Fuad Bajaber

Thermal-Aware Multiconstrained Intrabody QoS Routing for Wireless Body Area Networks

Wireless body area networks (WBANs) can be formed including implanted biosensors for health monitoring and diagnostic purposes. However, implanted biosensors could cause thermal damages on human tissue as it exhibits temperature rise due to wireless communication and processing tasks inside the human body. Again, Quality of Service (QoS) provisioning with multiconstraints (delay and reliability) is a striking requirement for diverse application types in WBANs to meet their objectives. This paper proposes TMQoS, a thermal-aware multiconstrained intrabody QoS routing protocol for WBANs, with the aim of ensuring the desired multiconstrained QoS demands of diverse applications along with keeping the temperature of the nodes to an acceptable level preventing thermal damages. We develop a cross-layer proactive routing framework that constructs an ongoing routing table which includes multiple shortest-path routes to address diverse QoS requirements. To avoid the packets to traverse through heated areas known as hotspot, we devise a hotspot avoidance mechanism. The route selection algorithm of TMQoS selects forwarder(s) based on the intended QoS demands of diverse traffic classes. The performance of TMQoS has been evaluated through simulation which demonstrates that the protocol achieves desired QoS demands while maintaining low temperature in the network compared to the state-of-the-art thermal-aware approaches.

Big Data 2.0 Processing Systems: Taxonomy and Open Challenges

Data is key resource in the modern world. Big data has become a popular term which is used to describe the exponential growth and availability of data. In practice, the growing demand for large-scale data processing and data analysis applications spurred the development of novel solutions from both the industry and academia. For a decade, the MapReduce framework, and its open source realization, Hadoop, has emerged as a highly successful framework that has created a lot of momentum in both the research and industrial communities such that it has become the defacto standard of big data processing platforms. However, in recent years, academia and industry have started to recognize the limitations of the Hadoop framework in several application domains and big data processing scenarios such as large scale processing of structured data, graph data and streaming data. Thus, we have witnessed an unprecedented interest to tackle these challenges with new solutions which constituted a new wave of mostly domain-specific, optimized big data processing platforms. In this article, we refer to this new wave of systems as Big Data 2.0 processing systems. To better understand the latest ongoing developments in the world of big data processing systems, we provide a taxonomy and detailed analysis of the state-of-the-art in this domain. In addition, we identify a set of the current open research challenges and discuss some promising directions for future research.

An efficient cluster-based communication protocol for wireless sensor networks

A wireless sensor network is a network of large numbers of sensor nodes, where each sensor node is a tiny device that is equipped with a processing, sensing subsystem and a communication subsystem. The critical issue in wireless sensor networks is how to gather sensed data in an energy-efficient way, so that the network lifetime can be extended. The design of protocols for such wireless sensor networks has to be energy-aware in order to extend the lifetime of the network because it is difficult to recharge sensor node batteries. We propose a protocol to form clusters, select cluster heads, select cluster senders and determine appropriate routings in order to reduce overall energy consumption and enhance the network lifetime. Our clustering protocol is called an Efficient Cluster-Based Communication Protocol (ECOMP) for Wireless Sensor Networks. In ECOMP, each sensor node consumes a small amount of transmitting energy in order to reach the neighbour sensor node in the bidirectional ring, and the cluster heads do not need to receive any sensed data from member nodes. The simulation results show that ECOMP significantly minimises energy consumption of sensor nodes and extends the network lifetime, compared with existing clustering protocol.

McMAC: Towards a MAC Protocol with Multi-Constrained QoS Provisioning for Diverse Traffic in Wireless Body Area Networks

The emergence of heterogeneous applications with diverse requirements for resource-constrained Wireless Body Area Networks (WBANs) poses significant challenges for provisioning Quality of Service (QoS) with multi-constraints (delay and reliability) while preserving energy efficiency. To address such challenges, this paper proposes McMAC, a MAC protocol with multi-constrained QoS provisioning for diverse traffic classes in WBANs. McMAC classifies traffic based on their multi-constrained QoS demands and introduces a novel superframe structure based on the “transmit-whenever-appropriate” principle, which allows diverse periods for diverse traffic classes according to their respective QoS requirements. Furthermore, a novel emergency packet handling mechanism is proposed to ensure packet delivery with the least possible delay and the highest reliability. McMAC is also modeled analytically, and extensive simulations were performed to evaluate its performance. The results reveal that McMAC achieves the desired delay and reliability guarantee according to the requirements of a particular traffic class while achieving energy efficiency.

On Designing Thermal-Aware Localized QoS Routing Protocol for in-vivo Sensor Nodes in Wireless Body Area Networks

In this paper, we address the thermal rise and Quality-of-Service (QoS) provisioning issue for an intra-body Wireless Body Area Network (WBAN) having in-vivo sensor nodes. We propose a thermal-aware QoS routing protocol, called TLQoS, that facilitates the system in achieving desired QoS in terms of delay and reliability for diverse traffic types, as well as avoids the formation of highly heated nodes known as hotspot(s), and keeps the temperature rise along the network to an acceptable level. TLQoS exploits modular architecture wherein different modules perform integrated operations in providing multiple QoS service with lower temperature rise. To address the challenges of highly dynamic wireless environment inside the human body. TLQoS implements potential-based localized routing that requires only local neighborhood information. TLQoS avoids routing loop formation as well as reduces the number of hop traversal exploiting hybrid potential, and tuning a configurable parameter. We perform extensive simulations of TLQoS, and the results show that TLQoS has significant performance improvements over state-of-the-art approaches.

