Muhammad Usman Shahid Khan

OmniSuggest: A Ubiquitous Cloud-Based Context-Aware Recommendation System for Mobile Social Networks

The evolution of mobile social networks and the availability of online check-in services, such as Foursquare and Gowalla, have initiated a new wave of research in the area of venue recommendation systems. Such systems recommend places to users closely related to their preferences. Although venue recommendation systems have been studied in recent literature, the existing approaches, mostly based on collaborative filtering, suffer from various issues, such as: 1) data sparseness, 2) cold start, and 3) scalability. Moreover, many existing schemes are limited in functionality, as the generated recommendations do not consider group of “friends” type situations. Furthermore, the traditional systems do not take into account the effect of real-time physical factors (e.g., distance from venue, traffic, and weather conditions) on recommendations. To address the aforementioned issues, this paper proposes a novel cloud-based recommendation framework OmniSuggest that utilizes: 1) Ant colony algorithms, 2) social filtering, and 3) hub and authority scores, to generate optimal venue recommendations. Unlike existing work, our approach suggests venues at a finer granularity for an individual or a “group” of friends with similar interest. Comprehensive experiments are conducted with a large-scale real dataset collected from Foursquare. The results confirm that our method offers more effective recommendations than many state of the art schemes.

A survey on energy-efficient methodologies and architectures of network-on-chip

We present an overview of the research conducted on Network-on-Chip (NoC).We emphasize on the energy efficiency of the NoC architectures and methodologies.We present taxonomies of buffered, bufferless, and energy efficient routing schemes.Strengths and weaknesses of the discussed techniques are highlighted.The survey also highlights possible directions for future research. Integration of large number of electronic components on a single chip has resulted in complete and complex systems on a single chip. The energy efficiency in the System-on-Chip (SoC) and its communication subset, the Network-on-Chip (NoC), is a key challenge, due to the fact that these systems are typically battery-powered. We present a survey that provides a broad picture of the state-of-the-art energy-efficient NoC architectures and techniques, such as the routing algorithms, buffered and bufferless router architectures, fault tolerance, switching techniques, voltage islands, and voltage-frequency scaling. The objective of the survey is to educate the readers with the latest design-improvements that are carried out in reducing the power consumption in the NoCs.

Personalized healthcare cloud services for disease risk assessment and wellness management using social media

We propose a cloud based framework that effectively manages the health related Big-data and benefits from the ubiquity of the Internet and social media. The framework facilitates the mobile and desktop users by offering: (a) disease risk assessment service and (b) consultation service with the health experts on Twitter. The disease risk assessment is performed through a collaborative filtering based approach whereas the hubs and authorities based approach is employed to identify the health experts from Twitter. The framework is implemented as Software as a Service (SaaS) to provide the disease risk assessment and expert user interaction services. Experimental results exhibit that the proposed framework achieves high accuracy as compared to the state-of-the-art approaches in terms of disease risk assessment and expert user recommendation.

On using the electrical characteristics of graphene-based conductors for designing a conformal monopole on a transparent substrate

Conformal antennas printed on thin flexible substrates typically use copper conductors for the radiating portion of the designs. This paper presents an alternative to using copper conductors. More particularly, a novel compact monopole on a thin transparent substrate with 25 μm thick flexible graphene-based conductors for the radiating portion of the design is presented. The design was modeled in a full-wave simulation tool, and a prototype was manufactured and measured in a full anechoic chamber. Overall, it was shown that the S-parameter simulations agreed well with measurements and that the electrical benefits of the flexible graphene-based conductors could be used to design a useful conformal monopole.

Analysis of Online Social Network Connections for Identification of Influential Users: Survey and Open Research Issues

Online social networks (OSNs) are structures that help users to interact, exchange, and propagate new ideas. The identification of the influential users in OSNs is a significant process for accelerating the propagation of information that includes marketing applications or hindering the dissemination of unwanted contents, such as viruses, negative online behaviors, and rumors. This article presents a detailed survey of influential users’ identification algorithms and their performance evaluation approaches in OSNs. The survey covers recent techniques, applications, and open research issues on analysis of OSN connections for identification of influential users.

