Average energy consumption, network life time, and throughput.
A minimum spanning tree-based clustering approach for RVPs selection and then use a low-computation geometry algorithm called MS trajectory construction (MSTC).
Total energy consumption, network lifetime, network delay. Coverage and overlapped coverage rate.
An improved (PSO) combined with mutation operator is presented to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks.
Energy consumption, network lifetime, throughput, end-to-end delay, packet delivery ratio, packet loss ratio, latency, and the jitter performance.
RVPs are chosen based on weighted evaluation of transmitted data packets and hop distance. A hybrid neural network with group teaching optimization algorithm then identifies the best path through these selected RVPs.
Energy consumption, network lifetime, and average tour length.
A novel ACO-based mobile sink algorithm seeks to determine an almost optimal set of RVPs and tour length for the mobile sink, considering nonuniform data constraints.
Energy consumption, network lifetime, buffer utilization, and average tour length.
Extended ACO-MSPD optimizes mobile sink paths in event-driven WSNs with a reselection mechanism for RVPs to balance energy and virtual RVPs to minimize data transmissions.
Energy consumption, residual energy, and network lifetime.
Proposing an artificial bee colony (ABC)-based metaheuristic for mobile sink (MS) relocalization, it optimizes MS paths to balance energy consumption during communication with CHs.