Energy-aware Graph Job Allocation in Software Defined Air-Ground Integrated Vehicular Networks

The software defined air-ground integrated vehicular (SD-AGV) networks have emerged as a promising paradigm, which realize the flexible on-ground resource sharing to support innovative applications for UAVs with heavy computational overhead. In this paper, we investigate a vehicular cloud-assisted graph job allocation problem in SD-AGV networks, where the computation-intensive jobs carried by UAVs, and the vehicular cloud are modeled as graphs. To map each component of the graph jobs to a feasible vehicle, while achieving the trade-off among minimizing UAVs' job completion time, energy consumption, and the data exchange cost among vehicles, we formulate the problem as a mixed-integer non-linear programming problem, which is Np-hard. Moreover, the constraint associated with preserving job structures poses addressing the subgraph isomorphism problem, that further complicates the algorithm design. Motivated by which, we propose an efficient decoupled approach by separating the template (feasible mappings between components and vehicles) searching from the transmission power allocation. For the former, we present an efficient algorithm of searching for all the subgraph isomorphisms with low computation complexity. For the latter, we introduce a power allocation algorithm by applying convex optimization techniques. Extensive simulations demonstrate that the proposed approach outperforms the benchmark methods considering various problem sizes.Comment: 14 pages, 7 figure

車両のエッジコンピューティングにおけるネットワークリソースの最適化手法

車両インターネット（IoV）の発展伴い、インテリジェント交通サービスやインフォテインメントサービスなど、多くのIoVサービスが人々の生活に浸透してきている。IoVの出現は、ドライバーに革新的で便利なサービスを提供するが、大規模なデータ処理には不向きである。従来のIPネットワークとは異なり、車両ネットワークの無線リンクは低品質かつ切れやすいため、タスク処理のためにクラウドへ大量のデータ伝送をすることは現実的でない。車両エッジコンピューティングネットワーク（VECN）は、これらのタスクを車両に近いエッジノードにオフロードすることにより、高速にタスク処理をする有望な方法である。VECNアーキテクチャには2つの難点がある。1つは、車両エッジノードのコンピューティングリソースが限られているため、軽量なアルゴリズムの開発が必要である。 もう1つは、一部のエッジノードと車両が同じパーティーに属していないことがあるため、合理的な資源割当が必要である。本論文では、まず、機械学習のブロードラーニングを用いて車両エッジコンピューティングネットワークにおける軽量トラフィック分析システムを提案する。そしてVECNでの公平なリソース割り当て問題を解決するため、マルチ属性に基づくダブルオークションメカニズムを設計する。次に、VECNシナリオでユーザーのダイナミクスを満たすオンラインオークションメカニズムを提案する。このメカニズムは、買いユーザーと売りユーザーのマッチングを構築するときに非価格属性も考慮する。最後に、提案されたスキームを検証するためにシミュレーション実験ならびに実機実験を行った。With the development of the Internet of Vehicles (IoV), more and more vehicular services are coming to people’s daily life, including intelligent transportation services and infotainment services. While the emergence of numerous services could provide innovative and convenient services for drivers, how to process large-scale data effectively still needs in-depth research in the IoV scenario. Different from traditional networks, the vehicular network has poor-quality wireless links which may lead to poor communication quality. Therefore, moving data to the cloud for processing is not feasible in the vehicular network. Vehicular edge computing network (VECN) is a promising way to provide fast task processing services for vehicles by offloading these tasks to edge nodes close to vehicles. There are two challenges in the VECN architecture: one is that vehicular edge nodes always have limited computing resources. Therefore, lightweight algorithms need to be deployed to promote the development of VECN. And the other is that some edge nodes and vehicles do not belong to the same party. Therefore, how to conduct fair trade between them for providing reasonable resource allocation is an important issue. In this dissertation, we firstly propose the broad learning based lightweight traffic analysis system in the vehicular edge computing network. Secondly, a multi-attribute based double auction mechanism is designed to solve the problems of fair resource allocation in the VECN. Then we propose an online auction mechanism to satisfy the dynamics of users in the VECN scenario, which also considers the non-price attributes when constructing the matching between buyers and sellers. Finally, we conduct multiple experiments to verify the proposed schemes.室蘭工業大学 (Muroran Institute of Technology)博士（工学

Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure

A Hybrid SDN-based Architecture for Wireless Networks

With new possibilities brought by the Internet of Things (IoT) and edge computing, the traffic demand of wireless networks increases dramatically. A more sophisticated network management framework is required to handle the flow routing and resource allocation for different users and services. By separating the network control and data planes, Software-defined Networking (SDN) brings flexible and programmable network control, which is considered as an appropriate solution in this scenario.Although SDN has been applied in traditional networks such as data centers with great successes, several unique challenges exist in the wireless environment. Compared with wired networks, wireless links have limited capacity. The high mobility of IoT and edge devices also leads to network topology changes and unstable link qualities. Such factors restrain the scalability and robustness of an SDN control plane. In addition, the coexistence of heterogeneous wireless and IoT protocols with distinct representations of network resources making it difficult to process traffic with state-of-the-art SDN standards such as OpenFlow. In this dissertation, we design a novel architecture for the wireless network management. We propose multiple techniques to better adopt SDN to relevant scenarios. First, while maintaining the centralized control plane logically, we deploy multiple SDN controller instances to ensure their scalability and robustness. We propose algorithms to determine the controllers\u27 locations and synchronization rates that minimize the communication costs. Then, we consider handling heterogeneous protocols in Radio Access Networks (RANs). We design a network slicing orchestrator enabling allocating resources across different RANs controlled by SDN, including LTE and Wi-Fi. Finally, we combine the centralized controller with local intelligence, including deploying another SDN control plane in edge devices locally, and offloading network functions to a programmable data plane. In all these approaches, we evaluate our solutions with both large-scale emulations and prototypes implemented in real devices, demonstrating the improvements in multiple performance metrics compared with state-of-the-art methods

