Real Human Mobility Research Articles

Multi-Wavelength Path Loss Model for Indoor VLC with Mobile Human Blockage

Visible light communication (VLC) is one of the candidate technologies for the sixth generation (6G) networks. The path loss model is particularly important for link budget estimation and network planning in VLC. Due to the wideband nature and the extremely poor diffraction capacity of light, the path loss of the VLC channel is susceptible to wavelength dependence and blockage effect. In this paper, we propose a novel path loss model which can characterize the impact of wavelength dependence combined with mobile human blockage for both the single-LED (light emitting diode) and the multi-LED scenario. When there is no blockage in the channel, the multi-wavelength path loss under free space propagation is modeled with a small standard deviation of 0.262 in the single-LED scenario and a small root mean square error of 0.009 in the multi-LED scenario which indicates the high accuracy of the model. When considering the mobile human blockage, the blockage probability (BP) is modeled with full consideration of realistic human mobility and human body shadowing. The results indicate that the BP in single-LED scenario can reach 0.08, while the BP in multi-LED scenario is 0.022. This demonstrates that the distributed deployment of transmitters can effectively reduce the occurrence of the blockage state in VLC.

Analyzing and forecasting service demands using human mobility data: A two-stage predictive framework with decomposition and multivariate analysis

Accurate service demand forecasts at critical facilities are fundamental for efficiently managing resources and promptly providing essential services to people and community. However, it has received little attention in the literature, mainly due to the unavailability of granular data and the lack of sophisticated forecasting methods. To address this gap, we provide a new perspective on sensing service demands at critical facilities leveraging fine-grained human mobility data, and propose a novel data-driven framework to forecast mobility patterns at the neighborhood level. Specifically, we develop a two-stage forecasting scheme to manage large-scale and complex human movement information. The first stage is to decompose the large-scale mobility data into spatial and temporal patterns, whereas the second stage is to model complex temporal dynamics using multivariate time series analysis. The proposed framework is implemented using real human mobility data obtained from mobile phone users. The results show that our model demonstrates the best predictive performance for varying forecast horizons, when compared to multiple benchmark methods including traditionally-used statistical and deep learning models. We also performed model robustness checks, showing that the proposed model is robust in making short-term and long-term forecasts. The proposed predictive framework could help businesses and local governments accurately forecast service demands for critical facilities for better allocating their resources.

Revealing the fractal and self-similarity of realistic collective human mobility

Revealing the fractal scaling laws of collective human mobility is benefit to understand the complexity of collective human mobility behaviours and has broad application prospects, such as infectious disease prevention, because fractal networks are potentially more robust against targeted attacks than non-fractal networks. Although many statistical indicators of individual human mobility had been observed in power-law distributions, there is still no evidence so far to verify the fractal or self-similarity of collective human mobility behaviours. This study uncovered the fractal and self-similarity properties on realistic weighted human mobility networks at world-, nation-, and city-wide scales. After observing the necessary conditions of network fractal, i.e., the evident power-law probability distributions of weight-related metrics, the fractal dimensions of weighted human mobility networks were measured to be about 0.67∼0.88. Furthermore, experimental results demonstrated their distinct statistical self-similarity and approximately structural self-similarity. These exciting findings can enhance the fire-new understandings of collective human mobility behaviours, and provide novel solutions for many real-world applications.

A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility.

