![]() ![]() suggested a distributed node deployment approach dubbed MuGWO. To deploy nodes in areas of user-specified relevance, Nematzadeh et al. However, current techniques often apply to areas with stable weights, and the relevance of regions has received little attention. ![]() The evaluation of node distribution based on criteria such as density, data traffic, coverage, energy consumption, and other factors is an important research area in HWSNs. Real-world deployment situations often involve deploying nodes into complicated 3D scenarios, with different parts of the intended area having varying degrees of relevance. A suitable sleep–wake system regulates the active state of nodes to maximize the network’s lifespan while maintaining coverage quality. In HWSNs, scheduling that minimizes energy use is a pressing and difficult issue. This leads to coverage overlap and redundancy in large-scale installations, which is a waste of resources. In addition, sensor nodes are often deployed at random by devices like drones and aerial vehicles in locations that are inaccessible to people. Coin-cell batteries, which are often used to power sensor nodes, are hard to replace and don’t provide reliable long-term monitoring of the designated region. Environmental monitoring, intelligent transportation, agriculture, healthcare, and industrial control are just some of the numerous areas where HWSNs have been used. The hardware, software, and communication protocol of these nodes might vary widely to suit a wide variety of use cases. Heterogeneous wireless sensor networks (HWSNs) are composed of a collection of wireless sensor nodes with varying characteristics and deployment methods. Simulation results show that IMA–NCS-3D has superior scheduling capability, cross-network load balancing capability, and a longer network lifespan than other current coverage optimization approaches. Node-to-node cooperation solutions are offered during data transmission to deal with unforeseen traffic abnormalities and reduce congestion and channel conflicts when traffic volumes are high. This phase of the process is known as node scheduling. A multi-objective fitness function is created to encode the active and inactive states of nodes as genes, and the optimal scheduling set of the network is built via selection, crossover, variation, and local search. Therefore, this research proposes an energy-efficient scheduling technique, IMA–NCS-3D for three-dimensional HWSNs on the basis of an improved memetic algorithm and node cooperation strategy. Energy as a major constrained resource requires an effective energy-efficient scheduling mechanism to balance node energy consumption to extend the network lifespan. Additionally, coverage redundancy and channel conflicts may adversely influence the quality of service in a network when many nodes have been deployed at once. Nodes in performance heterogeneous wireless sensor networks (HWSNs) often have varying levels of available energy, storage space, and processing power due to the network’s limited resources. ![]()
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