Hoseinitabatabaei SA, Gluhak A, tafazolli R, Headly W (2013) Design, Realization, and Evaluation of uDirect-An approach for Pervasive Observation of User Facing Direction on Mobile Phones, IEEE Transactions on Mobile Computing
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
Palaghias N, Hoseinitabatabaei SA, Nati M, Gluhak A, Moessner K (2016) A Survey on Mobile Social Signal Processing, ACM COMPUTING SURVEYS48(4)ARTN 57 ASSOC COMPUTING MACHINERY
Hoseinitabatabaei SA, Fleury M, Qadri NN (2011) Improving Propagation Modeling in Urban Environments for Vehicular Ad Hoc Networks, IEEE Transactions on Intelligent Transportation Systems12(3)pp. 705-716 IEEE
Developing applications, particularly real-time applications, 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, particularly 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 (NLOS) component, the propagation distance is more closely calculated according to the reflection distance, the effect of roadside obstacles is included, and for the modeling of fast fading, a phase factor is introduced, all without necessarily overly increasing the computational load. In the line-of-sight (LOS) component, as well as the 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 more closely model the ground reflection ray, and the reflection coefficient is also accordingly adjusted 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, particularly in the case of the NLOS 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. This paper therefore represents a way forward for VANET wireless channel modeling in simulations.
Hoseinitabatabaei SA, barnaghi P, tafazolli R, wang C Method and apparatus for scalable data discovery in IoT systems,
This patent is based on our novel data discovery mechanism for large scale, highly distributed and heterogeneous data networks. Managing Big Data harvested from IoT environments is an example application
Palaghias N, Hoseinitabatabaei SA, Nati M, Gluhak A, Moessner K (2015) Accurate Detection of Real-world Social Interactions with Smartphones, 2015 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC)pp. 579-585 IEEE
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. © 2011 IEEE.
Hoseinitabatabaei SA, Gluhak A, Tafazolli R, Headley W (2013) Design, Realization, and Evaluation of uDirect - An approach for Pervasive Observation of User Facing Direction on Mobile Phones, IEEE Transactions on Mobile Computing99PrePrints IEEE Computer Society
Hoseinitabatabaei SA, gluhak A, tafazolli R Information determination in a portable device,
Hoseinitabatabaei SA, Gluhak A, Tafazolli R (2015) Towards a position and orientation independent approach for pervasive observation of user direction with mobile phones, Pervasive and Mobile Computing17(PA)pp. 23-42
© 2014 Elsevier B.V. All rights reserved.This paper presents a novel approach for mobile phone centric observation of a user's facing direction, relying solely on built-in accelerometer and magnetometer. Our approach achieves greater accuracy and independence by an automatic detection of the wearing position of the mobile device on the user's body and subsequent selection of an optimum strategy for estimating the user direction. We report on a detailed analysis of various features sets and classifiers in order to determine an optimum selection of those for recognizing the wearing position and evaluate the accuracy and reliability of our overall implementation through an extensive measurement campaign.
Hoseinitabatabaei SA, Gluhak A, Tafazolli R (2014) Towards a position and orientation independent approach for pervasive observation of user direction with mobile phones, Pervasive and Mobile Computing
This paper presents a novel approach for mobile phone centric observation of a user's facing direction, relying solely on built-in accelerometer and magnetometer. Our approach achieves greater accuracy and independence by an automatic detection of the wearing position of the mobile device on the user's body and subsequent selection of an optimum strategy for estimating the user direction. We report on a detailed analysis of various features sets and classifiers in order to determine an optimum selection of those for recognizing the wearing position and evaluate the accuracy and reliability of our overall implementation through an extensive measurement campaign. © 2014 Elsevier B.V. All rights reserved.
The ever-growing computation and storage capability of mobile phones have given rise to mobile-centric context recognition systems, which are able to sense and analyze the context of the carrier so as to provide an appropriate level of service. As nonintrusive autonomous sensing and context recognition are desirable characteristics of a personal sensing system; efforts have been made to develop opportunistic sensing techniques on mobile phones. The resulting combination of these approaches has ushered in a new realm of applications, namely opportunistic user context recognition with mobile phones.
This article surveys the existing research and approaches towards realization of such systems. In doing so, the typical architecture of a mobile-centric user context recognition system as a sequential process of sensing, preprocessing, and context recognition phases is introduced. The main techniques used for the realization of the respective processes during these phases are described, and their strengths and limitations are highlighted. In addition, lessons learned from previous approaches are presented as motivation for future research. Finally, several open challenges are discussed as possible ways to extend the capabilities of current systems and improve their real-world experience.
