Energy-efficient data transmission with clustering and compressive sensing in wireless sensor networks / Mukil Alagirisamy - Mukil , Alagirisamy (2020)
One of the most important application of wireless sensor network is environmental monitoring. The application involves lifetime of sensor nodes for longer duration associating its energy module. Wireless sensor nodes deployed in sensing field aggregate enormous amount of sensed data and transfer them to the sink. The inherent limitation of energy carried within the battery of sensor nodes fetches extreme difficulty to acquire adequate network lifetime, becoming a bottleneck in forwarding data to sink. Hence the motivation is to reduce the amount of data transfer and attain energy efficiency. This is achieved by clustering and compressive sensing techniques. First objective is to reduce the transmission burden on sensors to attain energy efficiency. The solution is achieved by unequal clustering with appropriate cluster head selection and dual sink. These two criterions minimizes the energy holes and preserves the network lifetime. Energy-Aware Unequal Clustering routing algorithm with Dual sink (EAUC-DUAL) and Energy based Cluster Head selection Unequal Clustering with Dual sink (ECH-DUAL) are proposed. EAUC –DUAL uses static and mobile sink. EAUC-DUAL suggests smaller size clusters around static sink and the entire clusters in the network transmit their data only to the nearest sink providing load balancing and minimizes the hotspot. In the extended ECH-DUAL algorithm in addition to dual sink a new cluster head selection method is proposed for unequal clustering. It focuses on balancing the burden of cluster heads by suitable selection of Tentative Cluster Head (TCH) and Final Cluster Head (FCH). Simulation results interprets the network lifetime of EAUC –DUAL is two times more than the LEACH algorithm. In the extended ECH-DUAL algorithm the network lifetime of ECH-DUAL is twice greater than the network lifetime of EAUC-DUAL. Second objective is to reduce the transfer volume of sensed data and attaining energy efficiency. Conventional sampling results in high amount of sensed data. Hence the framework of Intelligent Neighbor-Aided Compressive Sensing (INACS) is proposed emphasizing on compressive sensing at source, data forwarding based on highest correlation to the neighbor node and exact recovery at the sink. Compressive sensing techniques and data forwarding reduces the volume of sensed data and the number of transmissions. Simulation results conclude better energy efficiency and reconstruction accuracy. The energy consumption in INACS is 0.29 times lesser than the existing protocol.The third objective focus on the reduction in observational cost and transmission cost through compressive sensing and data forwarding techniques respectively. The data forwarding should be performed considering the link capacity of nodes and available bandwidth. A framework Perceptron-based Optimal Routing (POR) and Perceptronbased Routing with Moderate Traffic Intensity (PRMTI) is proposed for data forwarding. The data forwarding process is initiated considering the network resources. POR is suggested for scarce network resources and PRMTI for abundant network resources. Simulation has been performed on energy consumption and number of transmissions. Residual energy of POR is 0.26 times higher and PRMTI is 0.14 times higher than the existing protocol. The simulation results of clustering algorithms, INACS and Perceptron framework are validated using various data analysis methods.