Cloud Computing in VANETs: Architecture, Taxonomy, and Challenges

Cloud Computing in VANETs (CC-V) has been investigated into two major themes of research including Vehicular Cloud Computing (VCC) and Vehicle using Cloud (VuC). VCC is the realization of autonomous cloud among vehicles to share their abundant resources. VuC is the efficient usage of conventional cloud by on-road vehicles via a reliable Internet connection. Recently, number of advancements have been made to address the issues and challenges in VCC and VuC. This paper qualitatively reviews CC-V with the emphasis on layered architecture, network component, taxonomy, and future challenges. Specifically, a four-layered architecture for CC-V is proposed including perception, co-ordination, artificial intelligence and smart application layers. Three network component of CC-V namely, vehicle, connection and computation are explored with their cooperative roles. A taxonomy for CC-V is presented considering major themes of research in the area including design of architecture, data dissemination, security, and applications. Related literature on each theme are critically investigated with comparative assessment of recent advances. Finally, some open research challenges are identified as future issues. The challenges are the outcome of the critical and qualitative assessment of literature on CC-V

Mobile Services Meet Distributed Cloud: Benefits, Applications, and Challenges

As the explosive growth of smart devices and enormous new applications, the variety of corresponding cloud services has been growing quickly. The conventional centralized cloud was faced with an overhead on backhaul links and high latency. Accordingly, a decentralized cloud paradigm including edge computing, mobile edge computing, cloudlet, and so on, was introduced to distribute cloud services to the edge network which located in proximity to mobile devices few years ago. However, this paradigm was not paid attention at that time since cloud technology and mobile network communication were immature to motivate mobile services. Recently, with the overwhelming growth of mobile communication technology and cloud technology, distributed cloud is emerging as a paradigm well equipped with technologies to support a broad range of mobile services. The 5G mobile communication technology provides high-speed data and low latency. Cloud services can be automatically deployed in the edge networks quickly and easily. Distributed cloud can prove itself to bring many benefits for mobile service such as reducing network latency, as well as computational and network overhead at the central cloud. Besides, we present some applications to emphasize the necessity of distributed cloud for mobile service and discuss further technical challenges in distributed cloud

On distributed mobile edge computing

Mobile Cloud Computing (MCC) has been proposed to offload the workloads of mobile applications from mobile devices to the cloud in order to not only reduce energy consumption of mobile devices but also accelerate the execution of mobile applications. Owing to the long End-to-End (E2E) delay between mobile devices and the cloud, offloading the workloads of many interactive mobile applications to the cloud may not be suitable. That is, these mobile applications require a huge amount of computing resources to process their workloads as well as a low E2E delay between mobile devices and computing resources, which cannot be satisfied by the current MCC technology. In order to reduce the E2E delay, a novel cloudlet network architecture is proposed to bring the computing and storage resources from the remote cloud to the mobile edge. In the cloudlet network, each mobile user is associated with a specific Avatar (i.e., a dedicated Virtual Machine (VM) providing computing and storage resources to its mobile user) in the nearby cloudlet via its associated Base Station (BS). Thus, mobile users can offload their workloads to their Avatars with low E2E delay (i.e., one wireless hop). However, mobile users may roam among BSs in the mobile network, and so the E2E delay between mobile users and their Avatars may become worse if the Avatars remain in their original cloudlets. Thus, Avatar handoff is proposed to migrate an Avatar from one cloudlet into another to reduce the E2E delay between the Avatar and its mobile user. The LatEncy aware Avatar handDoff (LEAD) algorithm is designed to determine the location of each mobile user\u27s Avatar in each time slot in order to minimize the average E2E delay among all the mobile users and their Avatars. The performance of LEAD is demonstrated via extensive simulations. The cloudlet network architecture not only facilitates mobile users in offloading their computational tasks but also empowers Internet of Things (IoT). Popular IoT resources are proposed to be cached in nearby brokers, which are considered as application layer middleware nodes hosted by cloudlets in the cloudlet network, to reduce the energy consumption of servers. In addition, an Energy Aware and latency guaranteed dynamic reSourcE caching (EASE) strategy is proposed to enable each broker to cache suitable popular resources such that the energy consumption from the servers is minimized and the average delay of delivering the contents of the resources to the corresponding clients is guaranteed. The performance of EASE is demonstrated via extensive simulations. The future work comprises two parts. First, caching popular IoT resources in nearby brokers may incur unbalanced traffic loads among brokers, thus increasing the average delay of delivering the contents of the resources. Thus, how to balance the traffic loads among brokers to speed up IoT content delivery process requires further investigation. Second, drone assisted mobile access network architecture will be briefly investigated to accelerate communications between mobile users and their Avatars