Adaptive learning-based resource management strategy in fog-to-cloud

Technology in the twenty-first century is rapidly developing and driving us into a new smart computing world, and emerging lots of new computing architectures. Fog-to-Cloud (F2C) is among one of them, which emerges to ensure the commitment for bringing the higher computing facilities near to the edge of the network and also help the large-scale computing system to be more intelligent. As the F2C is in its infantile state, therefore one of the biggest challenges for this computing paradigm is to efficiently manage the computing resources. Mainly, to address this challenge, in this work, we have given our sole interest for designing the initial architectural framework to build a proper, adaptive and efficient resource management mechanism in F2C. F2C has been proposed as a combined, coordinated and hierarchical computing platform, where a vast number of heterogeneous computing devices are participating. Notably, their versatility creates a massive challenge for effectively handling them. Even following any large-scale smart computing system, it can easily recognize that various kind of services is served for different purposes. Significantly, every service corresponds with the various tasks, which have different resource requirements. So, knowing the characteristics of participating devices and system offered services is giving advantages to build effective and resource management mechanism in F2C-enabled system. Considering these facts, initially, we have given our intense focus for identifying and defining the taxonomic model for all the participating devices and system involved services-tasks. In any F2C-enabled system consists of a large number of small Internet-of-Things (IoTs) and generating a continuous and colossal amount of sensing-data by capturing various environmental events. Notably, this sensing-data is one of the key ingredients for various smart services which have been offered by the F2C-enabled system. Besides that, resource statistical information is also playing a crucial role, for efficiently providing the services among the system consumers. Continuous monitoring of participating devices generates a massive amount of resource statistical information in the F2C-enabled system. Notably, having this information, it becomes much easier to know the device's availability and suitability for executing some tasks to offer some services. Therefore, ensuring better service facilities for any latency-sensitive services, it is essential to securely distribute the sensing-data and resource statistical information over the network. Considering these matters, we also proposed and designed a secure and distributed database framework for effectively and securely distribute the data over the network. To build an advanced and smarter system is necessarily required an effective mechanism for the utilization of system resources. Typically, the utilization and resource handling process mainly depend on the resource selection and allocation mechanism. The prediction of resources (e.g., RAM, CPU, Disk, etc.) usage and performance (i.e., in terms of task execution time) helps the selection and allocation process. Thus, adopting the machine learning (ML) techniques is much more useful for designing an advanced and sophisticated resource allocation mechanism in the F2C-enabled system. Adopting and performing the ML techniques in F2C-enabled system is a challenging task. Especially, the overall diversification and many other issues pose a massive challenge for successfully performing the ML techniques in any F2C-enabled system. Therefore, we have proposed and designed two different possible architectural schemas for performing the ML techniques in the F2C-enabled system to achieve an adaptive, advance and sophisticated resource management mechanism in the F2C-enabled system. Our proposals are the initial footmarks for designing the overall architectural framework for resource management mechanism in F2C-enabled system.La tecnologia del segle XXI avança ràpidament i ens condueix cap a un nou món intel·ligent, creant nous models d'arquitectures informàtiques. Fog-to-Cloud (F2C) és un d’ells, i sorgeix per garantir el compromís d’acostar les instal·lacions informàtiques a prop de la xarxa i també ajudar el sistema informàtic a gran escala a ser més intel·ligent. Com que el F2C es troba en un estat preliminar, un dels majors reptes d’aquest paradigma tecnològic és gestionar eficientment els recursos informàtics. Per fer front a aquest repte, en aquest treball hem centrat el nostre interès en dissenyar un marc arquitectònic per construir un mecanisme de gestió de recursos adequat, adaptatiu i eficient a F2C.F2C ha estat concebut com una plataforma informàtica combinada, coordinada i jeràrquica, on participen un gran nombre de dispositius heterogenis. La seva versatilitat planteja un gran repte per gestionar-los de manera eficaç. Els serveis que s'hi executen consten de diverses tasques, que tenen requisits de recursos diferents. Per tant, conèixer les característiques dels dispositius participants i dels serveis que ofereix el sistema és un requisit per dissenyar mecanismes eficaços i de gestió de recursos en un sistema habilitat per F2C. Tenint en compte aquests fets, inicialment ens hem centrat en identificar i definir el model taxonòmic per a tots els dispositius i sistemes implicats en l'execució de tasques de serveis. Qualsevol sistema habilitat per F2C inclou en un gran nombre de dispositius petits i connectats (conegut com a Internet of Things, o IoT) que generen una quantitat contínua i colossal de dades de detecció capturant diversos events ambientals. Aquestes dades són un dels ingredients clau per a diversos serveis intel·ligents que ofereix F2C. A més, el seguiment continu dels dispositius participants genera igualment una gran quantitat d'informació estadística. En particular, en tenir aquesta informació, es fa molt més fàcil conèixer la disponibilitat i la idoneïtat dels dispositius per executar algunes tasques i oferir alguns serveis. Per tant, per garantir millors serveis sensibles a la latència, és essencial distribuir de manera equilibrada i segura la informació estadística per la xarxa. Tenint en compte aquests assumptes, també hem proposat i dissenyat un entorn de base de dades segura i distribuïda per gestionar de manera eficaç i segura les dades a la xarxa. Per construir un sistema avançat i intel·ligent es necessita un mecanisme eficaç per a la gestió de l'ús dels recursos del sistema. Normalment, el procés d’utilització i manipulació de recursos depèn principalment del mecanisme de selecció i assignació de recursos. La predicció de l’ús i el rendiment de recursos (per exemple, RAM, CPU, disc, etc.) en termes de temps d’execució de tasques ajuda al procés de selecció i assignació. Adoptar les tècniques d’aprenentatge automàtic (conegut com a Machine Learning, o ML) és molt útil per dissenyar un mecanisme d’assignació de recursos avançat i sofisticat en el sistema habilitat per F2C. L’adopció i la realització de tècniques de ML en un sistema F2C és una tasca complexa. Especialment, la diversificació general i molts altres problemes plantegen un gran repte per realitzar amb èxit les tècniques de ML. Per tant, en aquesta recerca hem proposat i dissenyat dos possibles esquemes arquitectònics diferents per realitzar tècniques de ML en el sistema habilitat per F2C per aconseguir un mecanisme de gestió de recursos adaptatiu, avançat i sofisticat en un sistema F2C. Les nostres propostes són els primers passos per dissenyar un marc arquitectònic general per al mecanisme de gestió de recursos en un sistema habilitat per F2C.Postprint (published version

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas