Conventional mobility management schemes tend to hit the core network with increased signaling load when the cell size is shrinking and the user mobility speed increases. To mitigate this problem research community has proposed various intelligent mobility management schemes that take advantage of the predictability of the users mobility pattern. However, most of the proposed solutions are only focused on signaling of the active-state (i.e., handover signaling) and proposals on improvement of the idle-state signaling has been limited and were not well received from the industrial practitioners. This paper first surveys the major shortcomings of the existing proposals for the idle mode mobility management and then proposes a new architecture, namely predictive mobility management (PrMM) to mitigate the identified challenges. An analytical framework is developed and a closed form solution for the expected signaling overhead of the PrMM is presented. The results of numerical evaluations confirm that, depending on user mobility and network configuration, the PrMM efficiency can surpass the long term evolution (LTE) 4G signaling scheme by over 90%. Analysis of the results shows that the best performance is achieved at highly dense paging areas and lower cell crossing rates.

This article proposes a novel approach for enhancing the video popularity prediction models. Using the proposed approach, we enhance three popularity prediction techniques that outperform the accuracy of the prior state-of-the-art solutions. The major components of the proposed approach are two novel mechanisms for ” user grouping ” and ” content classification .” The user grouping method is an unsupervised clustering approach that divides the users into an adequate number of user groups with similar interests. The content classification approach identifies the classes of videos with similar popularity growth trends. To predict the popularity of the newly-released videos, our proposed popularity prediction model trains its parameters in each user group and its associated video popularity classes. Evaluations are performed through a 5-fold cross validation and on a dataset containing one month video request records of 26,706 users of BBC iPlayer. Using the proposed grouping technique, user groups of similar interest and up to two video popularity classes for each user group were detected. Our analysis shows that the accuracy of the proposed solution outperforms the state-of-the-art, including Szabo-Huberman (SH), Multivariate Linear (ML), and Multivariate linear Radial Basis Functions (MRBF) models by an average of 45%, 33%, and 24%, respectively. Finally, we discuss how various systems in the network and service management domain such as cache deployment, advertising, and video broadcasting technologies benefit from our findings to illustrate the implications.

When dealing with a large number of devices, the existing indexing solutions for the discovery of Internet of Things (IoT) sources often fall short to provide an adequate scalability. This is due to the high computational complexity and communication overhead that is required to create and maintain the indices of the IoT sources particularly when their attributes are dynamic. This paper presents a novel approach for indexing distributed IoT sources and paves the way to design a data discovery service to search and gain access to their data. The proposed method creates concise references to IoT sources by using Gaussian mixture models. Furthermore, a summary update mechanism is introduced to tackle the change of sources availability and mitigate the overhead of updating the indices frequently. The proposed approach is benchmarked against a standard centralized indexing and discovery solution. The results show that the proposed solution reduces the communication overhead required for indexing by three orders of magnitude while depending on IoT network architecture it may slightly increase the discovery time.

In research community, a new radio access network architecture with a logical separation between control plane (CP) and data plane (DP) has been proposed for future cellular systems. It aims to overcome limitations of the conventional architecture by providing high data rate services under the umbrella of a coverage layer in a dual connection mode. This configuration could provide significant savings in signalling overhead. In particular, mobility robustness with minimal handover (HO) signalling is considered as one of the most promising benefits of this architecture. However, the DP mobility remains an issue that needs to be investigated. We consider predictive DP HO management as a solution that could minimise the out-of-band signalling related to the HO procedure. Thus we propose a mobility prediction scheme based on Markov Chains. The developed model predicts the user's trajectory in terms of a HO sequence in order to minimise the interruption time and the associated signalling when the HO is triggered. Depending on the prediction accuracy, numerical results show that the predictive HO management strategy could significantly reduce the signalling cost as compared with the conventional non-predictive mechanism.

