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Literature Review Analysis on WSN Clustering-Free-Thesis

WSN literature


  • Discusses the WSN clustering general issues
  • A literature review on WSN clustering is listed as original content
  • Few WSN algorithms are discussed with the relevant code download link

WSN Clustering Issues

Wireless sensor nodes are backed by the sensor’s energy limitation. To resolve the issue several methodologies for data transmission have been formulated. The energy consumption reduction process starts from the very first step of the WSN communication setup. That step is clustering. numerous works by researchers have been published for it. Maximum clustering algorithms by Asian researchers revolve around optimization algorithms for clustering of WSN.

Though the optimization is efficient, yet, its adaption to real-life applications is still a question. The deep learning algorithms require complex hardware due to optimization requirements in network training. Unfortunately, this complex hardware is not available in sensor nodes or if used, a massive battery would have been required. This would have limited their usage in that case. With so many algorithms for efficient clustering for better data transmission with lesser packet delay, packet drop and higher throughput. In this blog at, we have listed a few most cited papers on WSN clustering algorithms and presented an analytical analysis of those.

Table: Literature Review of research papers on WSN Clustering Algorithms

WSN Optimization Steps

1. Define the Problem

For every Optimization algorithm for WSN, it is of utmost importance to define the problem space in the first place. You can find the template for it at the link below which can be integrated with every optimization algorithm.

2. Identify the tuning variables

The optimization algorithm will ask for the searching space dimension. These are your actual problem variables that need to be tuned. For example: in optimal CH selection, the x-y Coordinates are the tuning variables. So if ‘N’ is nodes then the search space dimension would be 2*N.

N=10;                       % number of nodes
area=[10,10];              % nodes deployment area in meter
Trange=2;                   % transmission range of sensor node in meter
nodes.pos=area(1).*rand(N,2);% nodes geographical locations
nvars = 2*(N);  % search space dimension

3. Define the constraints

The Constraints are those which limit the updated tuning variables during the optimization process. For example, the CH selection problem has the number of nodes as the constraint. The selected CH should be within nodes.

4. Download the Free Hybrid Optimization Code

Once all necessary steps are taken care of, you can use any of the #free Optimization codes from to get the final optimized values.

Hybrid Grey Wolf and Cuckoo Search Optimization

Hybrid Particle Swarm and Grey Wolf Optimization

BAT Optimization Algorithm

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