In current years, Internet of Things (IoT) applications has been an enormous outpouring. In the IoT network, the sensor nodes produce data uninterruptedly which directly affects the network longevity. Although the IoT applications probable are enormous, there are abundant challenges such as privacy, security, storage, load balancing, devices heterogeneity, as well as energy optimization that needs to be identified. Of those, the utilization of energy for the network is important which needs to be optimized. Numerous factors namely remaining energy, temperature, number of alive nodes, a load of Cluster Head (CH), and cost function affects energy utilization of sensor nodes. In this work, a hybrid self-adaptive Particle Swarm Optimization (PSO) -Differential Evolution (DE) algorithm is modeled to choose the optimal CH that in order to optimize aforesaid factors. Finally, the developed method performance is calculated with conventional methods regarding the energy-specific factors. The outcomes attained demonstrate that the developed technique performs better than the conventional algorithms.
Summary Wireless sensor network (WSN) comprises automatic sensors that are dispersed into a huge region. WSN is constructed from huge sensors, which is allocated to a particular task and the majority of task involves reporting and monitoring. However, as the network can be extended to several sensor nodes, there is a high chance of collision. Thus, this paper devises a novel technique for performing both collision detection and mitigation in WSN. Initially, the simulation of WSN is performed, and then the selection of cluster head is done using fractional artificial bee colony (FABC). Here, the network‐based parameter is extracted that involves received signal strength index (RSSI), priority level, delivery rate, and energy consumed. The deep recurrent neural network (DRNN) is adapted for collision detection. Here, the training of DRNN is done using lion crow search optimizer (LCSO). After collision detection, the collision mitigation is performed with a pre‐scheduling algorithm, namely dolphin ant lion optimizer (Dolphin ALO). Here, fitness is considered for collision mitigation that includes energy, sleep index (SI), delivery rate, priority level, E‐waste, and E‐save. The proposed method outperformed with the smallest energy consumption of 0.185, highest throughput of 0.815, highest packet delivery ratio (PDR) of 0.815, and highest collision detection rate of 0.930.
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