Distributed Data Retrieval for Real-Time Decision-Making under Freshness Constraints

This paper describes a distributed data retrieval algorithm for crowd-sensing application, which aims to collect data with minimized bandwidth cost while satisfying data freshness constraints. In a resource-limited setting, data loses freshness very fast. For instance, the condition of a road during a rush hour may be dynamic due to the rapid change of the traffic. In order to schedule an optimized route to a destination from a given location, we have to know its real-time condition. The protocol we design is to exploit logic dependencies among data by using and-or tree to reduce the overhead of the network and handle concurrent requests at the same time. Meanwhile, we further modify the centralized system into a distributed form so that each node in this network is able to calculate the best retrieval order locally. Furthermore, we integrate some ideas of other literature to let each node store the retrieved data locally due to the fact that the price of storage is lower and lower these days. Finally, we implement part of the algorithms and test the efficiency of using varying probabilities and earliest deadline first to sort queries.Ope

Decision-centric resource-efficient semantic information management

For the past few decades, we have put significant efforts in building tools that extend our senses and enhance our perceptions, be it the traditional sensor networks, or the more recent Internet-of-Things. With such systems, the lasting strives for efficiency and effectiveness have driven research forces in the community to keep seeking smarter ways to manage bigger data with lower resource consumptions, especially resource-poor environments such as post-disaster response and recovery scenarios. In this dissertation, we base ourselves on the state-of-the-arts studies, and build a set of techniques as well as a holistic information management system that not only account for data level characteristics, but, more importantly, take advantage of the higher information semantic level features as well as the even higher level decision logic structures in achieving effective and efficient data acquisition and dissemination. We first introduce a data prioritization algorithm that accounts for overlaps among data sources to maximize information delivery. We then build a set of techniques that directly optimize the efficiency of decision making, as opposed to only focusing on traditional, lower-level communication optimizations, such as total network throughput or average latency. In developing these algorithms, we view decisions as choices of a course of action, based on several logical predicates. Making a decision is thus reduced to evaluating a Boolean expression on these predicates; for example, "if it is raining, I will carry an umbrella." To evaluate a predicate, evidence is needed (e.g., a picture of the weather). Data objects, retrieved from sensors, supply the needed evidence for predicate evaluation. By using a decision-making model, our retrieval algorithms are able to take into consideration historical/domain knowledge, logical dependencies among data items, as well as information freshness decays, in order to prioritize data transmission to minimize overhead of transferring information needed by a variety of decision makers, while at the same time coping with query level timeliness requirements, environment dynamics, and system resource limitations. Finally we present the architecture for a distributed semantic-aware information management system, which we call Athena. We discuss its key design choices, and how we incorporate various techniques, such as interest book-keeping and label sharing, to improve information dissemination efficiency in realistic scenarios. For all the components as well as the whole Athena system, we will discuss our implementations and evaluations under realistic settings. Results show that our techniques improve the efficiency of information gathering and delivery in support of post-disaster situation assessment and decision making in the face of various environmental and systems constraints

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)

Privacy-Aware and Reliable Complex Event Processing in the Internet of Things - Trust-Based and Flexible Execution of Event Processing Operators in Dynamic Distributed Environments

The Internet of Things (IoT) promises to be an enhanced platform for supporting a heterogeneous range of context-aware applications in the fields of traffic monitoring, healthcare, and home automation, to name a few. The essence of the IoT is in the inter-networking of distributed information sources and the analysis of their data to understand the interactions between the physical objects, their users, and their environment. Complex Event Processing (CEP) is a cogent paradigm to infer higher-level information from atomic event streams (e.g., sensor data in the IoT). Using functional computing modules called operators (e.g., filters, aggregates, sequencers), CEP provides for an efficient and low-latency processing environment. Privacy and mobility support for context processing is gaining immense importance in the age of the IoT. However, new mobile communication paradigms - like Device-to-Device (D2D) communication - that are inherent to the IoT, must be enhanced to support a privacy-aware and reliable execution of CEP operators on mobile devices. It is crucial to preserve the differing privacy constraints of mobile users, while allowing for flexible and collaborative processing. Distributed mobile environments are also susceptible to adversary attacks, given the lack of sufficient control over the processing environment. Lastly, ensuring reliable and accurate CEP becomes a serious challenge due to the resource-constrained and dynamic nature of the IoT. In this thesis, we design and implement a privacy-aware and reliable CEP system that supports distributed processing of context data, by flexibly adapting to the dynamic conditions of a D2D environment. To this end, the main contributions, which form the key components of the proposed system, are three-fold: 1) We develop a method to analyze the communication characteristics of the users and derive the type and strength of their relationships. By doing so, we utilize the behavioral aspects of user relationships to automatically derive differing privacy constraints of the individual users. 2) We employ the derived privacy constraints as trust relations between users to execute CEP operators on mobile devices in a privacy-aware manner. In turn, we develop a trust management model called TrustCEP that incorporates a robust trust recommendation scheme to prevent adversary attacks and allow for trust evolution. 3) Finally, to account for reliability, we propose FlexCEP, a fine-grained flexible approach for CEP operator migration, such that the CEP system adapts to the dynamic nature of the environment. By extracting intermediate operator state and by leveraging device mobility and instantaneous characteristics, FlexCEP provides a flexible CEP execution model under varying network conditions. Overall, with the help of thorough evaluations of the above three contributions, we show how the proposed distributed CEP system can satisfy the requirements established above for a privacy-aware and reliable IoT environment

Deep Learning in Mobile and Wireless Networking: A Survey

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research