Fog computing scheduling algorithm for smart city

With the advent of the number of smart devices across the globe, increasing the number of users using the Internet. The main aim of the fog computing (FC) paradigm is to connect huge number of smart objects (billions of object) that can make a bright future for smart cities. Due to the large deployments of smart devices, devices are expected to generate huge amounts of data and forward the data through the Internet. FC also refers to an edge computing framework that mitigates the issue by applying the process of knowledge discovery using a data analysis approach to the edges. Thus, the FC approaches can work together with the internet of things (IoT) world, which can build a sustainable infrastructure for smart cities. In this paper, we propose a scheduling algorithm namely the weighted round-robin (WRR) scheduling algorithm to execute the task from one fog node (FN) to another fog node to the cloud. Firstly, a fog simulator is used with the emergent concept of FC to design IoT infrastructure for smart cities. Then, spanning-tree routing (STP) protocol is used for data collection and routing. Further, 5G networks are proposed to establish fast transmission and communication between users. Finally, the performance of our proposed system is evaluated in terms of response time, latency, and amount of data used

Cloud-Edge Orchestration for the Internet-of-Things: Architecture and AI-Powered Data Processing

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordThe Internet-of-Things (IoT) has been deeply penetrated into a wide range of important and critical sectors, including smart city, water, transportation, manufacturing and smart factory. Massive data are being acquired from a fast growing number of IoT devices. Efficient data processing is a necessity to meet diversified and stringent requirements of many emerging IoT applications. Due to the constrained computation and storage resources, IoT devices have resorted to the powerful cloud computing to process their data. However, centralised and remote cloud computing may introduce unacceptable communication delay since its physical location is far away from IoT devices. Edge cloud has been introduced to overcome this issue by moving the cloud in closer proximity to IoT devices. The orchestration and cooperation between the cloud and the edge provides a crucial computing architecture for IoT applications. Artificial intelligence (AI) is a powerful tool to enable the intelligent orchestration in this architecture. This paper first introduces such a kind of computing architecture from the perspective of IoT applications. It then investigates the state-of-the-art proposals on AI-powered cloud-edge orchestration for the IoT. Finally, a list of potential research challenges and open issues is provided and discussed, which can provide useful resources for carrying out future research in this area.Engineering and Physical Sciences Research Council (EPSRC

On the use of intelligent models towards meeting the challenges of the edge mesh

Nowadays, we are witnessing the advent of the Internet of Things (IoT) with numerous devices performing interactions between them or with their environment. The huge number of devices leads to huge volumes of data that demand the appropriate processing. The “legacy” approach is to rely on Cloud where increased computational resources can realize any desired processing. However, the need for supporting real-time applications requires a reduced latency in the provision of outcomes. Edge Computing (EC) comes as the “solver” of the latency problem. Various processing activities can be performed at EC nodes having direct connection with IoT devices. A number of challenges should be met before we conclude a fully automated ecosystem where nodes can cooperate or understand their status to efficiently serve applications. In this article, we perform a survey of the relevant research activities towards the vision of Edge Mesh (EM), i.e., a “cover” of intelligence upon the EC. We present the necessary hardware and discuss research outcomes in every aspect of EC/EM nodes functioning. We present technologies and theories adopted for data, tasks, and resource management while discussing how machine learning and optimization can be adopted in the domain

Edge Computing for Internet of Things

The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond

Task Allocation among Connected Devices: Requirements, Approaches and Challenges

Task allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to the best devices, according to the context and task requirements. The main purpose of this paper is to study the different connotations of the concept of TA efficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. The paper first analyzes the domains of connected devices where TA has an important role, which brings to this classification: Internet of Things (IoT), Sensor and Actuator Networks (SAN), Multi-Robot Systems (MRS), Mobile Crowdsensing (MCS), and Unmanned Aerial Vehicles (UAV). The paper then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-of-the-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. The paper also discusses the issues that need to be addressed by future research activities, i.e.: allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; extending the list of tasks performed by objects; designing TA strategies where network service providers have a role in TA functionalities’ provisioning

SDN-enabled Workload Offloading Schemes for IoT Video Analytics Applications

Increasing demand for using IoT applications, such as video analytics, leverages the importance of developing an architecture to meet the requirements in terms of the latency, reliability, and energy consumption. IoT video cameras combined with the power of machine learning algorithms introduce real-time video analytics applications that can be used in diverse domains, such as security surveillance, sports, and retail stores. However, processing captured video frames using machine learning algorithms needs resources that are beyond the capability of these IoT devices. IoT task offloading is a new paradigm to aim IoT applications to deliver processing intensive applications to their users. IoT devices, which have limited resources by nature, offload their tasks to more powerful servers, i.e., edge/cloud servers . Nonetheless, selecting an appropriate destination for offloading the tasks is the first incoming problem for the IoT task offloading. There are some criteria which needs to be considered when it comes to IoT task offloading, for example transmission latency, queuing delay, as well as processing latency. Although edge servers have limited resources compared to cloud servers, the end-to-end latency for sending the packets to the edge servers is less than the cloud servers. On the other hand, because of the limited available resources in the edge servers, distributing the offloaded tasks between these devices is necessary to avoid overloaded servers. Considering the above mentioned facts, in this thesis, we present load-balancing algorithms benefits from Software Defined Networking (SDN) to distribute offloaded tasks to reduce the chance of using overloaded servers and processing latency of offloaded packets of IoT video analytics applications. Taking into account the aforementioned facts, we propose a scoring metric to balance the incoming offloaded packets between edge servers. The introduced algorithm takes advantage of underlying SDN to collect information about the load of each edge server in the network. Then, the SDN controller uses the scoring metric and sorts the edge servers accordingly. The offloaded task will be directed to the edge server with the lowest processing load to avoid overloaded edge servers. Since the number of IoT devices in the network is not predictable, increasing number of IoT devices will lead to overloaded edge servers. Hence, offloading a part of the IoT tasks to the cloud server might be a better option, even though the packets should pass through the core network. In this regard, we developed a hierarchical edge/cloud system for IoT task offloading. We modeled each of edge/cloud servers by M/M/1 queue model. By benefiting from SDN as an underlying network, the SDN calculates the processing latency and transmission latency to edge and cloud servers, and decides the best destination in terms of the minimum latency that directs the offloaded tasks to one of the desired servers. We have conducted extensive performance evaluation to demonstrate the out-performance of the developed solutions compared with other related approaches in terms of total experienced latency and load distribution between the available servers. The results are comprehensively discussed in their related chapters to clarify the performance of the developed solution

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