A Novel Bat Algorithm for Line-of-Sight Localization in Internet of Things and Wireless Sensor Network

A Novel Bat Algorithm for Line-of-Sight Localization in Internet of Things and Wireless Sensor Network

Mihoubi Miloud (Djillali Liabes University, Algeria), Rahmoun Abdellatif (École Supérieure en Informatique, Algeria) and Pascal Lorenz (University of Haute Alsace, France)
DOI: 10.4018/978-1-5225-8100-0.ch009
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WSNs have recently been extensively investigated due to their numerous applications where processes have to be spread over a large area. One of the important challenges in WSNs is secure node localization. Its main objective is to protect the circulated information in WSN for any attack with low energy. For this reason, recent approaches relying on swarm intelligence techniques are called and the node localization is seen as an optimization problem in a multi-dimensional space. In this chapter, the authors present an improvement to the original bat algorithm for information protecting during the localization task. Hence, the proposed approach computes iteratively the position of the nodes and studied the scalability of the algorithm on a large WSN with hundreds of sensors that shows pretty good performance. Moreover, the parameters are simulated in different scenarios of simulation. In addition, a comparative study is conducted to give more performance to the proposed algorithm.
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A wireless sensor network (WSN) is an ad-hoc network with a large number of nodes that are micro sensors capable of collecting and transmitting environmental data in an autonomous way. The nodes’ positions are not necessarily predetermined, they can be randomly dispersed in a geographical area, called the “sensing area,” corresponding to the area of interest for the phenomenon being captured. In WSN, a large number of nodes are deployed in the network, the information detected by the sensor node will be gathered and transmitted through multi-hop techniques to sink (Figure 1). I.e. each node sends the information to its neighbour (so one hop between two neighbours) until it reaches the last and sends it to sink.

Recently, WSN has become a very active research field, and the fruitful employment of WSN has opened various new research areas for application, such as climate prediction, analysis of sane, atmospheric pressure, and so on (Adnan et al., 2014), where several issues are addressed in the WSN, such as energy minimization, compression schemes, self-organizing network algorithms, routing, Protocols, security, cyber security (He et al., 2017) and quality of service management.

Figure 1.

WSN communication architecture


(Mao et al., 2007) Node localization is one of the important challenges of WSN, (Boukerche et al., 2007) it plays a vital role in several fields, such as coverage, deployment purposes, routing information, location service, target tracking, and mortar launching and monitoring in underwater WSN (see figure 2) .

Figure 2.

Monitoring in Underwater WSN


The main objective of node localization is to estimate the sensor’s location with initially unknown location information; in order, the process uses knowledge of the absolute positions of a few sensors and inter-sensor measurements, such as: distance and bearing measurements. The sensors with unknown location information are called non anchor nodes, while sensors with known location information are called anchor or beacons nodes. Self-localization capability is highly desirable in environmental monitoring applications such as intrusion detection, road traffic monitoring, health monitoring, and so on. One possible solution of node localization is to equip each node with global positioning system (GPS) devices, but this solution is not suitable for two reasons:

  • The high cost of the device in terms of value, energy, computation power, and space.

  • The poor precision of the service in special environments (indoors, underground, etc.).

The first tentative vs. node localization is proposed in Reference (Doherty et al., 2001), where convex optimization is proposed to localize the network nodes. Currently, localization requires each unknown node to have GPS installed but uses only a few anchor nodes, and it uses communication techniques proposed in References (Yick et al., 2008, Kulkarni et al., 2009) to localize the unknown nodes, where their coordinates will be estimated by the sensor network localization algorithm. In fact, the node localization problem has been considered a multidimensional optimization problem, where optimization algorithms are used to resolve this matter, and the recently invented Bat algorithm is proposed (Yang, 2011) as a metaheuristic algorithm. The frequency parameter is unchanged in the Bat algorithm during the shifting of the Bats; this factor makes the algorithm heavy, but the execution time is really considerable. An efficient Bat algorithm is proposed by updating the frequency parameter, moreover, the velocity and the location parameters are also modified, and the concept of the Doppler Effect is integrated into the original algorithm.

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