Research On Energy-efficient Data Gathering Algorithm In Wireless Sensor Networks

As an important emerging technology of information perception and data collection in the 21 st century,wireless sensor networks(WSNs)has become an active area of research in international industry and academia.In the application of random deployment of sensor nodes with limited energy,maximizing the lifespan is one of the important research topics in WSNs applications,and improving energy efficiency as much as possible is crucial to prolong the lifespan of WSNs.Focusing on reducing communication energy consumption and balancing energy,this paper proposes environment-adaptive and energy-efficient data collection algorithms with to improve network energy efficiency.To tackle the energy efficiency optimization problem,this paper accomplishes the following innovative research results by simplifying network clustering,optimizing cluster head election,ensuring network coverage,coordinating inter-cluster data forwarding,utilizing mobile sink and scheduling charging vehicles to replenish energy to nodes to achieve sustainable data collection.In the application of WSNs with fixed base stations,the convergence of data flow direction makes the communication load of the nearest nodes of base stations too heavy,and the energy hole phenomenon is inevitable.As to the energy imbalance caused by the“hot spot problem” in this application scenario,an energy-efficient cluster-head multihop data collection algorithm,EEMR,is proposed.This algorithm can enhance the energy cohesion of clusters by incorporating energy-based nearest-neighbor propagation clustering.It proposes a cluster head election strategy combining distance and energy factors,adopts a cluster head rotation mechanism based on energy level,and designs a data forwarding strategy based on the "energy-distance-bias angle" relay node competency calculation and merit selection.Simulation results show that the EEMR algorithm has the advantages of late failure of the first node,concentration of failed nodes and decreasing convergence,which effectively improves energy efficiency and extends the lifespan of WSNs.In order to solve the problem of excessive involvement of base stations in network clustering decision in EEMR algorithm,a dynamic migration data collection algorithm with fixed clustering,FGMRP,is proposed.Referring to the theoretical research results of optimal number of cluster heads,this paper puts forward a dynamic migration clustering strategy of "constant number of clusters-dynamic regional migration",assist WSNs to form clusters at one time through the nearest neighbor propagation algorithm,adopts the cluster head election method of "energy-location-node density",optimizes the number of data forwarding,designs a relaying strategy where nodes with a larger number of surviving nodes forward have priority in forwarding data,and builds a comprehensive evaluation model with multiple indicators to measure the performance of the algorithm.The simulation results show that the FGMRP algorithm reduces the energy consumption of network clustering and further optimizes the network lifetime.In the application scenarios with dense deployment of sensor nodes,an energy-efficient and reliable data collection algorithm based on virtual lattice,GRMRP,is proposed.In this algorithm,the network is divided by the virtual grid method of reliable connectivity,and full network coverage is ensured based on the sensing capability of nodes.Meanwhile,dormancy mechanism to reduce energy consumption in dense areas is introduced,and the grid head rotation mechanism triggered by energy threshold is adopted,and the regional game negotiation strategy of "coexistence and death" is used to strengthen the local knowledge of regional energy for inter-grid data forwarding to achieve inter-grid energy balance.Simulation results show that the GRMRP algorithm significantly reduced the energy consumption per data collection round,shortened the data collection delay and significantly prolonged lifespan of WSNs.In the applications of mobile base station-assisted WSNs,sensor nodes switch from "edge nodes" to "near-neighbor nodes" with improved energy efficiency and reliable data transmission.However,it has the disadvantages of frequent network topology changes and significantly increased data collection latency.As for delay-constrained application scenarios,a delay-tolerant base station mobile assistance data collection algorithm,GESDGS,is proposed.The algorithm sets the virtual dwell point level by the quadratic virtual lattice method,adopts the dwell point adaptive selection method that follows the system tolerated time delay change,enables the dwell time allocation strategy based on the regional data collection volume,and builds the virtual point-based adaptive patrol route by using the quadrant movement rule of Hilbert curve.Simulation results show that the GESDGS algorithm is adaptively tolerant of time delay,and capable of reducing the energy consumption per data collection round and extending the network lifespan.In the application scenario where small and medium scale WSNs can work sustainably,an adaptive real-time on-demand charging scheduling algorithm CSS-MEE is proposed.The algorithm solves the NP-hard problem of scheduling path optimization for mobile vehicles through a hierarchy-based symmetric path construction method.It jointly considers the supply and demand relationship between the available energy of mobile vehicles and the demanded energy of network requesting nodes,alternates between full charging mode and adaptive charging mode,and enables a scheduling path update algorithm that considers the urgency of new requests and the cost of charging services to achieve a win-win situation for mobile charge efficiency and network requesting charging throughput.Simulation experiments show that the CSS-MEE algorithm performs better in terms of charging efficiency,request throughput and average charging service delay.