillance system. In this paper, a blockchain enabled scheme is proposed to protect the index data through an encrypted secure channel between the edge and fog nodes. It reduces the chance of attacks on the small edge and fog devices. The feasibility of the proposal is validated through intensive experimental analysis. 
9. Abstract of Cited Paper (BIB009):  Hyperledger Fabric is a "permissioned" blockchain architecture, providing a consistent distributed ledger, shared by a set of "peers." As with every blockchain architecture, the core principle of Hyperledger Fabric is that all the peers must have the same view of the shared ledger, making it challenging to support private data for the different peers. Extending Hyperledger Fabric to support private data (that can influence transactions) would open the door to many exciting new applications, in areas from healthcare to commerce, insurance, finance, and more. In this work we explored adding private-data support to Hyperledger Fabric using secure multiparty computation (MPC). Specifically, in our solution the peers store on the chain encryption of their private data, and use secure MPC whenever such private data is needed in a transaction. This solution is very general, allowing in principle to base transactions on any combination of public and private data. We created a demo of our solution over Hyperledger Fabric v1.0, implementing a bidding system where sellers can list assets on the ledger with a secret reserve price, and bidders publish their bids on the ledger but keep secret the bidding price itself. We implemented a smart contract (aka "chaincode") that runs the auction on this secret data, using a simple secure-MPC protocol that was built using the EMP-toolkit library. The chaincode itself was written in Go, and we used the SWIG library to make it possible to call our protocol implementation in C++. We identified two basic services that should be added to Hyperledger Fabric to support our solution, and are now working on implementing them.

Literature Review:
In the existing literature on blockchains, a number of open issues have been discussed regarding the non-consensus layers in blockchains, e.g., the issues of security and privacy  and quantitative analysis of smart contract performance BIB006 . In the following, we discuss issues and emerging research directions that have not been covered in the surveyed works. 1) Cost of Decentralization: The properties of permissionless blockchains such as trustlessness and self-organization have been widely recognized as the advantage over the conventional ledger/brokering systems. However, decentralization with blockchain networks is not "at no cost". As we have partly discussed in Section VI, even the scalable consensus protocols do not completely solve the problem of balancing between the requirement of security and resource efficiency. For instance, hwo to adaptively control the replication factor in shards still remains an open issue. Furthermore, consider that historical data such as spent transactions become huge as the blockchain grows. With the current design of append-only chains, it seems inevitable for ordinary nodes to eventually run out of storage and for the blockchain network to be controlled by a few powerful nodes. Then, it is plausible to seek an approach for "pruning" the blockchain data without undermining its immutability. Although hard forks such as SegWit [168] can be considered a manual pruning process, it is better expected that the outof-date blocks "have the right to be forgotten"  . Unfortunately, except a handful of experimental proposal BIB004 ,  , the issues of data pruning, e.g., how to delete obsolete transactions and migrate UTXOs buried in the chain, also remains an open issue. 2) Support for Secure Big-Data Computation: In the existing research, privacy concerns for blockchains are mostly placed on the levels of identity registration and encrypted data delivery (see Section VII-B). With more and more demands for big-data processing in various fields BIB002 , BIB007 , the question arises regarding whether it is also possible to provide onor off-blockchain support for secure Multi-Party Computation (MPC). For example, hospitals may want to learn patterns for diagnosis by using the private electronic medical records from the patients without seeing the raw data. In such a scenario, the existing privacy policies offered by blockchains (e.g., access authentication) turn out to be insufficient. This issue is partially touched in BIB008 for mobile federated learning, where each node connected to the blockchain trains on the same structure of deep neural network with the local data. Then, they only exchange the locally trained model for global model aggregation BIB003 . Note that in BIB008 the blockchain is merely used to conduct a convoy of the locally trained parameters as in BIB009 . Following such design arises a natural question, namely, how can we directly offer general-purpose, privacypreserving MPC on-chain (e.g., in blocking mining work) or off-chain with decentralized providers (cf. Figure 26) ? The question above generally remains unaddressed, and only a few works BIB001 , BIB010 can be found in the literature with limited strength for specific-purpose MPC provision. These works are based on the framework of cryptographic MPC techniques and allow mutually trustless parties to compute a joint function directly on their encrypted inputs to obtain the right outcome. In BIB001 , the multi-parties store their public-key-encrypted data on an off-chain storage plain as in BIB005 , while in BIB010 the encrypted data is stored directly on a permissioned blockchain (e.g., HyperLedger). However, due to the quadratic message complexity of the existing MPC protocols BIB001 , only a small number of computation parties can be supported on-chain BIB010 . Moreover, only a limited number of mathematical operations (e.g., polynomial functions) are supported by the protocols, and the MPC-based blockchain framework is still far from matured.