Large-scale sensing devices spread over a wide area and compose the supervisory control and data acquisition (SCADA) system to remotely control and monitor a specific process through collecting the sensing data from the working field. However, the trustworthy and energy efficient data collection is still a challenging issue for large-scale Internet of thing systems. In this article, a trust based energy efficient data collection with unmanned aerial vehicle (TEEDC-UAV) scheme is proposed to prolong lifetime with trustworthy style. First, in TEEDC-UAV scheme, an ant colony based unmanned aerial vehicle (UAV) trajectory optimization algorithm is proposed in which form the most data anchors in the working field with the trajectory as short as possible. Thus, the sensor nodes in SCADA system can be responsible for the least amount of data and greatly extend network life. Second, a trust reasoning and evolution mechanism is proposed to identify the trust degree of sensor nodes, and only trusted data will be collected so that the quality of data collection can be proved. In our proposed trust mechanism, the UAV can sense and collect data itself, so that data can be used as the baseline to identify the trust degree of sensor nodes. Finally, proved by sufficient experiment results, our proposed TEEDC-UAV scheme can find an optimized data collection trajectory efficiently, which helps the energy consumption of the network become much more balanced. Compared with previous strategies, the network life is greatly improved by 48.9%. Meanwhile, the trust mechanism proposed in this article can also greatly improve the identification accuracy of node trust degree, which reached 91% when consuming only 8% network life.

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Crowd sensing networks play a critical role in big data generation where a large number of mobile devices collect various kinds of data with large-volume features. Although what information should be collected is essential for the success of crowd-sensing applications, few research efforts have been made so far. On the other hand, an efficient incentive mechanism is required to encourage all crowd-sensing participants including data collectors, service providers, and service consumers to join the networks. In this article, we propose a new incentive mechanism called QUOIN, which simultaneously ensures Quality and Usability Of INformation for crowdsensing application requirements. We apply a Stackelberg game model to the proposed mechanism to guarantee each participant achieves a satisfactory level of profits. Performance of QUOIN is evaluated with a case study and experimental results demonstrate that it is efficient and effective to collect valuable information for crowd-sensing applications.

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One goal of the fifth-generation (5G) cellular network is to support much higher data capacity (e.g., 1000 times higher than today), where device-to-device (D2D) communication is one of the key enabling technologies. In this paper, we focus on the D2D relaying functionality to improve cellular downlink throughput. Based on the shortages of the latest relevant work, we propose a new scheme that leverages multi-hop relay-assisted outband D2D communications. First of all, by extending two-hop connection to three-hop connection, our scheme can cut down receiving bit error ratio (BER) of cell edge nodes far away from a cellular base station (BS), which improves cellular downlink throughput. Then, it balances network lifetime and throughput by the proposed ratio of income and expenditure (RIE) metric with respect to remaining energy and throughput. Moreover, it reduces the computational overhead of searching relay and also ensures that the optimal relay is selected by the adjustment of searching scope. Compared with the most relevant work, our scheme outperforms it in terms of throughput, delay, and network lifetime.

Directional communication is helpful to improve the performance of millimeter Wave (mmWave) links. However, the dynamic nature of vehicular scenarios raises the complexity of directional mmWave vehicular communications. Also, a mmWave link is susceptible to blockages. Therefore, a mmWave vehicular communication system requires high environmental adaptability and context-awareness. Due to inadequate context information and insufficient beam settings in the existing related algorithm, it is difficult to pick out the set of beams with more reasonable widths and directions, which hinders the further promotion of network capacity in vehicular networks. Therefore, we propose an improved fast machine learning (IFML) algorithm to overcome this shortcoming. In order to improve network capacity while suppressing the additional beam search overhead, a partitioned search method is designed in the IFML. Also, in order to be robust to occasional fluctuations and timely adapt to significant changes in communication environments, the IFML adopts a flexible beam performance update approach based on adjustable weight coefficient. The simulation results show that the IFML significantly outperforms the existing related algorithm in terms of aggregate received data after a certain number of online learning time periods.

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