Abstract
Being limited by the node energy, the Internet of Things (IoT) is prone to data redundancy in the process of data acquisition and collection, resulting in a large amount of packet losses in data transmission and making it impossible to guarantee the transmission mechanism with data security. In order to study the credibility of IoT sensing data transmission further, Edge Computing in Internet of Things: A Novel Sensing-data Reconstruction Algorithm under Intelligent-migration strategy (RdS-ImS) is proposed in this paper. From the beginning, this algorithm can build the packet loss model of sensing data on the link into the form which is random and give the data packet loss predictive model based on the compressed sensing theory. Secondly, preventing the random data packet from lossing, the predictive model is applied to recover through the sensing data retransmission mechanism. If it is unavailable to determine random data packet loss, time series prediction algorithm may be applied for recovery. In addition, in case of interruption of the transmission path, such predictive model can upload the sensing data to the cloud computing platform through an alternative path. At the same time, iterative calculation is performed on the path using intelligent algorithms to optimize the path According to simulation results; this predictive model can improve the network runtime quickly but reducing the sensing data packet loss rate effectively, thereby further verifying this method, which is put forward in this paper, is of relatively strong stability and adaptability.
Highlights
As the underlying application system of the Internet of Things (IoT), Wireless Sensor Network (WSN) is a new form of the network enabling the collection, transmission and processing of related sensing data which is in the deployment area, which generally consists of a large amount of sink nodes (Sink) and sensing nodes [1]–[4]
For a Compressed Sensing (CS) data collection algorithm that is highly sensitive to packet loss, can a limited quantiy of retransmissions improve the data reconstruction accuracy? In order to answer this question, this paper models the packet loss of a lossy link and analyzes the impact of retransmission on the performance of CS data collection algorithms which are under models of different packet loss based on experiments
During the data collection process of the algorithm, in case of data packet loss on the link, the dynamic real-time system design is completed by means of dynamic parameter fitting
Summary
As the underlying application system of the Internet of Things (IoT), Wireless Sensor Network (WSN) is a new form of the network enabling the collection, transmission and processing of related sensing data which is in the deployment area, which generally consists of a large amount of sink nodes (Sink) and sensing nodes [1]–[4]. To enable the data collection which is efficient and extend the service life of WSN, research on collection strategies of new data are needed urgently in order that the network balanced energy consumption can be guaranteed and the ‘‘energy hole’’ can be avoided and the redundancy of the data within the network can be reduced and the consumption of the energy of the network can be cut down. During the process of CS data collection, each node participates in the same number of sampling projections, and the numbers of data packets sent or received are the same, thereby realizing the energy balance of the network and avoiding the ‘‘energy hole’’ problem for traditional data collection processes
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