Big Data Processing Systems: State-of-the-Art and Open Challenges

The growing demand for large-scale data processing and data analysis applications spurred the development of novel solutions from both the industry and academia. In the last decade, the MapReduce framework has emerged as a highly successful framework that has created a lot of momentum in both the research and industrial communities such that it has become the defacto standard of big data processing platforms. In particular, the MapReduce framework has been introduced to provide a simple but powerful programming model and runtime environment that eases the job of developing scalable parallel applications to process vast amounts of data on large clusters of commodity machines. However, recently, academia and industry have started to recognize the limitations of the Hadoop framework in several application domains such as large scale processing of structured data, graph data and streaming data. Thus, in recent years, we have witnessed an unprecedented interest to tackle these challenges which constitutes a new wave of domain-specific optimized big data processing platforms. To better understand the latest ongoing developments in the world of big data processing systems, in this paper, we provide a detailed overview and analysis of the state-of-the-art in this domain. In addition, we identify a set of the current open research challenges and discuss some promising directions for future research.

Large Scale Environmental Monitoring and Maintaining Sensing Coverage in Sensor Networks

Wireless sensor networks are capable of observing the environment, processing data, and making decisions based on these observations. They have high potential impacts in many fields, including: education, healthcare, monitoring, retail, and science. Wireless sensor networks are composed of a large number of sensor nodes with limited energy resources. There is a need to design protocols that adapt much better to the constraints of the sensor network environment. I propose a protocol called Large Scale Environmental Monitoring and Maintaining Sensing Coverage in Sensor Networks. When an event is expected to occur in an area, the application requires a minimum percentage of sensor coverage. Since the sensor nodes are deployed randomly in the sensing field, it may consist of sensor nodes with overlapping coverage areas providing redundant data. It would be desirable to save energy in some sensor nodes by allowing them to sleep, when sensor node is covered by the neighbors of sensor nodes.

Towards Differentiated Rate Control for Congestion and Hotspot Avoidance in Implantable Wireless Body Area Networks

Implantable wireless body area networks (WBANs) are gaining considerable attention to the researchers due to their high potential in healthcare applications. However, one of the biggest challenges of WBANs is the heat produced by wireless implants because of the continuous sensing of physiological parameters that could cause thermal impairment to the human tissue. Again, an implantable WBAN can be equipped with heterogeneous nodes having diverse throughput, fidelity, and latency demands. Also, un-controlled traffic reporting rate could cause high contention as well as congestion in nodes, which are usually organized forming a many-to-one routing paradigm in a WBAN. The problem of congestion not only restrains in satisfying the desired QoS requirements of the diverse healthcare applications, but also increases the dissipated energy and the temperature of an implant biosensor. This paper proposes a novel rate control mechanism with the aim of providing a unified solution for both congestion and hotspot avoidance in an implantable WBAN. The proposed scheme also presents a scheduling rate allocation mechanism reflecting the relative priority of biosensors. The performance of the protocol is evaluated using simulations, which demonstrate that the proposed protocol maintains lower temperature rise as well as avoid the creation of hotspot(s). The results also indicate that the proposed protocol significantly improves the performance of healthcare applications in terms of throughput, reliability, latency, as well as energy consumption.

A Cloud-Based Platform for Democratizing and Socializing the Benchmarking Process

Performances evaluation, benchmarking and re-producibility represent significant aspects for evaluating the practical impact of scientific research outcomes in the Computer Science field. In spite of all the benefits (e.g., increasing visibility, boosting impact, improving the research quality) which can be obtained from conducting comprehensive and extensive experi-mental evaluations or providing reproducible software artifacts and detailed description of experimental setup, the required effort for achieving these goals remains prohibitive. In this article, we present the design and the implementation details of the Liquid Benchmarking platform as a social and cloud-based platform for democratizing and socializing the software benchmarking processes. Particularly, the platform facilitates the process of sharing the experimental artifacts (computing resources, datasets, software implementations, benchmarking tasks) as services where the end users can easily design, mashup, execute the experiments and visualize the experimental results with zero installation or configuration efforts. Moreover, the social features of the platform enable the users to share and provide feedback on the results of the executed experiments in a form that can guarantee a transparent scientific crediting process. Finally, we present four benchmarking case studies that have been realized via the Liquid Benchmarking platform in the following domains: XML compression techniques, graph indexing and querying techniques, string similarity join algorithms and reverse K nearest neighbors algorithms.

A novel approach of data sanitization by noise addition and knowledge discovery by clustering

Security of published data cannot be less important as compared to unpublished data or the data which is not made public. Therefore, PII (Personally Identifiable Information) is removed and data sanitized when organizations recording large volumes of data publish that data. However, this approach of ensuring data privacy and security can result in loss of utility of that published data for knowledge discovery. Therefore, a balance is required between privacy and the utility needs of published data. In this paper we study this delicate balance by evaluating four data mining clustering techniques for knowledge discovery and propose two privacy/utility quantification parameters. We subsequently perform number of experiments to statistically identify which clustering technique is best suited with desirable level of privacy/utility while noise is incrementally increased by simultaneously degrading data accuracy, completeness and consistency.