Unmanned aerial vehicles (UAVs) can be used as flying base stations (BSs) to offload Macro-BSs in hotspots. However, due to the limited battery on-board, UAVs can typically stay in operation for less than 1.5 hours. Afterward, the UAV has to fly back to a dedicated charging station that recharges/replaces the UAV's battery. In this paper, we study the performance of a UAV-enabled cellular network while capturing the influence of the spatial distribution of the charging stations. In particular, we use tools from stochastic geometry to derive the coverage probability of a UAV-enabled cellular network as a function of the battery size, the density of the charging stations, and the time required for recharging/replacing the battery.

When monitoring the environment with wireless sensor networks, the data sensed by the nodes within event backbone regions can adequately represent the events. As a result, identifying event backbone regions is a key issue for wireless sensor networks. With this aim, we propose a distributed and morphological operation-based data collection algorithm. Inspired by the use of morphological erosion and dilation on binary images, the proposed distributed and morphological operation-based data collection algorithm calculates the structuring neighbors of each node based on the structuring element, and it produces an event-monitoring map of structuring neighbors with less cost and then determines whether to erode or not. The remaining nodes that are not eroded become the event backbone nodes and send their sensing data. Moreover, according to the event backbone regions, the sink can approximately recover the complete event regions by the dilation operation. The algorithm analysis and experimental results show that the proposed algorithm can lead to lower overhead, decrease the amount of transmitted data, prolong the network lifetime, and rapidly recover event regions.

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An automatic, real-time detection approach to video scene change detection is presented. Owing to the high correlation of two consecutive video frames, it is proposed that only the eigenvector corresponding to the largest eigenvalue is retained in the principal component analysis (PCA) for video data. A one-dimensional PCA feature of video data is then generated from the PCA. It shows superior performance compared to the histogram feature and the pixel feature. The detection algorithm based on this PCA feature is then designed to detect both abrupt and gradual transitions. The proposed approach is tested on the TREC video test repository to validate its performance. © 2006 The Institution of Engineering and Technology

Using drones for cellular coverage enhancement is a recent technology that has shown a great potential in various practical scenarios. However, one of the main challenges that limits the performance of drone-enabled wireless networks is the limited flight time. In particular, due to the limited on-board battery size, the drone needs to frequently interrupt its operation and fly back to a charging station to recharge/replace its battery. In addition, the charging station might be responsible to recharge multiple drones. Given that the charging station has limited capacity, it can only serve a finite number of drones simultaneously. Hence, in order to accurately capture the influence of the battery limitation on the performance, it is required to analyze the dynamics of the time spent by the drones at the charging stations. In this paper, we use tools from queuing theory and stochastic geometry to study the influence of each of the charging stations limited capacity and spatial density on the performance of a drone-enabled wireless network.