Quantifying social interactions requires accurate, reliable and real-time recognition, of both users' interpersonal distance and relative orientation. DARSIS is the outcome of our research towards fulfilling these requirements based upon a non-intrusive opportunistic mechanism that solely relies on sensors and communication capabilities of off-the-shelf smartphones. We developed a novel hierarchical classifier for interpersonal distance estimation, produced by a substantive training set of Bluetooth Received Signal Strength Indicator (RSSI) and a concrete feature selection process. The presented relative orientation estimation mechanism addresses problems associated with lack of facing direction information in prior works, independently of the wearing position. In addition, DARSIS introduces a collaborative sensing scheme which allows on-the-fly exchange of facing direction information between users and facilitates the interpersonal distance recognition process by sharing RSSI values among devices. We show that the proposed interpersonal distance estimation models outperform state-of-the-art solutions and achieve up to 93.52% accuracy while DARSIS as a coherent system detects accurately 81.40% of the interactions in a real-world environment.

A novel method for a mobile phone centric observation of a user's facing direction is presented. To estimate this direction, our proposed technique exploits the acceleration pattern that can be measured by a smartphone as the user is walking. For an accurate analysis of the acceleration pattern, the proposed approach benefits from a new trigonometric interpolation scheme. Our algorithm is independent of the initial orientation of the device and is adaptable to various wearing positions on a user's body, which gives the user a larger degree of freedom. A detailed description of the algorithm, which has been customized for a trouser pocket is presented. In addition, complementary hints for adaptation of the algorithm to other wearing positions along with an example of chest pocket position are provided. We have evaluated a prototype implementation of our algorithm on a smartphone, through several field experiments. It has been observed that our algorithm outperforms the conventional GPS and PCA-based techniques in terms of accuracy, reliability and energy consumption. The results also show that our approach has been able to handle the sudden variations of the user's direction. We have further incorporated our algorithm into a dead-reckoning application as an example of its real-world utility.

In this paper we present the uDirect algorithm as a novel approach for mobile phone centric observation of a user's facing direction, through which the device and user orientations relative to earth coordinate are estimated. While the device orientation estimation is based on accelerometer and magnetometer measurements in standing mode, the unique behavior of measured acceleration during stance phase of a human's walking cycle is used for detecting user direction. Furthermore, the algorithm is independent of initial orientation of the device which gives the user higher space of freedom for long term observations. As the algorithm only relies on embedded accelerometer and magnetometer sensors of the mobile phone, it is not susceptible to shadowing effect as GPS. In addition, by performing independent estimations during each step of walking the model is robust to error accumulation. Evaluating the algorithm with 180 data samples from 10 participates has empirically confirmed the assumptions of our analytical model about the unique characteristics of the human stance phase for direction estimation. Moreover, our initial inspection has shown a system based on our algorithm outperforms conventional use of GPS and PCA analysis based techniques for walking distances more than 2 steps.

Abstract — Developing applications, especially real-time ones, for wireless vehicular ad hoc networks (VANETs) requires a reasonable assurance of the likely performance of the network, at the least in terms of packet loss ratios and end-to-end delay. Because wireless propagation strongly influences performance, especially in an urban environment, this paper improves on simpler propagation models for simulations by augmenting ray-tracing derived models of propagation. In the non-line-of-sight component: the propagation distance is more closely calculated according to the reflection distance; the effect of roadside obstacles is included; and for modeling of fast fading a phase factor is introduced, all without necessarily overly increasing computational load. In the line-of-sight component, as well as roadside obstacle modeling: single and double reflections from roadside buildings are added to the standard two-ray ground-propagation model; the distribution of vehicles within a street segment is used to model the ground reflection ray more closely; and the reflection coefficient is also adjusted accordingly to account for reflections from vehicles. The results have been compared with widely-used measurement studies of city streets in the literature, which have confirmed the overall advantage of the improvements, especially in the case of the non-line-of-sight component. A simulation case study shows that in general optimistic performance predictions of packet loss occur with the two-ray ground propagation model when indiscriminately applied. The paper, therefore, represents a way forward for VANET wireless channel modeling in simulations. Index Terms—simulation, urban environment, vehicle-to-vehicle communication, wireless propagation G I.