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

DESIGN OF MOBILE DATA COLLECTOR BASED CLUSTERING ROUTING PROTOCOL FOR WIRELESS SENSOR NETWORKS

Wireless Sensor Networks (WSNs) consisting of hundreds or even thousands of nodes, canbe used for a multitude of applications such as warfare intelligence or to monitor the environment. A typical WSN node has a limited and usually an irreplaceable power source and the efficient use of the available power is of utmost importance to ensure maximum lifetime of eachWSNapplication. Each of the nodes needs to transmit and communicate sensed data to an aggregation point for use by higher layer systems. Data and message transmission among nodes collectively consume the largest amount of energy available in WSNs. The network routing protocols ensure that every message reaches thedestination and has a direct impact on the amount of transmissions to deliver messages successfully. To this end, the transmission protocol within the WSNs should be scalable, adaptable and optimized to consume the least possible amount of energy to suite different network architectures and application domains. The inclusion of mobile nodes in the WSNs deployment proves to be detrimental to protocol performance in terms of nodes energy efficiency and reliable message delivery. This thesis which proposes a novel Mobile Data Collector based clustering routing protocol for WSNs is designed that combines cluster based hierarchical architecture and utilizes three-tier multi-hop routing strategy between cluster heads to base station by the help of Mobile Data Collector (MDC) for inter-cluster communication. In addition, a Mobile Data Collector based routing protocol is compared with Low Energy Adaptive Clustering Hierarchy and A Novel Application Specific Network Protocol for Wireless Sensor Networks routing protocol. The protocol is designed with the following in mind: minimize the energy consumption of sensor nodes, resolve communication holes issues, maintain data reliability, finally reach tradeoff between energy efficiency and latency in terms of End-to-End, and channel access delays. Simulation results have shown that the Mobile Data Collector based clustering routing protocol for WSNs could be easily implemented in environmental applications where energy efficiency of sensor nodes, network lifetime and data reliability are major concerns

A comparative study of clusterhead selection algorithms in wireless sensor networks

In Wireless Sensor Network, sensor nodes life time is the most critical parameter. Many researches on these lifetime extension are motivated by LEACH scheme, which by allowing rotation of cluster head role among the sensor nodes tries to distribute the energy consumption over all nodes in the network. Selection of clusterhead for such rotation greatly affects the energy efficiency of the network. Different communication protocols and algorithms are investigated to find ways to reduce power consumption. In this paper brief survey is taken from many proposals, which suggests different clusterhead selection strategies and a global view is presented. Comparison of their costs of clusterhead selection in different rounds, transmission method and other effects like cluster formation, distribution of clusterheads and creation of clusters shows a need of a combined strategy for better results.Comment: 12 pages, 3 figures, 5 tables, Int JournaL, International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.4, November 201

UDCA: Energy optimization in wireless sensor networks using uniform distributed clustering algorithms

Transceivers are the major energy consumption in a Wireless Sensor Network which is made of low-power, small in size, low cost and multi-functional nodes. These sensor nodes are operated by batteries which put significant constraint to the energy available to them. Each sensor node collects sensed data and forwards it to a single processing centre called the base station which uses all reported data to detect an event or determine the changes in an environment. In present study, we propose energy optimization in Wireless Sensor Networks (WSNs) using uniform distributed clustering algorithms. One of the algorithms distributes cluster heads uniformly in each cluster and each non-cluster head transmit its data to the cluster heads with short distance which reduces the communication distance of each node. Thus, minimizes the energy consumption of sensor nodes. The second algorithm generates cluster heads in hierarchical form in order to transmit the aggregate data to the base station. It was observed that there is increase in energy savings as we move from bottom up in the hierarchy. Both UDCA protocol and Low Energy Adaptive Cluster Hierarchy protocol (LEACH) were simulated. The simulation results show significant reduction in energy consumption of sensor nodes and cluster heads are more uniformly distributed among all nodes in UDCA compare with LEACH and extend the wireless sensor networks lifetime