Swarm Intelligent Data Aggregation in Wireless Sensor Network

Swarm Intelligent Data Aggregation in Wireless Sensor Network

Jinil Persis Devarajan (National Institute of Industrial Engineering (NITIE), Mumbai, India) and T. Paul Robert (Anna University, Chennai, India)
Copyright: © 2020 |Pages: 18
DOI: 10.4018/IJSIR.2020040101
OnDemand PDF Download:
No Current Special Offers


Data aggregation in WSNs is an interesting problem wherein data sensed by the sensors is routed to an aggregation node in an efficient way. Since the sensors are battery operated, it is very important for a routing protocol to conserve energy and also ensure load balancing and faster delivery. In this study, a multi-objective linear programming model is developed for this problem and solved using an exact algorithm applying dominance principle. In order to ensure faster convergence, routing algorithms incorporating strategies of swarms in nature such as Ants, Bees and Fireflies are adapted. In the simulation study, it is quite evident from the convergence characteristics, swarm intelligent algorithms could converge earlier than the exact algorithm with convergence time lesser by 90%. Moreover, when exact algorithm could solve smaller networks, the swarm intelligent algorithms could solve even larger network instances. Firefly algorithm is able to yield approximated pareto – optimal routes which outperforms ant colony optimization and bee colony optimization algorithms.
Article Preview

1. Introduction

With the development of cyber physical systems, wireless sensor networks (WSNs) are now effectively being used in monitoring industrial/production layouts in problems such as equipment health, industrial safety, quality/defect identification, remote monitoring of underwater ecosystem, battlefield/border surveillance, traffic control, patient health monitoring, mining, so forth (Kwong et al., 2011; Lee & Moon, 2014; Arora et al., 2016; Sarkar & Murugan, 2016). WSN is typically a network consisting of sensors and one or more aggregation nodes that are used to monitor a given environment (Abdulsalam et al., 2014; Dhand & Tyagi, 2016). WSN faces lot of challenges such as data aggregation, node deployment (Abdollahzadeh & Navimipour, 2015), secure routing, bandwidth allocation, failure diagnosis (Mahapatro & Mohan, 2013) and so forth. This study deals with data aggregation where in, the data sensed by the sensors in the network has to be collected and aggregated in a central node for further processing. The delivery of data to the central node from various other nodes in the network occurs through multi-hop communication (Behdani et al., 2012; Elnaggar et al., 2015; Rosset et al., 2017). These routes/paths that every node uses for data delivery are required to have less energy consumption (Ok et al., 2009; Zeng & Dong, 2015), faster data delivery (Korteweg et al., 2009) and load balancing (Abdollahzadeh & Navimipour, 2015; Arya & Sharma, 2015). The routing protocol that performs this challenging task in less computational effort needs to be developed.

Literature reports several network routing protocols proposed by various researchers. Commonly used hop count based routing protocols for WSNs protocols are - Adhoc On-Demand Vector (AODV) protocol [22], Dynamic source routing (DSR) protocol (Ng & Tsimenidis, 2013), destination-sequenced distance-vector routing (DSDV) (Mohamad et al., 2015), Optimized link state routing (OLSR) (T. Yang et al., 2009). Several variants of these protocols are also proposed to overcome the drawbacks of these protocols. Other strategies such as Hierarchical routing protocols, Location-based routing protocols, Multipath routing protocols and QoS-based routing are also developed (Sohraby et al., 2007; Anasane & Satao, 2016; Asif et al., 2017; Sabor et al., 2017).

Complete Article List

Search this Journal:
Open Access Articles
Volume 13: 4 Issues (2022): 2 Released, 2 Forthcoming
Volume 12: 4 Issues (2021)
Volume 11: 4 Issues (2020)
Volume 10: 4 Issues (2019)
Volume 9: 4 Issues (2018)
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing