Contention energy-aware real-time task mapping on NoC based heterogeneous MPSoCs

© 2018 IEEE. Network-on-Chip (NoC)-based multiprocessor system-on-chips (MPSoCs) are becoming the de-facto computing platform for computationally intensive real-time applications in the embedded systems due to their high performance, exceptional quality-of-service (QoS) and energy efficiency over superscalar uniprocessor architectures. Energy saving is important in the embedded system because it reduces the operating cost while prolongs lifetime and improves the reliability of the system. In this paper, contention-aware energy efficient static mapping using NoC-based heterogeneous MPSoC for real-time tasks with an individual deadline and precedence constraints is investigated. Unlike other schemes task ordering, mapping, and voltage assignment are performed in an integrated manner to minimize the processing energy while explicitly reduce contention between the communications and communication energy. Furthermore, both dynamic voltage and frequency scaling and dynamic power management are used for energy consumption optimization. The developed contention-aware integrated task mapping and voltage assignment (CITM-VA) static energy management scheme performs tasks ordering using earliest latest finish time first (ELFTF) strategy that assigns priorities to the tasks having shorter latest finish time (LFT) over the tasks with longer LFT. It remaps every task to a processor and/or discrete voltage level that reduces processing energy consumption. Similarly, the communication energy is minimized by assigning discrete voltage levels to the NoC links. Further, total energy efficiency is achieved by putting the processor into a low-power state when feasible. Moreover, this approach resolves the contention between communications that traverse the same link by allocating links to communications with higher priority. The results obtained through extensive simulations of real-world benchmarks demonstrate that CITM-VA approach outperforms state-of-the-art technique and achieves an average 30% total energy improvement. Additionally, it maintains high QoS and robustness for real-time applications

Self-adaptive mobile web service discovery framework for dynamic mobile environment

The advancement in mobile technologies has undoubtedly turned mobile web service (MWS) into a significant computing resource in a dynamic mobile environment (DME). The discovery is one of the critical stages in the MWS life cycle to identify the most relevant MWS for a particular task as per the request's context needs. While the traditional service discovery frameworks that assume the world is static with predetermined context are constrained in DME, the adaptive solutions show potential. Unfortunately, the effectiveness of these frameworks is plagued by three problems. Firstly, the coarse-grained MWS categorization approach that fails to deal with the proliferation of functionally similar MWS. Secondly, context models constricted by insufficient expressiveness and inadequate extensibility confound the difficulty in describing the DME, MWS, and the user’s MWS needs. Thirdly, matchmaking requires manual adjustment and disregard context information that triggers self-adaptation, leading to the ineffective and inaccurate discovery of relevant MWS. Therefore, to address these challenges, a self-adaptive MWS discovery framework for DME comprises an enhanced MWS categorization approach, an extensible meta-context ontology model, and a self-adaptive MWS matchmaker is proposed. In this research, the MWS categorization is achieved by extracting the goals and tags from the functional description of MWS and then subsuming k-means in the modified negative selection algorithm (M-NSA) to create categories that contain similar MWS. The designing of meta-context ontology is conducted using the lightweight unified process for ontology building (UPON-Lite) in collaboration with the feature-oriented domain analysis (FODA). The self-adaptive MWS matchmaking is achieved by enabling the self-adaptive matchmaker to learn MWS relevance using a Modified-Negative Selection Algorithm (M-NSA) and retrieve the most relevant MWS based on the current context of the discovery. The MWS categorization approach was evaluated, and its impact on the effectiveness of the framework is assessed. The meta-context ontology was evaluated using case studies, and its impact on the service relevance learning was assessed. The proposed framework was evaluated using a case study and the ProgrammableWeb dataset. It exhibits significant improvements in terms of binary relevance, graded relevance, and statistical significance, with the highest average precision value of 0.9167. This study demonstrates that the proposed framework is accurate and effective for service-based application designers and other MWS clients

Security and blockchain convergence with internet of multimedia things : current trends, research challenges and future directions

