Mobile User Behavior Research Articles

Truthful Multi-Resource Transaction Mechanism for P2P Task Offloading Based on Edge Computing

Peer-to-Peer (P2P) resource sharing promotes local resource-hungry task offloading to other mobile devices and balances the resource consumption between mobile devices. Most of existing P2P task offloading systems aims to solve the resource sharing between one pair exclusively without considering the cost of resource supply and the strategic behaviors of mobile users. In this paper, we propose two user models for the P2P task offloading system: honest user model and strategy user model. For the honest user model, we formulate the resource allocation maximization problem with latency and energy consumption constraints as an Integer Linear Programming. We show that the solution for honest user model can output 189% resource transactions of that for the strategic users. For the strategy user model, we propose a double auction-based P2P task offloading system, and design a truthful multi-resource transaction mechanism to maximize the number of resource transactions. We first group the mobile users based on the connected components to improve the efficiency of double auction. Then we utilize the McAfee Double Auction to price the resource transactions. Finally, we split each winning mobile user of double auction into multiple virtual mobile users, and use the matching approach to calculate the resource allocation. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the designed multi-resource transaction mechanism satisfies the desirable properties of computational efficiency, individual rationality, budget balance, truthfulness for resource request/supply, and general truthfulness for bid/ask price.

A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks

Mobile networks are expected to face major problems such as low network capacity, high latency, and limited resources but are expected to provide seamless connectivity in the foreseeable future. It is crucial to deliver an adequate level of performance for network services and to ensure an acceptable quality of services for mobile users. Intelligent mobility management is a promising solution to deal with the aforementioned issues. In this context, modeling user mobility behaviour is of great importance in order to extract valuable information about user behaviours and to meet their demands. In this paper, we propose a hybrid user mobility prediction approach for handover management in mobile networks. First, we extract user mobility patterns using a mobility model based on statistical models and deep learning algorithms. We deploy a vector autoregression (VAR) model and a gated recurrent unit (GRU) to predict the future trajectory of a user. We then reduce the number of unnecessary handover signaling messages and optimize the handover procedure using the obtained prediction results. We deploy mobility data generated from real users to conduct our experiments. The simulation results show that the proposed VAR-GRU mobility model has the lowest prediction error in comparison with existing methods. Moreover, we investigate the handover processing and transmission costs for predictive and non-predictive scenarios. It is shown that the handover-related costs effectively decrease when we obtain a prediction in the network. For vertical handover, processing cost and transmission cost improve, respectively, by 57.14% and 28.01%.

Influencing Factors of Acceptance and Use Behavior of Mobile Health Application Users: Systematic Review.

Purpose/Significance: Mobile health applications provide a convenient way for users to obtain health information and services. Studying the factors that influence users’ acceptance and use of mobile health applications (apps or Apps) will help to improve users’ actual usage behavior. Method/Process: Based on the literature review method and using the Web of Science core database as the data source, this paper summarizes the relevant research results regarding the influencing factors of the acceptance and use behavior of mobile health application users and makes a systematic review of the influencing factors from the perspectives of the individual, society, and application (app or App) design. Result/Conclusion: In terms of the individual dimension, the users’ behavior is influenced by demographic characteristics and motivations. Social attributes, source credibility, and legal issues all affect user behavior in the social dimension. In the application design dimension, functionality, perceived ease of use and usefulness, security, and cost are the main factors. At the end of the paper, suggestions are given to improve the users’ acceptability of mobile health applications and improve their use behavior.

Exploitation whale optimization based optimal offloading approach and topology optimization in a mobile ad hoc cloud environment

Widespread availability of network technologies, mobile user request is increased day by day. The larger amount of energy utilization and resource sufficiency of cloud computing is to create the maximum capacity of exploration and exploitation as troublesome. In this paper, we proposed the formation of mobile user behavior based topology and its optimization. During the offloading process, the minimization of response time and the energy consumption is the major goal of this paper. The topology node formations are performed via improved text rank algorithm (ITRA) and neural network (NN) classifiers with Euclidian distance. In this paper, we introduced an effective optimization algorithm of the exploitation whale optimization algorithm (EWOA) and it is the combination of differential evaluation (DE) and whale optimization algorithms (WOA). The offloading process of proposed EWOA produces an optimal outcome of minimized energy consumption and response time. The implementation works of the proposed EWOA are carried out in the VMware platform. The performance of the proposed method is evaluated using different size puzzles, face detection applications, and state-of-art methods. Ultimately, our proposed method produces optimal accuracy and convergence speed with the minimized offloading process.

