Wireless sensor network (WSN) consists of a large number of sensors and sink nodes which are used to monitor events or environmental parameters, such as movement, temperature, humidity, etc. Reinforcement learning (RL) has been applied in a wide range of schemes in WSNs, such as cooperative communication, routing and rate control, so that the sensors and sink nodes are able to observe and carry out optimal actions on their respective operating environment for network and application performance enhancements. This article provides an extensive review on the application of RL to WSNs. This covers many components and features of RL, such as state, action and reward. This article presents how most schemes in WSNs have been approached using the traditional and enhanced RL models and algorithms. It also presents performance enhancements brought about by the RL algorithms, and open issues associated with the application of RL in WSNs. This article aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented

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Through time domain observation, typical wireless signal strength values seems to exhibit some forms of mean-reverting and discontinuous ''jumps'' behaviour. Motivated by this fact, we propose a wireless link prediction and triggering (LPT) technique using a modified mean-reverting Ornstein-Uhlenbeck (OU) jump diffusion process. The proposed technique which we refer as OU-LPT is an integral component of wireless mesh network monitoring system developed by ICT FP7 CARrier grade wireless MEsh Network project. In particular, we demonstrate how this technique can be applied in the context of wireless mesh networks to support link switching or handover in the event of predicted link degradation or failure. The proposed technique has also been implemented and evaluated in a real-time experimental testbed. The results show that OU-LPT technique can significantly enhance the reliability of wireless links by reducing the rate of false triggers compared to a conventional linear prediction technique and therefore offers a new direction on how wireless link prediction, triggering and switching process can be conducted in the future.

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Small-cell deployment within a wireless Heterogeneous Network (HetNet) presents backhauling challenges that differ from those of conventional macro-cells. Due to the lack of availability of fixed-lined backhaul at desired locations and due to cost saving reasons, operators may deploy a variety of backhaul technologies in a given network, combining available technologies such as fiber, xDSL, wireless backhaul and multihop mesh networks to backhaul small-cells. As a consequence, small-cells capacity may be non-uniform in the HetNet. Furthermore, some small-cells backhaul capacity may fluctuate if wireless backhaul is chosen. With such concerns in mind, a new network selection strategy considering small-cell backhaul capacity is proposed to ensure that users enjoy the best possible user experience especially in terms of connection throughput and fairness. The study compares performance of several common Network Selection Schemes (NSSs) such as WiFi First (WF) and Physical Data Rate (PDR) with the proposed Dynamic Backhaul Capacity Sensitive (DyBaCS) NSS in LTE-WiFi HetNets. The downlink performance of HetNet is evaluated in terms ofaverage throughput per user and fairness among users. The effects of varying WiFi backhaul capacity form the focus for the evaluation. Results show that the DyBaCS scheme generally provides superior performance in terms of fairness and average throughput per user across the range of backhaul capacities considered.

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INTRODUCTIONWireless mesh networks (WMNs) are a very promising technology to provide an easily deployable and cost-efficient solution for access to packet-based services for metropolitan areas with high population densities. Thus, WMNs may be a key technology in future 4G wireless networks and are currently becoming attractive in situations where it is not convenient to deploy wired backhaul connectivity. For example, it is often impractical to deploy wired infrastructure cost effectively or under tight time constraints. This is particularly true if the deployment is only transient in nature. Another key feature of WMNs is that unlike wireless multihop relay networks, WMNs are not restricted to tree-shaped topologies rooted at the gateway to the wired network and hence do not suffer from the same performance bottlenecks. Instead, any mesh node may communicate with any other one over multiple paths, allowing more efficient utilization of network resources. In contrast to ad hoc networks, WMNs are operated by a single entity, and their components have far fewer restrictions in terms of energy, resilience, and processing power.The main aim of the Carrier Grade Mesh Networks (CARMEN) project is to design WMN architecture capable of delivering carrier grade triple-play services at significantly lower capital and operational expenditures than comparable wired backhaul networks. There is a particular emphasis on providing a solution for both fast deployment and transient usage scenarios. This goal presents a number of significant research challenges. One key challenge is to reduce the investment required to deploy and operate WMNs. This will be achieved by applying advanced self-configuration and management techniques in all stages from planning to deployment and operation. Currently neither coordinated (e.g., WiMAX-like) nor uncoordinated (e.g., WiFi-like) medium access control (MAC) technologies use wireless resources efficiently for multihop mesh scenarios, making it difficult to provide any guaranteed quality of service (QoS) levels. Therefore, methods for provisioning the QoS required by carrier grade services over coordinated or uncoordinated wireless MAC protocols are a further research topic. In order to develop a robust and long lasting solution, the CARMEN architecture will not be bound to a specific radio technology. Rather, an abstract interface will be defined that can support heterogeneous radio technologies within CARMEN systems.Although WMNs have been the subject of several research initiatives (e.g., [2][3][4]) over the last few years, some of which have been developed into products, current WMN solutions fail to meet the stringent quality and reliability requirements of service providers. THE CARMEN PROJECTThe CARMEN Project is a three-year project partially funded by the EU's 7th Research Framework Program and has a total budget of approximately 6 million. The CARMEN project focuses on developing a heterogeneous mesh backhaul solution to provide carrier grade services with greater flexibility and at...

Abstract-We present an insight on the sensitivity of total cost (CAPEX+OPEX) towards various key input parameters for CARrier Grade Wireless MEsh Networks (CARMEN). deployment These input parameters span across three main categories namely the network design options, environment conditions and cost. Various boundary conditions are imposed to allow network operator to understand the impacts of parameters' changes with the highest level of uncertainty. A simple Tabu optimization method is adopted to optimize the node density against target data rate and range.