The COVID-19 pandemic has created an urgent need for mathematical models that can project epidemic trends and evaluate the effectiveness of mitigation strategies. A major challenge in forecasting the transmission of COVID-19 is the accurate assessment of the multiscale human mobility and how it impacts infection through close contacts. By combining the stochastic agent-based modeling strategy and hierarchical structures of spatial containers corresponding to the notion of geographical places, this study proposes a novel model, Mob-Cov, to study the impact of human traveling behavior and individual health conditions on the disease outbreak and the probability of zero-COVID in the population. Specifically, individuals perform power law-type local movements within a container and global transport between different-level containers. It is revealed that frequent long-distance movements inside a small-level container (e.g., a road or a county) and a small population size reduce both the local crowdedness and disease transmission. It takes only half of the time to induce global disease outbreaks when the population increases from 150 to 500 (normalized unit). When the exponent of the long-tail distribution of distance k moved in the same-level container, , increases, the outbreak time decreases rapidly from 75 to 25 (normalized unit). In contrast, travel between large-level containers (e.g., cities and nations) facilitates global spread of the disease and outbreak. When the mean traveling distance across containers increases from 0.5 to 1 (normalized unit), the outbreak occurs almost twice as fast. Moreover, dynamic infection and recovery in the population are able to drive the bifurcation of the system to a "zero-COVID" state or to a "live with COVID" state, depending on the mobility patterns, population number and health conditions. Reducing population size and restricting global travel help achieve zero-COVID-19. Specifically, when is smaller than 0.2, the ratio of people with low levels of mobility is larger than 80% and the population size is smaller than 400, zero-COVID can be achieved within fewer than 1000 time steps. In summary, the Mob-Cov model considers more realistic human mobility at a wide range of spatial scales, and has been designed with equal emphasis on performance, low simulation cost, accuracy, ease of use and flexibility. It is a useful tool for researchers and politicians to apply when investigating pandemic dynamics and when planning actions against disease. The online version contains supplementary material available at 10.1007/s11071-023-08489-5.

Improving Traffic Load Distribution Fairness in Mobile Social Networks

Mobile social networks suffer from an unbalanced traffic load distribution due to the heterogeneity in mobility of nodes (humans) in the network. A few nodes in these networks are highly mobile, and the proposed social-based routing algorithms are likely to choose these most “social” nodes as the best message relays. Finally, this could lead to inequitable traffic load distribution and resource utilisation, such as faster battery drain and/or storage consumption of the most (socially) popular nodes. We propose a framework called Traffic Load Distribution Aware (TraLDA) to improve traffic load balancing across network nodes. We present a novel method for calculating node popularity which takes into account both node inherent and social-relations popularity. The former is purely determined by the node’s sociability level in the network, and in TraLDA is computed using the Kalman prediction which considers the node’s periodicity behaviour. However, the latter takes the benefit of interactions with more popular neighbours (acquaintances) to boost the popularity of lower (social) level nodes. Using extensive simulations in the Opportunistic Network Environment (ONE) driven by real human mobility scenarios, we show that our proposed strategy enhances the traffic load distribution fairness of the classical, yet popular social-aware routing algorithms BubbleRap and SimBet without negatively impacting the overall delivery performance.

Realistic Human Mobility Model for Opportunistic Networks

Within the opportunistic wireless network research community, the evaluation of a new network protocol depends almost entirely on simulation. Using simulation enables a researcher to test a wide variety of scenarios at a low cost compared to using a real system. In opportunistic networks, the mobil

Modeling Adversarial Behavior Against Mobility Data Privacy

Privacy risk assessment is a crucial issue in any privacy-aware analysis process. Traditional frameworks for privacy risk assessment systematically generate the assumed knowledge for a potential adversary, evaluating the risk without realistically modelling the collection of the background knowledge used by the adversary when performing the attack. In this work, we propose Simulated Privacy Annealing (SPA), a new adversarial behavior model for privacy risk assessment in mobility data. We model the behavior of an adversary as a mobility trajectory and introduce an optimization approach to find the most effective adversary trajectory in terms of privacy risk produced for the individuals represented in a mobility data set. We use simulated annealing to optimize the movement of the adversary and simulate a possible attack on mobility data. We finally test the effectiveness of our approach on real human mobility data, showing that it can simulate the knowledge gathering process for an adversary in a more realistic way.

