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

MEC vs MCC: performance analysis of real-time applications

Hoje em dia, numerosas são as aplicações que apresentam um uso intensivo de recursos empurrando os requisitos computacionais e a demanda de energia dos dispositivos para além das suas capacidades. Atentando na arquitetura Mobile Cloud, que disponibiliza plataformas funcionais e aplicações emergentes (como Realidade Aumentada (AR), Realidade Virtual (VR), jogos online em tempo real, etc.), são evidentes estes desafios directamente relacionados com a latência, consumo de energia, e requisitos de privacidade. O Mobile Edge Computing (MEC) é uma tecnologia recente que aborda os obstáculos de desempenho enfrentados pela Mobile Cloud Computing (MCC), procurando solucioná-los O MEC aproxima as funcionalidades de computação e de armazenamento da periferia da rede. Neste trabalho descreve-se a arquitetura MEC assim como os principais tipos soluções para a sua implementação. Apresenta-se a arquitetura de referência da tecnologia cloudlet e uma comparação com o modelo de arquitetura ainda em desenvolvimento e padronização pelo ETSI. Um dos propósitos do MEC é permitir remover dos dispositivos tarefas intensivas das aplicações para melhorar a computação, a capacidade de resposta e a duração da bateria dos dispositivos móveis. O objetivo deste trabalho é estudar, comparar e avaliar o desempenho das arquiteturas MEC e MCC para o provisionamento de tarefas intensivas de aplicações com uso intenso de computação. Os cenários de teste foram configurados utilizando esse tipo de aplicações em ambas as implementações de MEC e MCC. Os resultados do teste deste estudo permitem constatar que o MEC apresenta melhor desempenho do que o MCC relativamente à latência e à qualidade de experiência do utilizador. Além disso, os resultados dos testes permitem quantificar o benefício efetivo tecnologia MEC.Numerous applications, such as Augmented Reality (AR), Virtual Reality (VR), real-time online gaming are resource-intensive applications and consequently, are pushing the computational requirements and energy demands of the mobile devices beyond their capabilities. Despite the fact that mobile cloud architecture has practical and functional platforms, these new emerging applications present several challenges regarding latency, energy consumption, context awareness, and privacy enhancement. Mobile Edge Computing (MEC) is a new resourceful and intermediary technology, that addresses the performance hurdles faced by Mobile Cloud Computing (MCC), and brings computing and storage closer to the network edge. This work introduces the MEC architecture and some of edge computing implementations. It presents the reference architecture of the cloudlet technology and provides a comparison with the architecture model that is under standardization by ETSI. MEC can offload intensive tasks from applications to enhance computation, responsiveness and battery life of the mobile devices. The objective of this work is to study and evaluate the performance of MEC and MCC architectures for provisioning offload intensive tasks from compute-intensive applications. Test scenarios were set up with use cases with this kind of applications for both MEC and MCC implementations. The test results of this study enable to support evidence that the MEC presents better performance than cloud computing regarding latency and user quality of experience. Moreover, the results of the tests enable to quantify the effective benefit of the MEC approach

Multi-Access Edge Computing: Open issues, Challenges and Future Perspective

Latency minimization is a pivotal aspect in provision of real time services while adhering to Quality of Experience (QoE) parameters for assuring spectral efficiency. Edge Cloud Computing, being a potential research dimension in the realm of 5G networks, targets to enhance the network efficiency by harnessing activeness of both cloud computing and mobile devices in user's proximity. Keeping in view the far ranging impact of Edge Cloud Computing in future mobile generations, a comprehensive review of the prevalent Edge Cloud Computing frameworks and approaches is presented with a detailed comparison of its classifications through various QoS metrics (pertinent to network performance and overheads associated with deployment/migration). Considering the knowledge accumulated, procedures analysed and theories discussed, the paper provides a comprehensive overview on sate-of-the-art and future research directions for multi-access mobile edge computing

New Waves of IoT Technologies Research – Transcending Intelligence and Senses at the Edge to Create Multi Experience Environments

The next wave of Internet of Things (IoT) and Industrial Internet of Things (IIoT) brings new technological developments that incorporate radical advances in Artificial Intelligence (AI), edge computing processing, new sensing capabilities, more security protection and autonomous functions accelerating progress towards the ability for IoT systems to self-develop, self-maintain and self-optimise. The emergence of hyper autonomous IoT applications with enhanced sensing, distributed intelligence, edge processing and connectivity, combined with human augmentation, has the potential to power the transformation and optimisation of industrial sectors and to change the innovation landscape. This chapter is reviewing the most recent advances in the next wave of the IoT by looking not only at the technology enabling the IoT but also at the platforms and smart data aspects that will bring intelligence, sustainability, dependability, autonomy, and will support human-centric solutions.acceptedVersio

