Cooperative beamsteering in wireless sensor network based on backtracking search algorithm

The progressive development of Wireless Sensor Network (WSNs) contributes to many applications such as in the intelligent transport system (ITS), safety monitoring, military and in natural disasters prevention. In parallel to WSNs, the idea of internet of things (IoT) is developed where IoT can be defined as an interconnection between identifiable devices within the internet connection in sensing and monitoring processes. With recent growth in both size and power efficient computing, the concept of the ubiquitous WSN has aggressively emerged as an acknowledged research topic. As the capabilities of individual nodes in WSNs increase, so does the opportunity to perform more complicated tasks, such as cooperative beamsteering (CB). This CB manages to improve the range of communications and save precious battery power during the transmission. Therefore, this research proposes a meta-heuristic algorithm to organize node location in array arrangement. It is expected to effectively improve radiation beampattern fluctuations, exhibits lower complexity and less energy. From the simulation that has been done, it's observed that the proposed algorithm helps to reduce the side lobe level, thus better radiation beampattern is achieved

Backtracking Search Optimization for Collaborative Beamforming in Wireless Sensor Networks

Due to energy limitation and constraint in communication capabilities, the undesirable high battery power consumption has become one of the major issues in wireless sensor network (WSN). Therefore, a collaborative beamforming (CB) method was introduced with the aim to improve the radiation beampattern in order to compensate the power consumption. A CB is a technique which can increase the sensor node gain and performance by aiming at the desired objectives through intelligent capabilities. The sensor nodes were located randomly in WSN environment. The nodes were designed to cooperate among each other and act as a collaborative antenna array. The configuration of the collaborative nodes was modeled in circular array formation. The position of array nodes was determined by obtaining the optimum parameters pertaining to the antenna array which implemented by using Backtracking Search Optimization Algorithm (BSA). The parameter considered in the project was the side-lobe level minimization. It was observed that, the suppression of side-lobe level for BSA was better compared to the radiation beampattern obtained for conventional uniform circular array

Hybrid backtracking search optimization algorithm and K-means for clustering in wireless sensor networks

Rapid technology evolvement in the area of wireless sensor networks (WSNs) has led to many application-specific protocols that are particularly developed to cover different fields of usage and various network scenarios. Energy efficiency is one of the apparent challenges facing WSNs which has impacted immensely on the network performance. Hence, clustering protocols that eliminate energy inefficiencies in the network is essential. As finding an optimal set of cluster heads is an NP-hard problem, the application of heuristic algorithm is required to produce good clustering. In this paper, we propose a clustering solution for WSNs using a hybrid algorithm based on Backtracking Search Optimization Algorithm (BSA) and K-Means. A fitness function that incorporates aspects such as expected energy consumption in the network and maximum intra-cluster distance is utilized to address the problem of energy efficiency. Performance comparison against well-known clustering protocols such as LEACH and LEACH-C reveals that the hybrid of BSA and K-Means clustering algorithm is able to deliver more data to the base station and extends the network lifetime