Construction of Optimal Membership Functions for a Fuzzy Routing Scheme in Opportunistic Mobile Networks

This article proposes FRIMF, a fuzzy routing scheme for opportunistic mobile networks (OMNs). In FRIMF, we exploit the pairwise intercontact times to evaluate the connection strength between nodes. Instead of assuming a random movement model, in the present case we consider node contact processes in OMNs as bursty events. Consequently, we introduce a burstiness parameter to characterize the variability in the dynamics of pairwise interactions. This variance metric, along with the statistical mean of pairwise intercontact times, is used to define a single FRIMF routing metric called closeness through a fuzzy inference system. This reflects the tie strength of the pair nodes. To improve the transmission environment, we further propose a method to develop optimal membership functions for the FRIMF’s fuzzy parameters based on the contact information. Particularly, we leverage the membership function elicitation techniques commonly used in collective opinion aggregations based on a direct rating process to establish the relevancy between vagueness estimates of the routing parameters and statistical distributions of the pairwise intercontact times in a way that eventually presents asymmetric triangular fuzzy numbers. In turn, these TFNs are used to properly define the fuzzy sets of the FRIMF’s routing parameters. Through simulations in the real human mobility environments, we show that FRIMF utilizing the enhanced asymmetric TFNs can outperform that using the typical symmetric TFNs developed based on our subjective preferences. Lastly, comparing with several algorithm benchmarks, we confirm the efficiency of FRIMF in transmission cost and delay.

Precise: Predictive Content Dissemination Scheme exploiting realistic mobility patterns

Device-to-Device (D2D) communications have expanded the way of managing available network resources to efficiently distribute data between users. D2D exploits communication alternatives, in Opportunistic Networks, based on short range wireless radio technologies such as Bluetooth and WiFi-Direct. Besides, nowadays in most urban areas, realistic human mobility is characterized by often repeated patterns that can be used to accurately predict the next visited regions—we call these regions hotspots (or Replication Zones (RZs)). In this work, we present Predictive Content Dissemination Scheme (Precise), to explore and combine the D2D paradigm along with real mobility and predictions focused on the dissemination of content among hotspots. To analyze the viability of such scheme, we show simulation results and evaluate the average content availability, lifetime and delivery delay, storage usage and network utilization metrics. We compare the performance of Precise with state-of-the-art approaches, such as Epidemic, restricted Epidemic, and Proximity-Interest-Social (PIS) routing protocols. Our results underline the need for smart usage of communication opportunities and storage. We demonstrate that Precise allows for a neat reduction in network activity by decreasing the number of data exchanges by up to 92%, requiring the use of up to 50% less of on-device storage. This comes at negligible costs. In particular, the delivery delay with Precise shows an increase with respect to epidemic dissemination schemes that varies from 0.03 s in the most dynamic case to at most 1.91 s for the least dynamic case, and which however does not hinder the possibility to use Precise for real-time applications. Regarding how contents are spread, we observe that Precise requires 2% to 20% less mobile users to carry them within a target hotspot, especially under slow dynamics. This however does not impact on the probability that mobile users entering the hotspots obtain contents, and barely shortens the lifetime of contents in our experiments from 100 min down to about 95, in the worst case. This demonstrates that the reduction of content availability among mobile users with Precise is either negligible or not impactful, thus guaranteeing the dissemination of contents as with legacy epidemic dissemination protocols.

Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data

Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Large scale lockdown of human movements are effective in reducing the spread, but they come at a cost of significantly limited societal functions. We show that natural human movements are statistically diverse, and the spread of the disease is significantly influenced by a small group of active individuals and gathering venues. We find that interventions focused on these most mobile individuals and popular venues reduce both the peak infection rate and the total infected population while retaining high social activity levels. These trends are seen consistently in simulations with real human mobility data of different scales, resolutions, and modalities from multiple cities across the world. The observation implies that compared to broad sweeping interventions, more heterogeneous strategies that are targeted based on the network effects in human mobility provide a better balance between pandemic control and regular social activities.