Hoseinitabatabaei SA, gluhak A, tafazolli R (2013) A Fast Calibration Method for Triaxial-Magnetometers, IEEE Transactions on Instrumentation and Measurement
This paper presents a novel iterative calibration algorithm for tri-axial magnetometers. The proposed algorithm estimates and compensates the effects of deterministic interference parameters using only nine distinct measurements. The simulation results confirm that the proposed method outperforms the conventional ellipsoid fitting based models in terms of both accuracy and reliability even with the presence of a moderate wideband noise. The algorithm also achieves fast convergence , which makes it suitable for real-time applications
This work proposes a pattern identification and
online prediction algorithm for processing Internet of
Things (IoT) time-series data. This is achieved by
first proposing a new data aggregation and datadriven
discretisation method that does not require data
segment normalisation. We apply a dictionary based
algorithm in order to identify patterns of interest along
with prediction of the next pattern. The performance
of the proposed method is evaluated using synthetic
and real-world datasets. The evaluations results shows
that our system is able to identify the patterns by up to
85% accuracy which is 16.5% higher than a baseline
using the Symbolic Aggregation Approximation (SAX)
this paper presents a novel approach in targeting load balancing in ad hoc networks utilizing the properties of quantum game theory. This approach benefits from the instantaneous and information-less capability of entangled particles to synchronize the load balancing strategies in ad hoc networks. The Quantum Load Balancing (QLB) algorithm proposed by this work is implemented on top of OLSR as the baseline routing protocol; its performance is analyzed against the baseline OLSR, and considerable gain is reported regarding some of the main QoS metrics such as delay and jitter. Furthermore, it is shown that QLB algorithm supports a solid stability gain in terms of throughput which stands a proof of concept for the load-balancing properties of the proposed theory.
Data discovery for sensor data in an M2M network uses probabilistic models, such as Gaussian Mixing Models (GMMs) to represent attributes of the sensor data. The parameters of the probabilistic models can be provided to a discovery server (DS) that respond to queries concerning the sensor data. Since the parameters are compressed compared to the attributes of the sensor data itself, this can simplify the distribution of discovery data. A hierarchical arrangement of discovery servers can also be used with multiple levels of discovery servers where higher level discovery servers using more generic probabilistic models.
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.
When dealing with a large number of devices, the existing indexing solutions for the discovery of 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 (GMM). 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
Owing to growth in the popularity of mobile phones, solutions for more efficient mobile network resource management have been increasingly demanded by network operators. Predicting the future state of the network and allocating the network resources based on the predicted state has been proposed as an effective method for efficient management of the network resources by the research community. One of the major factors that changes the future state of network is changes in the behavior of users. As the result, to forecast the future state of network, a major task is to predict the future behaviors of users. This task is accomplished by User Behavior Prediction Models (UBPrMs). In order to maintain the quality of the service, such methods are expected to provide sufficiently accurate prediction. However, the existing methods often are not able to meet this performance requirement.
The accuracy of a predictive model is affected by two distinct sources of error, namely Modeling Error (ME) and Sampling Error (SaE). As the result, one ought to consider both sources of error while improving the performance of a model. To do this, this thesis aims to study and alleviate the impact of the mentioned sources of error on the performance of a UBPrM.
To treat the ME, we propose a novel group-level user behaviors prediction framework as a more accurate alternative for population-level user behaviors prediction models and a more computationally efficient alternative for individual-level user behaviors prediction. The novel framework is called Event Profiling Method (EPM). To diminish the impact of ME, the proposed event-based method takes advantage of similarities amongst users' behavior and the existing underlying patterns that repetitively occur in the network.
To evaluate the proposed framework, EPM method needs to be implemented in real-world scenarios. Video popularity prediction is considered as a suitable use case for EPM. For this purpose, this thesis utilizes the ideas of EPM framework to propose 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 three novel mechanisms for "user grouping", "content classification" and "dominant-follower users identification". 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 early popularity trends. The dominant-follower identification technique divides the users in each group into two distinct subgroups based on their reaction time to the released videos. 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 and user subgroups. Evaluations are performed through a 5-fold cross validation and on a dataset containing one month video request records of 26,706 number of BBC iPlayer users. Our analysis shows that the accuracy of the proposed solution outperforms the state-of-the-art including S-H, ML, MRBF models on average by 59%, 27% and 21%, respectively.
Afterwards, this thesis proposes a novel combination technique for multi-dimensional user profiles that is able to treat the SaE. In doing so, the proposed technique considers the samples of other users' behavior (or in general, other items) as a biased approximation of each user (or an item). The method utilizes two conditions on the magnitude and sign of the estimated bias between two users to decide on combining their profiles or not. The proposed technique is evaluated against synthesized and real-world datasets. Our results show that the proposed method provides better estimations of the st
This paper 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 number of BBC iPlayer users. Using the
proposed grouping technique, user groups of similar interest and up to 2 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 SH, ML, MRBF models on average by 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.