Key Technologies Of Random Deployment Wireless Sensor Networks

Randomly deployed wireless sensor networks are widely used in intelligent agriculture,modern military,ecological environment and other fields.In order to solve the problem of positioning difficulty caused by sparse nodes,partial coverage of heterogeneous wireless sensor networks,energy consumption efficiency in data acquisition,data redundancy of dense nodes and transmission delay in multi-to-one communication,this paper conducts in-depth research in theory and practice,and the main work and innovation results are as follows:Aiming at the problem of location difficulty caused by sparse nodes in wireless sensor networks,a method of passive event auxiliary nodes localization(PEAATAL)is proposed.Passive events refer to some signals detected by sensors in the sensing area.This study proves that passive events can increase the connectivity of the network and make some unlocatable nodes locatable.For the nodes that can not be located,a power amplification method is proposed that is to meet the positioning conditions by amplifying the power of most of the unlocatable nodes.The feasibility of the method is verified by simulation experiments.Compared with the localization auxiliary location algorithm(LAL),it is shown that the passive events save the cost of adjusting the network and improve the locability of the network.Aiming at the partial coverage problem of heterogeneous wireless sensor networks,a partial coverage algorithm(GHPC)based on greedy heuristic is proposed.Under the given coverage requirements,the greedy heuristic algorithm is used to deploy the sensing nodes,and the nodes with the maximum coverage contribution are selected to join the coverage set to meet the coverage requirements with the least nodes.The simulation results show that comparing with the deterministic and probabilistic coverage energy efficient algorithm(PCP)and the energy-saving comprehensive coverage and connection allocation algorithm(CCP),the GHPC algorithm can not only meet the coverage requirements,but also reduce the number of active nodes.Aiming at how to reduce the energy consumption effectively in multi-application data acquisition,a cooperative planning sampling method for data sharing is proposed.Firstly,the definition of task cooperation is given,and the theorem of cooperative sampling is proved.Then,a greedy multi-task collaborative sampling algorithm is proposed to optimize the sampling time and interval.Furthermore,the approximate method is compared with the optimal solution in different sample length task sets,which shows that the method is close to the optimal solution.In order to solve the problem that wireless sensor networks usually produce a large number of redundant data,resulting in a waste of storage space and increasing network energy consumption and data transmission collision probability,a data fusion algorithm(LTDA)based on hierarchical topology is proposed.Firstly,the logical topology is established,and the monitoring region is divided into different sub-regions according to the nodes duty.Before data transmission,the transmission node first evaluates the trust value of the perceived data,and when the trust value is higher than the judgment threshold,the data is allowed to be transmitted.The next hop node is selected as the node with the highest resource value by using the information of distance,energy and link quality to calculate the resource value of the uplink node.Simulation results show that the LTDA algorithm outperforms the reliable data fusion algorithm(RDAA)and the unstructured energy-balanced RDAA fusion algorithm(SEDA),which has better performance in average energy consumption,data packet loss rate,end-to-end transmission delay,etc.For multi-to-one communication in wireless sensor networks,in order to reduce the transmission delay,a cross-layer design and optimization data transmission algorithm for routing and MAC(J-R-MAC)is proposed.The J-R-MAC algorithm starts from the periodic structure of the sensor node and the sink so that the node has multiple opportunities to access the media in the same period.At the same time,the network nodes are divided into different sets,and the level of each node is calculated.Then the next hop forwarding node is selected according to the level information of the node to reduce the delay.Simulation results show that the proposed J-R-MAC algorithm can effectively reduce the end-to-end transmission delay and improve the packet delivery rate compared with CL-MAC algorithm.The simulation results show that the proposed J-R-MAC algorithm can effectively reduce the delay of end-to-end transmission and improve the rate of packet delivery.In this paper,the key technologies of wireless sensor network,such as node location,network coverage,data collection,data fusion and transmission,are studied,and the corresponding algorithms and models are proposed.Simulation experiments verify the effectiveness of the model algorithms,which can be widely used in intelligent agriculture,forestry,national defense and other industries.