Abstract
Considering the event-based WSNs routing, the frequent changes of topology may result to the large energy costs in the whole network. Therefore, this paper proposes a Compressed Sensing Routing-control-method with Intelligent Migration-mechanism based on Sensing Cloud-computing (CSR-IM). First, the method gives a determining the moving speed and position of the target node through compressed sensing theory, and at the same time, it gives the lower bound calculation process of the target node state estimation value at $k+1$ time by the probability knowledge. Second, with the purpose of reducing the network load, the routing tree with the center of fog nodes is established to obtain the data in the route effectively and optimize the data aggregation routing process, and then energy cost of the whole network is balanced. Finally, the simulation experiments show that method of this paper (CSR-IM) and other algorithms have improved the average data aggregation rate by 8.19%, and the average network coverage has increased by 12.65%, which proves that the proposed algorithm is effective and practical.
Highlights
As an important support for the Internet of Things (IoT), Wireless Sensor Networks (WSNs) has become a new research hotspot in the fields of wireless networks [1]–[5]
As the number of sensor nodes increases, the advantages brought by data aggregation gradually increase, compared to CSRIn terms of IM algorithm and Trust-Based Secure Routing (TBSR) algorithm, the performance enhancement speed of Mobile Data Collectors (MDCS) and DMOA is relatively slow, and the difference in data aggregation rate between the two algorithms is small
This paper studies dynamic data aggregation routing and network coverage with migration mechanism, and proposes the Compressed Sensing Routing-control- method with Intelligent Migration-mechanism based on Sensing Cloud-computing
Summary
As an important support for the Internet of Things (IoT), Wireless Sensor Networks (WSNs) has become a new research hotspot in the fields of wireless networks [1]–[5]. For data collection way of time-driven, the sensor nodes periodically sense the environment and transmit the sensed data to the base station at a fixed data rate according to pre-scheduling. This method of data collection is called periodic data collection and is suitable for applications that require global monitoring. In order to cope with problems such as node failure and inaccurate sensing data caused by the environment, WSNs usually deploy sensor nodes densely in the monitoring area, but this results in greater information redundancy in the whole network. It is an important technology to achieve energy- efficient data collection in WSNs
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