Design And Implement On Data Gathering With Mobile Sink For Wireless Sensor Networks

The Internet of Things plays a vital role in integration and interaction of information between network and physical world.It is of great significance to technologies such as big data and artificial intelligence.Wireless sensor networks(WSNs)are an important sensing technology in the Internet of Things.A large number of sensor nodes are deployed in target area,and they sense data in real time.These sensor nodes can communicate with each other,and transmit the collected data to base station or users.The energy of sensor nodes is limited in practical application scenarios,and it is very difficult to recharge the battery in general.In this circumstance,reducing energy consumption of network and improving the data collection rate have become the focus and difficulty in the design of data gathering in WSNs.In traditional data collection algorithms,once a sensor node is deployed successfully,it is difficult to change its position.So the sensor node transmits data to base station or the users with multi-hop way,which consumes a lot of energy of sensor nodes and results in so-called "hot spot" phenomenon.Furthermore,it leads to early death of certain nodes and connection failure to network,further affects data collection and transmission in WSNs.Recently,researchers have proposed to alleviate the "energy hole" and balance energy consumption of network by adding sink mobility.This thesis focuses on the research and implementation of data collection algorithm with mobile sink for WSNs in order to decrease energy consumption of nodes and prolong life cycle of network.The main work is as follows:(1)Basic concepts and application scenarios about WSNs are first reviewed.Then the thesis divides data collection algorithms with mobile sink into three categories and analyzes their characteristics.Advantages and disadvantages about LEACH are introduced in this paper.A Timer-Based Mobile Data Collection(TBMDC)algorithm for data collection is proposed which would mitigate the problem of "energy hole" in nodes.In clustering stage,the election of cluster head nodes takes the location and residual energy of nodes into consideration.Related simulation experiments demonstrate that the proposed algorithm can reduce energy consumption effectively and improve data collection rate.(2)To alleviate energy consumption nonuniform,this thesis proposes a data collection algorithm based on improved K-means and mobile sinks.This method improves the traditional K-means clustering algorithm.When selecting cluster head nodes,the algorithm considers geographical position between cluster head to other nodes and residual energy of nodes,instead of taking cluster center point as cluster head directly.In order to reduce moving path of mobile sinks,this algorithm uses several mobile sinks to access communication circle of cluster head.Hence this algorithm can solve the problem of network consumption nonuniform and improve the life cycle of network.(3)In order to apply our data collection algorithm in real life,the data collection algorithm based on improved K-means and mobile sinks is used into parking system.In this system,the parking sensors read information of free parking at first.Then RFID decides to allow the vehicle enter or leave by reading the information of it.Finally the parking system sends location of free parking to vehicle.At this time,this system completes whole behavior about parking.In this way,the system avoids vehicle waiting for parking and wasting time to find free parking.Meanwhile,this data collection algorithm reduces energy consumption of parking sensors,improves efficiency of data collection and transmission.