Publications

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In this work we present the concept of g'clipping', scheduling receive events for wireless transmissions only on receivers within some distance of the transmitter. Combined with spatial indexing, this technique enables faster simulation of large-scale wireless networks containing tens of thousands or even hundreds of thousands of wireless nodes. We detail our additions and changes to ns-3 to implement this feature, demonstrate how it yields a 2 × speedup for a complex 5G scenario with minimal impact on simulation fidelity, and show how under special circumstances a speedup of over 40 × is achievable while producing identical results.

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The explosion of both sensors and GPS-enabled devices has resulted in position/time data being the next big frontier for data analytics. However, many of the problems associated with large numbers of trajectories do not necessarily have an analog with many of the historic big-data applications such as text and image analysis. Modern trajectory analytics exploits much of the cutting-edge research in machine-learning, statistics, computational geometry and other disciplines. We will show that for doing trajectory analytics at scale, it is necessary to fundamentally change the way the information is represented through a feature-vector approach. We then demonstrate the ability to solve large trajectory analytics problems using this representation.

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Since the attacks carried out against the United States on September 11, 2001, which involved the commandeering of commercial aircraft, interest has increased in performing trajectory analysis of vehicle types not constrained by roadways or railways, i.e., aircraft and watercraft. Anomalous trajectories need to be automatically identified along with other trajectories of interest to flag them for further investigation. There is also interest in analyzing trajectories without a focus on anomaly detection. Various approaches to analyzing these trajectories have been undertaken with useful results to date. In this research, we seek to augment trajectory analysis by carrying out analysis of the trajectory curvature along with other parameters, including distance and total deflection (change in direction). At each point triplet in the ordered sequence of points, these parameters are computed. Adjacent point triplets with similar values are grouped together to form a higher level of semantic categorization. These categorizations are then analyzed to form a yet higher level of categorization which has more specific semantic meaning. This top level of categorization is then summarized for all trajectories under study, allowing for fast identification of trajectories with various semantic characteristics.

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