3D Multi-Objective Deployment of an Industrial Wireless Sensor Network for Maritime Applications Utilizing a Distributed Parallel Algorithm

Effective monitoring marine environment has become a vital problem in the marine applications. Traditionally, marine application mostly utilizes oceanographic research vessel methods to monitor the environment and human parameters. But these methods are usually expensive and time-consuming, also limited resolution in time and space. Due to easy deployment and cost-effective, WSNs have recently been considered as a promising alternative for next generation IMGs. This paper focuses on solving the issue of 3D WSN deployment in a 3D engine room space of a very large crude-oil carrier (VLCC), in which many power devices are also considered. To address this 3D WSN deployment problem for maritime applications, a 3D uncertain coverage model is proposed with a new 3D sensing model and an uncertain fusion operator, is presented. The deployment problem is converted into a multi-objective problems (MOP) in which three objectives are simultaneously considered: Coverage, Lifetime and Reliability. Our aim is to achieve extensive Coverage, long Lifetime and high Reliability. We also propose a distributed parallel cooperative co-evolutionary multi-objective large-scale evolutionary algorithm (DPCCMOLSEA) for maritime applications. In the simulation experiments, the effectiveness of this algorithm is verified in comparing with five state-of-the-art algorithms. The numerical outputs demonstrate that the proposed method performs the best with respect to both optimization performance and computation time

Survey on Various Aspects of Clustering in Wireless Sensor Networks Employing Classical, Optimization, and Machine Learning Techniques

A wide range of academic scholars, engineers, scientific and technology communities are interested in energy utilization of Wireless Sensor Networks (WSNs). Their extensive research is going on in areas like scalability, coverage, energy efficiency, data communication, connection, load balancing, security, reliability and network lifespan. Individual researchers are searching for affordable methods to enhance the solutions to existing problems that show unique techniques, protocols, concepts, and algorithms in the wanted domain. Review studies typically offer complete, simple access or a solution to these problems. Taking into account this motivating factor and the effect of clustering on the decline of energy, this article focuses on clustering techniques using various wireless sensor networks aspects. The important contribution of this paper is to give a succinct overview of clustering

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented

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

Differential Evolution-based 3D Directional Wireless Sensor Network Deployment Optimization

Wireless sensor networks (WSNs) are applied more and more widely in real life. In actual scenarios, 3D directional wireless sensors (DWSs) are constantly employed, thus, research on the real-time deployment optimization problem of 3D directional wireless sensor networks (DWSNs) based on terrain big data has more practical significance. Based on this, we study the deployment optimization problem of DWSNs in the 3D terrain through comprehensive consideration of coverage, lifetime, connectivity of sensor nodes, connectivity of cluster headers and reliability of DWSNs. We propose a modified differential evolution (DE) algorithm by adopting CR-sort and polynomial-based mutation on the basis of the cooperative coevolutionary (CC) framework, and apply it to address deployment problem of 3D DWSNs. In addition, to reduce computation time, we realize implementation of message passing interface (MPI) parallelism. As is revealed by the experimentation results, the modified algorithm proposed in this paper achieves satisfying performance with respect to either optimization results or operation time

A novel predictive optimization scheme for energy-efficient reliable operation of a sensor in dynamic scenarios

Wireless Sensor Network (WSN) has been studied for more than a decades that resulted in evolution of the significant applications towards assisting in sensing physical information from human inaccesible area. It was also observed from existing sysem that energy attribute is the root cause of majority of the problems associated with WSN that also gives rise to various operational reliability issue. Therefore, the prime goal of the proposed study is to present a novel predictive optimization approach of data fusion in order to jointly address the problems associated with energy efficiency and reliable operation of sensor nodes in WSN. An analytical research approach is carried out in order to ensure that a time-based synchronization scheme contributes to offer an evolutionary approach towards significant energy optimization. A simulation-based benchmarking analysis is carried out to find that proposed system offers good energy-efficient performance in comparison to existing approaches