Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Federating Heterogeneous Digital Libraries by Metadata Harvesting

This dissertation studies the challenges and issues faced in federating heterogeneous digital libraries (DLs) by metadata harvesting. The objective of federation is to provide high-level services (e.g. transparent search across all DLs) on the collective metadata from different digital libraries. There are two main approaches to federate DLs: distributed searching approach and harvesting approach. As the distributed searching approach replies on executing queries to digital libraries in real time, it has problems with scalability. The difficulty of creating a distributed searching service for a large federation is the motivation behind Open Archives Initiatives Protocols for Metadata Harvesting (OAI-PMH). OAI-PMH supports both data providers (repositories, archives) and service providers. Service providers develop value-added services based on the information collected from data providers. Data providers are simply collections of harvestable metadata. This dissertation examines the application of the metadata harvesting approach in DL federations. It addresses the following problems: (1) Whether or not metadata harvesting provides a realistic and scalable solution for DL federation. (2) What is the status of and problems with current data provider implementations, and how to solve these problems. (3) How to synchronize data providers and service providers. (4) How to build different types of federation services over harvested metadata. (5) How to create a scalable and reliable infrastructure to support federation services. The work done in this dissertation is based on OAI-PMH, and the results have influenced the evolution of OAI-PMH. However, the results are not limited to the scope of OAI-PMH. Our approach is to design and build key services for metadata harvesting and to deploy them on the Web. Implementing a publicly available service allows us to demonstrate how these approaches are practical. The problems posed above are evaluated by performing experiments over these services. To summarize the results of this thesis, we conclude that the metadata harvesting approach is a realistic and scalable approach to federate heterogeneous DLs. We present two models of building federation services: a centralized model and a replicated model. Our experiments also demonstrate that the repository synchronization problem can be addressed by push, pull, and hybrid push/pull models; each model has its strengths and weaknesses and fits a specific scenario. Finally, we present a scalable and reliable infrastructure to support the applications of metadata harvesting

Creating an Agent Based Framework to Maximize Information Utility

With increased reliance on communications to conduct military operations, information centric network management becomes vital. A Defense department study of information management for net-centric operations lists the need for tools for information triage (based on relevance, priority, and quality) to counter information overload, semi-automated mechanisms for assessment of quality and relevance of information, and advances to enhance cognition and information understanding in the context of missions [30]. Maximizing information utility to match mission objectives is a complex problem that requires a comprehensive solution in information classification, in scheduling, in resource allocation, and in QoS support. Of these research areas, the resource allocation mechanism provides a framework to build the entire solution. Through an agent based mindset, the lessons of robot control architecture are applied to the network domain. The task of managing information flows is achieved with a hybrid reactive architecture. By demonstration, the reactive agent responds to the observed state of the network through the Unified Behavior Framework (UBF). As information flows relay through the network, agents in the network nodes limit resource contention to improve average utility and create a network with smarter bandwidth utilization. While this is an important result for information maximization, the agent based framework may have broader applications for managing communication networks

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future

Softwarization of Large-Scale IoT-based Disasters Management Systems

The Internet of Things (IoT) enables objects to interact and cooperate with each other for reaching common objectives. It is very useful in large-scale disaster management systems where humans are likely to fail when they attempt to perform search and rescue operations in high-risk sites. IoT can indeed play a critical role in all phases of large-scale disasters (i.e. preparedness, relief, and recovery). Network softwarization aims at designing, architecting, deploying, and managing network components primarily based on software programmability properties. It relies on key technologies, such as cloud computing, Network Functions Virtualization (NFV), and Software Defined Networking (SDN). The key benefits are agility and cost efficiency. This thesis proposes softwarization approaches to tackle the key challenges related to large-scale IoT based disaster management systems. A first challenge faced by large-scale IoT disaster management systems is the dynamic formation of an optimal coalition of IoT devices for the tasks at hand. Meeting this challenge is critical for cost efficiency. A second challenge is an interoperability. IoT environments remain highly heterogeneous. However, the IoT devices need to interact. Yet another challenge is Quality of Service (QoS). Disaster management applications are known to be very QoS sensitive, especially when it comes to delay. To tackle the first challenge, we propose a cloud-based architecture that enables the formation of efficient coalitions of IoT devices for search and rescue tasks. The proposed architecture enables the publication and discovery of IoT devices belonging to different cloud providers. It also comes with a coalition formation algorithm. For the second challenge, we propose an NFV and SDN based - architecture for on-the-fly IoT gateway provisioning. The gateway functions are provisioned as Virtual Network Functions (VNFs) that are chained on-the-fly in the IoT domain using SDN. When it comes to the third challenge, we rely on fog computing to meet the QoS and propose algorithms that provision IoT applications components in hybrid NFV based - cloud/fogs. Both stationary and mobile fog nodes are considered. In the case of mobile fog nodes, a Tabu Search-based heuristic is proposed. It finds a near-optimal solution and we numerically show that it is faster than the Integer Linear Programming (ILP) solution by several orders of magnitude