Due to the use of acoustic channels with limited available bandwidth, Underwater Sensor Networks (USNs) often suffer from significant performance restrictions such as low reliability, low energy-efficiency, and high end-to-end packet delay. The provisioning of reliable, energy-efficient, and low-delay communication in USNs has become a challenging research issue. In this paper, we take noise attenuation in deep water areas into account and propose a novel layered multipath power control (LMPC) scheme in order to reduce the energy consumption as well as enhance reliable and robust communication in USNs. To this end, we first formalize an optimization problem to manage transmission power and control data rate across the whole network. The objective is to minimize energy consumption and simultaneously guarantee the other performance metrics. After proving that this optimization problem is NP-complete, we solve the key problems of LMPC including establishment of the energy-efficient tree and management of energy distribution and further develop a heuristic algorithm to achieve the feasible solution of the optimization problem. Finally, the extensive simulation experiments are conducted to evaluate the network performance under different working conditions. The results reveal that the proposed LMPC scheme outperforms the existing mechanism significantly.

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Despite the huge expansion of electric vehicle sales in the market, customers are discouraged by the possible catastrophic consequences brought by the safety issues of lithium-ion batteries, such as internal...

The Big Data problem is characterized by the so called 3V features: Volume -a huge amount of data, Velocity -a high data ingestion rate, and Variety -a mix of structured data, semi-structured data, and unstructured data. The state-of-the-art solutions to the Big Data problem are largely based on the MapReduce framework (aka its open source implementation Hadoop). Although Hadoop handles the data volume challenge successfully, it does not deal with the data variety well since the programming interfaces and its associated data processing model is inconvenient and inefficient for handling structured data and graph data. This paper presents epiC, an extensible system to tackle the Big Data's data variety challenge. epiC introduces a general Actor-like concurrent programming model, independent of the data processing models, for specifying parallel computations. Users process multi-structured datasets with appropriate epiC extensions, the implementation of a data processing model best suited for the data type and auxiliary code for mapping that data processing model into epiC's concurrent programming model. Like Hadoop, programs written in this way can be automatically parallelized and the runtime system takes care of fault tolerance and inter-machine communications. We present the design and implementation of epiC's concurrent programming model. We also present two customized data processing model, an optimized MapReduce extension and a relational model, on top of epiC. Experiments demonstrate the effectiveness and efficiency of our proposed epiC.

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