Clustering mobile trajectories for resource allocation in mobile environments

The recent developments in computer and communication technologies gave rise to Personal Communication Systems. Due to the nature of the PCS, the bandwidth allocation problem arises, which is based on the notion of bandwidth-on-demand. We deal with the problem of how to predict the position of a mobile client. We propose a new algorithm, called DCP, to discover user mobility patterns from collections of recorded mobile trajectories and use them for the prediction of movements and dynamic allocation of resources. The performance of the proposed algorithm is examined against two baseline algorithms. The simulation results illustrate that the proposed algorithm achieves recall that is comparable to that of the baseline algorithms and substantial improvement in precision. This improvement guarantees very good predictions for resource allocation with the advantage of very low resource consumption. © Springer-Verlag Berlin Heidelberg 2003

VIRTUALIZED BASEBAND UNITS CONSOLIDATION IN ADVANCED LTE NETWORKS USING MOBILITY- AND POWER-AWARE ALGORITHMS

Virtualization of baseband units in Advanced Long-Term Evolution networks and a rapid performance growth of general purpose processors naturally raise the interest in resource multiplexing. The concept of resource sharing and management between virtualized instances is not new and extensively used in data centers. We adopt some of the resource management techniques to organize virtualized baseband units on a pool of hosts and investigate the behavior of the system in order to identify features which are particularly relevant to mobile environment. Subsequently, we introduce our own resource management algorithm specifically targeted to address some of the peculiarities identified by experimental results

Dynamic distributed clustering in wireless sensor networks via Voronoi tessellation control

This paper presents two dynamic and distributed clustering algorithms for Wireless Sensor Networks (WSNs). Clustering approaches are used in WSNs to improve the network lifetime and scalability by balancing the workload among the clusters. Each cluster is managed by a cluster head (CH) node. The first algorithm requires the CH nodes to be mobile: by dynamically varying the CH node positions, the algorithm is proved to converge to a specific partition of the mission area, the generalised Voronoi tessellation, in which the loads of the CH nodes are balanced. Conversely, if the CH nodes are fixed, a weighted Voronoi clustering approach is proposed with the same load-balancing objective: a reinforcement learning approach is used to dynamically vary the mission space partition by controlling the weights of the Voronoi regions. Numerical simulations are provided to validate the approaches

A data mining approach for location prediction in mobile environments

Cataloged from PDF version of article.Mobility prediction is one of the most essential issues that need to be explored for mobility management in mobile computing systems. In this paper, we propose a new algorithm for predicting the next inter-cell movement of a mobile user in a Personal Communication Systems network. In the first phase of our threephase algorithm, user mobility patterns are mined from the history of mobile user trajectories. In the second phase, mobility rules are extracted from these patterns, and in the last phase, mobility predictions are accomplished by using these rules. The performance of the proposed algorithm is evaluated through simulation as compared to two other prediction methods. The performance results obtained in terms of Precision and Recall indicate that our method can make more accurate predictions than the other methods. 2004 Elsevier B.V. All rights reserved

Using a data mining approach for the prediction of user movements in mobile environments

Cataloged from PDF version of article.Mobility prediction is one of the most essential issues that need to be explored for mobility management in mobile computing systems. In this thesis, we propose a new algorithm for predicting the next inter-cell movement of a mobile user in a Personal Communication Systems network. In the first phase of our three-phase algorithm, user mobility patterns are mined from the history of mobile user trajectories. In the second phase, mobility rules are extracted from these patterns, and in the last phase, mobility predictions are accomplished by using these rules. The performance of the proposed algorithm is evaluated through simulation as compared to two other prediction methods. The performance results obtained in terms of Precision and Recall indicate that our method can make more accurate predictions than the other methods.Yavaş, GökhanM.S

Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure

Inferring Unusual Crowd Events From Mobile Phone Call Detail Records

The pervasiveness and availability of mobile phone data offer the opportunity of discovering usable knowledge about crowd behaviors in urban environments. Cities can leverage such knowledge in order to provide better services (e.g., public transport planning, optimized resource allocation) and safer cities. Call Detail Record (CDR) data represents a practical data source to detect and monitor unusual events considering the high level of mobile phone penetration, compared with GPS equipped and open devices. In this paper, we provide a methodology that is able to detect unusual events from CDR data that typically has low accuracy in terms of space and time resolution. Moreover, we introduce a concept of unusual event that involves a large amount of people who expose an unusual mobility behavior. Our careful consideration of the issues that come from coarse-grained CDR data ultimately leads to a completely general framework that can detect unusual crowd events from CDR data effectively and efficiently. Through extensive experiments on real-world CDR data for a large city in Africa, we demonstrate that our method can detect unusual events with 16% higher recall and over 10 times higher precision, compared to state-of-the-art methods. We implement a visual analytics prototype system to help end users analyze detected unusual crowd events to best suit different application scenarios. To the best of our knowledge, this is the first work on the detection of unusual events from CDR data with considerations of its temporal and spatial sparseness and distinction between user unusual activities and daily routines.Comment: 18 pages, 6 figure

Information-rich Task Allocation and Motion Planning for Heterogeneous Sensor Platforms

This paper introduces a novel stratified planning algorithm for teams of heterogeneous mobile sensors that maximizes information collection while minimizing resource costs. The main contribution of this work is the scalable unification of effective algorithms for de- centralized informative motion planning and decentralized high-level task allocation. We present the Information-rich Rapidly-exploring Random Tree (IRRT) algorithm, which is amenable to very general and realistic mobile sensor constraint characterizations, as well as review the Consensus-Based Bundle Algorithm (CBBA), offering several enhancements to the existing algorithms to embed information collection at each phase of the planning process. The proposed framework is validated with simulation results for networks of mobile sensors performing multi-target localization missions.United States. Air Force. Office of Scientific Research (Grant FA9550-08-1-0086)United States. Air Force. Office of Scientific Research. Multidisciplinary University Research Initiative (FA9550-08-1-0356