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 of Outlier Detection Methods in Network Anomaly Identification 
Chapter Title:  A Wavelet based approach 
1. Abstract of Cited Paper (BIB001):  The cost of a large wireless sensor network consisting of hundreds of devices may only be justified if it is deployed for multiple monitoring tasks. Introducing multiple tasks to the system however raises concerns regarding resource contention and quality of service (QoS). For example, rapid response tasks may have strict requirements with regards to data delivery latency, whereas data gathering tasks may be more lenient. In order to address the differential QoS challenge, we use the random re-routing (RRR) wireless sensor network routing protocol to prioritise the packets of some tasks above those of others. We extend the RRR protocol beyond that found in the previous literature to improve performance, and in simulation demonstrate an acknowledgement mechanism for adapting the packet priorities assigned to tasks. 
2. Abstract of Cited Paper (BIB002):  Environmental monitoring is one of the most popular applications in wireless sensor networks. Although it is important to obtain a continuous record of the environment, users may gain enough information without receiving every routine sensor measurement. Reducing unnecessary traffic allows better utilization of the network resources. However, it is uneasy to decide on when and what kind of traffic to reduce in a dynamically changing environment. In this paper, we study the problem of information-aware traffic reduction for wireless sensor networks. We propose a two-step information-aware traffic reduction algorithm to address this problem. First, we provide a distributed and real-time algorithm for sensors to classify their measurements and report them selectively based on the importance of information. Then, we propose a bandwidth allocation algorithm to assign different forwarding probabilities to packets considering both the information quality and the network load. Our algorithm can be implemented and integrated easily with existing routing protocols to maximize the quality of information to the users, while reducing the amount of network traffic. We evaluate our approach based on the real sensing measurements to demonstrate the quality of information achieved. Simulations are also conducted to evaluate the performance of our proposed scheme in a larger network. 
3. Abstract of Cited Paper (BIB003):  In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree. 
4. Abstract of Cited Paper (BIB004):  Network Anomaly Detection covers wide area of research. Current best practices for identifying and diagnosing traffic anomalies consist of visualizing traffic from different perspectives and identifying anomalies from prior experience. Different tools have been developed to automatically generate alerts to failures, but to automate the anomaly identification process remains a challenge. Recently, Signal Processing techniques have found applications in Network Intrusion Detection System because of their ability in detecting novel intrusions and attacks, which cannot be achieved by signature-based detection systems. Visualization techniques are ways of creating and handling graphical representations of data. This survey explains the main techniques known in the field of Statistical based and Wavelet based anomaly detection approaches and focuses on the role of data traffic visualization tools in network traffic anomaly detection. 
5. Abstract of Cited Paper (BIB005):  This paper presents an emergency communication system that is able to quickly deliver emergency messages over an unreliable and best-effort network such as the Internet. The proposed architecture employs application-layer multicast to rapidly deliver emergency traffic without the support of a dedicated network infrastructure. We introduce a distributed overlay tree construction and maintenance mechanism that produces consistent, loop-free and self-adaptive trees that dynamically change over time to effectively deal with varying network conditions and offer low message delays to end nodes. We evaluate the performance of the proposed approach through an experimental study conducted on a real-life networking testbed. 
6. Abstract of Cited Paper (BIB006):  Sensor networks (SNs) consist of spatially distributed sensors which monitor an environment, and which are connected to some sinks or backbone system to which the sensor data is being forwarded. In many cases, the sensor nodes themselves can serve as intermediate nodes for data coming from other nodes, on the way to the sinks. Much of the traffic carried by SNs will originate from routine measurements or observations by sensors that monitor a particular situation, such as the temperature and humidity in a room or the infrared observation of the perimeter of a house, so that the volume of routine traffic resulting from such observations may be quite high. When important and unusual events occur, such as a sudden fire breaking out or the arrival of an intruder, it will be necessary to convey this new information very urgently through the network to a designated set of sink nodes where this information can be processed and dealt with. This paper addresses the important challenge by avoiding the routine background traffic from creating delays or bottlenecks that impede the rapid delivery of high priority traffic resulting from the unusual events. Specifically we propose a novel technique, the ‘Randomized Re-Routing Algorithm (RRR)’, which detects the presence of novel events in a distributed manner, and dynamically disperses the background traffic towards secondary paths in the network, while creating a ‘fast track path’ which provides better delay and better quality of service (QoS) for the high priority traffic which is carrying the new information. When the surge of new information has subsided, this is again detected by the nodes and the nodes progressively revert to best QoS or shortest-path routing for all the ongoing traffic. The proposed technique is evaluated using a mathematical model as well as simulations, and is also compared with a standard node by a node priority scheduling technique.

Literature Review:
In a wireless sensor network (WSN), a large number of sensor nodes are distributed over a large area. The sensor nodes are endowed with wireless communication capabilities for sensing and processing. A measurement that significantly deviates from the normal pattern of sensed data detects an outlier in WSN. In BIB004 , a detailed overview of the existing outlier detection techniques for the WSN is given. A survey of wavelet based network anomaly detection approaches in the context of WSN can be found in BIB005 . In recent research, another significant work BIB002 can be found for anomaly detection by identifying outlier based on wavelets. In addition, research reported in BIB006 BIB003 BIB001 BIB007  ] present significant work dealing with automatic network response to unexpected events and improvement in quality of service (QoS).