An Improved Energy-Aware Routing Protocol Using Multiobjective Particular Swarm Optimization Algorithm

Table 1

A comparison of previous methods.

Metrics used	Advantage	Method	Objective/Target	Protocol name	Protocol type
Grid life, power consumption, CH count	Balanced and reduced energy consumption in each cycle, increasing life expectancy	Use of optimal sliding window, dynamic optimization of CHs	Obtain the desired number of clusters dynamically and energy efficiency	LEACH-SWDN	Classical
Energy consumption, latency, production rate, closed loss rate	Increases network time and performance	Use multicast tree optimization, minimally distributed transmission	Ensure energy efficiency and grid performance	EAODV
Energy consumption, package delivery ratio, average package delay, throughput, overhead	Low overhead and better network performance	Use of blind sending method, slope maintenance criterion	To enable mobility and robustness in routing	PHASeR
Package delivery rate, total energy consumption, operating power, average delay	Increased network life, PDR, and low overhead	Fuzzy- and nonfuzzy-based implementation	To increase network life and performance for heterogeneous networks	HEEDML
FND (death of the first node), HND (death of half of the nodes), LND (death of the last node), success rate	Increase network life time, balanced energy consumption	Genetic combination and thermal simulator, multipurpose fit function	To achieve increased network lifetime and energy savings of SNs	ASLPR
Average energy consumption, end-to-end latency and throughput	Ensure longer lifetime and reduced energy consumption	Improved mode routing, conscious energy path selection	To ensure an overall reduction in energy consumption	EA-FSR
Average energy latency and grid life	Energy efficiency and improved latency	Virtual cluster PSO	To achieve energy efficiency and better grid performance	EPMS	Swarm intelligence
FND, energy consumption per cycle, grid life, information received by BS	Better network lifetime, lower power consumption per cycle	Optimization of CHs selection using artificial fish algorithm	Reduce network power consumption	AFSA
Maximum and standard deviation of distance between clusters, FND, HND, LND, success rate	Save energy and prevent uncertainty during network operation	Use of fuzzy c-means, FA-SA hybrid	To achieve balanced clustering and minimal overall energy consumption	SIF
Network lifetime and residual energy	Optimal cluster formation leads to increased grid performance and balanced energy consumption	Bee colony optimization, optimal cluster	For a long network lifetime, energy consumption balance	PECE
Network lifetime, the average residual energy, the remaining energy standard deviation	Network lifetime is increased and energy consumption is reduced	Improved harmony search algorithm	To extend the lifetime of the network	IHSBEER
Total energy consumption, living nodes, average residual energy	Increase network lifetime	Using the bee mating algorithm	To provide trust based on proper clustering and energy consumption	LWTC-BMA
Energy consumption, energy efficiency, first dead node, package delivery rate, package loss rate and network coverage	Increase network life, coverage, package delivery ratio	Using the ABC algorithm, based on the cost function	To design a scalable grid and improve energy efficiency	ABC-SD
Data delivery rate, packet delay, power consumption, energy efficiency, lifetime network	Choose the optimal route	Using the PSO algorithm, based on meta-exploration techniques	To select the optimal route in routing	MOTPSO
Energy, energy efficiency, lifetime network	Increase network lifetime	Use of PSO algorithm, based on energy and latency	To reduce energy consumption, limited energy clustering	ECPSO
Price, packet delay, power consumption, network lifetime	Energy efficiency network	Use the PSO algorithm, based on energy and cost	To maximize network lifetime, cluster node organization, optimal location costing	CHSPSO
Intracluster distance, distance to hole, residual energy of sensor nodes, network lifetime	Effective reduction of energy consumption	Use of PSO algorithm, based on energy and latency	For energy efficiency, efficient particle encoding scheme and proportion function, energy efficiency	PSO-ECHS
Node residual energy, network life, latency and data accuracy	Energy-limited clustering	Use of PSO algorithm, based on energy and latency	Reduce energy consumption, extend the lifetime of the network, data collection	DAPSO
Energy consumption, energy efficiency, network lifetime	Effective choice of header	Use PSO algorithm based on distance error function	For data aggregation in wireless sensor networks, optimal point coordination for reliable communication	PSDEO