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

APPLICATION OF SOFT COMPUTING TECHNIQUES OVER HARD COMPUTING TECHNIQUES: A SURVEY

Soft computing is the fusion of different constituent elements. The main aim of this fusion to solve real-world problems, which are not solve by traditional approach that is hard computing. Actually, in our daily life maximum problem having uncertainty and vagueness information. So hard computing fail to solve this problems, because it give exact solution. To overcome this situation soft computing techniques plays a vital role, because it has capability to deal with uncertainty and vagueness and produce approximate result. This paper focuses on application of soft computing techniques over hard computing techniques

An ACO Algorithm for Effective Cluster Head Selection

This paper presents an effective algorithm for selecting cluster heads in mobile ad hoc networks using ant colony optimization. A cluster in an ad hoc network consists of a cluster head and cluster members which are at one hop away from the cluster head. The cluster head allocates the resources to its cluster members. Clustering in MANET is done to reduce the communication overhead and thereby increase the network performance. A MANET can have many clusters in it. This paper presents an algorithm which is a combination of the four main clustering schemes- the ID based clustering, connectivity based, probability based and the weighted approach. An Ant colony optimization based approach is used to minimize the number of clusters in MANET. This can also be considered as a minimum dominating set problem in graph theory. The algorithm considers various parameters like the number of nodes, the transmission range etc. Experimental results show that the proposed algorithm is an effective methodology for finding out the minimum number of cluster heads.Comment: 7 pages, 5 figures, International Journal of Advances in Information Technology (JAIT); ISSN: 1798-2340; Academy Publishers, Finlan

Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks

This book presents collective works published in the recent Special Issue (SI) entitled "Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks”. These works expose the readership to the latest solutions and techniques for MANETs and VANETs. They cover interesting topics such as power-aware optimization solutions for MANETs, data dissemination in VANETs, adaptive multi-hop broadcast schemes for VANETs, multi-metric routing protocols for VANETs, and incentive mechanisms to encourage the distribution of information in VANETs. The book demonstrates pioneering work in these fields, investigates novel solutions and methods, and discusses future trends in these field

Tom and Jerry Based Multipath Routing with Optimal K-medoids for choosing Best Clusterhead in MANET

Given the unpredictable nature of a MANET, routing has emerged as a major challenge in recent years. For effective routing in a MANET, it is necessary to establish both the route discovery and the best route selection from among many routes. The primary focus of this investigation is on finding the best path for data transmission in MANETs. In this research, we provide an efficient routing technique for minimising the time spent passing data between routers. Here, we employ a routing strategy based on Tom and Jerry Optimization (TJO) to find the best path via the MANET's routers, called Ad Hoc On-Demand Distance Vector (AODV). The AODV-TJO acronym stands for the suggested approach. This routing technique takes into account not just one but three goal functions: total number of hops. When a node or connection fails in a network, rerouting must be done. In order to prevent packet loss, the MANET employs this rerouting technique. Analyses of AODV-efficacy TJO's are conducted, and results are presented in terms of energy use, end-to-end latency, and bandwidth, as well as the proportion of living and dead nodes. Vortex Search Algorithm (VSO) and cuckoo search are compared to the AODV-TJO approach in terms of performance. Based on the findings, the AODV-TJO approach uses 580 J less energy than the Cuckoo search algorithm when used with 500 nodes