Improved bounds on information dissemination by Manhattan Random Waypoint model

Rezaei, Aria; Gao, Jie; Phillips, Jeff M.; Tóth, Csaba D.

doi:10.1145/3274895.3274917

Cited by 3 publications

(4 citation statements)

References 39 publications

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“…To analyze the tracking effect, we deployed 500 nodes randomly in a rectangular area of 100 * 100 m. The target node moves at a speed of 0.5 m/s, pausing for 2 seconds after every interval of 20 seconds [36], [37]. Again, we use the state-space models presented in Section IV-A.…”

Section: B Tracking Effectmentioning

confidence: 99%

“…In this subsection, we evaluate the performance of algorithms in highly dynamic environments, such as those in water pollution source tracing and air quality monitoring. We use the random path mobility model [27], [28], [33], [36], which provides random node movement in many mobile WSN applications. The model includes random pauses along the x, y, and z directions and speed changes [27], [36].…”

Section: E Overall Mobility Networkmentioning

confidence: 99%

“…We use the random path mobility model [27], [28], [33], [36], which provides random node movement in many mobile WSN applications. The model includes random pauses along the x, y, and z directions and speed changes [27], [36]. The network delay parameter is set to 3 s according to [13], [25], [36], [38].…”

Section: E Overall Mobility Networkmentioning

confidence: 99%

“…The model includes random pauses along the x, y, and z directions and speed changes [27], [36]. The network delay parameter is set to 3 s according to [13], [25], [36], [38]. The simulation time is 300 s. We consider the RF radius as a fixed value and evaluate the performances of various algorithms at a node density of 100 and speeds of 5, 10 and 15 m/s.…”

Section: E Overall Mobility Networkmentioning

confidence: 99%

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Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs

Zhang

et al. 2020

IEEE Access

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The localization problem of target nodes remains unresolved, especially in large-scale and complex environments. In this paper, we propose a particle centroid drift (PCD) algorithm to reduce the distance errors between nodes and obtain the particle aggregation region by using the drift vector. First, we use the particle quality prediction function to obtain the particles in a high-likelihood region. The high-quality particles have high probability in the calculation, which can increase the number of effective particles and enable avoiding particle degradation. Then, the centroid drift vector is used to make the particle distribution similar to the actual reference distribution. Experiments are conducted on state-space models: the local movement where 55% nodes are moving and the globe movement where 100% nodes are moving. The results show that the proposed algorithm has low estimation errors, a good tracking effect and an acceptable time complexity.

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Section: B Tracking Effectmentioning

confidence: 99%

Section: E Overall Mobility Networkmentioning

confidence: 99%

Section: E Overall Mobility Networkmentioning

confidence: 99%

Section: E Overall Mobility Networkmentioning

confidence: 99%

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Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs

Zhang

et al. 2020

IEEE Access

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CellSense

Fang

Yang

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Data from the cellular network have been proved as one of the most promising way to understand large-scale human mobility for various ubiquitous computing applications due to the high penetration of cellphones and low collection cost. Existing mobility models driven by cellular network data suffer from sparse spatial-temporal observations because user locations are recorded with cellphone activities, e.g., calls, text, or internet access. In this paper, we design a human mobility recovery system called CellSense to take the sparse cellular billing data (CBR) as input and outputs dense continuous records to recover the sensing gap when using cellular networks as sensing systems to sense the human mobility. There is limited work on this kind of recovery systems at large scale because even though it is straightforward to design a recovery system based on regression models, it is very challenging to evaluate these models at large scale due to the lack of the ground truth data. In this paper, we explore a new opportunity based on the upgrade of cellular infrastructures to obtain cellular network signaling data as the ground truth data, which log the interaction between cellphones and cellular towers at signal levels (e.g., attaching, detaching, paging) even without billable activities. Based on the signaling data, we design a system CellSense for human mobility recovery by integrating collective mobility patterns with individual mobility modeling, which achieves the 35.3% improvement over the state-of-the-art models. The key application of our recovery model is to take regular sparse CBR data that a researcher already has, and to recover the missing data due to sensing gaps of CBR data to produce a dense cellular data for them to train a machine learning model for their use cases, e.g., next location prediction.

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Mover

Lyu

Wang

Yang

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Human mobility models typically produce mobility data to capture human mobility patterns individually or collectively based on real-world observations or assumptions, which are essential for many use cases in research and practice, e.g., mobile networking, autonomous driving, urban planning, and epidemic control. However, most existing mobility models suffer from practical issues like unknown accuracy and uncertain parameters in new use cases because they are normally designed and verified based on a particular use case (e.g., mobile phones, taxis, or mobile payments). This causes significant challenges for researchers when they try to select a representative human mobility model with appropriate parameters for new use cases. In this paper, we introduce a MObility VERification framework called MOVER to systematically measure the performance of a set of representative mobility models including both theoretical and empirical models based on a diverse set of use cases with various measures. Based on a taxonomy built upon spatial granularity and temporal continuity, we selected four representative mobility use cases (e.g., the vehicle tracking system, the camera-based system, the mobile payment system, and the cellular network system) to verify the generalizability of the state-of-the-art human mobility models. MOVER methodically characterizes the accuracy of five different mobility models in these four use cases based on a comprehensive set of mobility measures and provide two key lessons learned: (i) For the collective level measures, the finer spatial granularity of the user cases, the better generalization of the theoretical models; (ii) For the individual-level measures, the lower periodic temporal continuity of the user cases, the theoretical models typically generalize better than the empirical models. The verification results can help the research community to select appropriate mobility models and parameters in different use cases.

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Improved bounds on information dissemination by Manhattan Random Waypoint model

Cited by 3 publications

References 39 publications

Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs

Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs

CellSense

Mover

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