Perceived Quality Model for Supporting Full Session Mobility in Multimedia Service Delivery Process

Personalized multimedia (PMM) services are the emerging area in multimedia technology. A PMM service is a multimedia service requested according to the users personal profile which typically includes individual preferences, keeping the technical constraints of mobile terminal in use and operating environment. An important issue of authentication in PMM service platform is, "how does it determines who the mobile user is, if requests are allowed to come in from various communication channels and devices." We propose a transaction based authentication scheme for PMM service using cognitive agents. The proposed approach dynamically deploys authentication challenges based on mobile transaction sensitivity, users mobility and behaviors. The method has been simulated with a typical PMM service over in-house established wireless testbed, the results are time efficient and quite encouraging.

Understanding user behavior of mobile Internet is vital for network infrastructure planning and radio resource allocation. In this paper, we investigate the spatial-temporal characteristics of users' access behavior and mobility patterns based on a data set collected from a cellular network in a city of China. We first propose a method called Maximum Distance Criterion (MDC) to distinguish users' access stability in different areas and periods. The analysis shows that urban users' access behavior is less stable than that of rural users. Furthermore, urban users' access behavior is more stable in holidays than in workdays, but contrary for that of rural users. We then introduce the concept of centroid to explore users' mobility patterns at the collective level. We find that users in different areas all move toward a highway during holidays while come back when workdays approaching. These results give us a better understanding of user behavior in the mobile Internet from both spatial and temporal perspective, which is essential for network planning.

Accessing the Internet from a mobile computer can currently be done by opening a circuit switched call to the ISP (Internet service provider). New packet based services are now being introduced, such as the GPRS (General Packet Radio Service) for the GSM system. The solution described gives a simpler access to the Internet, at the same time providing complete user anonymity. However, the solution here does not exclude the use of traditional accounting methods, or more sophisticated electronic cash solutions which do not provide anonymity at the same level, as the solution described here. A MH (mobile host) attaching to a visited radio network receives an IPv6 address prefix from the access router in the visited network. The MH configures the whole IPv6 address for its interface. After initialization, it starts paying for the radio and Internet access to the visited radio operator. Paying method is e-cash, providing full anonymity for the mobile user. After receiving access to the network, the mobile user registers its current location information on a location server (dynamic DNS or some other), so that other hosts can resolve the mobile user's current IPv6 address and contact the roaming mobile user. The same e-cash, which is used to pay for the network access, will be used to buy services provided in the network. Services can be either local or global. Local service could be, for example, buying a soda, global such as receiving a video stream from a video server.

Recently, wireless LAN systems have been widely deployed for public mobile Internet services. Public wireless LAN systems can provide high speed Internet connectivity using portable devices such as laptop computers, personal digital assistants (PDAs), etc. In public wireless LAN systems, reliable user authentication and mobility support are essential issues. However, re-authentication during handoff procedures causes long handoff latency and this affects the quality of service in real-time multimedia applications. A fast handoff scheme based on mobility prediction is proposed. In this scheme, a mobile host entering the area covered by an access point (AP) performs authentication procedures for multiple APs, rather than just the current AP. These multiple APs are selected by a prediction method called the frequent handoff region (FHR) selection algorithm, which takes into account users' mobility patterns, service classes, etc. Since a mobile host is registered and authenticated for an FHR in advance, handoff latency resulting from re-authentication can be significantly reduced. Simulation results show that the proposed scheme is more efficient than other schemes in terms of handoff delay and buffer requirements.

Mobile host (MH) has to be tracked in personal communication service (PCS) network, for which update and paging signals are required. The number of PCS network subscribers skyrocketed in recent years. To reuse channels over a distance, cell size is reduced and the number of cell crossing by user is becoming high. That makes optimal use of paging and update signal very important. In fact, most MH has unique movement profile, that contains the information of time, route, direction, etc., which is possible to learn and used to predict location. In this paper, we propose mobility pattern based location management scheme using the movement profile. Mobility pattern is learned and system will page only the restricted probable area. We compared the proposed scheme with distance-based location management. Improved cost saving is achieved.