A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting

Smart Cities (or Cities 2.0) are an evolution in citizen habitation. In such cities, transport commuting is changing rapidly with the proliferation of contemporary vehicular technology. New models of vehicle ride sharing systems are changing the way citizens commute in their daily movement schedule. The use of a private vehicle per single passenger transportation is no longer viable in sustainable Smart Cities (SC) because of the vehicles’ resource allocation and urban pollution. The current research on car ride sharing systems is widely expanding in a range of contemporary technologies, however, without covering a multidisciplinary approach. In this paper, the focus is on performing a multidisciplinary research on car riding systems taking into consideration personalized user mobility behavior by providing next destination prediction as well as a recommender system based on riders’ personalized information. Specifically, it proposes a predictive vehicle ride sharing system for commuting, which has impact on the SC green ecosystem. The adopted system also provides a recommendation to citizens to select the persons they would like to commute with. An Artificial Intelligence (AI)-enabled weighted pattern matching model is used to assess user movement behavior in SC and provide the best predicted recommendation list of commuting users. Citizens are then able to engage a current trip to next destination with the more suitable user provided by the list. An experimented is conducted with real data from the municipality of New Philadelphia, in SC of Athens, Greece, to implement the proposed system and observe certain user movement behavior. The results are promising for the incorporation of the adopted system to other SCs.

Performance Analysis of Dynamic Downlink PPP Cellular Networks Over Generalized Fading Channels With MRC Diversity

This paper proposes novel and generalized expressions to characterize the performance of modern cellular networks under realistic user mobility behavior. The η-μ distribution is employed to derive the received power probability density function, the average bit error rate for different modulation schemes, and the coverage probability assuming a Poisson point process spatial distribution of base stations in downlink. The user is assumed to experience fading with Maximum Ratio Combining (MRC) and move according to a random way-point mobility model. The proposed expressions are applicable to different widely-used fading environments, such as Rayleigh and Nakagami-m as particular cases, by an appropriate selection of the η-μ parameters. Monte Carlo simulation was used to show the validity of the proposed expressions. The generalized expressions allow the system designer to quantify the effects of user mobility on the cellular network performance, in different propagation environments, and network topologies as a function of the number of base stations and MRC branches.

Fuzzy trust based collaborative filtering analysis for mobile user preferences

With the rapid development of mobile terminal devices, mobile user activities can be carried out anytime and anywhere through various mobile terminals. The current research on mobile communication network is mainly focused on extracting useful and interesting information for mobile user from massive and disordered information. However, the sparsity of scoring data matrix results in low quality of recommendation algorithm. In order to overcome this drawback, the traditional collaborative filtering algorithm is improved. First, the user-interest matrix and item-feature matrix were obtained by analyzing mobile user behavior and item attributes. Fuzzy trust based model is utilized for collaborative filtering analysis for mobile user preferences. Then, the similarity between different mobile users was calculated by weighted calculation. With this method, mobile user preference can be predicted effectively, making it possible to recommend rational resource and waste less time in extracting resources out of the massive information. Experimental results show that the proposed algorithm reduces the mean absolute error (MAE) and the impact of sparse scoring matrix data compared with the traditional collaborative filtering algorithm, and improves the recommendation effect to a certain extent.

Causal Factors Affecting Mobile Banking Services Acceptance by Customers in Thailand

The study investigates the causal factors affecting mobile banking services acceptance by customers in Thailand. This study employs quantitative research methodology and non-probability sampling to draw 400 mobile banking users from the population who are the mobile banking users of commercial banks in Thailand. The online questionnaires were used as a research tool to collect data with the Cronbach's alpha coefficient as 0.931. By using the structural equation model to analyze data, the results have shown that service quality, perceived usefulness, perceived ease-of-use, safety in use, and social factors influenced mobile banking services acceptance by customers in Thailand.By checking the harmony with the statistics χ² = 108.618, df. = 86, χ²/df. = 1.263, p-value = .050, CMIN/DF =1.263, GFI = .989, AGFI=.962, TLI= .962, CFI = .976, RMSEA = .037,along with testing the weight factor. In conclusion, the research model was found to be harmonious with the empirical data at the significance level 0.05. The findings of this study suggest that the commercial banks should apply this research to understand the acceptance behavior of mobile banking users, also to determining marketing strategies, identifying opportunities and creating a competitive advantage for their services in the future.

