Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things

The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad RTI2018-098062-A-I00European Union’s Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence

A resilient approach for distributed MPC-based economic dispatch in interconnected microgrids

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Economic dispatch of interconnected microgrids that is based on distributed model predictive control (DMPC) requires the cooperation of all agents (microgrids). This paper discusses the case in which some of the agents might not comply with the decisions computed by performing a DMPC algorithm. In this regard, these agents could obtain a better performance at the cost of degrading the performance of the network as a whole. A resilient distributed method that can deal with such issues is proposed and studied in this paper. The method consists of two parts. The first part is to ensure that the decisions obtained from the algorithm are robustly feasible against most of the attacks with high confidence. In this part, we employ a two-step randomization-based approach to obtain a feasible solution with a predefined level of confidence. The second part consists in the identification and mitigation of the adversarial agents, which utilizes hypothesis testing with Bayesian inference and requires each agent to solve a mixed-integer problem to decide the connections with its neighbors. In addition, an analysis of the decisions computed using the stochastic approach and the outcome of the identification and mitigation method is provided. The performance of the proposed approach is also shown through numerical simulations.Peer ReviewedPostprint (author's final draft

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy

Assessment of Cyber Risks in an IoT-based Supply Chain using a Fuzzy Decision-Making Method

Purpose: The Internet of Things (IoT) is a relatively new paradigm that is growing rapidly in modern wireless communication scenarios. The main idea of this concept is the pervasive presence of all kinds of objects around us. This technology is the basis of today's intelligent life and is known as one of the most important sources of big data. Meanwhile, businesses are no exception to this rule and try to use the Internet of Things to make their business smarter. Supply chain management is a goal-based goal of linking business operations to provide a common view of market opportunity. Methodology: Using IoT technology, all major parts of the supply chain, including supply, production, distribution and sales, can be affected. Because this evolutionary technology is intertwined with Internet technology, the use of network-based tools can always create risks for business owners who use these technologies. Therefore, understanding and investigating a variety of cyber risks in this area can It is very important and by understanding their hands, we can prevent many future risks. Linear analysis based on hierarchical analysis is used. Findings: The results show that privacy is very important in interaction with suppliers as well as customers, and therefore those effective measures to deal with these risks can reduce many of the problems caused by this technology. Originality/Value: This paper attend to assessment of cyber risks in an IoT-based supply chain using a fuzzy decision-making method

Division of Research and Economic Development Monthly Report for June 2014

The monthly reports include statistics for project proposals and awards received by individual academic and administrative departments at the University of Rhode Island

Oblivious Network Optimization and Security Modeling in Sustainable Smart Grids and Cities

Today\u27s interconnected world requires an inexpensive, fast, and reliable way of transferring information. There exists an increasingly important need for intelligent and adaptable routing of network flows. In the last few years, many researchers have worked toward developing versatile solutions to the problem of routing network flows in unpredictable circumstances. These attempts have evolved into a rich literature in the area of oblivious network design which typically route the network flows via a routing scheme that makes use of a spanning tree or a set of trees of the graph representation of the network. In the first chapter, we provide an introduction to network design. This introductory chapter has been designed to clarify the importance and position of oblivious routing problems in the context of network design as well as its containing field of research. Part I of this dissertation discusses the fundamental role of linked hierarchical data structures in providing the mathematical tools needed to construct rigorous versatile routing schemes and applies hierarchical routing tools to the process of constructing versatile routing schemes. Part II of this dissertation applies the routing tools generated in Part I to address real-world network optimization problems in the area of electrical power networks, clusters of micrograms, and content-centric networks. There is an increasing concern regarding the security and privacy of both physical and communication layers of smart interactive customer-driven power networks, better known as smart grids. Part III of this dissertation utilizes an advanced interdisciplinary approach to address existing security and privacy issues, proposing legitimate countermeasures for each of them from the standpoint of both computing and electrical engineering. The proposed methods are theoretically proven by mathematical tools and illustrated by real-world examples