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

Table 2

Summary of related works: benefits and limitations.

References	Scheme/algorithm	Benefits	Limitations
[22]	An enhanced, high-performance cluster-based secure routing protocol to improve the data management quality.	Maximize network lifetime, throughput, and decrease the delay.	The real-world scalability remains unspecified.
[23]	An energy-efficient routing algorithm extends network lifespan by managing cluster sizes and energy use.	Balancing the energy consumption and decreases the overhead.	Issues arise when the energy-hole problem aligns with the sink’s trajectory.
[25]	An energy-aware algorithm to minimize energy use in data collection by efficiently selecting points and distributing the workload.	Increase network lifetime, minimize energy consumption.	Suffers from excess transmission delays and network partition problem.
[26]	An efficient path planning for reliable data gathering algorithm.	Less computation overhead, reduced energy consumption, and better utilize of buffers.	Defines numerous RPs that significantly extend data gathering time and risk buffer overflow.
[27]	An energy-efficient data collection algorithm balances intercluster and intracluster energy to prolong network life.	Maximize the network lifetime by balancing energy and reduce communication costs.	Using few RVPs leads to maximizing the data gathering time and causing the buffer overflow problem.
[20]	Introducing ORPSTC for MS data collection base on a minimum spanning tree-based clustering approach for RVP selection.	Extend the network lifetime and ensure a balanced energy distribution among SNs.	The loss of data due to the buffer overflow.
[28]	An optimal rendezvous points selection method to construct MS trajectory for data collection in WSNs.	Balance the energy among the SNs and prolongs the network lifetime.	The intended path may not be the shortest one.
[29]	An intelligent data routing technique for WSNs based on MS for data collection.	Maximize stability, lifetime, and throughput; minimize energy consumption, latency, and overhead.	Evaluating rendezvous points without considering CHs’ actual positions is unsuitable for disconnected networks.
[30]	A genetic algorithm (GA) is used to obtain an optimal number of clusters and a sink movement trajectory.	Minimizes the average communication distance by determining the best location for each cluster head.	Maximizes the consumed energy and decreases the network lifetime.
[31]	A hybrid approach of genetic and particle swarm optimization algorithms are used for help in optimized the CH selection and the route for sink mobility.	Obtains the best possible network performance.	Fails to apply the fitness parameters for routing.
[32]	A trajectory scheduling method based on PSO and GA is introduced to search the parking positions with optimal coverage rate.	Decrease energy and increased network lifetime.	GA fails in addressing the permanence period of the network.
[33]	A hybrid of the mean shift and Bald Eagle Search algorithms are used for clustering and selecting the cluster head.	Increase network lifetime and decrease energy consumption and delay.	Endures massive message overhead and transmission delay.
[34]	Ant colony optimization-based MS is introduced to maximize the network lifetime and minimize the delay in collecting data.	Balance the energy consumption and maximize the network lifetime.	High computational complexity.
[35]	An extended ant colony optimization (ACO)-based MS path construction for selecting the best set of the RVPs and determining the efficient MS path.	Decreasing data loss and increasing network lifetime.	Increasing the RVPs selection time.
[36]	A hybrid of artificial Bee Colony and Differential Evolution is used for routing method in WSNs.	Balance the energy among the CHs.	Minimal convergence rate and decline of the network lifetime.
[37]	A game theory and enhanced ant colony-based MS route selection and data gathering technique to manage data transfer, management, energy consumption.	Reduced energy consumption and data delivery delay.	Ignoring of the CH selection process.