Market-based dynamic resource allocation in Mobile Edge Computing systems with multi-server and multi-user

Mobile Edge Computing (MEC) is critical to the development of the Internet of things (IoTs) and 5G networks. However, the computation and communication resources of edge servers are limited, so it is challenging to perform resource allocation especially when the competition among edge servers is also taken into consideration. In this paper, we propose a trading model to investigate both the computation and communication resources allocation in MEC systems with multi-server and multi-user. We model the dynamic behavior of mobile users (MUs) using an evolutionary game, and then we build the deterministic and stochastic models to study the evolution of MUs where the evolutionary equilibrium is considered as the solution. We propose an evolution algorithm to obtain the evolutionary equilibrium. Furthermore, we analyze the competition among edge cloud servers (ECSs) by a noncooperative game, and propose an iteration algorithm to obtain Nash equilibrium where the ECSs can adjust the amount of resources provided to MUs and the corresponding price charged in order to attract more MUs. The existences of evolutionary equilibrium and Nash equilibrium are validated in performance evaluation.

Mobile APP Micro‐Usage during the mobile search

AbstractMobile users' micro‐usage behavior on smartphones has got researchers' attention, while current studies have not focused on the mobile search context. This study aimed to understand the characteristics of micro‐usage behavior in mobile search sessions and the triggers for them, through mixed methods. The findings revealed that micro‐usage mainly occurred on social and communication APPs. Searching for information about games and health can trigger more micro‐usage. Besides, chatting with others was the most important causes of micro‐usage. This study helps to better understand mobile usage behavior and micro‐usage in a search session.

Factors affecting consumer acceptance of mobile banking in Tejarat Bank in city of Karaj, Iran

Objective: The growth in mobile phone usage in the last decade has been pretty amazing. Today, for most people mobile phones are a necessity. So that you have to carry them wherever you go. Mobile technology has changed commercial and business behaviors of mobile users. Methodology: Commerce via mobile is a business solution nowadays and has led to a change in existing businesses. Among these services, is mobile banking. It should be said that mobile banking is one of the primary components to help expansion of business operations using mobiles. The main objective of this study is to investigate factors influencing the adoption of mobile banking services by customers and testing six hypotheses. Results: The present research is casual by method and nature, survey by implementation, and practical in purpose. The population in this study, customers are Tejarat Bank city customers in Karaj in the year 2015.Sh. To achieve objectives of the research, 384 questionnaires distributed among the bank customers and then analyzed. In this study all considered assumptions confirmed. Conclusion: Finally, it can be said that mobile phone services can lead to creation of comparative advantage and an acceptable profitability compared with other banks’ services.

Epidemic Heterogeneity and Hierarchy: A Study of Wireless Hybrid Worm Propagation

With the growth in the use of smart mobile devices and the development of information technologies, worms and malware can spread from mobile networks into heterogeneous and hierarchical networks. Thus, the spread of these worms constitutes an increasing potential threat. For understanding the propagation of the aforementioned wireless hybrid worms, current researches have three critical problems: <i>Structural simplification of network topologies</i> (previous research object for wireless worms is the mobile network), <i>Homogenous population of network devices</i> (properties of network devices are the same), and <i>Inaccuracy of propagation models</i> (traditional deterministic differential or stochastic difference models cannot model propagation of wireless hybrid worms accurately). To address them, we propose a novel compartmental population-based propagation model oriented towards heterogeneous and hierarchical networks with human behaviors, and then study the impacts of user mobility and operation behaviors on worm propagation. Meanwhile, we conduct extensive simulations to show our model can characterize propagation features accurately. The results in this paper not only provide a deep understanding of new worm propagation, but also serve as fundamental defense guidelines.

Human Mobility Prediction Using Sparse Trajectory Data

Human mobility prediction techniques are instrumental for many important applications including service management and city planning. Previous work looks at the inherent patterns of a user's historical trajectories to predict his/her future location. Such method suffers when only a small number of historical locations are available, which is a main challenge for mobility prediction on sparse trajectory datasets. In this paper, we propose a multi-task learning based algorithm to predict users' mobility by learning the mobility behaviors of different users at the same time and exploiting the similarities among them to overcome the sparse issue and improve the prediction performance. Specifically, we use a Bayesian mixture model to describe users' spatio-temporal mobility patterns, where we introduce a novel von Mises distribution to model the temporal distribution of users' mobility to better preserve its continuity across time. Then, we use the hierarchical Dirichlet process to perform joint prediction of all users' mobility. This model allows us to leverage similar mobility patterns among users to improve the prediction performance for users with sparse trajectory data. We conduct extensive evaluations using data collected by a cellular network operator and mobile applications. Our evaluations show that our algorithms achieve prediction accuracy of 53.9% on average and 73.2% for users with high-quality mobility data, with the performance gap over 8.7% on average compared with the state-of-the-art algorithms, demonstrating the effectiveness of our proposed algorithm.

