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

The work summarises a study of the data communications requirements for agricultural livestock monitoring applications using wireless sensor networks (WSNs). Several design challenges are identified and analysed in depth based on actual global positioning system positioning data gathered from an actual herd of cattle. A wireless system including antennae diversity together with data downloads optimisation schemes utilising data collector and routers are developed and tested in a working farm environment. Two analysis metrics, connection availability and connection duration, are used to quantify the impact of cattle movement on network connectivity. The major contributions of this study stem from a definition of the communication issues in deploying animal monitoring platforms in free-ranging farm environments and the analysis and optimisation of the wireless data download performance using as the foundation knowledge gained from a series of working farm trials. Additionally, the data download protocols are designed particularly to treat animal movement. The results prove the viability of WSN-based solutions for livestock monitoring applications

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This paper investigates an adaptation of Wireless Sensor Networks (WSNs) to cattle monitoring application. The proposed solution facilitates a desired requirement of continuously assessing the condition of individual animal, aggregating and reporting these data to the farm manager. There are several existing approaches to animal monitoring, from using a store and forward mechanisms to employing a GSM technique. These approaches for monitoring livestock health can only provide sporadic information and introduce a considerable cost in staffing and physical hardware. The core of this study is to overcome the aforementioned drawbacks by using alternative low cost, low power consumption sensor nodes, which are capable of providing real-time communications at a reasonable hardware cost. In this paper, the hardware and software have been carefully designed to provide early indication of possible outbreaks while conforming to WSNs' stringent limitations.

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.