Handbook of Integration of Cloud Computing, Cyber Physical Systems and Internet of Things

The next-generation cyber-physical systems (CPSs) will not only be limited to industries but will span across multiple application-areas regarding smart cities and regions. These CPSs will leverage the recent advancements in the areas of cloud computing, Internet of Things and big data to provision citizen-centric applications and services such as smart hybrid energy grids, smart waste management, smart healthcare and smart transportation. Challenges regarding context-awareness, quality of service and quality of experience, mobility management, middleware platforms, service level agreements, trust, and privacy needs to be solved to realize such CPSs. This chapter discusses these challenges in detail and proposes ICICLE – a context-aware IoT-enabled cyber-physical system as a blueprint for next-generation CPSs.

In recent years cloud computing has been gaining enormous momentum. Cloud service providers around the world have publicized a large number of cloud services. Increasing numbers of users are drawn to the convenience and economic affordability of cloud services. As cloud services rapidly proliferate, users are faced with the dilemma of service selection, especially since many services offer similar functionalities with equal quality of service (QoS). In a dynamic cloud environment, the uncertain QoS of cloud services, the fuzzy and personalized QoE (quality of experience) of cloud consumers, are now becoming the central challenges to trustworthy service selection for a potential user. Therefore, evaluating the trustworthiness of cloud services and helping potential user select the trustworthy cloud services among abundant candidates, have been the urgent need at the current development stage of cloud computing. This chapter provides an overview of the related work on trustworthy cloud service selection and a case study on the user feature-aware trustworthy service selection for potential users. At the end of this chapter, a discussion is given based on the identified issues and future research prospects.

Cloud computing has recently emerged as a paradigm for a cloud provider to host and deliver computing services to enterprises and consumers [1]. Usually, the provided services mainly refer to Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), which are all made available to the general public in a pay-as-you-go manner [2, 3]. In most systems, the service provider provides the architecture for users to make reservations/price bidding in advance [4, 5].

This chapter presents a study on Cloud Computing (CC) Sustainability Assessment methodology. Taking into consideration the characteristics of the Sustainable Development Goals models we proposed the multi-objective cloud computing sustainability assessment proposed, provided the comparison methodology, and applied it to the proposed and the general United Nations framework. The chapter additionally tackles the emerging issues of the use of open, private, and public data. Special attention is given to the concept of Big Data, Cyber Physical CC and Internet of Things paradigms in CC environment.

In this talk, we first survey the latest deep learning technology, presenting both theoretical and practical perspectives that are most relevant to our topic. Next, we review general problems and tasks in text/language processing, and underline the distinct properties that differentiate language processing from other tasks such as speech and image object recognition. More importantly, we highlight the general issues of language processing, and elaborate on how new deep learning technologies are proposed and fundamentally address these issues. We then place particular emphasis on several important applications: 1) web search, 2) online recommendation and 3) machine translation. For each of the tasks we discuss what particular architectures of deep learning models are suitable given the nature of the task, and how learning can be performed efficiently and effectively using end-to-end optimization strategies. Beyond providing a systematic review of the general theory, we also present hands-on experience in building state-of-the-art systems. In the talk, we will share our practice with concrete examples drawn from our first-hand experience in major research benchmarks and some industrial scale applications which we have been working on extensively in recent years.

The Internet of Things (IoT) is growing rapidly but faces constant challenges to its deployment. Consumer and industry fears about the security of connected systems, as well as the high resource costs of connectivity, limit IoT’s use to low-criticality, inexpensive, and easy to service systems. Reducing energy and bandwidth resource requirements, as well as improving system security, would enable IoT on devices and systems with greater potential for impact. We propose the use of Cloud resources to apply knowledge of system models to building context and cognition for connected systems, thereby reducing resource requirements through the use of estimation and improving security by way of abstraction and forward simulation. We envision an architecture comprised of “Data Proxies” and a “Cognitive Layer” to supervise system behavior and validate the impact of commands prior to execution of actuation requests. These elements work in concert to apply system models to meet prescribed “Quality of Data” targets with minimal resource use. The use of this architecture will reduce economic and sentiment barriers to the adoption of the IoT, allow more devices to connect, and generate data more rapidly than ever before, enabling novel opportunities for data-informed actuation.

