Table 1.

Comparison of advantages and disadvantages of related work.

Method	Key Feature	Advantages	Disadvantages
Distributed clustering with data fusion [10,11]	Multi-hop and intra-cluster communication	The need for centralized stations for cluster CH formation is eliminated	Selfishness of nodes is ignored.
Cognitive radio WSN (CR-WSN) [12,13,14,15]	Utilization of increased spectrum	Extends WSNs lifetime	Clustering in CR-WSNs has not yet been matured.
Energy conservation via efficient routing algorithms like LEACH, LEACH-C [16], PEGASIS [17,18]	Nodes of clusters made rich in information about neighboring nodes	Extend WSNs’ lifetime	Selfishness of the node is ignored.
One Hop Cluster-Head Algorithm (OHCH) [19].	CH selection	Prolonging the network lifetime and reducing the network data latency	Ignoring the distance factor as nodes get away and deplete energy quickly
Dynamic network state learning model (NSLM) [20]	Hidden Markov model (HMM) and Lagrange multiplier-based approach	Outperformed in terms of buffer cost, holding cost, overflow, energy consumption, and bandwidth usage	Other optimization approaches are ignored to compare.
Noncooperative game theoretic approach in clustering [22]	Dependability assessment mechanism for heterogeneous WSNs	Reliability and availability measures for susceptible sensor nodes improved	Not all possible security measures considered.
Coalitional game theory [24]	Based on topological structure	Extend WSNs’ lifetime via finding the cheapest route	How to choose corresponding leaders is not mentioned in the work.
Bayesian game [25]	Bayesian game to form static game	Extend WSNs’ lifetime	Imperfect information.
Game theory [28]	A trade-off between energy conservation and network throughput, double-time Nash equilibrium	Balances energy consumption	Selfishness of nodes are ignored.
Game theory-based energy efficient clustering routing protocol (GEEC) [30]	The proposed mechanism is compared with LEACH and LEACH-C	Extend WSNs’ lifetime	No guarantee of the connectivity and robustness of the network.
An evolutionary game [31]	Combined the evolutionary game with the classical GT.	Extend WSNs’ lifetime and provides a better delivery ratio.	Selfishness of nodes are ignored.