“VešKajPiješ?” (You know what you drink?) - mobile app for reducing alcohol related harm in Slovenia

Abstract Regulation (EU) No. 1169/2011 on the provision of food information to consumers, provides that the labelling of ingredients and nutritional declaration is not mandatory for alcoholic beverages with more than 1.2% alcohol by volume. Consequently, consumers generally do not have access to similar information on alcoholic beverages as they do for food and non-alcoholic beverages. In Slovenia, drinking alcohol is a serious public health problem, both among adolescents and adults. Per capita consumption of pure alcohol in Slovenia is above the EU average. In order to achieve better results and reduce alcohol related harm, we need a comprehensive alcohol policy that incorporates proven effective measures in different areas of action. In 2019, in the frame of Slovenian programme “VesKajJes” (Know what you eat) supported by MoH, a mobile application was developed that enables consumers to scan EAN code for gaining the nutrition profile of selected food item. An analysis of the behaviour of mobile app users revealed that alcoholic beverages are one of the most frequently sought products; of all searches, as many as 36% of users wanted to get information about alcohol beverages. This was a major reason for upgrading the mobile application with information about alcoholic beverages. Additionally this newly developed communication channel will be used for raising awareness about alcohol-related harm. Mobile application “VešKajPiješ?” (You know what you drink?) will be developed within which we will: (1) collect information on at least 1,500 alcoholic beverages; (2) develop the protocol for a reliable assessment of the energy values depending of the type of alcoholic beverages; (3) prepare short health messages to raise awareness about the effects of hazardous and harmful alcohol use; (4) develop an application upgrade to show the alcohol content, energy value of alcoholic beverages, health messages and a link to a screening tool for assessing the alcohol consumption (AUDIT). Key messages There is a growing support for mandatory labelling of alcohol-containing beverages including health warnings. The program presents a solution consisting of a mobile app for consumers seeking information.

The Temporal Nature of Mobile Push Notification Alerts: A Study of European News Outlets’ Dissemination Patterns

Push notifications provide news outlets with direct access to audiences amid concerns around information overload, disinformation, and heightened competition for reader attention. Such news distribution is relevant because it (a) bypasses social media and news aggregators, reaching readers directly; (b) alters the agency and control of temporal news personalisation; and (c) reinforces mobile as the locus of contact between news organisations and audiences. However, push notifications are a relatively under-researched topic. We explore news organisations’ use of alerts, considering whether they attempt to integrate with existing mobile-user behaviour patterns or seek to be a disruptive element, garnering attention when audiences are not typically using devices. Through quantitative content analysis, this study examines the temporality of push notifications (n = 7092) from nine Northwestern European countries, comprising 34 news outlets. These data allow for comparisons at two levels: publisher type and national context. The study shows how the temporal patterns of push notifications’ dissemination align with existing news consumption behaviours; concepts of content-snacking and audiences’ rhythms and rituals are a useful lens through which these immediate, concise texts can be considered. Our findings show that news organisations use the mobile channel for attracting and maintaining users’ attention, with varying interpretations of temporal customisability.

Design of virtual reality augmented reality mobile platform and game user behavior monitoring using deep learning

To explore the design of the virtual reality (VR) augmented reality (AR) mobile platform and game decision model based on deep learning (DL), the gesture-based interaction of VR games based on Leap Motion is researched. Based on the interactive features of gestures, a set of general gesture interaction rules in VR games is established. In the meantime, according to the theoretical basis and the characteristics of VR, a set of general models of VR gesture interaction is designed, the factors affecting the efficiency of VR gesture interaction are studied, and reasonable interaction feedback is designed. By using the computer vision and image processing technology, gesture-based interaction can collect natural gestures, extract gesture features, recognize gesture indications, and respond to the user demands. Also, it can extract the basic gestures from gesture-based interaction in VR, analyze the basic features of gestures and gesture-based interaction in VR games, and describes the gesture features by mathematical vectors and sets. The research results show that the application of gesture feature design method in the game can analyze the factors affecting the interaction efficiency. Also, the usability of the gesture-based interaction designed by the gesture design method is verified by tests. Therefore, the AR&amp;DL platform of “AR+DL” establishes a learning platform supported by DL and AR technology. The game decision model is used to describe the process of gesture-based interaction in the game, and the factors affecting the interaction efficiency are reduced, which has certain reference and guidance for VR applications using gesture-based interaction.

