Purpose

In 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/approach

The 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.

Findings

The 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/value

This 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.

中文翻译：

---

使用基于改进的基于狮子算法的图像传输模型来延长无线多媒体传感器网络的寿命

目的

在过去的几十年中，无线传感器网络（WSN）在传统的WSN和无线多媒体传感器网络（WMSN）的参与下变得更加重要。由低功耗，小尺寸，低成本传感器组成的网络被称为WSN。在此，通信信息使用多跳进行处理，并且仅提供简单的感测数据，例如湿度，温度等，而WMSN被称为分布式感测网络，该网络由包含扇区感测区域的摄像机组成。 。这些WMSN可以发送，接收和处理视频信息数据，而通过包装无线收发器，这些信息将变得更加密集和复杂。WSN和WMSN的可旋转性和方向性特征各不相同。

设计/方法/方法

本文的主要目的是通过使用高级优化算法来最大程度地延长网络寿命，并降低能耗。通过优化系统参数以将传感器信息传输到后续传感器节点（包含在该范围内），可以实现最佳传输半径。对于这种最佳选择，本文提出了一种新的改进的狮子算法（LA），即所谓的“幼崽池联狮子算法”（CLA）。下一个贡献是通过提出的算法对簇头（CH）的最佳选择。最后，在网络能量，收敛速度和活跃节点方面，与其他传统方法相比，所提出模型的性能得到了验证和比较。

发现

拟议模型的成本函数在74-78之间。从结果可以明显看出，在第六次迭代中，所提出的模型的性能获得了较少的成本函数，即分别比遗传算法（GA），蜻蜓算法（DA），粒子群算法提高了11.14、9.78、7.26、4.49和4.13％优化（PSO），萤火虫群优化（GSO）和萤火虫（FF）。所提出的模型在第八次迭代时的性能分别比GA，DA，PSO，GSO，FF和LA分别高14.15、7.96、4.36、7.73、7.38和3.39％，收敛速度更小。

创意/价值

本文提出了一种新的优化技术，可通过减少能耗来延长网络寿命。这是利用CLA解决优化问题的第一项工作。