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.