Bangotra Deep Kumar, Singh Yashwant, Kumar Nagesh, Kumar Singh Pradeep, Ojeniyi Adegoke
Department of Higher Education, J&K Govt., India.
Department of Computer Science and Information Technology, Central University of Jammu, J&K, India.
Biomed Res Int. 2022 Mar 25;2022:1976694. doi: 10.1155/2022/1976694. eCollection 2022.
Wireless sensor network (WSN) is made up of tiny sensor nodes. The application of WSN in diverse fields has seen a tremendous escalation in recent years. WSN applications are constrained by the limited set of computing resources possessed by the sensor nodes and the security aspects of data communication in the WSN. Many algorithms based on nature-inspired optimization (NIO) have been proposed in the past to optimize the issue of energy efficiency and security in WSN. In the proposed work, two opportunistic routing algorithms, i.e., intelligent opportunistic routing protocol (IOP) and trust-based secure intelligent opportunistic routing protocol (TBSIOP), are compared against two NIO algorithms developed for achieving energy efficiency and security in WSN for performance analysis. The performance is evaluated by simulating the algorithms on MATLAB and comparing the obtained results with existing ACO-based and PSO-based routing algorithms. It is observed that the TBSIOP outperforms the NIO-based algorithms in terms of energy efficiency, network lifetime, packet delivery ratio, end-to-end delay, and average risk level. All the parameters under consideration are recorded in the presence of a maximum of 50% malicious nodes for 25, 50, and 100 nodes' test cases. The increasing size of the network has a significant effect on the performance of TBSIOP, as the packet delivery ratio is close to 100%. Also, TBSIOP can easily avoid malicious nodes during the routing process as reflected from the results. This will improve the network lifetime of TBSIOP compared to other protocols. As far as the application of the work is concerned, it would be beneficial for smart healthcare services. It can also help in better communication during the sharing of data by providing energy-efficient services and keeping the network alive for a longer period.
无线传感器网络(WSN)由微型传感器节点组成。近年来,WSN在各个领域的应用急剧增加。WSN应用受到传感器节点所拥有的有限计算资源集以及WSN中数据通信安全方面的限制。过去已经提出了许多基于自然启发式优化(NIO)的算法来优化WSN中的能源效率和安全问题。在这项拟议的工作中,将两种机会路由算法,即智能机会路由协议(IOP)和基于信任的安全智能机会路由协议(TBSIOP),与为实现WSN中的能源效率和安全而开发的两种NIO算法进行比较,以进行性能分析。通过在MATLAB上模拟这些算法并将获得的结果与现有的基于蚁群优化(ACO)和基于粒子群优化(PSO)的路由算法进行比较来评估性能。可以观察到,TBSIOP在能源效率、网络寿命、数据包交付率、端到端延迟和平均风险水平方面优于基于NIO的算法。在25、50和100个节点的测试用例中,在最多50%恶意节点存在的情况下记录所有考虑的参数。网络规模的增加对TBSIOP的性能有显著影响,因为数据包交付率接近100%。此外,从结果可以看出,TBSIOP在路由过程中可以轻松避免恶意节点。与其他协议相比,这将提高TBSIOP的网络寿命。就这项工作的应用而言,它将有利于智能医疗服务。它还可以通过提供节能服务并使网络长时间保持运行来帮助在数据共享期间实现更好的通信。