| Popis: |
A wireless sensor network (WSN) collects data from various monitoring systems using small sensor nodes (SNs). The WSN analyses and sends found data to the BS. SNs have a short lifespan, therefore the sensor network must gather and transmit data efficiently. Energy efficiency is the main difficulty in sensor network organisation. Sensor nodes require energy efficiency due to their high cost and limited capacity. This limitation significantly compromises the longevity of the network. The anticipated demands on a node’s battery determine its lifespan. Sensor networks must be resilient to sensor node lifetime reduction. Energy-efficient routing algorithms may increase the lifetime of multihop networks by selecting the most energy-efficient approach. Unfortunately, the present technique has performance issues. Modified Fuzzy C-Means with Particle Swarm Optimization (MFCM-PSO) is a novel hybrid clustering method that combines fuzzy c-means with particle swarm optimisation to address the shortcomings of existing methods that use FCM to determine cluster centres and then optimise them using PSO. The PSO helps choose the optimum number of clusters and CHs (Cluster Heads). This method uses 100 and 200 sensor nodes. We devised a hierarchical packet routing technique by introducing the concepts of Direct Cluster Head (DCH) and PCH (Parent Cluster Head), which are selected based on distinct FF (Fitness Functions) and act as relays for a limited number of other CHs, saving network energy. The simulation results show that the proposed MFCM-PSO method outperformed existing EEHCHR on scenario-1, with 28% for FND (First Node Dead), 23% for HND (Half Node Dead), and 19% for LND (Last Node Dead), and on scenario-2, 8% for FND, 23% for HND, and assumed 36% for LND since no node dies up to 1500 rounds. Thus, the simulation results suggest that the MFCM-PSO method improves coverage, network energy, and lifetime efficiency over the EEHCHR method. |