Mobility-Aware Joint Task Scheduling and Resource Allocation for Cooperative Mobile Edge Computing

Mobile edge computing (MEC) has emerged as a new paradigm to assist low latency services by enabling computation offloading at the network edge. Nevertheless, human mobility can significantly impact the offloading decision and performance in MEC networks. In this context, we propose device-to-device (D2D) cooperation based MEC to expedite the task execution of mobile user by leveraging proximity-aware task offloading. However, user mobility in such distributed architecture results in dynamic offloading decision that instigates mobility-aware task scheduling in our proposed framework. We jointly formulate task assignment and power allocation to minimize the total task execution latency by taking account of user mobility, distributed resources, tasks properties, and energy constraint of the user device. We first propose Genetic Algorithm (GA)-based evolutionary scheme to solve our formulated mixed-integer non-linear programming (MINLP) problem. Then we propose a heuristic named mobility-aware task scheduling (MATS) to obtain effective task assignment with low complexity. The extensive evaluation under realistic human mobility trajectories provides useful insights into the performance of our schemes and demonstrates that, both GA and MATS achieve better latency than other baseline schemes while satisfying the energy constraint of mobile device.

The rhythm of the crowd: Properties of evolutionary community detection algorithms for mobile edge selection

The Multi-access Edge Computing (MEC) paradigm increases the computational capabilities of distributed sensing architectures, such as Mobile CrowdSensing platforms, which are designed to collect heterogeneous data from the crowd by exploiting mobile devices. In this context, our work focusses on the impact of three community detection algorithms to our edge selection strategy. In particular, we study TILES, Infomap, and iLCD which are specifically designed to identify evolving communities of users in dynamic networks. Our analysis is based on the ParticipAct data set that offers real human mobility data. We first measure the quality of the data set during an observation period of 1 year, during which the data set provides the 75% of the expected traces collected by approximately 170 users. We then compare some structural properties of the communities detected, namely Similarity, Forward Stability, Cohesion and Coverage. We conclude our study with a performance analysis of the selected Mobile MECs by varying the community detection algorithms adopted. In particular, we measure the latency and the number of satisfied requests and we show that the average latency obtained with Infomap is slightly lower than that of the other algorithms, while the average number of satisfied requests is higher when we adopt the TILES algorithm.

Simulation of the self-similar least-action walk model

ABSTRACT Simulation of ad hoc networks requires the use of a realistic human mobility model to give credibility to computer-based performance evaluations. To this end, a number of models have appeared in the literature from which the self-similar least-action walk model (SLAW) stands out as an approach mainly intended to incorporate several statistical properties of human motion. Although it is generally accepted that this model is able to produce realistic traces, so far not enough attention has been given towards creating an implementation with an upfront clear design that is also reliable and computationally efficient. This paper addresses these issues by presenting the design, implementation, and validation of a SLAW simulator. This simulator follows the discrete-event simulation paradigm for generation of mobility traces and is flexible enough for incorporating future walker models. This simulator can be used, among other purposes, to perform computer-based studies of networking technologies where human mobility plays a major role.

Energy-Efficient Mobility-Aware Caching Algorithms for Clustered Small Cells in Ultra-Dense Networks

We consider the energy-efficient mobility-aware content caching problem with clustered small cells (SCs) in ultra-dense networks (UDNs). The UDN is regarded as one of the key solutions to overcome the explosive growth of mobile data traffic and consequential energy consumption. In addition, caching the popular contents at the edge of UDNs can further mitigate the challenges of traffic and energy. In this paper, we group the SCs into disjoint clusters and model the mobility of users between clusters as a Markov chain. We formulate the caching problem aimed at minimizing the overall energy consumption for content delivery to the moving users. Then we decompose the complicated task of energy consumption optimization in cache-enabled UDNs into two sub-problems. In the first sub-problem, we formulate the inter-cluster caching problem as minimizing the energy consumption for serving a user request by the macro base station. We show that the first sub-problem is NP-hard, and develop a polynomial-heuristic algorithm. Further, we define the mobility-oblivious version of the sub-problem, and provide a 1/2k-approximation solution for the mobility-aware based on this mobility-oblivious version. In the second sub-problem, we consider the heterogeneity of small cells and define the intra-cluster caching problem. The aim of this sub-problem is the placement of content in different SCs within the cluster so that the energy consumption for the content delivery in that cluster is minimized. In particular, the second sub-problem fits the general framework of the generalized assignment problem, which allows us to exploit its rich literature, to determine the optimal solution for our problem. Through extensive simulations based on real wireless data and human mobility patterns, we demonstrate the benefits of our approach that is even up to 56% better compared to the conventional caching schemes.

