Understanding Access Stability and Mobility Patterns of Mobile Internet Users

The unprecedented growth in mobile device adoption and the rapid advancement of mobile technologies a wireless networks have created new opportunities in mobile marketing and adverting. The opportunities for Mobile Marketers and Advertisers include real-time customer engagement, improve customer experience, build brand loyalty, increase revenues, and drive customer satisfaction. The challenges, however, for the Marketers and Advertisers include how to analyze troves of data that mobile devices emit and how to derive customer engagement insights from the mobile data. This research paper addresses the challenge by developing Big Data Mobile Marketing analytics and advertising recommendation framework. The proposed framework supports both offline and online advertising operations in which the selected analytics techniques are used to provide advertising recommendations based on collected Big Data on mobile user's profiles, access behaviors, and mobility patterns. The paper presents prototyping solution design as well as its application and certain experimental results.

We consider per-user profile replication as a mechanism for faster location lookup of mobile users in a personal communications service system. We present a minimum-cost maximum-flow based algorithm to compute the set of sites at which a user profile should be replicated given known calling and user mobility patterns. We show the costs and benefits of our replication algorithm against previous location lookup approaches through analysis. We also simulate our algorithm against other location lookup algorithms on a realistic model of a geographical area to evaluate critical system performance measures. A notable aspect of our simulations is that we use well-validated models of user calling and mobility patterns.

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.

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.

The mobile Internet brings tremendous opportunities for researchers to analyze user mobility pattern, which is of great importance for Internet Service Providers (ISP) to provide better location-based services. This paper focuses on predicting user mobility patterns based on their different mobility characteristics. For that, we collect real-world data from Long Term Evolution (LTE) mobile network by a specially developed network traffic analysis platform followed by clustering the user into stationary one or mobile one with a location-entropy-based method for distinguishing groups with distinct mobility characteristics, and then we present the tailored Intelligent Time Division (ITD) method and Time-Based Markov (TBM) predictor for the location prediction of stationary and mobile users respectively. Extensive experiments demonstrate the effectiveness and better performance of our proposed methods compared with the baselines, as well as the adaptabilities of different predictors according to individual's mobility characteristics.

The advent of modern WLAN standards, beginning with IEEE 802.11ax, has established a new paradigm for uplink multi-user access through foundational technologies like MU-MIMO and OFDMA. While these technologies promise unprecedented concurrent transmission speeds for dense deployments, their practical performance is severely constrained in networks with a massive number of users exhibiting heterogeneous states. Specifically, a user with a poor channel can contaminate an entire MU-MIMO group via error propagation, while a highly mobile user exacerbates the accumulation of channel sounding overhead. This paper systematically investigates these performance bottlenecks and their root causes. We propose Nexus, a simple yet effective cross-layer solution that consistently improves the uplink throughput in various environments. At the core of Nexus are two synergistic designs: (1) channel qualityaware user grouping, which isolates users with poor channel conditions to mitigate error propagation; and (2) opportunistic CSI multiplexing, which dynamically adapts the reuse of channel information according to each user's mobility pattern, thereby drastically reducing sounding overhead. We have prototyped Nexus on a software-defined radio platform, and our experimental results demonstrate that it improves the overall uplink throughput by up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2 \times$</tex> compared to the standard 802.11ax, significantly outperforming existing state-of-the-art baselines.

With proliferation of smart phones and an increasing number of services provisioned by clouds, it is commonplace for users to request cloud services from their mobile devices. Accessing services directly from the Internet data centers inherently incurs high latency due to long RTTs and possible congestions in WAN. To lower the latency, some researchers propose to 'cache' the services at edge clouds or smart routers in the access network which are closer to end users than the Internet cloud. Although 'caching' is a promising technique, placing the services and dispatching users' requests in a way that can minimize the users' access delay and service providers' cost has not been addressed so far. In this paper, we study the joint optimization of service placement and load dispatching in the mobile cloud systems. We show this problem is unique to both the traditional caching problem in mobile networks and the content distribution problem in content distribution networks. We develop a set of efficient algorithms for service providers to achieve various trade-offs among the average latency of mobile users' requests, and the cost of service providers. Our solution utilizes user's mobility pattern and services access pattern to predict the distribution of user's future requests, and then adapt the service placement and load dispatching online based on the prediction. We conduct extensive trace driven simulations. Results show our solution not only achieves much lower latency than directly accessing service from remote clouds, but also outperforms other classical benchmark algorithms in term of the latency, cost and algorithm running time.

