The Flexible Group Spatial Keyword Query

We present a new class of service for location based social networks, called the Flexible Group Spatial Keyword Query, which enables a group of users to collectively find a point of interest (POI) that optimizes an aggregate cost function combining both spatial distances and keyword similarities. In addition, our query service allows users to consider the tradeoffs between obtaining a sub-optimal solution for the entire group and obtaining an optimimized solution but only for a subgroup. We propose algorithms to process three variants of the query: (i) the group nearest neighbor with keywords query, which finds a POI that optimizes the aggregate cost function for the whole group of size n, (ii) the subgroup nearest neighbor with keywords query, which finds the optimal subgroup and a POI that optimizes the aggregate cost function for a given subgroup size m (m <= n), and (iii) the multiple subgroup nearest neighbor with keywords query, which finds optimal subgroups and corresponding POIs for each of the subgroup sizes in the range [m, n]. We design query processing algorithms based on branch-and-bound and best-first paradigms. Finally, we provide theoretical bounds and conduct extensive experiments with two real datasets which verify the effectiveness and efficiency of the proposed algorithms.Comment: 12 page

The K Group Nearest-Neighbor Query on Non-indexed RAM-Resident Data

Data sets that are used for answering a single query only once (or just a few times) before they are replaced by new data sets appear frequently in practical applications. The cost of buiding indexes to accelerate query processing would not be repaid for such data sets. We consider an extension of the popular (K) Nearest-Neighbor Query, called the (K) Group Nearest Neighbor Query (GNNQ). This query discovers the (K) nearest neighbor(s) to a group of query points (considering the sum of distances to all the members of the query group) and has been studied during recent years, considering data sets indexed by efficient spatial data structures. We study (K) GNNQs, considering non-indexed RAM-resident data sets and present an existing algorithm adapted to such data sets and two Plane-Sweep algorithms, that apply optimizations emerging from the geometric properties of the problem. By extensive experimentation, using real and synthetic data sets, we highlight the most efficient algorithm

On CSI-Free Multiantenna Schemes for Massive RF Wireless Energy Transfer

AbstractRadio-frequency wireless energy transfer (RF-WET) is emerging as a potential green enabler for massive Internet of Things (IoT). Herein, we analyze channel state information (CSI)free multiantenna strategies for powering wirelessly a large set of single-antenna IoT devices. The CSI-free schemes are AASS (AA-IS), where all antennas transmit the same (independent) signal(s), and SA, where just one antenna transmits at a time such that all antennas are utilized during the coherence block. We characterize the distribution of the provided energy under correlated Rician fading for each scheme and find out that while AA-IS and SA cannot take advantage of the multiple antennas to improve the average provided energy, its dispersion can be significantly reduced. Meanwhile, AA-SS provides the greatest average energy, but also the greatest energy dispersion, and the gains depend critically on the mean phase shifts between the antenna elements. We find that consecutive antennas must be π-phase shifted for optimum average energy performance under AA-SS. Our numerical results evidence that correlation is beneficial under AA-SS, while a greater line of sight (LOS) and/or the number of antennas is not always beneficial under such a scheme. Meanwhile, both AA-IS and SA schemes benefit from small correlation, large LOS, and/or a large number of antennas. Finally, AA-SS (SA and AA-IS) is (are) preferable when devices are (are not) clustered in specific spatial directions.Abstract Radio-frequency wireless energy transfer (RF-WET) is emerging as a potential green enabler for massive Internet of Things (IoT). Herein, we analyze channel state information (CSI)free multiantenna strategies for powering wirelessly a large set of single-antenna IoT devices. The CSI-free schemes are AASS (AA-IS), where all antennas transmit the same (independent) signal(s), and SA, where just one antenna transmits at a time such that all antennas are utilized during the coherence block. We characterize the distribution of the provided energy under correlated Rician fading for each scheme and find out that while AA-IS and SA cannot take advantage of the multiple antennas to improve the average provided energy, its dispersion can be significantly reduced. Meanwhile, AA-SS provides the greatest average energy, but also the greatest energy dispersion, and the gains depend critically on the mean phase shifts between the antenna elements. We find that consecutive antennas must be π-phase shifted for optimum average energy performance under AA-SS. Our numerical results evidence that correlation is beneficial under AA-SS, while a greater line of sight (LOS) and/or the number of antennas is not always beneficial under such a scheme. Meanwhile, both AA-IS and SA schemes benefit from small correlation, large LOS, and/or a large number of antennas. Finally, AA-SS (SA and AA-IS) is (are) preferable when devices are (are not) clustered in specific spatial directions