Table 1 Comparison between various methodologies.

Nguyen et al.⁶

K-means and LEACH

Tree based

K-means selects cluster heads (CHs) at the center of each cluster

Minimized intra-cluster power consumption

Uneven cluster sizes may impact power consumption at sometimes

Devika et al.⁷

Combined Particle Swarm Optimization and Wolf Search

Flat routing and hierarchical routing.

LEACH-PWO used to identify energy-efficient and reliable CHs

Increased throughput and network lifetime.

Self-configuration and restoring strategies not adopted

Subramanyam et al.⁸

Hybrid HGWCSOA-OCHS scheme for cluster head selection

LEACH and LEACH-C, hybrid HGWCSOA-OCHS scheme for cluster head selection

Hybrid HGWCSOA-OCHS scheme for cluster head selection

Prevents premature convergence and enabling effective exploration of the search space.

Self-configuration is not possible

Nigam et al.⁹

Shortest path tree

Tree structure

Traditional method

Reduced number of data transmissions and scalability

No detailed CH selection process

Yuan et al.¹⁰

Plane routing

Two-tier hierarchy

Dynamic selection

Balanced data load across the network

No appropriate clustering methods discussed

Manchanta et al.¹¹

Energy-efficient compression sensing-based clustering framework (ECSCF)

Node to CH hierarchy

Based on probability and threshold value for each node

Better load balancing and prolonged network lifetime.

Node mobility and scalability

Prabha et al.¹²

Heterogeneous clustering approach (HCA)

Hierarchical structure from node to CH

Based on a probability of 0.1, and the number of CHs is limited to 10 in each round.

Improved energy efficiency, Enhanced network lifetime and data prediction accuracy

Self-election of CH

Aziz et al.¹³

Efficient Multi-hop Cluster-based Aggregation scheme using Hybrid Compressive Sensing (EMCA-CS)

Multi-hop hierarchy

Lexicographical model

Hexagonal clustering pattern covers the entire sensing area, reduced overall energy consumption, improved network lifetime and stability.

Due to multi-hop transmission shortest path cannot be achieved

Pacharaney et al.¹⁴

Spatially correlated clustering approach

Hexagonal topology

Stochastically elect the CH

Reduced number of transmissions, reduced intra-cluster communication, high success rate

Scalability

Zhang et al.¹⁵

CBA with K-means clustering algorithm

Tree based

Highet energy node

Effective parallel computing with enhanced efficiency

Directed and weighted networks not discussed

Ghaderi et al.¹⁶

Equal-size Clusters, Non-equal Clusters, Hierarchical Clustering

Grid-based Routing, Cluster-based Routing

Hierarchical model by nodes themselves

Error rate reduction and traffic load balancing

Energy consumption not discussed effectively

Kaur et al.¹⁷

Fuzzy C-means (FCM) algorithm, GSTEB

Tree-based routing

REAC-IN and swarm intelligence algorithms

Energy efficiency, Improved network lifetime,

Shortest path selection issue

Ahmed et al.¹⁸

Block Sparse Bayesian learning and 1-norm recovery

Effective speech signal recovery, Enhanced performance, Effective reconstruction accuracy

Not able to recover speech signals continuously, noise infiltration issue due to sampling process

Xue et al.¹⁹

Secure block parallel compressive sensing wit bit level XOR

Better reconstruction, Enhanced security, reduced computational complexity

Poor comparison with existing methods, optimal block size selection not discussed

Canh et al.²⁰

CS with Restricted Structural Random Matrix

Preserves democracy of CS, successful reconstruction even at low sampling rates, reduced bit rate overhead

Additional storage required, Higher computational complexity

Sekar et al.²¹

Compressed Tensor Completion with Randomized Singular Value Decomposition

Minimized energy usage and extended lifetime

Implementation flaws, computational challenge, high overheads affect lifetime

Li et al.²²

A tail-Hadamard product parametrization

Greater signal recovery, best quality image reconstruction

Performance at non-linear equations not discussed