Table of Contents Author Guidelines Submit a Manuscript
Mobile Information Systems
Volume 2016, Article ID 8475820, 13 pages
http://dx.doi.org/10.1155/2016/8475820
Research Article

Energy Efficient Data Dissemination in Multi-UAV Coordinated Wireless Sensor Networks

1Computer Science and Engineering Department, Thapar University, Patiala, Punjab 147004, India
2Department of Information Security Engineering, Soonchunhyang University, Asan-si 31538, Republic of Korea

Received 29 March 2016; Accepted 4 May 2016

Academic Editor: Daniel G. Reina

Copyright © 2016 Vishal Sharma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

With the requirement of better connectivity and enhanced coverage, collaborative networks are gaining a lot of popularity these days. One of such collaborative networks is formed between the wireless sensor networks (WSNs) and the unmanned aerial vehicles (UAVs). WSNs comprise static nodes arranged in a flat grid topology which may be randomly deployed or by some particular distribution. With the sensors operating on batteries, WSNs face a crucial issue of energy depletion during network operations. Integration of WSNs with UAVs can provide a solution to this excessive utilization of energy resources. UAVs provide a maneuvering support by playing a pivotal role of a manager node in these networks. However, integration of these networks demands an improved approach for data dissemination for effective utilization of network resources. For this, we propose a new data dissemination approach, which utilizes the attraction properties of fire fly optimization algorithm to provide energy efficient relaying. The proposed approach provides continuous connectivity, better lifetime, and improved coverage in the UAV coordinated WSNs. The performance of the proposed model is presented in terms of significant gains attained for parameters, namely, throughput, coverage, mean hops, lifetime, and delays, in comparison with the EEGA, ERIDSR, and I-ERIDSR approaches.