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

Unlocking the deployment of spectrum sharing with a policy enforcement framework

Spectrum sharing has been proposed as a promising way to increase the efficiency of spectrum usage by allowing incumbent operators (IOs) to share their allocated radio resources with licensee operators (LOs), under a set of agreed rules. The goal is to maximize a common utility, such as the sum rate throughput, while maintaining the level of service required by the IOs. However, this is only guaranteed under the assumption that all “players”respect the agreed sharing rules. In this paper, we propose a comprehensive framework for licensed shared access (LSA) networks that discourages LO misbehavior. Our framework is built around three core functions: misbehavior detection via the employment of a dedicated sensing network; a penalization function; and, a behavior-driven resource allocation. To the best of our knowledge, this is the first time that these components are combined for the monitoring/policing of the spectrum under the LSA framework. Moreover, a novel simulator for LSA is provided as an open access tool, serving the purpose of testing and validating our proposed techniques via a set of extensive system-level simulations in the context of mobile network operators, where IOs and several competing LOs are considered. The results demonstrate that violation of the agreed sharing rules can lead to a great loss of resources for the misbehaving LOs, the amount of which is controlled by the system. Finally, we promote that including a policy enforcement function as part of the spectrum sharing system can be beneficial for the LSA system, since it can guarantee compliance with the spectrum sharing rules and limit the short-term benefits arising from misbehavior

Energy-Efficient UAV Trajectories: Simulation vs Emulation

This paper uses an emulator to verify an energy-efficient trajectory for an unmanned aerial vehicle (UAV) acting as a portable access point (PAP) to serve a set of users. Specifically, we use the Common Open Research Emulator (CORE), and Extendable Mobile Ad-hoc Network Emulator (EMANE), which allow us to take theoretical assumptions regarding data transfer rates and transmission characteristics and test them in the virtualized wireless networking setting the two tools provide us. The optimal fly-hover-communicate trajectory that maximizes the system's energy efficiency is obtained using a circle-packing algorithm. The CORE-EMANE emulator results match the simulated results, thereby verifying the practicality of the obtained trajectory solution

Once-yearly zoledronic acid in the prevention of osteoporotic bone fractures in postmenopausal women

Zoledronic acid is a nitrogen-containing, third-generation bisphosphonate that has recently been approved for the treatment of postmenopausal osteoporosis as an annual intravenous infusion. Zoledronic acid is an antiresorptive agent which has a high affinity for mineralized bone and especially for sites of high bone turnover. Zoledronic acid is excreted by the kidney without further metabolism. Zoledronic acid administered as a 5 mg intravenous infusion annually increases bone mineral density in the lumbar spine and femoral neck by 6.7% and 5.1% respectively and reduces the incidence of new vertebral and hip fractures by 70% and 41% respectively in postmenopausal women with osteoporosis. Most common side effects are post-dose fever, flu-like symptoms, myalgia, arthralgia, and headache which usually occur in the first 3 days after infusion and are self-limited. Rare adverse effects include renal dysfunction, hypocalcemia, atrial fibrillation, and osteonecrosis of the jaw

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