Mobiilse värkvõrgu protsessihaldus

Värkvõrk, ehk Asjade Internet (Internet of Things, lüh IoT) edendab lahendusi nagu nn tark linn, kus meid igapäevaselt ümbritsevad objektid on ühendatud infosüsteemidega ja ka üksteisega. Selliseks näiteks võib olla teekatete seisukorra monitoorimissüsteem. Võrku ühendatud sõidukitelt (nt bussidelt) kogutakse videomaterjali, mida seejärel töödeldakse, et tuvastada löökauke või lume kogunemist. Tavaliselt hõlmab selline lahendus keeruka tsentraalse süsteemi ehitamist. Otsuste langetamiseks (nt milliseid sõidukeid parasjagu protsessi kaasata) vajab keskne süsteem pidevat ühendust kõigi IoT seadmetega. Seadmete hulga kasvades võib keskne lahendus aga muutuda pudelikaelaks. Selliste protsesside disaini, haldust, automatiseerimist ja seiret hõlbustavad märkimisväärselt äriprotsesside halduse (Business Process Management, lüh BPM) valdkonna standardid ja tööriistad. Paraku ei ole BPM tehnoloogiad koheselt kasutatavad uute paradigmadega nagu Udu- ja Servaarvutus, mis tuleviku värkvõrgu jaoks vajalikud on. Nende puhul liigub suur osa otsustustest ja arvutustest üksikutest andmekeskustest servavõrgu seadmetele, mis asuvad lõppkasutajatele ja IoT seadmetele lähemal. Videotöötlust võiks teostada mini-andmekeskustes, mis on paigaldatud üle linna, näiteks bussipeatustesse. Arvestades IoT seadmete üha suurenevat hulka, vähendab selline koormuse jaotamine vähendab riski, et tsentraalne andmekeskust ülekoormamist. Doktoritöö uurib, kuidas mobiilsusega seonduvaid IoT protsesse taoliselt ümber korraldada, kohanedes pidevalt muutlikule, liikuvate seadmetega täidetud servavõrgule. Nimelt on ühendused katkendlikud, mistõttu otsuste langetus ja planeerimine peavad arvestama muuhulgas mobiilseadmete liikumistrajektoore. Töö raames valminud prototüüpe testiti Android seadmetel ja simulatsioonides. Lisaks valmis tööriistakomplekt STEP-ONE, mis võimaldab teadlastel hõlpsalt simuleerida ja analüüsida taolisi probleeme erinevais realistlikes stsenaariumites nagu seda on tark linn.The Internet of Things (IoT) promotes solutions such as a smart city, where everyday objects connect with info systems and each other. One example is a road condition monitoring system, where connected vehicles, such as buses, capture video, which is then processed to detect potholes and snow build-up. Building such a solution typically involves establishing a complex centralised system. The centralised approach may become a bottleneck as the number of IoT devices keeps growing. It relies on constant connectivity to all involved devices to make decisions, such as which vehicles to involve in the process. Designing, automating, managing, and monitoring such processes can greatly be supported using the standards and software systems provided by the field of Business Process Management (BPM). However, BPM techniques are not directly applicable to new computing paradigms, such as Fog Computing and Edge Computing, on which the future of IoT relies. Here, a lot of decision-making and processing is moved from central data-centers to devices in the network edge, near the end-users and IoT sensors. For example, video could be processed in mini-datacenters deployed throughout the city, e.g., at bus stops. This load distribution reduces the risk of the ever-growing number of IoT devices overloading the data center. This thesis studies how to reorganise the process execution in this decentralised fashion, where processes must dynamically adapt to the volatile edge environment filled with moving devices. Namely, connectivity is intermittent, so decision-making and planning need to involve factors such as the movement trajectories of mobile devices. We examined this issue in simulations and with a prototype for Android smartphones. We also showcase the STEP-ONE toolset, allowing researchers to conveniently simulate and analyse these issues in different realistic scenarios, such as those in a smart city.  https://www.ester.ee/record=b552551

DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications

This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft SystemsUnmanned Aerial Vehicles (UAVs) have been very effective in collecting aerial images data for various Internet-of-Things (IoT)/smart cities applications such as search and rescue, surveillance, vehicle detection, counting, intelligent transportation systems, to name a few. However, the real-time processing of collected data on edge in the context of the Internet-of-Drones remains an open challenge because UAVs have limited energy capabilities, while computer vision techniquesconsume excessive energy and require abundant resources. This fact is even more critical when deep learning algorithms, such as convolutional neural networks (CNNs), are used for classification and detection. In this paper, we first propose a system architecture of computation offloading for Internet-connected drones. Then, we conduct a comprehensive experimental study to evaluate the performance in terms of energy, bandwidth, and delay of the cloud computation offloading approach versus the edge computing approach of deep learning applications in the context of UAVs. In particular, we investigate the tradeoff between the communication cost and the computation of the two candidate approaches experimentally. The main results demonstrate that the computation offloading approach allows us to provide much higher throughput (i.e., frames per second) as compared to the edge computing approach, despite the larger communication delays.info:eu-repo/semantics/publishedVersio

Suporte a gerenciamento do trânsito baseado em computação na névoa para os sistemas de transporte inteligentes

Orientadores: Leandro Aparecido Villas, Daniel Ludovico GuidoniTese (doutorado) ¿ Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O trânsito nos grandes centros urbanos contribui com problemas que vão desde diminuição da qualidade de vida e segurança da população até o aumento de custos financeiros às pessoas, cidades e empresas. Um dos motivos para um maior tráfego de veículos é o vertiginoso crescimento populacional dos centros urbanos. Além disso, o fluxo de veículos é prejudicado por situações adversas recorrentes nas vias, como o aumento súbito do tráfego durante os horários de pico, gargalos nas infraestruturas de transporte, e acidentes de trânsito. Com o avanço das tecnologias de comunicação, processamento e sensoriamento, os Sistemas de Transporte Inteligentes (ITS) surgem como uma alternativa para mitigar esses problemas. A interoperabilidade dos ITS com novas tecnologias tais como as redes veiculares (VANETs) e computação em névoa, os tornam mais promissores e eficazes. As VANETs preveem que veículos possuam poder computacional e capacidade de comunicação sem fio com outros veículos e com as infraestruturas fixa de comunicação, assim, uma nova gama de serviços de segurança e entretenimento aos motoristas e passageiros podem ser desenvolvidas. Entretanto, estes tipos de serviços, em especial o de gerenciamento de trânsito, demandam uma análise contínua das condições de fluxo de veículos nas vias e um vasto recurso de rede e processamento, tornando o desenvolvimento de soluções para ITS mais complexo e de difícil escalabilidade. A computação em névoa é uma infraestrutura de computação descentralizada na qual dados, processamento, armazenamento e aplicações são distribuídos na borda da rede, assim, aumentando a escalabilidade do sistema. Na literatura, os sistemas de gerenciamento de tráfego não tratam de maneira adequada o problema de escalabilidade, implicando em problemas relacionados ao balanceamento de carga e tempo de resposta. Esta tese de doutorado propõe um sistema de gerenciamento de tráfego baseado no paradigma de computação em névoa, para detectar, classificar e controlar o congestionamento de tráfego. O sistema proposto apresenta um framework distribuído e escalável que reduz os problemas supracitados em relação ao estado da arte. Para tanto, utilizando da natureza distribuída da computação em névoa, a solução implementa um algoritmo de roteamento probabilístico que faz o