MEC vs MCC: performance analysis of interactive and real-time applications

A evolução das redes de telecomunicações tem promovido o desenvolvimento de novas aplicações para dispositivos móveis. Algumas destas aplicações exigem requisitos computacionais e energéticos que vão para além das capacidades dos dispositivos móveis. Neste contexto, pode ser utilizada a arquitetura Mobile Cloud Computing (MCC), que permite executar as aplicações em datacenters na cloud e aliviar o processamento nos dispositivos móveis. No entanto, algumas aplicações mais exigentes, e.g. interativas e de tempo real, são mais sensíveis ao atraso no processamento e comunicação da informação. Para estas aplicações, a arquitetura Mobile Edge Computing (MEC) pode ser utilizada como uma tecnologia intermédia que disponibiliza recursos computacionais e de armazenamento a partir da periferia da rede. Este artigo apresenta um estudo que avalia o desempenho das arquiteturas MCC e MEC na execução de duas aplicações tomadas como representativas do espectro das aplicações interativas, de tempo real e de processamento intensivo: o Fluid e o FaceSwap. São apresentados resultados que permitem quantificar o desempenho destas arquiteturas em diferentes circunstâncias.Telecommunication networks evolution is driving the development of new applications for mobile devices. Some of these applications are resource-intensive and push computational and energy demands of mobile devices beyond the mobile hardware capabilities. In this context, Mobile Cloud Computing (MCC) architecture emerges as a solution for offloading mobile devices that allows to execute these applications in cloud datacenters thus reducing the processing demand in mobile devices. However, more demanding applications, e.g. interactive and real-time applications, are sensitive to processing and communications delay. For these applications, Mobile Edge Computing (MEC) can be used as an intermediary technology, providing computing and storage resources in the network edge. This paper presents a study carried out to evaluate the performance of MEC and MCC architectures when executing two applications, Fluid and FaceSwap, representative of real time and computing intensive applications. A set of scenarios were designed to quantify the performance of these architectures in different settings.info:eu-repo/semantics/publishedVersio

New Secure IoT Architectures, Communication Protocols and User Interaction Technologies for Home Automation, Industrial and Smart Environments