Internet of Things (IoT) applications such as disaster early warning systems are increasingly being used in many areas of human life and business activities by using container-related technologies. However, there are still numerous technological challenges to be solved, particularly related to the time-critical Quality of Service (QoS) aspects of such applications. These types of systems should therefore be continuously monitored at various levels including infrastructure, container and application. Currently, there is a great lack of adequate monitoring systems for such a purpose. In this study, we present an architecture and implementation of our multi-level monitoring framework to ensure system health and adapt the performance of disaster early warning systems to runtime variations in running conditions. These changing conditions, which are out of service providers’ control, could be e.g. quantity, size and computational requirements of arrival requests to be processed or e.g. network quality (e.g. delay, packet loss and throughput) intrinsic to connections between individual application components deployed on different cloud infrastructures. Adaptation possibilities in this work include dynamically changing paths between containerized application components on one hand and also horizontal scaling of container-based application instances from the other side.

With the diverse availability of computation and communication provided by cloud and edge systems, Cyber Physical System (CPS) generate a new way to visualize the interaction between physical and computation systems. In addition to computation and communication technology, CPS also depends on control systems, electronics and electrical engineering, chemical and biological advancements and other new design technologies to give a better interaction among these technologies. The rise of CPS technology revolutionizes our way of living by influencing society in numerous ways e.g. smart home, smart traffic, smart city, smart shopping, smart healthcare, smart agriculture, etc.

Cyber-Physical Systems (CPS) is a very complex system where a new management layer must be developed. This chapter presents the benefits and challenges of deploying real-time Scheduling, Monitoring, and End-to-End SLA (SMeSLA) in CPS. We propose an SMeSLA conceptual architecture which allows end-users to submit their service level requirements to an SLA manager; as a result, scheduling and monitoring managers would operate accordingly. The SMeSLA management layer empowers CPS system to meet consumers’ satisfaction and achieve optimal performance. However, in order to successfully deploy SMeSLA in CPS, many technical and general challenges must be addressed.

Elderly population across the world is on the rise and municipalities along with caregivers are struggling to provide care due to limited resources. Sweden’s elderly population is set to grow significantly by 2050 where the number of people between 65–79 years and 80 years and over is expected to increase by 45% and 87% respectively [1]. The same trend continues within Europe where 25% of the population will be over 65 years of age by the year 2020, and the age group of 65–80 years is predicted to rise by 40% from the year 2010 to 2030 [2]. The rise in elderly population has increased the stress on municipalities and caregivers; and has created the need for new healthcare solutions that are feasible, affordable and easily accessible to all. Smart homes equipped with sensors have already made life easier for those living in them for many decades now by providing home automation solutions. We are also witnessing an increase in the use of Information Communication Technologies (ICT) to assist elderly population and decrease in operational costs. ICT systems in assisting elderly population have an immense potential for providing in-home care to the elderly [3]. The advent of the Internet of Things (IoT) with low-cost and prolific sensors has furthered this trend of home automation and monitoring solutions being used for elderly healthcare [4, 5]. Alongside, the field of ambient assisted homes has continuously paved the way for providing an improved quality of life for those in need such as patients with dementia or chronic conditions as well as elderly living alone at home [5–7].

Disaster management demands a near real-time information dissemination so that the emergency services can be provided to the right people at the right time. Recent advances in information and communication technologies enable collection of real-time information from various sources. For example, sensors deployed in the fields collect data about the environment. Similarly, social networks like Twitter and Facebook can help to collect data from people in the disaster zone. On one hand, inadequate situation awareness in disasters has been identified as one of the primary factors in human errors with grave consequences such as loss of lives and destruction of critical infrastructure. On the other hand, the growing ubiquity of social media and mobile devices, and pervasive nature of the Internet-of-Things means that there are more sources of outbound traffic, which ultimately results in the creation of a data deluge, beginning shortly after the onset of disaster events, leading to the problem of information tsunami. In addition, security and privacy has crucial role to overcome the misuse of the system for either intrusions into data or overcome the misuse of the information that was meant for a specified purpose. These problems can be addressed by processing the collected data in real-time and extracting meaningful and actionable information for emergency providers while taking care of security and privacy aspects. Such situation-awareness applications demand a large amount of computing resources. The cloud, which provides elastic and scalable infrastructure, becomes the natural choice for such applications. In this chapter, we provide such a situation aware application to support disaster management data lifecycle, i.e. from data ingestion and processing to alert dissemination. We utilize cloud computing, Internet of Things and social computing technologies to achieve a scalable, efficient, and usable situation-aware application called Cloud4BigData.

In 2010, the building sector accounted for 19% of all global greenhouse gas (GHG) emissions and 32% of global final energy use. Of that final energy consumption, space heating was the most significant end-use responsible for one-third [36]. In the same year, residential, commercial, and industrial buildings jointly accounted for 41% of the primary energy use of the US with close to 75% of this consumption being served by fossil fuels [16].