AERAA-DAR: Algorithm for Energy-Resourceful Attribute-aware Data Aggregation and Routing In Wireless Sensor Networks

摘要

This paper proposes a new algorithm for energy-resourceful attributeaware data aggregation and routing in wireless sensor network (WSN) named AERAA-DAR. The proposed AERAA-DAR combine advantages of both query and time-driven routing. In AERAA-DAR routing design, a joint K-means unequal-clustering scheme is applied to partition the nodes into unequal into clusters. Also, two weight functions, contending and proper weights are implemented to select the suitable cluster heads. A piece of link-state learning information and threshold function are combined to serves the CH nodes in close range establish vertical chain hierarchies. Chain leaders are chosen to allocate distinct duties dynamically, and cooperatively work as agents between other nodes and the sink. A cost-driven gradient-based routing scheme is implemented to realize multi-path from the chain leaders to the sink. The routing decisions are realized in consideration of the attenuation, residual energy, and shortest route conditions of the established paths. At the cluster heads and chain leaders, discrete wavelet transform (DWT) compression is carried out on the sensed data secured within the hierarchical sections. At the shared links of the paths to the sink, intersection linear network coding (LNC) is carried out on the extracted data sent from the chain regions. The obtained simulation results disclose that by consolidating the benefits of a cost-driven gradient-based multi-path routing scheme, LNC, and DWT in a cluster-chain topology, AERAA-DAR enhances the data reporting rate and accuracy. It is disclosed equally, that AERAA-DAR considerable lessens energy consumption, latency and increases network longevity compared to similar state-of-art routing schemes.