Programa Oficial de Doutoramento en Tecnoloxías da Información e das Comunicacións en Redes Móbiles. 5029V01Tese por compendio de publicacións[Abstract] The Internet of Things (IoT) presents a communication network where heterogeneous physical devices such as vehicles, homes, urban infrastructures or industrial machinery are interconnected and share data. For these communications to be successful, it is necessary to integrate and embed electronic devices that allow for obtaining environmental information (sensors), for performing physical actuations (actuators) as well as for sending and receiving data (network interfaces). This integration of embedded systems poses several challenges. It is needed for these devices to present very low power consumption. In many cases IoT nodes are powered by batteries or constrained power supplies. Moreover, the great amount of devices needed in an IoT network makes power e ciency one of the major concerns of these deployments, due to the cost and environmental impact of the energy consumption. This need for low energy consumption is demanded by resource constrained devices, con icting with the second major concern of IoT: security and data privacy. There are critical urban and industrial systems, such as tra c management, water supply, maritime control, railway control or high risk industrial manufacturing systems such as oil re neries that will obtain great bene ts from IoT deployments, for which non-authorized access can posse severe risks for public safety. On the other hand, both these public systems and the ones deployed on private environments (homes, working places, malls) present a risk for the privacy and security of their users. These IoT deployments need advanced security mechanisms, both to prevent access to the devices and to protect the data exchanged by them. As a consequence, it is needed to improve two main aspects: energy e ciency of IoT devices and the use of lightweight security mechanisms that can be implemented by these resource constrained devices but at the same time guarantee a fair degree of security. The huge amount of data transmitted by this type of networks also presents another challenge. There are big data systems capable of processing large amounts of data, but with IoT the granularity and dispersion of the generated information presents a new scenario very di erent from the one existing nowadays. Forecasts anticipate that there will be a growth from the 15 billion installed devices in 2015 to more than 75 billion devices in 2025. Moreover, there will be much more services exploiting the data produced by these networks, meaning the resulting tra c will be even higher. The information must not only be processed in real time, but data mining processes will have to be performed to historical data. The main goal of this Ph.D. thesis is to analyze each one of the previously described challenges and to provide solutions that allow for an adequate adoption of IoT in Industrial, domestic and, in general, any scenario that can obtain any bene t from the interconnection and exibility that IoT brings.[Resumen] La internet de las cosas (IoT o Internet of Things) representa una red de intercomunicaciones en la que participan dispositivos físicos de toda índole, como vehículos, viviendas, electrodomésticos, infraestructuras urbanas o maquinaria y dispositivos industriales. Para que esta comunicación se pueda llevar a cabo es necesario integrar elementos electr onicos que permitan obtener informaci on del entorno (sensores), realizar acciones f sicas (actuadores) y enviar y recibir la informaci on necesaria (interfaces de comunicaciones de red). La integración y uso de estos sistemas electrónicos embebidos supone varios retos. Es necesario que dichos dispositivos presenten un consumo reducido. En muchos casos deberían ser alimentados por baterías o fuentes de alimentación limitadas. Además, la gran cantidad de dispositivos que involucra la IoT hace necesario que la e ciencia energética de los mismos sea una de las principales preocupaciones, por el coste e implicaciones medioambientales que supone el consumo de electricidad de los mismos. Esta necesidad de limitar el consumo provoca que dichos dispositivos tengan unas prestaciones muy limitadas, lo que entra en conflicto con la segunda mayor preocupación de la IoT: la seguridad y privacidad de los datos. Por un lado existen sistemas críticos urbanos e industriales, como puede ser la regulación del tráfi co, el control del suministro de agua, el control marítimo, el control ferroviario o los sistemas de producción industrial de alto riesgo, como refi nerías, que son claros candidatos a benefi ciarse de la IoT, pero cuyo acceso no autorizado supone graves problemas de seguridad ciudadana. Por otro lado, tanto estos sistemas de naturaleza publica, como los que se desplieguen en entornos privados (viviendas, entornos de trabajo o centros comerciales, entre otros) suponen un riesgo para la privacidad y también para la seguridad de los usuarios. Todo esto hace que sean necesarios mecanismos de seguridad avanzados, tanto de acceso a los dispositivos como de protección de los datos que estos intercambian. En consecuencia, es necesario avanzar en dos aspectos principales: la e ciencia energética de los dispositivos y el uso de mecanismos de seguridad e ficientes, tanto computacional como energéticamente, que permitan la implantación de la IoT sin comprometer la seguridad y la privacidad de los usuarios. Por otro lado, la ingente cantidad de información que estos sistemas puede llegar a producir presenta otros dos retos que deben ser afrontados. En primer lugar, el tratamiento y análisis de datos toma una nueva dimensión. Existen sistemas de big data capaces de procesar cantidades enormes de información, pero con la internet de las cosas la granularidad y dispersión de los datos plantean un escenario muy distinto al actual. La previsión es pasar de 15.000.000.000 de dispositivos instalados en 2015 a más de 75.000.000.000 en 2025. Además existirán multitud de servicios que harán un uso intensivo de estos dispositivos y de los datos que estos intercambian, por lo que el volumen de tráfico será todavía mayor. Asimismo, la información debe ser procesada tanto en tiempo real como a posteriori sobre históricos, lo que permite obtener información estadística muy relevante en diferentes entornos. El principal objetivo de la presente tesis doctoral es analizar cada uno de estos retos (e ciencia energética, seguridad, procesamiento de datos e interacción con el usuario) y plantear soluciones que permitan una correcta adopción de la internet de las cosas en ámbitos industriales, domésticos y en general en cualquier escenario que se pueda bene ciar de la interconexión y flexibilidad de acceso que proporciona el IoT.[Resumo] O internet das cousas (IoT ou Internet of Things) representa unha rede de intercomunicaci óns na que participan dispositivos físicos moi diversos, coma vehículos, vivendas, electrodomésticos, infraestruturas urbanas ou maquinaria e dispositivos industriais. Para que estas comunicacións se poidan levar a cabo é necesario integrar elementos electrónicos que permitan obter información da contorna (sensores), realizar accións físicas (actuadores) e enviar e recibir a información necesaria (interfaces de comunicacións de rede). A integración e uso destes sistemas electrónicos integrados supón varios retos. En primeiro lugar, é necesario que estes dispositivos teñan un consumo reducido. En moitos casos deberían ser alimentados por baterías ou fontes de alimentación limitadas. Ademais, a gran cantidade de dispositivos que se empregan na IoT fai necesario que a e ciencia enerxética dos mesmos sexa unha das principais preocupacións, polo custo e implicacións medioambientais que supón o consumo de electricidade dos mesmos. Esta necesidade de limitar o consumo provoca que estes dispositivos teñan unhas prestacións moi limitadas, o que entra en con ito coa segunda maior preocupación da IoT: a seguridade e privacidade dos datos. Por un lado existen sistemas críticos urbanos e industriais, como pode ser a regulación do tráfi co, o control de augas, o control marítimo, o control ferroviario ou os sistemas de produción industrial de alto risco, como refinerías, que son claros candidatos a obter benefi cios da IoT, pero cuxo acceso non autorizado supón graves problemas de seguridade cidadá. Por outra parte tanto estes sistemas de natureza pública como os que se despreguen en contornas privadas (vivendas, contornas de traballo ou centros comerciais entre outros) supoñen un risco para a privacidade e tamén para a seguridade dos usuarios. Todo isto fai que sexan necesarios mecanismos de seguridade avanzados, tanto de acceso aos dispositivos como de protección dos datos que estes intercambian. En consecuencia, é necesario avanzar en dous aspectos principais: a e ciencia enerxética dos dispositivos e o uso de mecanismos de seguridade re cientes, tanto computacional como enerxéticamente, que permitan o despregue da IoT sen comprometer a seguridade e a privacidade dos usuarios. Por outro lado, a inxente cantidade de información que estes sistemas poden chegar a xerar presenta outros retos que deben ser tratados. O tratamento e a análise de datos toma unha nova dimensión. Existen sistemas de big data capaces de procesar cantidades enormes de información, pero coa internet das cousas a granularidade e dispersión dos datos supón un escenario moi distinto ao actual. A previsión e pasar de 15.000.000.000 de dispositivos instalados no ano 2015 a m ais de 75.000.000.000 de dispositivos no ano 2025. Ademais existirían multitude de servizos que farían un uso intensivo destes dispositivos e dos datos que intercambian, polo que o volume de tráfico sería aínda maior. Do mesmo xeito a información debe ser procesada tanto en tempo real como posteriormente sobre históricos, o que permite obter información estatística moi relevante en diferentes contornas. O principal obxectivo da presente tese doutoral é analizar cada un destes retos (e ciencia enerxética, seguridade, procesamento de datos e interacción co usuario) e propor solucións que permitan unha correcta adopción da internet das cousas en ámbitos industriais, domésticos e en xeral en todo aquel escenario que se poda bene ciar da interconexión e flexibilidade de acceso que proporciona a IoT