Detection of Dummy Trajectories Using Convolutional Neural Networks

Nowadays, privacy in trajectory is an important issue in the coming big data era. In order to provide better protection for trajectory privacy, a number of solutions have been proposed in the literature, and the dummy trajectory method has attracted great interests in both academia and industry recently due to the following advantages: (1) neither a third-party server nor other parties’ cooperation is necessary; (2) location-based services are not influenced; and (3) its algorithm is relatively simple and efficient. However, most of trajectory privacy generations usually consider the geometric shape of the trajectory; meanwhile the real human mobility feature is usually neglected. In fact, the real trajectory is not the product of random probability. In this paper, convolutional neural network (CNN) is used as the learning machine to train with lots of the real trajectory and the generated dummy trajectory sets. Then, the trained classifier is used to distinguish the dummy from the real trajectory. Experiments demonstrate that the method using CNN is very efficient, and more than 90% of dummy trajectories can be detected. Moreover, the real trajectory erroneous judgment rate is below 10% for most of real trajectories.

Performance Comparison of Deterministic and Stochastic Utility Ascent Routing Algorithms in Opportunistic Mobile Networks

Opportunistic mobile networks (OMNs) are a class of MANETs where complete endto-end paths rarely exist between sources and destinations; consequently, the end-to-end delays in these networks are much greater than typical MANETs. In this harsh environment networking algorithm actions and decisions are inherently local; the algorithms are mostly greedy, choosing the best solution among the locally ones. In this paper, we study the delivery performance of two classes of gradient utility ascent routing algorithms, deterministic (greedy) and stochastic utility ascent ones, in OMNs. We furthermore discuss the impact of these algorithms on traffic load distribution fairness among the network nodes, since this issue is critical in mobile networking whose nodes typically possess limited resources, e.g. power and storage capacity. Using simulation, driven by real human mobility traces, we investigate the performance of greedy ascent algorithms, Hill Climbing (HC) and Delegation Forwarding (DF), compared with that of stochastic ascent algorithms, Simulated Annealing (SA). The results under the scenario show an advantage of SA over HC and DF in terms of total delivered messages, average delivery delay, and traffic distribution fairness; however, SA negatively impacts the delivery cost performance, i.e. increasing the total message copies beyond those of HC and DF.

Understanding Motivations behind Inaccurate Check-ins

Check-in data from social networks provide researchers a unique opportunity to model human dynamics at scale. However, it is unclear how indicative these check-in traces are of real human mobility. Prior work showed that significant amounts of Foursquare check-ins did not match with the physical mobility patterns of users, and suggested that misrepresented check-ins were incentivized by external rewards provided by the system. In this paper, our goal is to understand the root cause of inaccurate check-in data, by studying the validity of check-in traces in social media platforms without external rewards for check-ins. We conduct a data-driven analysis using an empirical check-in data trace of more than 276,000 users from WeChat Moments, with matching traces of their physical mobility. We develop a set of hypotheses on the underlying motivations behind people's inaccurate check-ins, and validate them using a detailed survey study. Our analysis reveals that there are surprisingly high amount of inaccurate check-ins even in the absence of rewards: 43% of total check-ins are inaccurate and 61% of survey participants report they have misrepresented their check-ins. We also find that inaccurate check-ins are often a result of user interface design as well as for convenience, self-advertisement and self-presentation.

