Enhancing lifetime of wireless multimedia sensor networks using modified lion algorithm–based image transmission model

Abstract

PurposeIn the past few decades, the wireless sensor network (WSN) has become the more vital one with the involvement of the conventional WSNs and wireless multimedia sensor networks (WMSNs). The network that is composed of low-power, small-size, low-cost sensors is said to be WSN. Here, the communication information is handled using the multiple hop and offers only a simple sensing data, such as humidity, temperature and so on, whereas WMSNs are referred as the distributed sensing networks that are composed of video cameras, which contain the sector sense area. These WMSNs can send, receive and process the video information data, which is more intensive and complicated by wrapping with wireless transceiver. The WSNs and the WMSNs are varied in terms of their characteristic of turnablity and directivity.Design/methodology/approachThe main intention of this paper is to maximize the lifetime of network with reduced energy consumption by using an advanced optimization algorithm. The optimal transmission radius is achieved by optimizing the system parameter to transmit the sensor information to the consequent sensor nodes, which are contained within the range. For this optimal selection, this paper proposes a new modified lion algorithm (LA), the so-called cub pool-linked lion algorithm (CLA). The next contribution is on the optimal selection of cluster head (CH) by the proposed algorithm. Finally, the performance of proposed model is validated and compared over the other traditional methods in terms of network energy, convergence rate and alive nodes.FindingsThe proposed model's cost function relies in the range of 74–78. From the result, it is clear that at sixth iteration, the proposed model’s performance attains less cost function, that is, 11.14, 9.78, 7.26, 4.49 and 4.13% better than Genetic Algorithm (GA), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Glowworm Swarm Optimization (GSO) and Firefly (FF), correspondingly. The performance of the proposed model at eighth iteration is 14.15, 7.96, 4.36, 7.73, 7.38 and 3.39% superior to GA, DA, PSO, GSO, FF and LA, correspondingly with less convergence rate.Originality/valueThis paper presents a new optimization technique for increasing the network lifetime with reduced energy consumption. This is the first work that utilizes CLA for optimization problems.