A Taxonomy of Self-configuring Service Discovery Systems

We analyze the fundamental concepts and issues in service discovery. This analysis places service discovery in the context of distributed systems by describing service discovery as a third generation naming system. We also describe the essential architectures and the functionalities in service discovery. We then proceed to show how service discovery fits into a system, by characterizing operational aspects. Subsequently, we describe how existing state of the art performs service discovery, in relation to the operational aspects and functionalities, and identify areas for improvement

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Multi-Layer Cyber-Physical Security and Resilience for Smart Grid

The smart grid is a large-scale complex system that integrates communication technologies with the physical layer operation of the energy systems. Security and resilience mechanisms by design are important to provide guarantee operations for the system. This chapter provides a layered perspective of the smart grid security and discusses game and decision theory as a tool to model the interactions among system components and the interaction between attackers and the system. We discuss game-theoretic applications and challenges in the design of cross-layer robust and resilient controller, secure network routing protocol at the data communication and networking layers, and the challenges of the information security at the management layer of the grid. The chapter will discuss the future directions of using game-theoretic tools in addressing multi-layer security issues in the smart grid.Comment: 16 page

Multicast Scheduling and Resource Allocation Algorithms for OFDMA-Based Systems: A Survey

Multicasting is emerging as an enabling technology for multimedia transmissions over wireless networks to support several groups of users with flexible quality of service (QoS)requirements. Although multicast has huge potential to push the limits of next generation communication systems; it is however one of the most challenging issues currently being addressed. In this survey, we explain multicast group formation and various forms of group rate determination approaches. We also provide a systematic review of recent channel-aware multicast scheduling and resource allocation (MSRA) techniques proposed for downlink multicast services in OFDMA based systems. We study these enabling algorithms, evaluate their core characteristics, limitations and classify them using multidimensional matrix. We cohesively review the algorithms in terms of their throughput maximization, fairness considerations, performance complexities, multi-antenna support, optimality and simplifying assumptions. We discuss existing standards employing multicasting and further highlight some potential research opportunities in multicast systems

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

A quantitative comparison of load balancing approaches in distributed object computing systems

Several load balancing schemes are recently proposed for distributed object computing systems, which are widely envisioned to be the desired distributed software development paradigm due to the higher modularity and the capability of handling machine and operating system heterogeneity. However, while the rationales and mechanisms employed are dramatically different, the relative strengths and weaknesses of these approaches are unknown, making it difficult for a practitioner to choose an appropriate approach for the problem at hand. In this paper, we describe in detail three representative approaches, which are all practicable, and present a quantitative comparison using our experimental distributed object computing platform. Among these three approaches, namely, JavaSpaces based, request redirection based, and fuzzy decision based, we find that the fuzzy decision based algorithm outperforms the other two considerably.published_or_final_versio

Influence Analysis towards Big Social Data

Large scale social data from online social networks, instant messaging applications, and wearable devices have seen an exponential growth in a number of users and activities recently. The rapid proliferation of social data provides rich information and infinite possibilities for us to understand and analyze the complex inherent mechanism which governs the evolution of the new technology age. Influence, as a natural product of information diffusion (or propagation), which represents the change in an individual’s thoughts, attitudes, and behaviors resulting from interaction with others, is one of the fundamental processes in social worlds. Therefore, influence analysis occupies a very prominent place in social related data analysis, theory, model, and algorithms. In this dissertation, we study the influence analysis under the scenario of big social data. Firstly, we investigate the uncertainty of influence relationship among the social network. A novel sampling scheme is proposed which enables the development of an efficient algorithm to measure uncertainty. Considering the practicality of neighborhood relationship in real social data, a framework is introduced to transform the uncertain networks into deterministic weight networks where the weight on edges can be measured as Jaccard-like index. Secondly, focusing on the dynamic of social data, a practical framework is proposed by only probing partial communities to explore the real changes of a social network data. Our probing framework minimizes the possible difference between the observed topology and the actual network through several representative communities. We also propose an algorithm that takes full advantage of our divide-and-conquer strategy which reduces the computational overhead. Thirdly, if let the number of users who are influenced be the depth of propagation and the area covered by influenced users be the breadth, most of the research results are only focused on the influence depth instead of the influence breadth. Timeliness, acceptance ratio, and breadth are three important factors that significantly affect the result of influence maximization in reality, but they are neglected by researchers in most of time. To fill the gap, a novel algorithm that incorporates time delay for timeliness, opportunistic selection for acceptance ratio, and broad diffusion for influence breadth has been investigated. In our model, the breadth of influence is measured by the number of covered communities, and the tradeoff between depth and breadth of influence could be balanced by a specific parameter. Furthermore, the problem of privacy preserved influence maximization in both physical location network and online social network was addressed. We merge both the sensed location information collected from cyber-physical world and relationship information gathered from online social network into a unified framework with a comprehensive model. Then we propose the resolution for influence maximization problem with an efficient algorithm. At the same time, a privacy-preserving mechanism are proposed to protect the cyber physical location and link information from the application aspect. Last but not least, to address the challenge of large-scale data, we take the lead in designing an efficient influence maximization framework based on two new models which incorporate the dynamism of networks with consideration of time constraint during the influence spreading process in practice. All proposed problems and models of influence analysis have been empirically studied and verified by different, large-scale, real-world social data in this dissertation

Optimal epidemic information dissemination in uncertain dynamic environment

Optimization of stochastic epidemic information dissemination plays a significant role in enhancing the reliability of epidemic networks. This letter proposes a multi-stage decision making optimization model for stochastic epidemic information dissemination based on dynamic programming, in which uncertainties in a dynamic environment are taken into account. We model the inherent bimodal dynamics of general epidemic mechanisms as a Markov chain, and a state transition equation is proposed based on this Markov chain. We further derive optimal policies and a theoretical closed-form expression for the maximal expected number of successfully delivered messages. The properties of the derived model are theoretically analyzed. Simulation results show an improvement in reliability, in terms of accumulative number of successfully delivered messages, of epidemic information dissemination in stochastic situations