Mobility-Aware Transmission Scheduling Scheme for Millimeter-Wave Cells

With the explosive growth of mobile traffic, diverse mobile applications with high throughput demands have gained considerable attention from both academia and industry. However, dynamics due to human mobility impose serious issues on high throughput communications. Although millimeter-wave (mm-wave), relays, and concurrent transmission have been applied for throughput improvement, how to achieve high throughput transmission in mobility-aware scenarios is still challenging. In this paper, we propose a throughput-efficient service scheduling (TESS) scheme, which exploits multi-hop relay and concurrent transmissions with the consideration of human mobility. In TESS, we first propose a mobility-aware transmission scheduling scheme in single mm-wave cell. The proposed scheduling scheme consists of a relay path planning algorithm and a global time scheduling algorithm. In the relay path planning algorithm, we establish multi-hop relay transmission paths from base station to service points. In the global time scheduling algorithm, we schedule concurrent transmission in relay paths. Furthermore, the proposed TESS scheme is extended for multi-cell scenarios. Through extensive performance evaluations under realistic human mobility trajectories, we demonstrate the superior performance of TESS in terms of system throughput compared with the state-of-the-art schemes.

Mobility Assisted Content Transmission For Device-to-Device Communication Underlaying Cellular Networks

With the explosion growth of mobile data demands for proximal services, device-to-device communication is proposed as a vital technology for the next generation cellular network. With the extensive increase in personal mobile devices, content transmission of proximal devices underlaying cellular networks is gaining considerable attention. However, due to the mobility constraint of mobile devices, content transmission underlaying cellular networks greatly affects the overall transmission capacity of these proximal devices. In this paper, we investigate the problem of mobility assisted content transmission and resource allocation by exploiting the contact patterns regulated by these proximal devices’ mobility. We formulate the content transmission and resource allocation with the help of the statistic property of contact rates, and then utilize convex optimization to determine the successful content transmission and resource allocation scheme. We present the optimal resource allocated content transmission algorithm based on the pseudo-polynomial time algorithm using dynamic programming to solve the optimization problem. Real data traces are utilized in our proposed algorithm to find out the natural strategies of mobile homophily. Extensive simulations under the realistic human mobility factors are evaluated to demonstrate the efficiency of our proposed scheme.

Mobile Matrix: Routing under mobility in IoT, IoMT, and Social IoT

The explosive growth of “things” connected to the Internet (Internet of Things, IoT) raises the question of whether existing ready-to-go networking protocols are enough to cover social and mobile IoT’s demands. IoT aims to interconnect static devices attached to some physical infrastructure. However, mobility is a major factor present in everyday life, and naturally the “things” can move around (Internet of Mobile Things, IoMT) and create social ties (Social IoT, SIoT) in the cyber-physical space. In that context, we present Mobile Matrix (μMatrix), a routing protocol that uses hierarchical IPv6 address allocation to perform any-to-any routing and mobility management without changing a node’s address. In this way, device mobility is transparent to the application level favoring IoMT and SIoT implementation and broader adoption. The protocol has low memory footprint, adjustable control message overhead, and it achieves optimal routing path distortion. Moreover, it does not rely on any particular hardware for mobility detection (a key open issue), such as an accelerometer. Instead, it uses a passive mechanism to detect that a device has moved. We present analytic proofs for the computational complexity and efficiency of μMatrix, as well as an evaluation of the protocol through simulations. We evaluate the protocol performance under human and non-human mobility models. For human mobility, we generated mobility traces using Group Regularity Mobility (GRM) Model, setting its parameters based on real human mobility traces. For the non-human mobility, we propose a new mobility model, to which we refer as Cyclical Random Waypoint (CRWP), where nodes move using a simple Random Waypoint and, eventually, return to their initial position. We compared μMatrix with three baseline protocols: Routing Protocol for low-power and lossy networks (RPL), Mobility Management RPL (MMRPL), and Ad hoc On-Demand Distance Vector (AODV). The results show that μMatrix and RPL offer ≈ 99.9% of bottom-up delivery rate, but only μMatrix offer ≥ 95% of top-down traffic in highly dynamic and mobile scenarios, while other protocols ≤ 75%. Moreover, μMatrix uses up to 65% of the routing table while RPL and AODV fulfill theirs in all scenarios, which leads to poor top-down and any-to-any reliability.