Intelligent Technique Based on Enhanced Metaheuristic for Optimization Problem in Internet of Things and Wireless Sensor Network

Intelligent Technique Based on Enhanced Metaheuristic for Optimization Problem in Internet of Things and Wireless Sensor Network

Miloud Mihoubi (EEDIS Laboratory, Djillali Liabès University, Sidi Bel Abbès, Algeria & DAEI Laboratory, University of Ibn Khaldoun, Tiaret, Algeria), Abdellatif Rahmoun (LabRI-SBA Lab, École Supérieure en Informatique, Sidi Bel Abbes, Algeria), Meriem Zerkouk (University of Sciences and Technology - Mohamed Boudiaf, Bir El Djir, Algeria), Pascal Lorenz (IRIMAS Laboratory, University of Haute Alsace, Colmar, France) and Lotfi Baidar (LabRI-SBA Lab, École Supérieure en Informatique, Sidi Bel Abbes, Algeria)
Copyright: © 2020 |Pages: 26
DOI: 10.4018/IJGHPC.2020070102

Abstract

For the last decade, there has been an intensive research development in the area of wireless sensor networks (WSN). This is mainly due to their growing interest in several applications of the Internet of Things (IoT). Several issues are thus discussed such as node localization, a capability that is highly desirable for performance evaluation in monitoring applications. The localization aim is to look for precise geographical positions of sensors. Recently, swarm intelligence techniques are suggested to deal with localization challenge and localization is seen as an optimization problem. In this article, an Enhanced Fruit Fly Optimization Algorithm (EFFOA) is proposed to solve the localization. EFFOA has a strong capacity to calculate the position of the unknown nodes and converges iteratively to the best solution. Distributing and exploiting nodes is a chief challenge to testing the scalability performance. the EFFOA is simulated under variant studies and scenarios. in addition, a comparative experimental study proves that EFFOA outperforms some of the well-known optimization algorithms.
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Introduction

Wireless sensor networks (WSNs) consist of a large number of densely deployed nodes which are tiny, low power, inexpensive, multi-functional connected by a wireless medium. Its main task is to provide information of environment in real time, the sensors are installed for tracking and monitoring the environmental requirements and physical phenomenon such as climate prediction, analysis of sane, atmospheric pressure, etc. WSN is employed in several application domains such as security and surveillance, data aggregation, environment sensing, industrial process control, structural health monitoring (Akyildiz et al., 2002) (see Figure 1).

Figure 1.

Health monitoring

IJGHPC.2020070102.f01

The gathered information needs to be associated with sensor nodes rental to provide an accurate view of the sensor field. Each sensor node can monitor its region and proceed to send the collected data to sink node, the sink node is managed by base station via internet link (see Figure 2), the effectiveness of WSN is bound to its ability to collect data and transmit them in a minimum time and with greater precision (Jin et al., 2017; Yick et al., 2008). Numerous challenges are addressed in WSN and have been largely browsed: energy minimization (optimization), energy harvesting, compression schemes, self-organizing network algorithms, routing protocols, security and quality of service management.

Figure 2.

WSN communication architecture

IJGHPC.2020070102.f02

The three most essential problems are energy efficiency, quality of service and security management (Iqbal et al., 2015), last decades, WSN has seen an important attention in several application domain and detailed survey are proposed in (Pal 2010; Khelifi et al., 2018; Alrajeh et al., 2013), the most fruitful challenge of WSN is node localization, Localization algorithm has received much importance in the last years, firstly mathematical methods are proposed to deal with node localization issues.

The information location plays a vital role in coverage, deployment purpose, routing information, location service, target tracking, and rescue operations.

The simple solution is to equip every node with a Global Positioning System (GPS) receiver that can accurately provide the sensors with their exact location, localization by equipping each node with GPS is not appropriate, because it is less energy efficient and expensive, needs large size of hardware and has a line of sight problem. If GPS is installed on every node, then it increases the node size and deployment cost. Furthermore, GPS is not energy efficient as it consumes a lot of energy and not suitable for a network like (indoor, underground and underwater environment). Numerous localization methods have been proposed for solving the problem, in place of equipping each node by a GPS, the majority of methods allowed to use a certain number of nodes in the network equipped with GPS. These nodes are generally known by anchor nodes, beacons or landmark which their position is known.

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