Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services

The Internet of Things (IoT) has made it possible for devices around the world to acquire information and store it, in order to be able to use it at a later stage. However, this potential opportunity is often not exploited because of the excessively big interval between the data collection and the capability to process and analyse it. In this paper, we review the current IoT technologies, approaches and models in order to discover what challenges need to be met to make more sense of data. The main goal of this paper is to review the surveys related to IoT in order to provide well integrated and context aware intelligent services for IoT. Moreover, we present a state-of-the-art of IoT from the context aware perspective that allows the integration of IoT and social networks in the emerging Social Internet of Things (SIoT) term.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER) under the granted Project SEQUOIA-UA (Management requirements and methodology for Big Data analytics) TIN2015-63502-C3-3-R, by the University of Alicante, within the program of support for research, under project GRE14-10, and by the Conselleria de Educación, Investigación, Cultura y Deporte, Comunidad Valenciana, Spain, within the program of support for research, under project GV/2016/087. This work has also been partially funded by projects from the Spanish Ministry of Education and Competitivity TIN2015-65100-R and DIIM2.0 (PROMETEOII/2014/001)

Internet of Things Strategic Research Roadmap

Internet of Things (IoT) is an integrated part of Future Internet including existing and evolving Internet and network developments and could be conceptually defined as a dynamic global network infrastructure with self configuring capabilities based on standard and interoperable communication protocols where physical and virtual “things” have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network

Service Composition for IP Smart Object using Realtime Web Protocols: Concept and Research Challenges

The Internet of Things (IoT) refers to a world-wide network of interconnected physical things using standardized communication protocols. Recent development of Internet Protocol (IP) stacks for resource-constrained devices unveils a possibility for the future IoT based on the stable and scalable IP technology much like today's Internet of computers. One important question remains: how can data and events (denoted as services) introduced by a variety of IP networked things be exchanged and aggregated e ciently in various application domains. Because the true value of IoT lies in the interaction of several services from physical things, answers to this question are essential to support a rapid creation of new IoT smart and ubiquitous applications. The problem is known as service composition. This article explains the practicability of the future full-IP IoT with realtime Web protocols to formally state the problem of service composition for IP smart objects, provides literature review, and discusses its research challenges

Towards a methodology for the engineering of event-driven process applications

Successful applications of the Internet of Things such as smart cities, smart logistics, and predictive maintenance, build on observing and analyzing business-related objects in the real world for business process execution and monitoring. In this context, complex event processing is increasingly used to integrate events from sensors with events stemming from business process management systems. This paper describes a methodology to combine the areas and engineer an event-driven logistics processes application. Thereby, we describe the requirements, use cases and lessons learned to design and implement such an architecture

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

Unified Theory of Relativistic Identification of Information in a Systems Age: Proposed Convergence of Unique Identification with Syntax and Semantics through Internet Protocol version 6

Unique identification of objects are helpful to the decision making process in many domains. Decisions, however, are often based on information that takes into account multiple factors. Physical objects and their unique identification may be one of many factors. In real-world scenarios, increasingly decisions are based on collective information gathered from multiple sources (or systems) and then combined to a higher level domain that may trigger a decision or action. Currently, we do not have a globally unique mechanism to identify information derived from data originating from objects and processes. Unique identification of information, hence, is an open question. In addition, information, to be of value, must be related to the context of the process. In general, contextual information is of greater relevance in the decision making process or in decision support systems. In this working paper, I shall refer to such information as decisionable information. The suggestion here is to utilize the vast potential of internet protocol version six (IPv6) to uniquely identify not only objects and processes but also relationships (semantics) and interfaces (sensors). Convergence of identification of diverse entities using the globally agreed structure of IPv6 offers the potential to identify 3.4x10[subscript 38] instances based on the fact that the 128-bit IPv6 structure can support 3.4x10[subscript 38] unique addresses. It is not necessary that all instances must be connected to the internet or routed or transmitted simply because an IP addressing scheme is suggested. This is a means for identification that will be globally unique and offers the potential to be connected or routed via the internet. In this working paper, scenarios offer [1] new revenue potential from data routing (P2P traffic track and trace) for telecommunication industries, [2] potential for use in healthcare and biomedical community, [3] scope of use in the semantic web structure by transitioning URIs used in RDF, [4] applications involving thousands of mobile ad hoc sensors (MANET) that demand dynamic adaptive auto-reconfiguration. This paper presents a confluence of ideas

Decision Support and Systems Interoperability in Global Business Management

Globalization of business and volatility of financial markets has catapulted ‘cycle-time’ as a key indicator of operational efficiency in business processes. Systems automation holds the promise to augment the ability of business and healthcare networks to rapidly adapt to changes or respond, with minimal human intervention, under ideal conditions. Currently, system of systems (SOS) or organization of networks contribute minimally in making decisions because collaboration remains elusive due the challenges of complexity. Convergence and maturity of research offers the potential for a paradigm shift in interoperability. This paper explores some of these trends and related technologies. Irrespective of the characteristics of information systems, the development of various industry-contributed ontologies for knowledge and decision layers, may spur self-organizing SOS to increase the ability to sense and respond. Profitability from pervasive use of ontological frameworks and agent-based modeling may depend on the ability to use them through better enterprise and extraprise exchange