A secure and energy saving protocol for wireless sensor networks

The research domain for wireless sensor networks (WSN) has been extensively conducted due to innovative technologies and research directions that have come up addressing the usability of WSN under various schemes. This domain permits dependable tracking of a diversity of environments for both military and civil applications. The key management mechanism is a primary protocol for keeping the privacy and confidentiality of the data transmitted among different sensor nodes in WSNs. Since node's size is small; they are intrinsically limited by inadequate resources such as battery life-time and memory capacity. The proposed secure and energy saving protocol (SESP) for wireless sensor networks) has a significant impact on the overall network life-time and energy dissipation. To encrypt sent messsages, the SESP uses the public-key cryptography’s concept. It depends on sensor nodes' identities (IDs) to prevent the messages repeated; making security goals- authentication, confidentiality, integrity, availability, and freshness to be achieved. Finally, simulation results show that the proposed approach produced better energy consumption and network life-time compared to LEACH protocol; sensors are dead after 900 rounds in the proposed SESP protocol. While, in the low-energy adaptive clustering hierarchy (LEACH) scheme, the sensors are dead after 750 rounds.

A Novel Data Gathering Algorithm For Wireless Sensor Networks Using Artificial Intelligence

The most challenging task in wireless sensor network is energy efficiency, as energy is the major constraint in the wireless sensor network to improve the life time of the network. Hence developing algorithms to improve network life time is the major task. In wireless sensor network most of the energy is wasted while gathering the data, hence an efficient algorithm which conserves energy has to be designed. Thus our proposed work A Novel Data Gathering Algorithm for Wireless Sensor Networks using Artificial Intelligence (NDGAI) uses mobile element and deals with the conservation of energy while gathering the data. Appropriate clustering, cluster leader selection and proper path determination of mobile element helps to conserve energy and improve the over all network life time. In our proposed work initially the clusters are forged by using Amended Expectation Maximization(AEM) algorithm, which is the maximum likelihood estimate. It is used along with Gap statistic method to find the optimal number of clusters. AEM algorithm helps in obtaining the centres of the cluster with maximum number of nodes near the cluster centres. For each cluster, Cluster Leader (CL) is selected by using Fuzzy Logic. Fuzzy logic selects the node which is near to the cluster centre by using parameters such as Closeness of node to the Cluster Centroid, direction of node towards base station, number of Neighbouring Nodes. After the CL’s are determined, to reduce the path length virtual points(VP) are selected so that mobile element reaches this virtual point and collects the data. These VP’s are selected only when the CL has data in it. The mobile elements can reach these virtual points intelligently by using optimal path,that is obtained by using hybrid of Particle Swarm Optimization and Artificial Bee Colony algorithm. Thus the mobile element travels in the optimal path and gathers the data from the entire network intelligently and efficiently with less amount of energy. With this approach the performance and life time of the network is improved while gathering the data. The simulation results are compared with Scalable Grid-Based Data Gathering Algorithm for Environmental Monitoring Wireless Sensor Networks (SGBDN) and proved that the proposed method is better than SGBDN .

Secure and Shortest Path Routing Bypassing Attackers in Wireless Sensor Networks

In wireless sensor networks (WSNs), energy constraint of node is the major issue, as the sensor may be deployed in the area where energy backup or quick replacements may not be available. In such cases, preserving the node energy and prolonging the network life time play crucial role in wireless sensor networks. Similarly, sensor nodes are highly vulnerable to attacks, attackers can easily tamper the sensor node and compromise it. Thus to overcome above stated two problems, the proposed work ensures shortest path routing, which ensures network life time of sensor nodes and the trust based routing, which avoids node compromise attacks. The proposed shortest path routing algorithms takes route through multi-hop nodes to corresponding sink. The shortest path based on the geographical routing strategy chooses the nodes nearest to the routing node and sink node. The novel routing framework proposed in this work considered shortest path with trust based routes. The node's energy is considered to taking reliable node on the routing path, which ensure the packet delivery and avoids any node failure due to less energy. The node's trust value is evaluated with three type, which ensure that the paths created are more reliable

A hybrid Meta-heuristic solution for Energy-Efficient routing in Software-Defined Wireless Sensor Network

Software-Defined Networking (SDN) has been adopted as an emerging networking paradigm within Wireless Sensor Networks (WSNs). SDN enables WSNs with self-configuration and programmable control to dynamically and efficiently manage the network functionalities. Generally, in WSN, smart sensing devices suffer from the low battery issue and they may be deployed in such environments where frequent recharge is not possible after the deployment. Therefore, this work focuses on energy-efficient routing problem considering Software-Defined Wireless Sensor Networks (SD-WSN) architecture. In SD-WSN, Control Server (CS) assigns the tasks to selected Control Nodes (CNs) dynamically. Thus, the CNs' selection process is developed as one optimization (NP-Hard) problem to make the network functional. To solve this problem effectively, a nature-inspired algorithm i.e., Grey Wolf Optimization (GWO) is hybridized with Particle Swarm Optimization (PSO) in order to improve its convergence and overall performance. This hybrid variant of GWO is dedicated to offering a Balanced clustering (BC) based routing protocol, this variant is referred to as HGWO-BC. Further, to solve the problem effectively, a fitness function is designed that considers several parameters e.g., intracluster distance, CS to CNs distance, nodes' residual energy, and cluster size. Thus, the proposed approach performs balanced, energy-efficient, and scalable clustering and prolongs the network life-time. To verify its effectiveness, an exhaustive simulation study is done. Comparative results show that the HGWO-BC approach outperforms other state-of-the-art approaches concerning network life-time, residual energy, network throughput, and convergence rate.