• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.27-36
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• 2011

The sensor nodes of sensor network system are capable of wireless communication with sink nodes. They also acquire and transmit sensor data in broad region where people cannot access easily. However, the transmission of redundant data from sensor nodes reduces the lifetime of the entire system and substantial amount of resulted data needs to be resorted before implementing them to the specific applications. In this paper, the mobile agent middleware to eliminate the redundant sensor data is designed and implemented. In the proposed system, the mobile agent visits the destination sensor nodes according to the migration list offered by the meta table in the name space of the naming agent, eliminates the redundant sensor data corresponding to user condition, and acquires and transmits sensor data according to the purpose and needs. Thus, the excess transmission of the sensor data is avoided and the lifetime of the entire system can be extended. Moreover, the experiments using the mobile agent middleware with the conditions and limitations that are possible in real situation ore done to verify the successful elimination of the redundant sensor data and the efficiency of the data acquisition. Also, we show the potential applicability of the mobile agent middleware in various active sensor networks through the active rule based mobile agent middleware or the interaction with the active rule system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.227-247
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• 2018

For intermittently connected wireless sensor networks deployed in hash environments, sensor nodes may fail due to internal or external reasons at any time. In the process of data collection and recovery, we need to speed up as much as possible so that all the sensory data can be restored by accessing as few survivors as possible. In this paper a novel redundant data storage algorithm based on minimum spanning tree and quasi-randomized matrix-QRNCDS is proposed. QRNCDS disseminates k source data packets to n sensor nodes in the network (n>k) according to the minimum spanning tree traversal mechanism. Every node stores only one encoded data packet in its storage which is the XOR result of the received source data packets in accordance with the quasi-randomized matrix theory. The algorithm adopts the minimum spanning tree traversal rule to reduce the complexity of the traversal message of the source packets. In order to solve the problem that some source packets cannot be restored if the random matrix is not full column rank, the semi-randomized network coding method is used in QRNCDS. Each source node only needs to store its own source data packet, and the storage nodes choose to receive or not. In the decoding phase, Gaussian Elimination and Belief Propagation are combined to improve the probability and efficiency of data decoding. As a result, part of the source data can be recovered in the case of semi-random matrix without full column rank. The simulation results show that QRNCDS has lower energy consumption, higher data collection efficiency, higher decoding efficiency, smaller data storage redundancy and larger network fault tolerance.