Research On Data Acquisition Optimization Algorithm Based On Distributed Wireless Sensor Networks

Wireless sensor network is a kind of wireless self-organizing sensor network formed by a large number of sensor nodes,which is widely used in military,medical,industrial and other fields.Due to the limited capacity of each sensor node,wireless sensor networks usually perform specific tasks in a cooperative manner,such as area monitoring or positioning,and send the monitored or sensed data to the base station for analysis and processing by upper applications or users.The above process,called data collection,is one of the important functions of wireless sensor networks.In this process,congestion will inevitably occur between sensor nodes,leading to packet loss or retransmission of data,increasing node energy consumption and transmission delay.At the same time,the battery energy of nodes is limited and prone to damage,which can lead to data loss and communication link failure when the battery energy is depleted.Therefore,how to solve the problems of low latency,low energy consumption and extending the network life cycle of data acquisition in wireless sensor networks has become a hot topic for scholars at home and abroad.This paper focuses on the data collection problem in wireless sensor networks,and carries out the research on the data collection optimization algorithm based on distributed wireless sensor networks.The main work and conclusions are as follows:(1)Aiming at the problems of high delay and energy hole in static data acquisition in wireless sensor networks,an optimization algorithm for mobile data acquisition in duty cycle mode is proposed.The algorithm introduces a mobile data collector that can collect data from selected sensor nodes(referred to as anchors).To ensure the optimality of mobile data collection,an adaptive anchor selection algorithm was designed to determine the optimal number and position of anchors,reducing the impact of anchors on latency and energy consumption;Next,with the delay time in the duty cycle mode as the objective function,the mobile data collection problem is described as a delay optimization problem subject to flow conservation,congestion control,and energy balance.A distributed subgradient algorithm based on non-uniform step size is proposed to solve this optimization problem,and the convergence of the algorithm is strictly proved;Finally,the effectiveness of the proposed algorithm was verified by comparing it with existing methods in terms of convergence speed,delay time,and energy consumption.(2)Aiming at the problem that the objective function is unknown or the gradient cannot be established in the data acquisition of wireless sensor networks,a distributed data acquisition optimization algorithm with unknown objective function is proposed.Firstly,a data priority transmission mechanism is introduced in data collection,which achieves data storage and transmission of different priorities through queues.The optimal transmission path of nodes is also formulated to reduce the transmission delay of high priority data;Secondly,an adaptive duty cycle adjustment method for nodes was designed,where each sensor node can adjust the duty cycle time based on the amount of data and priority at different times;Finally,a random gradient free distributed optimization algorithm was proposed,which can obtain the optimal solution based on the input and output of variables when an explicit objective function cannot be obtained.Each sensor node only needs to communicate with neighboring nodes to complete the update.Simulation experiments have shown that in scenarios where the objective function is unknown,the optimization variables can converge to the optimal solution.In addition,the effectiveness of the algorithm was verified by reducing latency and energy consumption,achieving priority transmission,and adjusting the duty cycle.(3)Aiming at the problem of recovery accuracy of data transmission in wireless sensor networks,an optimized data acquisition algorithm based on asynchronous Nesterov acceleration is proposed.By minimizing reconstruction errors as the objective function and utilizing more historical information to accelerate the calculation process,the convergence speed is improvedfrom O(1/k)to O(1/k2).Meanwhile,the implementation process of the algorithm adopts an asynchronous computing model,where each node eliminates interference from other nodes by adding a buffer and an independent clock,thereby improving the utilization of computing resources by the nodes.Simulation experiments have shown that the proposed algorithm can effectively improve the computational efficiency and convergence speed of nodes,and has significant advantages in latency,energy consumption,and other aspects.