Research On Schemes Of Node Scheduling And Target Tracking In Internet Of Things

Internet of things (IoT) is a service network system based on sensing technology and integrated various applications, its operation base is the data of sensing layer. Sensing layer is the interface between IoT and the physical world, it is in the bottom of the architecture in IoT, and its own particularity fundamentally determines that its operation modes and control strategies are required to be energy-efficient. Node scheduling is an effective method to deal with limited energy of sensing layer in IoT, while target tracking is one of the important applications in IoT, which needs to combined with node scheduling. This thesis researched node scheduling and target tracking technology in IoT, which is significant for the development of IoT.This thesis studied the status of effective node scheduling schemes and target tracking schemes in IoT, analyzed the problems of geographical position independent node scheduling schemes, forecast-based target tracking schemes in IoT and node scheduling scheme for target tracking, and then did research based on these. The main work of the thesis is as follows.a) For dealing with the problem of IoT nodes non-uniform sleeping caused by border effect, this thesis analyzed the tolerance coverage area based node scheduling algorithm, and then improved the energy-efficiency and node states of the algorithm, firstly introducing residual energy of nodes as criterion factor in coverage redundancy judgment to improve the uniformity of the energy consumption, secondly optimizing node state distribution and conversion to minimize coverage redundancy, and then proposed an new energy-efficient coverage optimized node scheduling algorithm TCA-NS. In addition, for the event detecting and reporting application in IoT, a node data reliable gathering cross-layer protocol DRGC was proposed in this thesis. The protocol includes a clock synchronization algorithm that balanced synchronous precision and energy consumption, and a cross-layer data gathering algorithm that fuses MAC and routing scheme. TCA-NS algorithm and DRGC protocol can be fused and applied to sensing layer in IoT, forming a set of underlying protocol stack.b) Grid partition based node scheduling algorithm was discussed and three grid partition methods respectively based on Voronoi grid, triangular grid and squared rectangle grid were analyzed. The effect of value relationship between communication radius and sensing radius of sensor nodes to network performance was studied, and in the network model of communication radius is less than sensing radius, a squared rectangle partition based node scheduling algorithm SRPMNSA was studied. By extending network model to unlimited value between sensing radius and communication radius of nodes, and improving the value for side length of partitioned squared rectangle, an improved squared rectangle partition algorithm I-SRPMNSA was proposed. In the extended network model, when rs>rc, I-SRPMNSA is better than SRPMNSA in network coverage quality; when 1/2rc≤rs≤rc, I-SRPMNSA prolongs the network lifetime compared with SRPMNSAc) For Target Tracking in IoT, the concept of sensing subtraction was presented, combining sensing subtraction and compressed sensing, sparse sampling the distributed sensing information in IoT, and reconstructing sensing subtraction matrix by compressed sensing theory, and then locating and tracking moving target by sensing subtraction method. Based on this, a target tracking scheme based on compressed sensing and sensing subtraction was proposed. This scheme needs no grid partition, does not have specific requirement for the shape of monitoring area, which is a universally applicable target tracking scheme in IoT. It recovers sensing signal well with sparse sampling sensor signal, gets the positions of targets accurately by sensing subtraction method, and its sparse sampling strategy reduces network communication traffic and improves the energy-efficiency of system.d) For the network application of moving target tracking, node energy consumption model in IoT was analyzed in detail, and node scheduling schemes for target tracking were studied. Firstly, an energy-efficient adaptive node scheduling algorithm ANSTT was proposed. The algorithm automatically adjusts the working mode of sensor nodes according to the perceived ability on moving target and the relative remaining energy level (RREL) of nodes. Three performance indexes were presented to evaluate the performance of this algorithm, and the probabilistic model of the three performance indexes in this algorithm were analyzed with Markov process. ANSTT algorithm effectively reduces system energy consumption and prolongs network lifetime while maintaining low detection delay and high target perceived rate. Secondly, a forecast-based node collaborative scheduling algorithm for moving target tracking was proposed, and the model of target moving and node sensing was presented. This algorithm constructs sensing utility function by particle filtering,constructs sensing energy-efficiency function by a method balanced the residual energy of nodes, and then elects the members of perception group who participate in the process of target tracking. It has high accuracy in object tracking, and can effectively balance energy consumption distribution among nodes.