M. Ezhilarasi’s research while affiliated with Sri Ramakrishna Engineering College and other places

The application of wireless sensor networks is not limited to a particular domain. Technology advancements result in innovative solutions for simple communication to large applications via wireless sensor IoT networks. Besides the advancements, there is a serious issue in terms of threats or attacks on wireless sensor networks, which is common. Various intrusion detection methodologies have evolved so far to detect common network attacks. But it is essential to concentrate on other routing attacks like selective forwarding attack, black hole attack, Sybil attack, wormhole attack, identity replication attack, and hello flood attack. Existing research models concentrate on any one of the above-mentioned routing attacks and attain better detection performance. Detecting each attack through different detection mechanisms will increase the overall cost, and it is a tedious process. Considering this factor, in this research work, a novel intrusion detection system is introduced to detect routing attacks in wireless sensor networks using fuzzy and feed-forward neural networks. The experimental results demonstrate that the proposed model attains an average detection rate of 97.8% and a maximum detection accuracy of 98.8%, compared to existing techniques like support vector machine (SVM), decision tree (DT), and random forest (RF) models.

Wireless networks play an important role in science, including medicine, agriculture, the military, geography, and so on. The main issue with a network of wireless sensors is how to manage resource utilization to extend its lifetime. This paper investigates the various aspects of increased energy usage that may improve network life. Variables related to energy consumption and various performance metrics are investigated in terms of energy efficiency. To investigate how the network’s energy usage can be managed, a quick overview of clustering protocols, routing protocols, MAC protocols, and load balancing protocols is conducted. This paper can provide researchers with an idea of the various parameters that influence energy consumption and what methodologies could be adapted by each parameter to conserve energy, thereby extending the network’s lifetime.

Rapid development of energy efficient technique for wireless sensor network and its proliferation can relieve the energy constraints on sensor to a little extent but limited the lifetime of the batteries in the sensor. Upon exploration, rechargeable wireless sensor network has potential to mitigate this issue by prolonging the network through extraction of renewable energy to replenish the sensor on the deployed region. Energy-constrained deep learning architectures have to be utilized as optimization objective to the existing routing protocol to enhance the reliability and flexibility of the network. In this chapter, a novel deep learning algorithm, named as deep belief network, has been proposed to achieve energy efficiency. The dynamic source routing protocol is been employed on this paradigm; the mobile sink is utilized for data gathering and replenishing of energy in the cooperative manner towards data transmission. The deep belief network exploits the route with shortest path through information of the mobile sink on a random transmission. Priorization is employed to sensor nodes with least energy will be charged by the mobile node while computation of the node density. Furthermore, the proposed model eliminates the localization issues and latency issue of mobile node. Moving trajectory of the mobile sink is determined with optimal velocity control mechanism. The simulation results of the proposed architecture proves that the proposed paradigm exhibits a good performance in terms of throughput, latency, packet delivery ratio, routing overhead, and energy utilization of nodes compared with state of approaches.

Wireless sensor networks are being used in almost all remote monitoring applications which involve sensing of information from remote locations and transmitting them to the control base center in a wireless medium. This has gained wide spread importance and attention especially with the integration of Internet of things which effectively help in transmission of real-time data and multimedia content. Especially in transmission of multimedia content, energy of nodes is a critical issue as the power provided to the nodes is limited in nature. Hence, this necessitates an intelligent and query-based utilization of energy for the applications or transmission on demand basis. A cluster-based evolutionary algorithm evolutionary multipath energy-efficient routing protocol has been proposed in this research article with a network size of 200 nodes and experimented. The performance with respect to network lifetime and network efficiency has been compared against conventional clustering routing schemes, namely LEACH, PEGASIS and TEEN algorithms. An evolutionary algorithm in the form of cuckoo search algorithm has been used to optimally select the cluster head in the clustering process with attributes taken in terms of energy efficiency.