Edge Offloading in Smart Grid

The energy transition supports the shift towards more sustainable energy alternatives, paving towards decentralized smart grids, where the energy is generated closer to the point of use. The decentralized smart grids foresee novel data-driven low latency applications for improving resilience and responsiveness, such as peer-to-peer energy trading, microgrid control, fault detection, or demand response. However, the traditional cloud-based smart grid architectures are unable to meet the requirements of the new emerging applications such as low latency and high-reliability thus alternative architectures such as edge, fog, or hybrid need to be adopted. Moreover, edge offloading can play a pivotal role for the next-generation smart grid AI applications because it enables the efficient utilization of computing resources and addresses the challenges of increasing data generated by IoT devices, optimizing the response time, energy consumption, and network performance. However, a comprehensive overview of the current state of research is needed to support sound decisions regarding energy-related applications offloading from cloud to fog or edge, focusing on smart grid open challenges and potential impacts. In this paper, we delve into smart grid and computational distribution architec-tures, including edge-fog-cloud models, orchestration architecture, and serverless computing, and analyze the decision-making variables and optimization algorithms to assess the efficiency of edge offloading. Finally, the work contributes to a comprehensive understanding of the edge offloading in smart grid, providing a SWOT analysis to support decision making.Comment: to be submitted to journa

Resource Management in Multi-Access Edge Computing (MEC)

