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Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system
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Content introduction
With the rapid development of the Internet of Things (IoT), Wireless Sensor Networks (WSN) have become an important technology for connecting and collecting environmental data. However, WSN nodes usually cannot run for a long time due to energy constraints, so energy saving has become an important consideration in WSN design. In order to solve this problem, researchers have proposed many energy-saving routing protocols, and one of the widely studied and applied protocols is the Energy-Efficient Sleep-Awake Awareness (EESAA) intelligent routing protocol for WSNs.
The EESAA protocol is a perception-based routing protocol that uses the node’s environment awareness capability to decide whether the node enters sleep mode, thereby extending the life of the entire network. The design of this protocol is mainly based on the following key ideas:
- Sleep wake-up mechanism: The EESAA protocol determines whether the node enters sleep mode by sensing changes in the environment around the node. When a node senses no activity in the environment, it will automatically enter sleep mode to save energy. When activity occurs in the environment, nodes wake up and re-engage in network communications. This sleep-wake mechanism can greatly reduce the energy consumption of nodes.
- Routing decision: The EESAA protocol makes routing decisions by selecting the path with the lowest energy consumption. When selecting a path, the protocol considers factors such as the node’s energy level, the distance between nodes, and the expected communication load. By optimizing routing paths, the EESAA protocol can minimize energy consumption and extend the life of the entire network.
- Node activity scheduling: The EESAA protocol also further optimizes energy consumption through node activity scheduling. The protocol dynamically adjusts the node’s activity time and frequency based on the node’s energy level and expected communication needs. In this way, nodes with lower energy can reduce their active time, while nodes with higher energy can increase their active time, resulting in a more balanced energy consumption.
The application of EESAA protocol in WSN has achieved remarkable results. By using this protocol, the overall energy efficiency of WSN is greatly improved and the life of the network is significantly extended. In addition, the EESAA protocol has good scalability and robustness and can adapt to network environments of different sizes and complexity.
However, there are also some challenges and room for improvement in the EESAA protocol. For example, the protocol has a high dependence on environmental awareness. When the environment changes rapidly, the protocol may not be able to make appropriate decisions in time. In addition, the protocol still has some room for improvement in node activity scheduling. Issues such as how to more accurately predict the communication needs of nodes and how to dynamically adjust the activity time of nodes still require further research and improvement.
In general, the Energy Efficient Sleep Awakening Awareness (EESAA) intelligent routing protocol for WSN is a technology that can effectively extend the life of WSN. By leveraging the node’s context awareness capabilities and optimizing routing decisions, the protocol is able to minimize energy consumption and achieve a more balanced energy distribution. Although there are still some challenges, with further research and improvement, the EESAA protocol is expected to play a greater role in the WSN field and promote the development of IoT technology.
Part of the code
function [MeanMin, MeanMinNorm, BestMin, BestMinNorm, MeanCPU] = Monte
% Monte Carlo execution of population-based optimization software
% OUTPUT MeanMin is the mean of the best solution found. It is a
% nFunction x nBench array, where nFunction is the number of optimization
% functions that are used, and nBench is the number of benchmarks that
% are optimized.
% OUTPUT MeanMinNorm is MeanMin normalized to a minimum of 1 for each benchmark.
% OUTPUT BestMin is the best solution found by each optimization function
% for each benchmark.
% OUTPUT BestMinNorm is BestMin normalized to a minimum of 1 for each benchmark.
% OUTPUT MeanCPU is the mean CPU time required for each optimization function
% normalized to 1.
nMonte = 100; % number of Monte Carlo runs
% Optimization methods
OptFunction = [
'ACO '; % ant colony optimization
'BBO '; % biogeography-based optimization
'DE '; % differential evolution
'ES '; % evolutionary strategy
'GA '; % genetic algorithm
'PBIL '; % probability based incremental learning
'PSO '; % particle swarm optimization
'StudGA']; % stud genetic algorithm
% Benchmark functions
Bench = [ % multimodal? separable? regular?
'Ackley '; % y n y
'Fletcher '; % y n n
'Griewank '; % y n y
'Penalty1 '; % y n y
'Penalty2 '; % y n y
'Quartic '; % n y y
'Rastrigin '; % y y y
'Rosenbrock'; % n n y
'Schwefel '; % y y n
'Schwefel2 '; % n n y
'Schwefel3 '; % y n n
'Schwefel4 '; % n n n
'Sphere '; % n y y
'Step ']; % n y n
?nch = ['MAPSS'];
nFunction = size(OptFunction, 1);
nBench = size(Bench, 1);
MeanMin = zeros(nFunction, nBench);
BestMin = inf(nFunction, nBench);
MeanCPU = zeros(nFunction, nBench);
for i = 1 : nFunction
for j = 1 : nBench
disp(['Optimization method ', num2str(i), '/', num2str(nFunction), ...
', Benchmark function ', num2str(j), '/', num2str(nBench)]);
for k = 1 : nMonte
tic;
[Cost] = eval([OptFunction(i,:), '(@', Bench(j,:), ', false);']);
MeanCPU(i,j) = ((k - 1) * MeanCPU(i,j) + toc) / k;
MeanMin(i,j) = ((k - 1) * MeanMin(i,j) + Cost(end)) / k;
BestMin(i,j) = min(BestMin(i,j), Cost(end));
end
end
end
% Normalize the results
if min(MeanMin) == 0
MeanMinNorm = [];
else
MeanMinNorm = MeanMin * diag(1./min(MeanMin));
end
if min(BestMin) == 0
BestMinNorm = [];
else
BestMinNorm = BestMin * diag(1./min(BestMin));
end
MeanCPU = min(MeanCPU');
MeanCPU = MeanCPU / min(MeanCPU);
Running results
References
[1] Bhattacharya P P .Journal of Wireless Sensor Networks Performance Comparison of Heterogeneous EESAA in Two and Three Dimensional Wireless Sensor Networks[J]. 2016.