Assad Abbas

A taxonomy and survey on Green Data Center Networks

Abstract Data centers are growing exponentially (in number and size) to accommodate the escalating user and application demands. Likewise, the concerns about the environmental impacts, energy needs, and electricity cost of data centers are also growing. Network infrastructure being the communication backbone of the data center plays a pivotal role in the data center’s scalability, performance, energy consumption, and cost. Research community is endeavoring hard to overcome the challenges faced by the legacy Data Center Networks (DCNs). Serious efforts have been made to handle the problems in various DCN areas. This survey presents significant insights to the state-of-the-art research conducted pertaining to the DCN domain along with a detailed discussion of the energy efficiency aspects of the DCNs. The authors explored: (a) DCN architectures (electrical, optical, and hybrid), (b) network traffic management and characterization, (c) DCN performance monitoring, (d) network-aware resource allocation, (e) DCN experimentation techniques, and (f) energy efficiency. The survey presents an overview of the ongoing research in the broad domain of DCNs and highlights the challenges faced by the DCN research community.

A survey on context-aware recommender systems based on computational intelligence techniques

The demand for ubiquitous information processing over the Web has called for the development of context-aware recommender systems capable of dealing with the problems of information overload and information filtering. Contemporary recommender systems harness context-awareness with the personalization to offer the most accurate recommendations about different products, services, and resources. However, such systems come across the issues, such as sparsity, cold start, and scalability that lead to imprecise recommendations. Computational Intelligence (CI) techniques not only improve recommendation accuracy but also substantially mitigate the aforementioned issues. Large numbers of context-aware recommender systems are based on the CI techniques, such as: (a) fuzzy sets, (b) artificial neural networks, (c) evolutionary computing, (d) swarm intelligence, and (e) artificial immune systems. This survey aims to encompass the state-of-the-art context-aware recommender systems based on the CI techniques. Taxonomy of the CI techniques is presented and challenges particular to the context-aware recommender systems are also discussed. Moreover, the ability of each of the CI techniques to deal with the aforesaid challenges is also highlighted. Furthermore, the strengths and weaknesses of each of the CI techniques used in context-aware recommender systems are discussed and a comparison of the techniques is also presented.

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.

Big Data Reduction Methods: A Survey

Abstract Research on big data analytics is entering in the new phase called fast data where multiple gigabytes of data arrive in the big data systems every second. Modern big data systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity in the acquired data and consequently give rise to the 6Vs of big data. The reduced and relevant data streams are perceived to be more useful than collecting raw, redundant, inconsistent, and noisy data. Another perspective for big data reduction is that the million variables big datasets cause the curse of dimensionality which requires unbounded computational resources to uncover actionable knowledge patterns. This article presents a review of methods that are used for big data reduction. It also presents a detailed taxonomic discussion of big data reduction methods including the network theory, big data compression, dimension reduction, redundancy elimination, data mining, and machine learning methods. In addition, the open research issues pertinent to the big data reduction are also highlighted.

e-Health Cloud: Privacy Concerns and Mitigation Strategies

Cloud based solutions have permeated in the healthcare domain due to a broad range of benefits offered by the cloud computing. Besides the financial advantages to the healthcare organizations, cloud computing also offers large-scale and on-demand storage and processing services to various entities of the cloud based health ecosystem. However, outsourcing the sensitive health information to the third-party cloud providers can result in serious privacy concerns. This chapter highlights the privacy issues related to the health data and also presents privacy preserving requirements. Besides the benefits of the cloud computing in healthcare, cloud computing deployment models are also discussed from the perspective of healthcare systems. Moreover, some recently developed strategies to mitigate the privacy concerns and to fulfil the privacy preserving requirements are also discussed in detail. Furthermore, strengths and weaknesses of each of the presented strategies are reported and some open issues for the future research are also presented.

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.

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.

Handbook of Large-Scale Distributed Computing in Smart Healthcare

Conventional healthcare services have seamlessly been integrated with pervasive computing paradigm and consequently cost-effective and dependable smart healthcare services and systems have emerged. Currently, the smart healthcare systems use Body Area Networks (BANs) and wearable devices for pervasive health monitoring and Ambient Assisted Living. The BANs use smartphones and several handheld devices to ensure ubiquitous access to the healthcare information and services. However, due to the intrinsic architectural limitations in terms of CPU speed, storage, and memory, the mobile and other computing devices seem inadequate to handle huge volumes of sensor data being generated unceasingly. In addition, the sensor data is highly complex and multi-dimensional. Therefore, integrating the BANs with large-scale and distributed computing paradigms, such as the cloud, cluster, and grid computing is inevitable to handle the processing and storage needs. Moreover, the contemporary research efforts mostly focus on health information delivery methods to ensure the information exchange within a single BAN. Consequently, the efforts have been very limited in interconnecting several BANs remotely through the servers.