This PhD thesis investigates the effective ways of managing the resources of a Multi-Access Edge Computing Platform (MEC) in 5th Generation Mobile Communication (5G) networks. The main characteristics of MEC include distributed nature, proximity to users, and high availability. Based on these key features, solutions have been proposed for effective resource management. In this research, two aspects of resource management in MEC have been addressed. They are the computational resource and the caching resource which corresponds to the services provided by the MEC. MEC is a new 5G enabling technology proposed to reduce latency by bringing cloud computing capability closer to end-user Internet of Things (IoT) and mobile devices. MEC would support latency-critical user applications such as driverless cars and e-health. These applications will depend on resources and services provided by the MEC. However, MEC has limited computational and storage resources compared to the cloud. Therefore, it is important to ensure a reliable MEC network communication during resource provisioning by eradicating the chances of deadlock. Deadlock may occur due to a huge number of devices contending for a limited amount of resources if adequate measures are not put in place. It is crucial to eradicate deadlock while scheduling and provisioning resources on MEC to achieve a highly reliable and readily available system to support latency-critical applications. In this research, a deadlock avoidance resource provisioning algorithm has been proposed for industrial IoT devices using MEC platforms to ensure higher reliability of network interactions. The proposed scheme incorporates Banker’s resource-request algorithm using Software Defined Networking (SDN) to reduce communication overhead. Simulation and experimental results have shown that system deadlock can be prevented by applying the proposed algorithm which ultimately leads to a more reliable network interaction between mobile stations and MEC platforms. Additionally, this research explores the use of MEC as a caching platform as it is proclaimed as a key technology for reducing service processing delays in 5G networks. Caching on MEC decreases service latency and improve data content access by allowing direct content delivery through the edge without fetching data from the remote server. Caching on MEC is also deemed as an effective approach that guarantees more reachability due to proximity to endusers. In this regard, a novel hybrid content caching algorithm has been proposed for MEC platforms to increase their caching efficiency. The proposed algorithm is a unification of a modified Belady’s algorithm and a distributed cooperative caching algorithm to improve data access while reducing latency. A polynomial fit algorithm with Lagrange interpolation is employed to predict future request references for Belady’s algorithm. Experimental results show that the proposed algorithm obtains 4% more cache hits due to its selective caching approach when compared with case study algorithms. Results also show that the use of a cooperative algorithm can improve the total cache hits up to 80%. Furthermore, this thesis has also explored another predictive caching scheme to further improve caching efficiency. The motivation was to investigate another predictive caching approach as an improvement to the formal. A Predictive Collaborative Replacement (PCR) caching framework has been proposed as a result which consists of three schemes. Each of the schemes addresses a particular problem. The proactive predictive scheme has been proposed to address the problem of continuous change in cache popularity trends. The collaborative scheme addresses the problem of cache redundancy in the collaborative space. Finally, the replacement scheme is a solution to evict cold cache blocks and increase hit ratio. Simulation experiment has shown that the replacement scheme achieves 3% more cache hits than existing replacement algorithms such as Least Recently Used, Multi Queue and Frequency-based replacement. PCR algorithm has been tested using a real dataset (MovieLens20M dataset) and compared with an existing contemporary predictive algorithm. Results show that PCR performs better with a 25% increase in hit ratio and a 10% CPU utilization overhead

Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios

Health Monitoring System Models (HMSMs) need speed, efficiency, and security to work. Cascading components ensure data collection, storage, communication, retrieval, and privacy in these models. Researchers propose many methods to design such models, varying in scalability, multidomain efficiency, flexibility, usage and deployment, computational complexity, cost of deployment, security level, feature usability, and other performance metrics. Thus, HMSM designers struggle to find the best models for their application-specific deployments. They must test and validate different models, which increases design time and cost, affecting deployment feasibility. This article discusses secure HMSMs' application-specific advantages, feature-specific limitations, context-specific nuances, and deployment-specific future research scopes to reduce model selection ambiguity. The models based on the Internet of Things (IoT), Machine Learning Models (MLMs), Blockchain Models, Hashing Methods, Encryption Methods, Distributed Computing Configurations, and Bioinspired Models have better Quality of Service (QoS) and security than their counterparts. Researchers can find application-specific models. This article compares the above models in deployment cost, attack mitigation performance, scalability, computational complexity, and monitoring applicability. This comparative analysis helps readers choose HMSMs for context-specific application deployments. This article also devises performance measuring metrics called Health Monitoring Model Metrics (HM3) to compare the performance of various models based on accuracy, precision, delay, scalability, computational complexity, energy consumption, and security