Energy efficient rendezvous points based routing technique using multiple mobile sink in heterogeneous wireless sensor networks

Abstract

In recent decades, Sensor nodes (SNs) are used in numerous uses of heterogeneous wireless sensor networks (HWSNs) to obtain a variety of sensing data sources. Sink mobility shows a significant part in the enhancement of sensor system execution, energy utilization, and lifetime. To manage sink mobility, rendezvous points (RPs) are introduced where some SNs are chosen as RPs, and the non-RP nodes convey the information to the cluster heads (CHs). The CHs then forward their information to the nearby RPs. To determine the set of RPs and travelling path of mobile sinks (MSs) that visits these RPs is quite challenging. This work presents an energy-efficient SOSS based routing method that depends on RPs and multiple MSs in HWSNs. At first, all the heterogeneous nodes are distributed into the number of clusters using mean shift clustering (MSC). Then, the Bald eagle search (BES) algorithm is used for an optimal selection of CHs whereas multiple MS is employed for effective data gathering. The use of multiple MSs can enhance the data collection efficiency and decreases the energy utilization for HWSNs. Finally, the hybrid seagull optimization and salp swarm (SOSS) algorithm is used to find the RPs and travelling routes of MS. The entire simulation work of the heterogeneous network is simulated in the NS2 platform. The simulation outcomes display that the suggested method provides superior performance in HWSN than other current routing protocols.

An Energy-Optimized Artificial Intelligence of Things (AIoT)-Based Biosensor Networking for Predicting COVID-19 Outbreaks in Healthcare Systems

“…Some examples are [20] the method that uses FLSs based on three factors, [21] the method that reduces the overhead of CH selection in LEACH using FLS based on two factors, [22] the method that uses FLSs and an improved ant colony optimization (ACO) for uneven clustering and intercluster routing, [23] the method that uses type-2 FLSs and better ACO for the same purpose, and [24] the one that uses FLSs for creating unequal-sized clusters to balance the load. Many researchers discussed [4,[25][26][27][28][29] the CH election process based on residual energy, distance, and node density and how it affects cluster formation and load balancing. One is EE-LEACH [9,30,31], which uses rank-based FLSs for CH election and cluster formation but does not consider other parameters such as average energy and the number of neighbors.…”

“…One is EE-LEACH [9,30,31], which uses rank-based FLSs for CH election and cluster formation but does not consider other parameters such as average energy and the number of neighbors. Several scholarly papers [26,28,32] have focused on the Internet of Things (IoT) environment to prolong the longevity of heterogeneous networks, with a particular emphasis on the healthcare domain. Alshamraniʹs seminal work [6] deserves mention for its comprehensive classification of various components within the Internet of Medical Things (IoMT) systems, specifically tailored to the healthcare context.…”

Fuzzy inference logic assisted rendezvous points based effective routing strategy in wireless sensor networks

“…Section 6 gives a conclusion and future scope of a novel technique that is the improvement of the given method. Gupta et al 16 described an energy-efficient seagull optimization and salp swarm (SOSS) based routing approach that relies on RPs and several MSs. Initially, mean shift clustering (MSC) was used to divide all of the different nodes into several kinds of clusters.…”

“…The existing models considered for comparison are novel framework for energy-efficient compressive data gathering (NFECG),energy-adjusted high-level data total tree (EHDT) protocol, energy mindful CS-based data aggregation (ECDA), threshold-sensitive energy-efficient delay-aware routing protocol (TEDRP), enhanced threshold sensitive stable election routing procedure (ETSSEP), distance based enhanced threshold sensitive stable election routing procedure (DETSSEP), seagull optimization and salp swarm (SOSS) respectively. 16 Here, a total of 9,000 rounds are taken to analyze the status of nodes that are alive. The proposed strategy provides more alive nodes, about 100 for 5,000 rounds, while other models provide less than 100 at 5000 rounds.…”