Service-Oriented Reference Architecture for Smart Cities

The trend towards turning existing cities into smart cities is growing. Facilitated by advances in computing such as Cloud services and Internet of Things (IoT), smart cities propose to bring integrated, autonomous systems together to improve quality of life for their inhabitants. Systems such as autonomous vehicles, smart grids and intelligent traffic management are in the initial stages of development. However, as of yet there, is no holistic architecture on which to integrate these systems into a smart city. Additionally, the existing systems and infrastructure of cities is extensive and critical to their operation. We cannot simply replace these systems with smarter versions, instead the system intelligence must augment the existing systems. In this paper we propose a service oriented reference architecture for smart cities which can tackle these problems and identify some related open research questions. The abstract architecture encapsulates the way in which different aspects of the service oriented approach span through the layers of existing city infrastructure. Additionally, the extensible provision of services by individual systems allows for the organic growth of the smart city as required

Massive-Scale Automation in Cyber-Physical Systems: Vision & Challenges

The next era of computing is the evolution of the Internet of Things (IoT) and Smart Cities with development of the Internet of Simulation (IoS). The existing technologies of Cloud, Edge, and Fog computing as well as HPC being applied to the domains of Big Data and deep learning are not adequate to handle the scale and complexity of the systems required to facilitate a fully integrated and automated smart city. This integration of existing systems will create an explosion of data streams at a scale not yet experienced. The additional data can be combined with simulations as services (SIMaaS) to provide a shared model of reality across all integrated systems, things, devices, and individuals within the city. There are also numerous challenges in managing the security and safety of the integrated systems. This paper presents an overview of the existing state-of-the-art in automating, augmenting, and integrating systems across the domains of smart cities, autonomous vehicles, energy efficiency, smart manufacturing in Industry 4.0, and healthcare. Additionally the key challenges relating to Big Data, a model of reality, augmentation of systems, computation, and security are examined

Mobility-aware application scheduling in fog computing

Fog computing provides a distributed infrastructure at the edges of the network, resulting in low-latency access and faster response to application requests when compared to centralized clouds. With this new level of computing capacity introduced between users and the data center-based clouds, new forms of resource allocation and management can be developed to take advantage of the Fog infrastructure. A wide range of applications with different requirements run on end-user devices, and with the popularity of cloud computing many of them rely on remote processing or storage. As clouds are primarily delivered through centralized data centers, such remote processing/storage usually takes place at a single location that hosts user applications and data. The distributed capacity provided by Fog computing allows execution and storage to be performed at different locations. The combination of distributed capacity, the range and types of user applications, and the mobility of smart devices require resource management and scheduling strategies that takes into account these factors altogether. We analyze the scheduling problem in Fog computing, focusing on how user mobility can influence application performance and how three different scheduling policies, namely concurrent, FCFS, and delay-priority, can be used to improve execution based on application characteristics

Survey of advances and challenges in intelligent autonomy for distributed cyber-physical systems

With the evolution of the Internet of things and smart cities, a new trend of the Internet of simulation has emerged to utilise the technologies of cloud, edge, fog computing, and high-performance computing for design and analysis of complex cyber-physical systems using simulation. These technologies although being applied to the domains of big data and deep learning are not adequate to cope with the scale and complexity of emerging connected, smart, and autonomous systems. This study explores the existing state-of-the-art in automating, augmenting, and integrating systems across the domains of smart cities, autonomous vehicles, energy efficiency, smart manufacturing in Industry 4.0, and healthcare. This is expanded to look at existing computational infrastructure and how it can be used to support these applications. A detailed review is presented of advances in approaches providing and supporting intelligence as a service. Finally, some of the remaining challenges due to the explosion of data streams; issues of safety and security; and others related to big data, a model of reality, augmentation of systems, and computation are examined

A cooperative-based model for smart-sensing tasks in fog computing

OAPA Fog Computing is currently receiving a great deal of focused attention. Fog Computing can be viewed as an extension of cloud computing that services the edges of networks. A cooperative relationship among applications to collect data in a city is a fundamental research topic in Fog Computing (FC). When considering the Green Cloud (GC), people or vehicles with smart-sensor devices can be viewed as users in FC and can forward sensing data to the data center (DC). In a traditional sensing process, rewards are paid according to the distances between the users and the platform, which can be seen as the existing solution. Because users with smart-sensing devices tend to participate in tasks with high rewards, the number of users in suburban regions is smaller, and data collection is sparse and cannot satisfy the demands of the tasks. However, there are many users in urban regions, which makes data collection costly and of low quality. In this paper, a cooperative-based model for smartphone tasks, named a Cooperative-based Model for Smart-Sensing Tasks (CMST), is proposed to promote the quality of data collection in FC networks. In the CMST scheme, we develop an allocation method focused on improving the rewards in suburban regions. The rewards to each user with a smart sensor are distributed according to the region density. Moreover, for each task there is a cooperative relationship among the users; they cooperate with one another to reach the volume of data that the platform requires. Extensive experiments show that our scheme improves the overall data-coverage factor by 14.997% to 31.46%, and the platform cost can be reduced by 35.882

Cloud Computing At The Edges

Currently, most cloud computing deployments are generally supported through the use of large scale data centres. There is a common perception that by developing scalable computation, storage, network, and by energy-acquisition at preferential prices, data centres are able to provide more efficient, reliable and cost effective hosting environments for user applications. However, although the network capacity within and in the proximity of such a data centre may be high - the connectivity of a user to their first hop network may not be. Understanding how a distributed cloud can be provisioned, enabling capability to be made available "closer" to a user (geographically or based on network metrics, such as number of hops or latency), remains an important challenge aiming to provide the same benefits as a centralised cloud, but with better Quality of Service for mobile users. With increasing proliferation of mobile devices and sensor-based deployments, understanding how data from such devices can be processed in closer proximity to the device (ranging from capability directly available on the device or through first-hop network nodes from the device) also forms an important requirement of such distributed clouds. We review a number of technologies that could be useful enablers of distributed clouds - outlining common themes across them and identifying potential business models.5813125th International Conference on Cloud Computing and Services Science (CLOSER)MAY 20-22, 2015Lisbon, PORTUGA

Cloud computing at the edges

Currently, most cloud computing deployments are generally supported through the use of large scale data centres. There is a common perception that by developing scalable computation, storage, network, and by energy-acquisition at preferential prices, data centres are able to provide more efficient, reliable and cost effective hosting environments for user applications. However, although the network capacity within and in the proximity of such a data centre may be high – the connectivity of a user to their first hop network may not be. Understanding how a distributed cloud can be provisioned, enabling capability to be made available “closer” to a user (geographically or based on network metrics, such as number of hops or latency), remains an important challenge – aiming to provide the same benefits as a centralised cloud, but with better Quality of Service for mobile users. With increasing proliferation of mobile devices and sensor-based deployments, understanding how data from such devices can be processed in closer proximity to the device (ranging from capability directly available on the device or through first-hop network nodes from the device) also forms an important requirement of such distributed clouds. We review a number of technologies that could be useful enablers of distributed clouds – outlining common themes across them and identifying potential business models