Optimized Resource Management and Dynamic Routing Protocol for Wireless Sensor Networks Through Load Balancing, Packet Scheduling, and Intelligent Clustering

Heterogeneous Wireless Sensor Networks (HWSNs) are pivotal for providing weather-related event data, enabling universal location access, and facilitating remote monitoring through multi-hop transmission. Efficient energy utilization is critical in ensuring the optimal functioning of HWSNs. Previously, Compressive Sensing (CS) technology was established to enhance communication efficiency within HWSNs. While previous methods were effective in managing energy consumption and reducing transmission delays across network devices, the increased number of devices has impacted their efficacy. Consequently, energy becomes a vital limitation in constructing HWSNs. In order to address these challenges, this study introduces Load Balancing and Packet Scheduling with Intelligent Clustering based Improved Routing Protocol (LPICR). This integrates load balancing, packet scheduling, intelligent clustering, and enhanced routing techniques. The protocol is structured into three main categories: intelligent route selection, load balancing-based Cluster Head (CH) selection, and path scheduling. Initially, an efficient opportunistic routing is conducted by the intelligent route selection process. This routing method minimizes data forwarding during communication and significantly decreases energy consumption in the HWSN. Furthermore, by using a load balancing-oriented procedure for selecting cluster heads, the system achieves efficient determination of cluster heads and construction of clusters, resulting in the most efficient use of energy in communication. Path scheduling reduces the probability of delays by facilitating effective data flow between the source and destination in the HWSN. The NS2 platform is used to implement the proposed LPICR-HWSN protocol. The calculation of the result and comparison analysis is considered for the parameters are Data loss rate, communication time, packet success rate, malicious detection ratio, throughput, Routing overhead and energy efficiency. The results are thoroughly investigated by accounting for factors like the quantity of nodes and the varying speed of the network. To assess the efficacy of this proposed protocol, we conduct a comparative analysis using established methodologies such as CDAS-WSN, EEPC-WSN, TCCS-WSN, and MTODS-HWSN. The results suggest that the proffered LPICR-HWSN model demonstrates superior performance compared to previous methods.