With the onset of the digital era and the availability of the internet, the need for digital data in a huge manner can be fulfilled by peer-to-peer (P2P) network instead of a traditional client server-based solution. Generally, mobile communication is based on cellular networks or multi-hop wireless networks. Cellular networks have adequate fixed infrastructure whereas multi-hop wireless networks have limited infrastructure and hence there are many limitations. The P2P systems are mainly designed for wired networks and the routing is based on IP infrastructure. Chord based protocols are widely deployed in the structured P2P networks but it can not perform well when implemented for mobile P2P networks due to the mobility of the users. Mobility pattern of mobile users plays an important role in locating users and delivering data packets seamlessly. Today, many of the mobile users follow a fixed mobility pattern in urban cities and mobility pattern of the mobile users can be utilized to reduce table update cost and increase Lookup Success Rate (LSR). We have proposed Local P2P Group (LPG) based communication scheme for structured mobile P2P networks. We are focussed on the mobility pattern of the mobile users in urban cities. We have analytically evaluated the proposed scheme using fluid-flow and RWP (Random Waypoint) mobility models and found that the proposed scheme performs better than the existing schemes like MR-Chord and MobiStore. Our proposed scheme has up to 40% higher Lookup Success Rate and 81% less table update cost than existing schemes, MR-Chord and MobiStore.

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.

We formulate the optimum personal paging area problem as an interval estimation problem, with an aim to reduce the paging signaling cost by minimizing the size of the paging area. A paging algorithm is developed to set-up the optimal one-step paging area that contains the locations of a mobile user with a high probability and thus to gain a coverage that is matched to the user's time-varying mobility pattern. With a particular mobility model, the effects of the user's mobility characteristics and the system parameters on the performance of the paging procedure are investigated.

The advances in location-acquisition and smart phone technologies have led to a myriad of location-based social media. Therefore, analyzing the increasing amount of spatio-temporal data emerges as an important topic. Most studies on geographic data mining focus on exploring static mobility patterns. As the amount of incoming data streams increases, revealing the temporal aspect of user mobility patterns is worth investigating. This paper targets on mining user mobility patterns over time (referred to as mobility evolution) from streams of check-in records. Intuitively, at each time slot, a mobility pattern indicates spatial regions where users stay. Therefore, given a set of time slots, mobility evolution refers a sequence of spatial regions at each time slot. Note that nearby time slots may have similar spatial region distribution. Thus, given check-in datasets, we use the idea of data compression to obtain a sequence of representative segments, where each representative segment captures spatial region distribution at the corresponding time interval. To measure the quality of a segmentation result, we propose a representation cost function based on the Minimum Description Length (MDL) principle. In addition, because deriving the sequence of segments incurs expensive computational cost, we propose a family of greedy algorithms for segmentation to serve diverse requirements: efficient compression, informative compression, and cost-effective compression. Besides, to handle the massive amount of incoming check-in data, we also propose an incremental compression approach to incrementally update the mobility evolution. We conduct experiments on Foursquare datasets to demonstrate both the effectiveness and efficiency of our proposed algorithms.

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.

The IEEE 802.16e (Wimax) is an emerging broadband wireless access system standard, and the cells in Wimax networks are organized as paging group for the user mobility management. According to the standard, the paging group can have overlapped edges to reduce the location update numbers for users close to the paging group boundary. In this paper, we propose a scheme to determine the optimal paging group layout in Wimax networks considering both the overlapped area effect, user mobility pattern and session arrival rate. A theoretical model is developed to analyze the network mobility management costs under various paging group layouts. The results show that the overlapped area can reduce the mobility management cost effectively; however the area should not be too large and the cell residence time variance has little effect on the overall mobility management costs.

This paper presents a predictive framework for mobility-aware prefetching to enhance the experience of a mobile Web user roaming between heterogeneous wireless access networks. We consider a heterogeneous two-tier wireless access network system, composed of smaller, but faster and cheaper wireless local area networks (WLAN) placed within a much larger, but slower and more expensive cellular wireless data network. An optimal prefetch threshold algorithm is proposed, which takes into consideration the user mobility pattern, the relative characteristics of the networks, and the user perceived value of time. Using current industry network parameters, we study the performance of the proposed prefetching algorithm. Our numerical results show how user mobility and the heterogeneous network configuration significantly alter the prefetching threshold. The performance of this algorithm is compared with that of a static prefetching algorithm, demonstrating that mobility-aware prefetching can significantly improve the performance of future-generation heterogeneous wireless networks.

We consider per-user profile replication as a mechanism for faster location lookup of mobile users in a Personal Communications Service system. We present a minimum-cost maximum-flow based algorithm to compute the set of sites at which a user profile should be replicated given known calling and user mobility patterns. We then present schemes for replication plans that gracefully adapt to changes in the calling and mobility patterns.