Location-based services (LBSs) typically crowdsource geo-tagged data from mobile users. Collecting more data will generally improve the utility for LBS providers; however, it also leads to more privacy exposure of users’ mobility patterns. Although the tension between data utility and user privacy has been recognized, there lacks a solution that determines how much data to collect—in both spatial and temporal domains—is the “best” for both mobile users and the service provider. This article proposes a strategy toward making an optimal tradeoff such that a user submits data only if her mobility privacy will not be compromised and the data utility of the LBS provider will be sufficiently improved. To this end, we first define and formulate a concept called privacy exposure , which incorporates both the spatial distribution and the temporal transition of a user’s activity points . Second, we define and quantify data utility in terms of spatial repetitions and temporal closeness among data based on an economic principle. Then, we propose a PRivacy-preserving and UTility-Enhancing Crowdsourcing (PRUTEC) algorithm to determine, on behalf of each mobile user, whether a newly sensed piece of data should be submitted to the LBS provider. Our simulation demonstrates that PRUTEC improves the data utility of the service provider with a much less amount of data to collect and reduces privacy exposure for mobile users while collecting useful data continuously.

In recent years, there has been a growing demand in 3G data services, leading to deteriorating 3G service quality. Noting that Wireless Local Area Networks (WLANs) as well as 3G cellular networks are widely available today, WLANs could be effectively utilized to relieve the overload in the 3G networks. On the other hand, use of IEEE 802.11 WLAN Access Points (APs) has proliferated tremendously, resulting in a communication device inside a mobile vehicle to access the Internet. However, using Internet through APs in moving vehicles is challenging since WLAN APs have a short range and are typically not deployed to cover all roads. Several studies have investigated the performance of using intermittently available WLAN connectivity from moving vehicles for data transfers and predictive offloading in WLAN/3G networks. However, these works have not addressed mobility pattern from the viewpoint that drivers' mobility is generally known to have a daily routine. Therefore, in this paper, we consider the user's historical mobility to decide to offload data to WLAN instead of switching to 3G network. The user's application usage pattern is also considered into predicting available WLANs. To evaluate the performance of our offloading algorithm, we analyze the prediction error and conduct simulations. The simulation results show that the proposed algorithm achieves shorter transmission time than the existing schemes that do not consider user's mobility pattern by delivering more data to the WLANs.

In this paper, we study the problem of resource allocation in a cache-enabled software defined cellular network (SDCN) with mobile users, where the SDCN controller has global information of the network and the popular contents that the users request are stored at the content server and cache-enabled small base stations (SBSs). We propose a Markov chain based model to predict the users’ mobility patterns and then use the predicted mobility patterns to determine optimal resource allocation. The mobility prediction and resource allocation problem are jointly formulated as an optimization problem whose goal is to maximize the network throughput. Based on the predicted users’ mobility patterns, a distributed alternating direction method of multipliers (ADMM) is proposed to solve the resource allocation problem. The proposed ADMM algorithm enables the multiple SBSs implement their resource allocation simultaneously and, hence decreases the control overhead of the SDCN controller. Simulation results show that the proposed algorithm achieves up to 9.35% and 33.17% gains in terms of the average throughput compared to the random algorithm and the nearest association with equal allocated resource algorithm.

Many people increasingly rely on mobile communication services to carry out daily activities. Due to the limitation of the PCS network architecture, a constantly relocating user may encounter significant delay when requesting data or value-added services. Previous research showed that this inefficiency can be effectively reduced by predicting the user's mobile patterns. However, most research merely focused on the user's moving nodes without considering the traffic times and requested services which could dramatically affect the user's behavior. The research also did not consider how likely the user is going to relocate. Thus, in this work, we extend the hidden Markov model for modeling the behavior of the mobile users with regard to the following important factors: 1) moving node, 2) requested service, 3) user state, and 4) traffic time. Our novel approach requires only one scan of the target dataset. Moreover, the needed memory space and processing time can be independent of the transaction size. A user model can be built to predict the user's mobile patterns at different granularity levels, as well as for decision support and service improvement. Moreover, the built model can be easily adjusted later to reflect the latest user behavior without re-scanning the original dataset. Our approach can also be readily used to mine streaming data.