MacroServ: A Route Recommendation Service for Large-Scale Evacuations

To respond to emergencies in a fast and an effective manner, it is of critical importance to have efficient evacuation plans that lead to minimum road congestions. Although emergency evacuation systems have been studied in the past, the existing approaches, mostly based on multi-objective optimizations, are not scalable enough when involve numerous time varying parameters, such as traffic volume, safety status, and weather conditions. In this paper, we propose a scalable emergency evacuation service, termed the MacroServ that recommends the evacuees with the most preferred routes towards safe locations during a disaster. Unlike many existing approaches that model systems with static network characteristics, our approach considers real-time road conditions to compute the maximum flow capacity of routes in the transportation network. The evacuees are directed towards those routes that are safe and have least congestion resulting in decreased evacuation time. We utilized probability distributions to model the real-life stochastic behaviors of evacuees during emergency scenarios. The results indicate that recommendation of appropriate routes during emergency scenarios play a critical role in quicker and safe evacuation of the population.

Segregating Spammers and Unsolicited Bloggers from Genuine Experts on Twitter

Online Social Networks (OSNs) have not only significantly reformed the social interaction pattern but have also emerged as an effective platform for recommendation of services and products. The upswing in use of the OSNs has also witnessed growth in unwanted activities on social media. On the one hand, the spammers on social media can be a high risk towards the security of legitimate users and on the other hand some of the legitimate users, such as bloggers can pollute the results of recommendation systems that work alongside the OSNs. The polluted results of recommendation systems can be precarious to the masses that track recommendations. Therefore, it is necessary to segregate such type of users from the genuine experts. We propose a framework that separates the spammers and unsolicited bloggers from the genuine experts of a specific domain. The proposed approach employs modified Hyperlink Induced Topic Search (HITS) to separate the unsolicited bloggers from the experts on Twitter on the basis of tweets. The approach considers domain specific keywords in the tweets and several tweet characteristics to identify the unsolicited bloggers. Experimental results demonstrate the effectiveness of the proposed methodology as compared to several state-of-the-art approaches and classifiers.

On the Correlation of Sensor Location and Human Activity Recognition in Body Area Networks (BANs)

Accurate recognition of patients’ physical activities leads to correct diagnosis and treatments. However, currently deployed approaches are deficient in recognizing the activities requiring frequent interposture transitions, such as jogging, jumping, turning left, and going upstairs. The reason is that with the change in position and rotation, different activity signals are generated, which are difficult to distinguish from other activities and can therefore mislead the physicians. Therefore, we propose to employ a methodology that utilizes the energy expenditure for each activity and reduces the dimensions of the feature space to differentiate among the activities. In this regard, we employ a feature descriptor called local energy-based shape histogram to preserve the maximum information of local energy. Considering the high volumes of continuously generated data, our methodology integrates cloud computing services with the body area networks. We also investigate the effects of on-body sensors’ location on the activity recognition accuracy and also identify the best sensor position for a certain activity with the maximum accuracy. We used the wearable action recognition database dataset to perform the experiments. Our analysis shows that for each activity to be recognized at a decent level, it is imperative to observe the activity recognition performance by simultaneously applying different combinations of sensors.

A Parallel Framework for Object Detection and Recognition for Secure Vehicle Parking

Image analytics, biometrics access control, security, and surveillance applications utilize complex machine learning and computer vision algorithms, such as face detection andrecognition. Speedup and accuracy are two important factors that need to be addressed in all such complex applications. Parallel computing breaks down the complex tasks into discrete fragments to be solved concurrently on multiple processors. The parallel computing procedure significantly reduces the executiontime with improved speedup. This paper presents a parallel framework for object detection and recognition for a secure vehicle parking. The proposed framework is divided in to three steps: (1) vehicle detection at the parking entry junction, (2)drivers face detection, and (3) identification of drivers face from the huge database of stored facial images. On successful identification of authorized person, vehicle is allowed to enter inthe parking-lot. The adaptive boosting algorithm is used for vehicle and face detection, while Eigenfaces based approach is employed for face recognition. Moreover, the scalability comparison of parallely executed driver face recognition algorithm indicates high speedup compared to serial execution. Furthermore, the results of the proposed framework reveal promising performance and encourage outcomes to be deployed in real-time at entrance/exits of the public/private vehicle parking areas.

Smart Data Center

Recently, the increase in demand of online applications and services that are hosted at data centers, have increased the power consumption of the data centers tremendously. In this chapter, we have addressed the problem of minimizing the operational cost of the power consumption in the data center. We have presented a new idea to power up the data center from more than one Smart grid. The algorithm titled “Smart Data center” is also presented in the chapter that minimizes the operational cost of the data center. The algorithm operates on long term and real time price markets from the local grid, low cost surplus power from the remote grid, and the backup power in the batteries of Uninterrupted Power Supply (UPS). The performance of the algorithm in analyzed using theoretical analysis.