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A Load-Aware Energy-Efficient Clustering Algorithm in Sensor-Cloud
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AI
DOI：10.1007/s10723-023-09683-w.png)
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Abstract
En 中文
The development of sensing cloud provides new network architecture and optimization ideas for wireless sensor networks. In this paper, we study the service performance problems of sensor clouds in multi-user and multi-task service scenarios and optimize the sensing task distribution strategy in sensor clouds. Based on the characteristics of sensing task issuance and execution, a load-aware energy efficient clustering service algorithm for sensing cloud is proposed by introducing the measurement of regional load in the process of sensing cloud network services. The algorithm achieves load balancing and energy consumption balancing by improving the existing energy efficient clustering algorithm. The algorithm selects the best cluster head node by considering the energy of each sensor node and the distance from the data center. In addition, the algorithm assigns service tasks to the nodes that are most suitable to serve the current task based on the regional service load and selects the minimum number of nodes to collect packets to meet the user's requirements. The algorithm is verified through simulation experiments to be better than similar algorithms in key service metrics such as node energy consumption, node survival rate, and service area coverage.
Keywords:
Sensor-cloud
Clustering
QoS
Head selection
Multi-task service
Energy-balance