An Intelligent Energy-Efficient Data Routing Scheme for Wireless Sensor Networks Utilizing Mobile Sink

Table 1

Summary of related works: sink type, aggregation, metrics, and trajectory.

References	Sink type		Aggregation		Metrics	Trajectory algorithm
	MS	Fixed	RVPs	CHs
[22]					Network lifetime, ergodic residual energy, throughput, and delay.	Threshold-based secret sharing scheme with the assistance of various criteria and QoS requirements.
[23]					Number of alive nodes, network life time, and average energy consumption.	Fixed path where MS makes three trips on its fixed path to get each node’s information of its local density and its distance to MS’ trajectory.
[25]					Network lifetime, fairness index, and energy consumption.	An energy-aware path construction algorithm based on minimal spanning tree.
[26]					Path length, effect of SN, effect of buffer capacity.	An efficient path planning algorithm that implemented based on Dijkstra’s algorithm.
[27]					Network lifetime, network energy consumption, path length.	An efficient mechanism that applying PSO with a path encoding method to select a RVP for each cluster.
[20]					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).
[28]					Energy consumption, network life time, number of dropped packets, buffer capacity, and data gathering ratio.	A squirrel search algorithm-based RVPs selection (SSA-RVPs) method is used to minimize the trajectory of MS while visiting a set of optimal RVPs.
[29]					Stability period, network lifetime, throughput, average energy consumption, data transmission latency, and message overhead.	Path planning mechanism consists of two phases; constructing boundary of RVPs by connecting the outer RVPs and then connecting the leftover RVPs.
[30]					Stability interval, network lifetime, throughput, and end-to-end delay.	A genetic algorithm defines the optimal RVPs on the trajectory for each cluster.
[31]					Stability period, network longevity, number of dead nodes, throughput, and remaining energy.	A hybrid of genetic and particle swarm optimization algorithms are used for CH selection and sink mobility-based data transmission.
[32]					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.
[33]					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.
[34]					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.
[35]					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.
[36]					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.
[37]					Energy consumption, network lifetime, total travel time, and overlapping area.	A game theory and enhanced ant colony based MS route selection and data gathering (GTAC-DG) technique.