In this paper, the problem of proactive caching is studied for cloud radio access networks (CRANs). In the studied model, the baseband units (BBUs) can predict the content request distribution and mobility pattern of each user and determine which content to cache at remote radio heads and the BBUs. This problem is formulated as an optimization problem, which jointly incorporates backhaul and fronthaul loads and content caching. To solve this problem, an algorithm that combines the machine learning framework of echo state networks (ESNs) with sublinear algorithms is proposed. Using ESNs, the BBUs can predict each user's content request distribution and mobility pattern while having only limited information on the network's and user's state. In order to predict each user's periodic mobility pattern with minimal complexity, the memory capacity of the corresponding ESN is derived for a periodic input. This memory capacity is shown to capture the maximum amount of user information needed for the proposed ESN model. Then, a sublinear algorithm is proposed to determine which content to cache while using limited content request distribution samples. Simulation results using real data from Youku and the Beijing University of Posts and Telecommunications show that the proposed approach yields significant gains, in terms of sum effective capacity, that reach up to 27.8% and 30.7%, respectively, compared with two baseline algorithms: random caching with clustering and random caching without clustering.

Over the past few months, COVID-19 has emerged to the world as a new threat to humanity and communities, expanding from a few small infected cities to hundreds of countries around the world impacting businesses, education, economics, and almost every activity associated with human life. This had led many researchers and scientists to analyze and study different factors and variables that obtain timely information on the outbreak of COVID-19. One of the main factors that helped in spreading the corona-virus is human mobility. Since detailed information about human movement during outbreaks are difficult to obtain, social networks comes as an alternative with its massive volume of publicly available data. In this research, we propose mobility detection and identification of social media's spatio-temporal data, as a proxy for human mobility. We aim to discover and explore an in-depth level of mobility data extracted from social media applications to uncover the relation between COVID-19 spread and daily mobility ratio in Kuwait regional area. With the use of the latest mobility data extracted from Twitter users, we have shown that user mobility is linked to the positive cases of COVID-19, with a relatively high correlation coefficient. Moreover, we have analyzed and discussed how the impact of COVID-19 affected user behavior and mobility habits.

With the rapid development of mobile communication and packet-switched technologies, it is inevitable to discover the suitable solutions to integrate these networks and to provide users the consistency and continuity of services through diverse access networks. In this paper, we analyse the fundamental criteria for the suitable solutions based on 3GPP TR22.937 and WiMAX Forum requirements; the requirements are subcategorized into five criteria, which are common billing and customer care, common access control, policy and charging, service session continuity, seamless service access and unified user identity. Through these requirement criteria, several mobility support techniques are compared and evaluated. This paper further examines IP Multimedia Subsystem (IMS) approaches defined in 3GPP standards for supporting seamless service continuity from the aspects of these criteria. Consequently, the challenges and drawbacks of IMS from the aspects of architecture, protocol signalling and interoperability are later overviewed in this paper.

In this paper I seek to make a contribution to ‘geographies of mobility’ by arguing that assumptions of unrestricted movement and mobility in contemporary Western societies are hegemonic in prioritising specific bodies and modes of mobility and movement. In particular, mobility and movement are defined through ‘normalising’ discourses which serve to alienate impaired bodies and to prioritise what one might term the ‘mobile body’. This has the effect of (re)producing structured inequalities in peoples' movement and mobility patterns. Such ideas are developed and illustrated with reference to interviews with disabled people.

Mobile technology is a widely adopted technology and is used for mobile users to stay connected. Mobile data mining is an extension of data mining for the purpose of developing innovative ways to extract useful knowledge out from mobile users to the decision makers. Our proposed method, parallel pattern, provides a method for finding out similar decisions by mobile users. Parallel patterns are divided into physical parallel pattern and logical parallel pattern. Physical parallel pattern finds the similarities of physical movement decisions among mobile users. Logical parallel pattern finds the logical similarities of logical theme movements among mobile users. In physical parallel pattern, mobile users can only occupy one physical location at any one time while in logical parallel pattern, mobile users can occupy more than one logical theme at any one time and each static node can have more than one logical theme at any one time. Our performance evaluation shows that the method for mobile parallel pattern mining suits for the real world problem in both the physical and logical parallel pattern mobile mining paradigm.

Finding prevalent mobile user patterns in large amount of data has been one of the major problems in the area of mobile data mining. Particularly, the algorithms of discovering frequent user's behavior patterns in the mobile agent system have been studied extensively in recent years. The key feature in most of these algorithms is that they use a location log dataset with userpsilas requested services. In this case, some problems occur because they do not consider that mobile user's behavior patterns are dynamically variable as time passes. In this paper, we propose a novel data mining method based on temporal mobile access patterns that can efficiently discover mobile user's behavior patterns. An advantage of our method compactly stores user's behavior patterns according to location log dataset and requested services in memory. Thus, even the information datasets require large shared memory when they store, our approach still provides faster access and consume less memory than existing techniques.