Consuming energy at the maximal level is a major concern in wireless sensor networks (WSNs). Many researchers focus on reducing and preserving the energy. The duration of active network of WSNs is affected by energy consumption of sensor nodes. For typical applications such as structure monitoring, border surveillance, integrated into the external surface of a pipeline, and clambered along the sustaining structure of a bridge, sensor node energy efficiency is an important issue. The paper proposed an energy-efficient multi-hop routing protocol using hybrid optimization algorithm (E2MR-HOA) for WSNs. The proposed routing protocol consists of two algorithms, i.e., hybrid optimization algorithm. We present modified chemical reaction optimization (MCRO) algorithm to form clusters and select cluster head (CH) among the cluster members. Then the modified bacterial forging search (MBFS) algorithm is used to compute reliable route between source to destination. The proposed E2MR-HOA protocol is evaluated using NS2 simulations. The simulation result shows that the proposed routing protocol provides significant energy efficiency with network lifetime over the existing routing protocols.
Technology evaluation in the electronics field leads to the great development of Wireless Sensor Networks (WSN) for a variety of applications. The sensor nodes are deployed in hazardous environments, and they are operated by isolated battery sources. Network connectivity is purely based on power availability, which impacts the network lifetime. Hence, power must be used wisely to prolong the network lifetime. The sensor nodes that fail due to power have to detect quickly to maintain the network. In a WSN, classifiers are used to detect the faults for checking the data generated by the sensor nodes. In this paper, six classifiers such as Support Vector Machine, Convolutional Neural Network, Multilayer Perceptron, Stochastic Gradient Descent, Random Forest and Probabilistic Neural Network have been taken for analysis. Six different faults (Offset fault, Gain fault, Stuck-at fault, Out of Bounds, Spike fault and Data loss) are injected in the data generated by the sensor nodes. The faulty data are checked by the classifiers. The simulation results show that the Random Forest detected more faults and it also outperformed all other classifiers in that category.
The electromagnetic spectrum is one of nature’s meagre resources. The requirements of wireless communication cannot be satisfied by the new spectrum allocation plan. A policy of self-driven spectrum allocation results as a result. Cognitive radio (CR) engineering is a brilliant technique to maximise spectrum utilisation in rapidly changing environments by identifying unusable and underutilised bandwidth. One of the information strategies of intellectual radio is range detecting, which uses self-persuaded range allocation techniques to use open range to determine the existence of critical clients in the approved recurrence band. Energy location and cyclostationary highlight recognition are the two main factors that determine range detection. Energy recognition is a key method of range detection, but it becomes discouraging at low signal to noise ratios. With a cost of the highest degree of execution complexity, the critical cyclostationary highlight recognition based on cyclic range assessment may successfully identify weak signs from crucial clients. This project is aimed at implementing a useful range detecting mechanism in a field programmable door show with meticulous precision for CR. The adaptive absolute-self-coherent-restoral algorithm, specifically using the truncation multiplier, is a new spectrum sensing system. The proposed architecture, which makes use of a truncation multiplier, was created using the Xilinx approach. This study suggests an efficient spectrum sensing technique that makes use of the Adaptive Absolute Score (AAS) algorithm and SQRT-based Carry Select Adder (CSLA). The TM-CSLA design includes 228 LUT for the Spartan 6 device, which is fewer than the other architectures.
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