Extracting mobility pattern from target trajectory in wireless sensor networks

Misra, Sudip; Singh, Sukhchain; Khatua, Manas; Obaidat, Mohammad S.

doi:10.1002/dac.2649

Cited by 22 publications

(17 citation statements)

References 48 publications

(46 reference statements)

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“…RahimiZadeh and Kabiri present a trust‐based routing protocol that guarantees an end‐to‐end trustworthy and a clustering‐based optimal route. Misra et al propose an approach that utilizes filter techniques for target tracking in wireless sensor networks (WSN). Smaoui et al develop a secure scheme for network mobility management in the Host Identity Protocol (HIP), to ensure authentication, confidentiality, and integrity protection.…”

Section: Background and Related Workmentioning

confidence: 99%

CGAM: A community and geography aware mobility model

Mourchid

Kobbane

Othman

et al. 2017

Int J Communication

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SummaryThe advances of localization-enabled technologies have led to huge volumes of large-scale human mobility data collected from Call Data Records (CDR), Global Positioning System (GPS) tracking systems, and Location Based Social networks (LBSN). These location data that encompass mobility patterns could generate an important value for building accurate and realistic mobility models and hence important value for fields of application including context-aware advertising, city-wide sensing applications, urban planning, and more. In this paper, we investigate the underlying spatio-temporal and structural properties for human mobility patterns, and propose the Community and Geography Aware Mobility (CGAM) model, which characterizes user mobility knowledge through several properties such as home location distribution, community members' distribution, and radius of gyration. We validate the CGAM synthetic traces against real-world GPS traces and against the traces generated by the baseline mobility model SMOOTH and assess that CGAM is accurate in predicting the performance of flooding-based and community-based routing protocols. KEYWORDShuman mobility, mobility model, user patterns | INTRODUCTIONCharacterizing human mobility for different (daily or longtime) scales has received substantial attention, with the goal of designing mobility models that can express accurately several fundamental properties revealed in real traces of dynamic networks. The analysis, validation, and prediction of the performance of these mobility models could be fundamental for many fields of application ranging from context-aware advertising to epidemics prevention.Mobility models are classified into synthetic and trace-based mobility models. 1 The synthetic traces generated by a synthetic mobility model are far to be realistic, while those generated by a trace-based mobility model are expected to reproduce the statistical features present in human mobility. Therefore, the research community is more interested in the development and the use of mobility models that are based on real traces collected from several indoor and outdoor sites. 2 Moreover, many studies reported the discovery of fundamental statistical properties of human mobility such as flight length, flight duration, pause-times, and inter-contact times, extracted from real traces collected from bank notes tracking, Global Positioning System (GPS), CDR, Bluetooth encounters, and Location Based Social networks (LBSN) traces. 2 In this paper, we present a new mobility model, called Community and Geography Aware Mobility (CGAM) that is able to reproduce statistical properties of human mobility, to express geography, individual and community behavior features and thus simulating straightforwardly real-life scenarios. The CGAM model is proposed to combine the

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Section: Background and Related Workmentioning

confidence: 99%

CGAM: A community and geography aware mobility model

Mourchid

Kobbane

Othman

et al. 2017

Int J Communication

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“…Based on this model characteristic quantities determining the mobility behavior are derived, e.g., the distributions of cell residence and holding time as well as the average number of HOs. Misra et al [12] present an approach towards modeling the mobility pattern of a target node in a wireless sensor network based on available tracking data that are collected by sensing nodes. Different methods are presented to determine and predict the trajectory of the moving target node.…”

Section: Mobility Modelsmentioning

confidence: 99%

Trajectory estimation based on mobile network operator data for cellular network simulations

Ostermayer

Kieslich

Lindorfer

2016

J Wireless Com Network

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In this paper, we present a framework for estimating trajectories of cellular networks users based on mobile network operator data. We use handover and location area update events of both speech and packet data users captured in the core network of the Austrian MNO A1 to estimate the subscribers' mobility behavior. By utilizing publicly available data, i.e., environmental information, road infrastructure data, transmitter power ranges and antenna characteristics, our approach allows the estimation of subscriber trajectories for both urban and semi-rural environments with a good accordance to the actual trajectories. Additionally, we present a method to estimate a particular subscriber's movement velocity, on the basis of mentioned data. Furthermore, we propose a methodology to estimate when a particular user started or ended a speech or packet data session during his journey, based on mobility-related network events. With this, our framework enables the creation of reproducible mobility situations for cellular network simulations at system level.

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Section: Related Workmentioning

confidence: 99%

“…The usage of well-known mobility models was originally applied in the area of location predication [3,4], like Bayesian approaches [5][6][7], neural networks [8], Hidden Markov models [9], Markov models [10] and compression algorithms [11][12][13]. In addition, some recently proposed new algorithms [14,15] and frames [16][17][18] all presented very good results.Recently, regarding the location prediction, scholars start to consider many other factors that could influence the prediction results, like spatial context [4], temporal factors [19,20], spatio-temporal factors [21,22] and even demographics (such as gender and age) [23].…”

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confidence: 99%

User location prediction with energy efficiency model in the Long Term‐Evolution network

Qiao

Yang

et al. 2015

Int J Communication

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Predicting users' next location/place allows us to anticipate their future movement. It provides additional time to be ready for that movement and react consequently. Furthermore, many industries, including Internet Service Providers, are still requiring low cost and simple location/place prediction methods that can be implemented on mobile device. This paper studies domain-independent prediction algorithms and spatiotemporal based prediction method using 20-day-long records in Long Term-Evolution(LTE) network, which captures the mobility patterns of 3474 individuals. After examining the prediction accuracy and resource consumption of domain-independent prediction algorithms, we find Markov provides the best tradeoff. Furthermore, Active LeZi outperforms Markov if enough consecutive parsed patterns of users' history movement are captured. In addition, we further group users according to their spatio-temporal entropy profiles in order to predict not only user's future locations but also the place he or she most likely to appear within a specific period. By applying the simple spatio-temporal based method to each group of user, 83.3% accuracy can be achieved for some users. Yet Markov and Active LeZi algorithms perform better for some other users. This implies that we should consider applying different prediction methods to users with distinct spatio-temporal characteristics. RELATED WORKIndividuals display significant regularity, as they tend to visit a few highly frequented locations, like home or office. Numerous studies show that people's movement trajectory is far from random. By measuring the entropy of each individual's trajectory, [1] achieved 93% potential predictability in user mobility. When considering both the frequencies and temporal correlations of individual movements, the theoretical maximum predictability can reach 88% [2].Previous studies regarding location prediction include several models and methods. The usage of well-known mobility models was originally applied in the area of location predication [3,4], like Bayesian approaches [5-7], neural networks [8], Hidden Markov models [9], Markov models [10] and compression algorithms [11-13]. In addition, some recently proposed new algorithms [14, 15] and frames [16-18] all presented very good results.Recently, regarding the location prediction, scholars start to consider many other factors that could influence the prediction results, like spatial context [4], temporal factors [19,20], spatio-temporal factors [21,22] and even demographics (such as gender and age) [23].

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Extracting mobility pattern from target trajectory in wireless sensor networks

Cited by 22 publications

References 48 publications

CGAM: A community and geography aware mobility model

CGAM: A community and geography aware mobility model

Trajectory estimation based on mobile network operator data for cellular network simulations

User location prediction with energy efficiency model in the Long Term‐Evolution network

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