City Data Fusion: Sensor Data Fusion in the Internet of Things

Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. We then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. Our main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed Systems and Technologies (IJDST), 201

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges

The Internet of Things (IoT) refers to a network of connected devices collecting and exchanging data over the Internet. These things can be artificial or natural, and interact as autonomous agents forming a complex system. In turn, Business Process Management (BPM) was established to analyze, discover, design, implement, execute, monitor and evolve collaborative business processes within and across organizations. While the IoT and BPM have been regarded as separate topics in research and practice, we strongly believe that the management of IoT applications will strongly benefit from BPM concepts, methods and technologies on the one hand; on the other one, the IoT poses challenges that will require enhancements and extensions of the current state-of-the-art in the BPM field. In this paper, we question to what extent these two paradigms can be combined and we discuss the emerging challenges

Towards Run-Time Verification of Compositions in the Web of Things using Complex Event Processing

Following the vision of the Internet of Things, physical world entities are integrated into virtual world things. Things are expected to become active participants in business and social processes. Then, the Internet of Things could benefit from the Web Service architecture like today’s Web does, so Future ser-vice-oriented Internet things will offer their functionality via service-enabled in-terfaces. In previous work, we demonstrated the need of considering the behav-iour of things to develop applications in a more rigorous way, and we proposed a lightweight model for representing such behaviour. Our methodology relies on the service-oriented paradigm and extends the DPWS profile to specify the order with which things can receive messages. We also proposed a static verifi-cation technique to check whether a mashup of things respects the behaviour, specified at design-time, of the composed things. However, a change in the be-haviour of a thing may cause that some compositions do not fulfill its behaviour anymore. Moreover, given that a thing can receive requests from instances of different mashups at run-time, these requests could violate the behaviour of that thing, even though each mashup fulfills such behaviour, due to the change of state of the thing. To address these issues, we present a proposal based on me-diation techniques and complex event processing to detect and inhibit invalid invocations, so things only receive requests compatible with their behaviour.Work partially supported by projects TIN2008-05932, TIN2012-35669, CSD2007-0004 funded by Spanish Ministry MINECO and FEDER; P11-TIC-7659 funded by Andalusian Government; and Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tec

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Ontology-based context-aware model for event processing in an IoT environment

The Internet of Things (IoT) is more and more becoming one of the fundamental sources of data. The observations produced by these sources are made accessible with heterogeneous vocabularies, models and data formats. The heterogeneity factor in such an enormous environment complicates the task of sharing and reusing this data in a more intelligent way (other than the purposes it was initially set up for). In this research, we investigate these challenges, considering how we can transform raw sensor data into a more meaningful information. This raw data will be modelled using ontology-based information that is accessible through continuous queries for sensor streaming data.Interoperability among heterogeneous entities is an important issue in an IoT environment. Semantic modelling is a key element to support interoperability. Most of the current ontologies for IoT mainly focus on resources and services information. This research builds upon the current state-of-the-art ontologies to provide contextual information and facilitate sensor data querying. In this research, we present an Ontology to represent an IoT environment, with emphasis on temporal and geospatial context enrichment. Furthermore, the Ontology is used alongside a proposed syntax based on Description Logic to build an Event Processing Model. The aim of this model is to interconnect ontology-based reasoning with event processing. This model enables to perform event processing over high-level ontological concepts.The Ontology was developed using the NeOn methodology, which emphasises on the reuse and modularisation. The Competency Questions techniques was used to develop the requirements of this Ontology. This was later evaluated by domain experts in software engineering and cloud computing. The ontology was evaluated based on its completeness, conciseness, consistency and expandability, over 70% of the domain experts agreed on the core modules, concepts and relationships within the ontology. The resulted Ontology provides a core IoT ontology that could be used for further development within a specific IoT domain. IIThe proposed Ontology-Based Context-Aware model for Event-Processing in an IoT environment “OCEM-IoT”, implements all the time operators used in complex event processing engines. Throughput and latency were used as performance comparison metrics for the syntax evaluation; the results obtained show an improved performance over existing event processing languages