balanceamento do tráfego e evita o problema de deslocamento de congestionamentos para outras regiões. Utilizando às características da computação em névoa, foi desenvolvida uma metodologia distribuída baseada em regiões que faz a coleta de dados e classificação das vias em relação às condições do trânsito compartilhadas pelos veículos. Finalmente, foi desenvolvido um conjunto de algoritmos/protocolos de comunicação que comparado com outras soluções da literatura, reduz a perda de pacotes e o número de mensagens transmitidas. O serviço proposto foi comparado extensivamente com outras soluções da literatura em relação às métricas de trânsito, onde o sistema proposto foi capaz de reduzir em até 70% o tempo parado e em até 49% o planning time index. Considerando as métricas de comunicação, o serviço proposto é capaz de reduzir em até 12% a colisão de pacotes alcançando uma cobertura de 98% do cenário. Os resultados mostram que o framework baseado em computação em névoa desenvolvido, melhora o fluxo de veículos de forma eficiente e escalávelAbstract: Traffic in large urban centers contributes to problems that range from decreasing the population¿s quality of life and security to increasing financial costs for people, cities, and companies. One of the reasons for increased vehicle traffic is the population growth in urban centers. Moreover, vehicle flow is hampered by recurring adverse situations on roads, such as the sudden increase in vehicle traffic during peak hours, bottlenecks in transportation infrastructure, and traffic accidents. Considering the advance of communication, processing, and sensing technologies, Intelligent Transport Systems (ITS) have emerged as an alternative to mitigate these problems. The interoperability of ITS with new technologies, such as vehicular networks (VANETs) and Fog computing, make them more promising and effective. VANETs ensure that vehicles have the computing power and wireless communication capabilities with other vehicles and with fixed communication infrastructures; therefore, a new range of security and entertainment services for drivers and passengers can be developed. However, these types of services, especially traffic management, demand a continuous analysis of vehicle flow conditions on roads and a huge network and processing resource, making the development of ITS solutions more complex and difficult to scale. Fog computing is a decentralized computing infrastructure in which data, processing, storage, and applications are distributed at the network edge, thereby increasing the system¿s scalability. In the literature, traffic management systems do not adequately address the scalability problem, resulting in load balancing and response time problems. This doctoral thesis proposes a traffic management system based on the Fog computing paradigm to detect, classify, and control traffic congestion. The proposed system presents a distributed and scalable framework that reduces the aforementioned problems in relation to state of the art. Therefore, using Fog computing¿s distributed nature, the solution implements a probabilistic routing algorithm that balances traffic and avoids the problem of congestion displacement to other regions. Using the characteristics of Fog computing, a distributed methodology was developed based on regions that collect data and classify the roads concerning the traffic conditions shared by the vehicles. Finally, a set of communication algorithms/protocols was developed which, compared with other literature solutions, reduces packet loss and the number of messages transmitted. The proposed service was compared extensively with other solutions in the literature regarding traffic metrics, where the proposed system was able to reduce downtime by up to 70% and up to 49% of the planning time index. Considering communication metrics, the proposed service can reduce packet collision by up to 12% reaching 98% coverage of the scenario. The results show that the framework based on Fog computing developed improves the vehicles¿ flow efficiently and in a scalable wayDoutoradoCiência da ComputaçãoDoutor em Ciência da Computaçã