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The advent of the Internet of things (IoT) is changing the way how we interact with the physical world. However, the current Internet suffers from exponential increase in bandwidth demand. In order to resolve the bandwidth issue, we can consider aerial networks by unmanned aerial vehicles (UAV) or the so-called drones for establishing a three-dimensional mobile network in an ad hoc manner. By deploying a network from the sky, we can use the otherwise idle wireless medium and high mobility free from ground obstacles. Aerial networks are especially effective for supporting the temporary surge of population as well as disaster areas because building an additional network infrastructure requires extensive time. In this paper, we propose an efficient handover mechanism for aerial networks in the three-dimensional space, which significantly differs from the conventional two-dimensional schemes. The proposed scheme adjusts the height of a drone and the distance between the drones. To this end, we use the seamless handover success probability and the false handover initiation probability in order to evaluate the optimal coverage decision algorithm. To the best of our knowledge, the proposed scheme is the first attempt for resolving handover of net-drones in the three-dimensional space. [ABSTRACT FROM AUTHOR]

The key to realizing the crowd sensing network is to overcome the resource restrictions of energy, bandwidth, computing, and so on. First of all, due to the number of users and sensor availability will be dynamic change over time, crowd sensing system is difficult to accurately predict and allocate resource to accomplish a specific task. Secondly, there is a need to consider how to choose an effective subset from a large number of users with different sensing ability, so as to allocate the sensing devices in communication resources under the constraint conditions. This paper proposes a profit maximization algorithm for resource allocation component in crowd sensing environment. The proposed algorithm not only considers the current profit of crowd sensing service request but also considers the long-term expected profits, so as to ensure long-term maximum profit. The objective function is no longer to minimize the completion time but rather to achieve the target profit maximization. The experimental results show that the new algorithm is feasible and superior to the traditional algorithms. [ABSTRACT FROM AUTHOR]

In large-scale wireless sensor networks, massive sensor data generated by a large number of sensor nodes call for being stored and disposed. Though limited by the energy and bandwidth, a large-scale wireless sensor network displays the disadvantages of fusing the data collected by the sensor nodes and compressing them at the sensor nodes. Thus the goals of reduction of bandwidth and a high speed of data processing should be achieved at the second-level sink nodes. Traditional compression technology is unable to appropriately meet the demands of processing massive sensor data with a high compression rate and low energy cost. In this paper, Parallel Matching Lempel-Ziv-Storer-Szymanski (PMLZSS), a high speed lossless data compression algorithm, making use of the CUDA framework at the second-level sink node is presented. The core idea of PMLZSS algorithm is parallel matrix matching. PMLZSS algorithm divides the data compression files into multiple compressed dictionary window strings and prereading window strings along the vertical and horizontal axes of the matrices, respectively. All of the matrices are parallel matched in the different thread blocks. Compared with LZSS and BZIP2 on the traditional serial CPU platforms, the compression speed of PMLZSS increases about 16 times while, for BZIP2, the compression speed increases about 12 times when the basic compression rate unchanged. [ABSTRACT FROM AUTHOR]