Crowdsourcing Of Sensor Cloud Services

This book develops a crowdsourced sensor-cloud service composition framework taking into account spatio-temporal aspects. This book also unfolds new horizons to service-oriented computing towards the direction of crowdsourced sensor data based applications, in the broader context of Internet of Things (IoT). It is a massive challenge for the IoT research field how to effectively and efficiently capture, manage and deliver sensed data as user-desired services. The outcome of this research will contribute to solving this very important question, by designing a novel service framework and a set of unique service selection and composition frameworks. Delivering a novel service framework to manage crowdsourced sensor data provides high-level abstraction (i.e., sensor-cloud service) to model crowdsourced sensor data from functional and non-functional perspectives, seamlessly turning the raw data into “ready to go” services. A creative indexing model is developed to capture and manage the spatio-temporal dynamism of crowdsourced service providers. Delivering novel frameworks to compose crowdsourced sensor-cloud services is vital. These frameworks focuses on spatio-temporal composition of crowdsourced sensor-cloud services, which is a new territory for existing service oriented computing research. A creative failure-proof model is also designed to prevent composition failure caused by fluctuating QoS. Delivering an incentive model to drive the coverage of crowdsourced service providers is also vital. A new spatio-temporal incentive model targets changing coverage of the crowdsourced providers to achieve demanded coverage of crowdsourced sensor-cloud services within a region. The outcome of this research is expected to potentially create a sensor services crowdsourcing market and new commercial opportunities focusing on crowdsourced data based applications. The crowdsourced community based approach adds significant value to journey planning and map services thus creating a competitive edge for a technologically-minded companies incentivizing new start-ups, thus enabling higher market innovation. This book primarily targets researchers and practitioners, who conduct research work in service oriented computing, Internet of Things (IoT), smart city and spatio-temporal travel planning, as well as advanced-level students studying this field. Small and Medium Entrepreneurs, who invest in crowdsourced IoT services and journey planning infrastructures, will also want to purchase this book.

This book constitutes the proceedings of the 16th International Conference on Service-Oriented Computing, ICSOC 2018, held in Hangzhou, China, in November 2018. The 63 full papers presented together with 3 keynotes in this volume were carefully reviewed and selected from numerous submissions. The papers have been organized in the following topical sections: Microservices; Services and Processes; Service Trust and Security; Business Services and Processes; Edge + IoT Services; Social and Interactive Services; Recommendation; Service Analytics; Quality of Service; Service Engineering; Service Applications; Service Management.

The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies. This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based technologies for data storage, transmission and mashup, and AI techniques to analyze this data. This textbook is designed to train college students to master modern cloud computing systems in operating principles, architecture design, machine learning algorithms, programming models and software tools for big data mining, analytics, and cognitive applications. The book will be suitable for use in one-semester computer science or electrical engineering courses on cloud computing, machine learning, cloud programming, cognitive computing, or big data science. The book will also be very useful as a reference for professionals who want to work in cloud computing and data science. Cloud and Cognitive Computing begins with two introductory chapters on fundamentals of cloud computing, data science, and adaptive computing that lay the foundation for the rest of the book. Subsequent chapters cover topics including cloud architecture, mashup services, virtual machines, Docker containers, mobile clouds, IoT and AI, inter-cloud mashups, and cloud performance and benchmarks, with a focus on Google's Brain Project, DeepMind, and X-Lab programs, IBKai HwangM SyNapse, Bluemix programs, cognitive initiatives, and neurocomputers. The book then covers machine learning algorithms and cloud programming software tools and application development, applying the tools in machine learning, social media, deep learning, and cognitive applications. All cloud systems are illustrated with big data and cognitive application examples.

Mobile crowdsensing is a technology that allows large scale, cost-effective sensing of the physical world. In mobile crowdsensing, mobile personal devices such as smart phones or smart watches come equipped with a variety of sensors that can be leveraged to collect data related to environment, transportation, healthcare, safety and so on. This book presents the first extensive coverage of mobile crowdsensing, with examples and insights drawn from the authors’ extensive research on this topic as well as from the research and development of a growing community of researchers and practitioners working in this emerging field. Throughout the text, the authors provide the reader with various examples of crowdsensing applications and the building blocks to creating the necessary infrastructure, explore the related concepts of mobile sensing and crowdsourcing, and examine security and privacy issues introduced by mobile crowdsensing platforms. Provides a comprehensive description of mobile crowdsensing, a one-stop shop for all relevant issues pertaining to mobile crowdsensing, including motivation, applications, design and implementation, incentive mechanisms, and reliability and privacy. Describes the design and implementations of mobile crowdsensing platforms of great interest for the readers working in research and industry to quickly implement and test their systems. Identifies potential issues in building such mobile crowdsensing applications to ensure their usability in real life and presents future directions in mobile crowdsensing by emphasizing the open problems that have to be addressed.

This book is the first on Smart Grid 3.0. The book presents literature reviews of recent computational and communication technologies and their application in the evolution of smart grids to Smart Grid 3.0. It offers new control solutions, architectures and energy management strategies that are based on artificial intelligence and deep learning techniques. The book details the hardware and software implementation of fault identification or detection based on synchrophasor data and machine learning. It also discusses blockchain architectures for smart grid applications such as electric vehicles, home automation and automatic metering infrastructure.

Crowd computing, crowdsourcing, crowd-associated network (CrAN), crowd-assisted sensing are some examples of crowd-based concepts that harness the power of people on the web or connected via web-like infrastructure to do tasks that are often difficult for individual users or computers to do alone. This creates many challenging issues like assessing reliability and correctness of crowd generated information, delivery of data and information via crowd, middleware for supporting crowdsourcing and crowd computing tasks, crowd associated networking and its security, Quality of Information (QoI) issues, etc. This book compiles the latest advances in the relevant fields.

This book presents the latest research on the software crowdsourcing approach to develop large and complex software in a cloud-based platform. It develops the fundamental principles, management organization and processes, and a cloud-based infrastructure to support this new software development approach. The book examines a variety of issues in software crowdsourcing processes, including software quality, costs, diversity of solutions, and the competitive nature of crowdsourcing processes. Furthermore, the book outlines a research roadmap of this emerging field, including all the key technology and management issues for the foreseeable future. Crowdsourcing, as demonstrated by Wikipedia and Facebook for online web applications, has shown promising results for a variety of applications, including healthcare, business, gold mining exploration, education, and software development. Software crowdsourcing is emerging as a promising solution to designing, developing and maintaining software. Preliminary software crowdsourcing practices and platforms, including Apple's App Store and TopCoder, demonstrate the advantages of crowdsourcing in terms of software ecosystem expansion and product quality improvement.

This book constitutes the proceedings of the 19th International Conference on Service-Oriented Computing, ICSOC 2020, which is held virtually in November 2021. The 29 full, 28 short, and 3 vision papers included in this volume were carefully reviewed and selected from 189 submissions. They were organized in topical sections named: Blockchains and smart contracts, Architectures, microservices and APIs, Applications, Internet-of-Things, crowdsourced, social, and conversational services, Service composition and recommendation, Cloud computing, and Edge computing.