The Internet of Multimedia Things (IoMT) orchestration enables the integration of systems, software, cloud, and smart sensors into a single platform. The IoMT deals with scalar as well as multimedia data. In these networks, sensor-embedded devices and their data face numerous challenges when it comes to security. In this paper, a comprehensive review of the existing literature for IoMT is presented in the context of security and blockchain. The latest literature on all three aspects of security, i.e., authentication, privacy, and trust is provided to explore the challenges experienced by multimedia data. The convergence of blockchain and IoMT along with multimedia-enabled blockchain platforms are discussed for emerging applications. To highlight the significance of this survey, large-scale commercial projects focused on security and blockchain for multimedia applications are reviewed. The shortcomings of these projects are explored and suggestions for further improvement are provided. Based on the aforementioned discussion, we present our own case study for healthcare industry: a theoretical framework having security and blockchain as key enablers. The case study reflects the importance of security and blockchain in multimedia applications of healthcare sector. Finally, we discuss the convergence of emerging technologies with security, blockchain and IoMT to visualize the future of tomorrow's applications. © 2020 Elsevier Lt

A Reinforcement Learning Quality of Service Negotiation Framework For IoT Middleware

The Internet of Things (IoT) ecosystem is characterised by heterogeneous devices dynamically interacting with each other to perform a specific task, often without human intervention. This interaction typically occurs in a service-oriented manner and is facilitated by an IoT middleware. The service provision paradigm enables the functionalities of IoT devices to be provided as IoT services to perform actuation tasks in critical-safety systems such as autonomous, connected vehicle system and industrial control systems. As IoT systems are increasingly deployed into an environment characterised by continuous changes and uncertainties, there have been growing concerns on how to resolve the Quality of Service (QoS) contentions between heterogeneous devices with conflicting preferences to guarantee the execution of mission-critical actuation tasks. With IoT devices with different QoS constraints as IoT service providers spontaneously interacts with IoT service consumers with varied QoS requirements, it becomes essential to find the best way to establish and manage the QoS agreement in the middleware as a compromise in the QoS could lead to negative consequences. This thesis presents a QoS negotiation framework, IoTQoSystem, for IoT service-oriented middleware. The QoS framework is underpinned by a negotiation process that is modelled as a Markov Decision Process (MDP). A model-based Reinforcement Learning negotiation strategy is proposed for generating an acceptable QoS solution in a dynamic, multilateral and multi-parameter scenarios. A microservice-oriented negotiation architecture is developed that combines negotiation, monitoring and forecasting to provide a self-managing mechanism for ensuring the successful execution of actuation tasks in an IoT environment. Using a case study, the developed QoS negotiation framework was evaluated using real-world data sets with different negotiation scenarios to illustrate its scalability, reliability and performance

Sentiment analysis as a service

This research focuses on the design and development of a service composition based framework that enables the execution of services for social media based sentiment analysis. Our research develops novel analytical models, composition techniques and algorithms which use services as a mean for sentiment abstraction, processing and analysis from large scale social media data. Current sentiment analysis techniques require specialized skill of data science and machine learning. Moreover, traditional approaches rely on laborious and time-consuming activities such as manual dataset labelling, data model training and validation. This makes overall sentiment analysis process a challenging task. In comparison, services are `ready-made' software solutions that can be composed on-demand for developing complex applications without indulging in the domain specific details. This thesis investigates a novel approach that transforms traditional social media based sentiment analysis process into a service composition driven solution. In this thesis, we begin by developing a novel service framework that replaces the traditional sentiment analysis tasks with online services. Our framework includes a new service model to present services required for sentiment analysis. We develop a semantic service composition model and algorithm that dynamically composes various services for data collection, noise filtering and sentiment extraction. In particular, we focus on abstracting sentiment based on location and time. We then focus on enhancing the flexibility of our proposed service framework to compose appropriate sentiment analysis services for highly dynamic and changing features of social media platforms. In addition, we aim to efficiently process and analyze large scale social media data. In order to enhance our service composition framework, we propose a novel approach to formalize social media platforms as cloud enabled services. We develop a functional and quality of service (QoS) model that captures various dynamic features of social media platforms. In addition, we devise a cloud based service model to access social media platforms as services by using the Ontology Web Language for Service (OWL-S). Secondly, we integrate the QoS model into our existing composition framework. It enables our framework to dynamically assess the QoS of multiple social media platforms, and simultaneously compose appropriate services to extract, process, analyze and integrate the sentiment results from large scale data. Finally, we concentrate on efficient utilization of the sentiment analysis extracted from large scale data. We formulate a meta-information composition model that transforms and stores sentiment obtained from large streams of data into re-usable information. Later, the re-usable information is on-demand integrated and delivered to end users. To demonstrate the performance and test the effectiveness of our proposed models, we develop prototypes to evaluate our composition framework. We design several scenarios and conduct a series of experiments using real-world social media datasets. We present the results and discuss the outcomes which validate the performance of our research