Abstract-Wireless Sensor Network is monitored withContikiMAC Cooja flavor to diagnose the energy utilization ratio by nodes and the fault detection process in distributed approach; adopted the Low power Listening (LPL) mechanism with ContikiMAC to prolong the network's lifetime. LPL locate the root cause of communication issue, get rid of the interruption problems, and get back normal communication state. The LPL mechanism reduces the energy utilization in centralized and distribute approaches. Even more, the distributed approach is best suited for network monitoring when energy utilization is main objective in the presence of LPL. It is also important how soon the faulty node can be detected. In this case, latency has vital contributions in monitoring mechanism and latency is achieved by developing the efficient faulty node detection methodology.

The selection of optimal relay node ever remains a stern challenge for underwater routing. Due to a rigid and uncouth underwater environment, the acoustic channel faces inevitable masses that tarnish the transmission cycle. None of the protocols can cover all routing issues; therefore, designing underwater routing protocol demands a cognitive coverage that cannot be accomplished without meticulous research. An angle-based shrewd technique is being adopted to improve the data packet delivery, as well as revitalize the network lifespan. From source to destination, one complete cycle comprises three phases indeed; in the first phase, the eligibility of data packet belonging to the same transmission zone is litigated by Forwarder Hop Angle (FHA) and Counterpart Hop Angle (CHA). If FHA value is equal or greater than CHA, it presages that the generated packet belongs to the same transmission zone; otherwise, it portends that packet is maverick from other sectors. The second phase picks out the best relay node by computing a three-state link quality with prefix values using the Additive-Rise and Additive-Fall method. Finally, the third phase renders a decisive solution regarding exorbitant overhead fistula; a packet holding time is contemplated to prevent the packet loss probability. Simulation results using NS2 have been analyzed, regarding packet delivery ratio, packet error rate, communication overhead, and end-to-end delay. Comparing to HHVBF and GEDAR, USPF indeed has outperformed, leading into the evidence of applicability’s favor.

The accomplishment of sustainable communication among source and destination sink node is a rigors challenge and even establishing bodacious communication link between these nodes is nothing short of a miracle because data routes are governed by the underwater environment. Energy consumption has a significant influence as all active devices rely on the battery. As cost-effective data packet transmission is established as a norm, no charging or replacement can be achieved. Hop link evaluation and shrewd connection discovery by way of a resurrecting linking element were just a genuinely grim task, and only feasible to create the extra powered energy pods (URR-SAEP) that had never been carried out before after detailed study. After packet transfer, the sensor node performs the link inspection process, and when a link is deemed shaky at less than or equivalent to 50 percent of capacity, the target node incorporates its residual capacity status and returns it to the source node that attaches other unoptimizable energy pods to improve only the targeted node link from 50 percent to 90 percent. Performance evaluation using NS2 with Aqua-Sim 2.0 simulator has been obtained comparing with DBR and EEDBR protocols in terms of point-to-point delay, Packet dissemination ratio, Network lifespan and Energy Diminution.

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Due to unavoidable environmental factors, wireless sensor networks are facing numerous tribulations regarding network coverage. These arose due to the uncouth deployment of the sensor nodes in the wireless coverage area that ultimately degrades the performance and confines the coverage range. In order to enhance the network coverage range, an instance (node) redeployment-based Bodacious-instance Coverage Mechanism (BiCM) is proposed. The proposed mechanism creates new instance positions in the coverage area. It operates in two stages; in the first stage, it locates the intended instance position through the Dissimilitude Enhancement Scheme (DES) and moves the instance to a new position, while the second stage is called the depuration, when the moving distance between the initial and intended instance positions is sagaciously reduced. Further, the variations of various parameters of BiCM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius have been explored, and the optimized parameters are identified. The performance metric has been meticulously analyzed through simulation results and is compared with the state-of-the-art Fruit Fly Optimization Algorithm (FOA) and, one step above, the tuned BiCM algorithm in terms of mean coverage rate, computation time, and standard deviation. The coverage range curve for various numbers of iterations and sensor nodes is also presented for the tuned Bodacious-instance Coverage Mechanism (tuned BiCM), BiCM, and FOA. The performance metrics generated by the simulation have vouched for the effectiveness of tuned BiCM as it achieved more coverage range than BiCM and FOA.