A Hybrid Algorithm for Optimal Wireless Sensor Network Deployment with the Minimum Number of Sensor Nodes

Abstract

Abstract:Wireless sensor network (WSN) applications are rapidly growing and are widely used in various disciplines. Deployment is one of the key issues to be solved in WSNs, since the sensor nodes' positioning affects highly the system performance. An optimal WSN deployment should maximize the collection of the desired interest phenomena, guarantee the required coverage and connectivity, extend the network lifetime, and minimize the network cost in terms of energy consumption. Most of the research effort in this area aims to solve the deployment issue, without minimizing the network cost by reducing unnecessary working nodes in the network. In this paper, we propose a deployment approach based on the gradient method and the Simulated Annealing algorithm to solve the sensor deployment problem with the minimum number of sensor nodes. The proposed algorithm is able to heuristically optimize the number of sensors and their positions in order to achieve the desired application requirements.

Penerapan Keamanan WSN Berbasis Algoritma RSA 2048 dan SHA-3 pada Pemantauan Suhu

“…Kendala umum bagi pendekatan manajemen kunci adalah batas atas jumlah total node dalam jaringan [6]. Berbagai pendekatan kemudian digunakan untuk memaksimalkan cakupan dan konektifitas jaringan [10]. WSN saat ini banyak menggunakan teknologi Zigbee, namun kendala yang dihadapi pada teknologi ini adalah kelemahan pada protokol keamanannya [11], jangkauan yang pendek dan tidak terlalu jauh.…”

Deployment Techniques in Wireless Sensor Networks, Coverage and Connectivity: A Survey

“…Finally, monitoring events along international borders to detect illegal intrusion is an example of barrier coverage [14]. There are two barrier coverage types, full barrier coverage which means that every location of the barrier is covered by at least one sensor based on a Poisson point process model [15].…”

“…Annealing process by heating and cooling of a material to increase the size of its crystals and reduce their defects. A hybrid algorithm for optimal WSN deployment with a minimum number of sensor nodes was proposed in [14]. The proposed approach was built upon combining the gradient method with simulated annealing algorithm, which can solve both area and barrier coverage problems, for 1-coverage and 1-connectivity applications.…”

An IoT System for Social Distancing and Emergency Management in Smart Cities Using Multi-Sensor Data

“…Importantly, the applicability of the WSNs mainly depends on the lifetime of the sensor nodes, and, for this reason, it is important to design this type of system while bearing in mind this crucial aspect and selecting the more convenient energy efficient routing protocol [9][10][11]. Other important design parameters are [10,12,13]: (i) the limited storing and computational resources of each sensing nodes, (ii) the costs (i.e., cheap sensors are prone to failure, while expensive sensors need good housing and cannot be used for dense deployments), (iii) the position of each sensing node, which cannot be predetermined and depends on the accessibility of the point where the node should be placed, (iv) the sensing nodes' deployment (to collect the needed data, to have the required coverage and connectivity, to extend the network lifetime, and to minimize energy consumption), and (v) the minimum number of time slots required to aggregate data along the edges of a data-gathering tree spanning all the nodes in a WSN (a.k.a., minimum aggregation delay), if the gathered data are aggregated before the transmission to the control center. The solution presented in this paper was designed while bearing in mind all the design parameters mentioned above, focusing, in particular, on maximizing the exploitation of the nodes' storing and computational resources, on minimizing the system cost and on optimizing the system deployment.…”