Auction-Based Data Transaction in Mobile Networks: Data Allocation Design and Performance Analysis

Mobile data traffic is experiencing unprecedented increases due to the proliferation of highly capable smartphones, laptops and tablets, and mobile data offloading can be used to move traffic from cellular networks to other wireless infrastructures such as small-cell base stations. This work addresses the related issue of data allocation, by proposing a novel infrastructure independent method based on the hotspot function of smartphones. In the proposed scheme, smartphones transfer data allowances among mobile users, so that users with excess data allowances act as accessible Wi-Fi hotspots, selling their data allowance to other users who need extra data allowances. To achieve this objective, we propose to use auctions with single and multiple data sellers. Efficient schemes based on auction models are discussed to sell the data allowances over successive days in a month, and over different time slots during a single day. Overall system performance is considered based on the behavior of mobile users, such as changing demands for the sale or purchase of data allowances. Together with the analytical results presented, our simulation experiments also indicate that knowledge of user behavior can significantly improve the performance of data allowance transactions, leading to highly efficient allocations among users.

Analysis of Trip Distributions of Human Mobility Patterns and Their Transit Behaviors Using Mobile Call Detail Records

Rapid urbanization and modernization are increasing worldwide, including in Myanmar. Mobile call detail records (CDRs) provide new opportunities for measuring transport demands and problems in transportation planning. This research aims to analyze trip distributions and transit behaviors of mobile phone users based on their call activities. Origin-Destination (O-D) pairs were computed for the entire city, and the trip distributions help understand human mobility. It was found that zone-to-zone flow has the highest flow in commercial and industrial areas. Moreover, the logical assumptions were specified to extract the transit behaviors of users. The results indicate the degree of mode-to-mode transfer behaviors of users. Among the four categories of transit usage, only rail users do not transfer to other modes, having the lowest proportion, with other mode-to-other mode transfers having the highest proportion. The results were validated with the Person Trip Survey for Comprehensive Urban Transport Plan of the Greater Yangon. This study contributes significantly to the expansion of current and potential future transit systems, which can provide a new and improved transport system for Yangon City to meet its demands. This information is helpful in conducting disaster management and emergency preparedness in terms of trip distributions of human mobility patterns changing over space and time and the transit behaviors of the transferring mode in daily trips.

Tracking area list allocation scheme based on overlapping community algorithm

Reducing the singling overhead for tracking and mobile paging devices is a challenging issue in the study of location management of cellular networks. Cellular networks have become massive generators of data, and in the forthcoming years, this data is expected to increase drastically. Big data-based intelligence and analytics can improve network operational efficiency and user service quality. This work proposes to exploit massive handover and paging data from cellular networks to minimize singling due to user mobility. In this paper, we offer a new holistic tracking area lists (TAL) management methodology, considering group user mobility behavior and paging characteristics. Firstly, a series of graphs showing the evolution of user mobility and traffic is built from handover and paging statistics in the network management system (NMS). Then, the TAL allocation problem is formulated as a classical graph partitioning problem, which is then solved by detecting overlapping communities algorithm based on game theory. Results show that the proposed method can effectively reduce the location management singling overhead and improve the TAL configuration efficiency.

Mobile Privacy: Scalable Ensemble Matching for User Identification Attacks

Mobile privacy is broadly concerning in the mobile big data era, as user mobility behaviors are privacy-sensitive and unique. User identification attacks consist of one of the most critical privacy concerns on mobile big data. In this paper, we study mobile privacy in terms of user identifiability from the perspective of privacy adversaries. User identification in two datasets from the same data source or two different data sources is generally formulated as a linear assignment problem (LAP), in which the cost matrix of users is generated by a single distance measure. However, user identification via one single distance measure may lead to a large portion of false matches, especially when only a few users coexist across these two datasets. In addition, the cubic computational complexity of LAP limits the scale of user identification analysis. In this paper, we propose a multi-feature ensemble matching framework to improve the user identification precision based on a majority voting rule, by integrating multiple distance measures. The computational complexity of the proposed ensemble matching algorithm is an order of magnitude less than that of the single-distance based approach, which results from solving an LAP on a highly sparse matrix rather than a dense matrix. Experiments demonstrate the superior performance of our proposed scalable ensemble matching framework with respect to matching precision as well as the vulnerability of mobile network subscribers' privacy.