Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

G. Suseendran, D. Akila, Souvik Pal, Bikramjit Sarkar, A. Aly, Dac-Nhuong Le
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引用次数: 2

Abstract

This article presents a new scheme for data optimization in IoT assister sensor networks. The various components of IoT assisted cloud platform are discussed. In addition, a new architecture for IoT assisted sensor networks is presented. Further, a model for data optimization in IoT assisted sensor networks is proposed. A novel Membership inducing Dynamic Data Optimization (MIDDO) algorithm for IoT assisted sensor network is proposed in this research. The proposed algorithm considers every node data and utilized membership function for the optimized data allocation. The proposed framework is compared with two stage optimization, dynamic stochastic optimization and sparsity inducing optimization and evaluated in terms of performance ratio, reliability ratio, coverage ratio and sensing error. It was inferred that the proposed MIDDO algorithm achieves an average performance ratio of 76.55%, reliability ratio of 94.74%, coverage ratio of 85.75% and sensing error of 0.154.
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云平台上物联网辅助传感器网络的数据优化
本文提出了一种新的物联网辅助传感器网络数据优化方案。讨论了物联网辅助云平台的各个组成部分。此外,还提出了一种新的物联网辅助传感器网络架构。进一步,提出了物联网辅助传感器网络数据优化模型。提出了一种新的物联网辅助传感器网络成员诱导动态数据优化(MIDDO)算法。该算法考虑每个节点的数据,利用隶属度函数对数据进行优化分配。将该框架与两阶段优化——动态随机优化和稀疏性诱导优化进行了比较,并从性能比、可靠性比、覆盖率和感知误差等方面进行了评价。由此推断,所提出的MIDDO算法平均性能比为76.55%,信度比为94.74%,覆盖率为85.75%,感知误差为0.154。
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