Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Car-Park Management using Wireless Sensor Networks

A complete wireless sensor network solution for car-park management is presented in this paper. The system architecture and design are first detailed, followed by a description of the current working implementation, which is based on our DSYS25z sensing nodes. Results of a series of real experimental tests regarding connectivity, sensing and network performance are then discussed. The analysis of link characteristics in the car park scenario shows unexpected reliability patterns which have a strong influence on MAC and routing protocol design. Two unexpected link reliability patterns are identified and documented. First, the presence of the objects (cars) being sensed can cause significant interference and degradation in communication performance. Second, link quality has a high temporal correlation but a low spatial correlation. From these observations we conclude that a) the construction and maintenance of a fixed topology is not useful and b) spatial rather than temporal message replicates can improve transport reliability

Emerging Communications for Wireless Sensor Networks

Wireless sensor networks are deployed in a rapidly increasing number of arenas, with uses ranging from healthcare monitoring to industrial and environmental safety, as well as new ubiquitous computing devices that are becoming ever more pervasive in our interconnected society. This book presents a range of exciting developments in software communication technologies including some novel applications, such as in high altitude systems, ground heat exchangers and body sensor networks. Authors from leading institutions on four continents present their latest findings in the spirit of exchanging information and stimulating discussion in the WSN community worldwide

Analysis of Low Energy Adaptive Clustering Hierarchy (LEACH) protocol

Sensor network consists of tiny sensors and actuators with general purpose computing elements to cooperatively monitor physical or environmental conditions, such as temperature, pressure, etc. Wireless Sensor Networks are uniquely characterized by properties like limited power they can harvest or store, dynamic network topology, large scale of deployment. Sensor networks have a huge application in fields which includes habitat monitoring, object tracking, fire detection, land slide detection and traffic monitoring. Based on the network topology, routing protocols in sensor networks can be classified as flat-based routing, hierarchical-based routing and location-based routing. These protocols are quite simple and hence are very susceptible to attacks like Sinkhole attack, Selective forwarding, Sybil attack, Wormholes, HELLO flood attack, Acknowledgement spoofing or altering, replaying routing information. Low Energy Adaptive Clustering Hierarchy (LEACH) is an energy-efficient hierarchical-based routing protocol. Our prime focus was on the analysis of LEACH based upon certain parameters like network lifetime, stability period, etc. and also the effect of selective forwarding attack and degree of heterogeneity on LEACH protocol. After a number of simulations, it was found that the stability region’s length is considerably increased by choosing an optimal value of heterogeneity; energy is not properly utilized and throughput is decreased in networks compromised by selective forwarding attack but the number of cluster-heads per round remains unaffected in such networks

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial