Wireless Sensor Network (WSN) contains several sensor nodules that are linked to each other wirelessly. Errors in WSN may perhaps be because of several causes which bring about hardware damage, power thwarts, incorrect sensor impression, faulty communication, sensor deficiencies, etc. This damages the network process. In this paper, we propose to develop a Hierarchical Fault Detection and Recovery Framework (HDFR) for Self-Healing WSN. This framework consists of three modules: Fault detection, fault confirmation and fault recovery. In fault detection module, Particle Swarm Optimization (PSO) algorithm is applied for estimating the discrete Round Trip Paths (RTPs). Along the established RTPs, round trip delay (RTD) time values are estimated. Then based on the RTD, the suspected nodes are identified. In fault confirmation module, the nodes are confirmed to be either in FAULTY or ACTIVE state. In fault recovery module, the primary controller (PC) will establish an alternate route via the secondary controllers (SC) by excluding the faulty nodes. Then, it will resend the stored packets to the sink via the newly established route. By experimental results, it is shown that the HDFR framework achieves better detection accuracy and packet delivery ratio.

Cognitive radio (CR) is one of the promising and enabling technologies that allow dynamic spectrum access to increase the spectrum utilization for enhanced network performance. Clustering, network topology management technique, grouping the nodes logically based on common channels to achieve scalability and stability. However, clustering is susceptible to third party attacks by malicious SUs that create a random and intelligent attack by false information's. The security has become the main issue in CR networks to guarantee

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Underwater Wireless Sensor Networks is effective and intelligent utilization of energy for routing protocol in longer network lifetime. Energy Consumption and load balancing are the vital roles for a network lifetime. The use of load balancing in WSN is granted as the best resource of sink mobility which protects energy sources to organize. The aim of this paper is to evaluate various deployed strategies involving sink mobility. Multiple mobile sinks are capable of performing computational operations like collecting information from electric joints instantly, storage, and also communication capability. It evaluates the results and the effect of sink mobility by comparing with another routing protocol GEDAR.

Sensor networks are frequently employed to keep an eye on rapidly changing, dynamic environments. Low latency, energy efficiency, coverage difficulties, and network lifetime are seen to be the most important problems in wireless sensor networks. Cluster-based wireless sensor networks require additional study to overcome issues with energy efficiency and network lifespan. Finding the ideal number of clusters with the goal of reducing energy consumption is one of the primary challenges in cluster-based networks. The right value for k relies on the shape and size of the point distribution in a data collection, as well as the user's preferred level of clustering resolution. Additionally, if each data point is taken into account as its own cluster, increasing k without suffering any penalties diminishes the degree of accuracy in the resulting clustering until it reaches zero. Hence, Variance Difference Method (VDM) is proposed in order to determine the ideal number of clusters K and to carry out clustering in WSN. Elbow method, Silhouette method, and Gap statistic method performance is also reviewed and contrasted with that of the suggested VDM in order to demonstrate that the proposed VDM performs better than Elbow method, Silhouette method, and Gap Statistic method.