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Han T.,Huazhong University of Science and Technology | Feng Y.,Huazhong University of Science and Technology | Wang J.,Shanghai Research Center for Wireless Communication | Wang J.,CAS Shanghai Institute of Microsystem and Information Technology | And 3 more authors.
2015 IEEE International Conference on Communication Workshop, ICCW 2015 | Year: 2015

In this paper, we consider a two-dimensional heterogeneous cellular network scenario consisting of one base station (BS) and some mobile stations (MSs) whose locations follow a Poisson point process (PPP). The MSs are equipped with multiple radio access interfaces including a cellular access interface and at least one short-range communication interface. We propose a nearest-neighbor cooperation communication (NNCC) scheme by exploiting the short-range communication between a MS and its nearest neighbor to collaborate on their uplink transmissions. In the proposed cooperation scheme, a MS and its nearest neighbor first exchange data by the short-range communication. Upon successful decoding of the data from each other, they proceed to send their own data, as well as the data received from the other to the BS respectively in orthogonal time slots. The energy efficiency analysis for the proposed scheme is presented based on the characteristics of the PPP and the Rayleigh fading channel. Numerical results show that the NNCC scheme significantly improves the energy efficiency compared to the conventional non-cooperative uplink transmissions. © 2015 IEEE.

Zhao C.,CAS Shanghai Institute of Microsystem and Information Technology | Zhao C.,Shanghai Research Center for Wireless Communication | Zhang W.,CAS Shanghai Institute of Microsystem and Information Technology | Zhang W.,Shanghai Research Center for Wireless Communication | And 5 more authors.
IEEE Transactions on Vehicular Technology | Year: 2015

Compressive sensing (CS)-based data aggregation has become an increasingly important research topic for largescale wireless sensor networks since conventional data aggregations are shown to be inefficient and unstable in handling huge data traffic. However, for CS-based techniques, the discrete cosine transform, which is the most widely adopted sparsification basis, cannot sufficiently sparsify real-world signals, which are unordered due to random sensor distribution, thus weakening advantages of CS. In this paper, an energy-efficient CS-based scheme, which is called "treelet-based clustered compressive data aggregation" (T-CCDA), is proposed. Specifically, as a first step, treelet transform is adopted as a sparsification tool to mine sparsity from signals for CS recovery. This approach not only enhances the performance of CS recovery but reveals localized correlation structures among sensor nodes as well. Then, a novel clustered routing algorithm is proposed to further facilitate energy saving by taking advantage of the correlation structures, thus giving our T-CCDA scheme. Simulation results show that the proposed scheme outperforms other reference approaches in terms of communication overhead per reconstruction error for adopted data sets. © 2014 IEEE.

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