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

Integrating an agent-based wireless sensor network within an existing multi-agent condition monitoring system

The use of wireless sensor networks for condition monitoring is gaining ground across all sectors of industry, and while their use for power engineering applications has yet been limited, they represent a viable platform for next-generation substation condition monitoring systems. For engineers to fully benefit from this new approach to condition monitoring, new sensor data must be incorporated into a single integrated system. This paper proposes the integration of an agent-based wireless sensor network with an existing agent-based condition monitoring system. It demonstrates that multi-agent systems can be extended down to the sensor level while considering the reduced energy availability of low-power embedded devices. A novel agent-based approach to data translation is presented, which is demonstrated through two case studies: a lab-based temperature and vibration monitoring system, and a proposal to integrate a wireless sensor network to an existing technology demonstrator deployed in a substation in the UK

Flexible Sensor Network Reprogramming for Logistics

Besides the currently realized applications, Wireless Sensor Networks can be put to use in logistics processes. However, doing so requires a level of flexibility and safety not provided by the current WSN software platforms. This paper discusses a logistics scenario, and presents SensorScheme, a runtime environment used to realize this scenario, based on semantics of the Scheme programming language. SensorScheme is a general purpose WSN platform, providing dynamic reprogramming, memory safety (sandboxing), blocking I/O, marshalled communication, compact code transport. It improves on the state of the art by making better use of the little available memory, thereby providing greater capability in terms of program size and complexity. We illustrate the use of our platform with some application examples, and provide experimental results to show its compactness, speed of operation and energy efficiency

Middleware for Wireless Sensor Networks: An Outlook

In modern distributed computing, applications are rarely built directly atop operating system facilities, e.g., sockets. Higher-level middleware abstractions and systems are often employed to simplify the programmer’s chore or to achieve interoperability. In contrast, real-world wireless sensor network (WSN) applications are almost always developed by relying directly on the operating system. Why is this the case? Does it make sense to include a middleware layer in the design of WSNs? And, if so, is it the same kind of software system as in traditional distributed computing? What are the fundamental concepts, reasonable assumptions, and key criteria guiding its design? What are the main open research challenges, and the potential pitfalls? Most importantly, is it worth pursuing research in this field? This paper provides a (biased) answer to these and other research questions, preceded by a brief account on the state of the art in the field

Towards Business Processes Orchestrating the Physical Enterprise with Wireless Sensor Networks

The industrial adoption of wireless sensor net- works (WSNs) is hampered by two main factors. First, there is a lack of integration of WSNs with business process modeling languages and back-ends. Second, programming WSNs is still challenging as it is mainly performed at the operating system level. To this end, we provide makeSense: a unified programming framework and a compilation chain that, from high-level business process specifications, generates code ready for deployment on WSN nodes

Towards Python-based Domain-specific Languages for Self-reconfigurable Modular Robotics Research

This paper explores the role of operating system and high-level languages in the development of software and domain-specific languages (DSLs) for self-reconfigurable robotics. We review some of the current trends in self-reconfigurable robotics and describe the development of a software system for ATRON II which utilizes Linux and Python to significantly improve software abstraction and portability while providing some basic features which could prove useful when using Python, either stand-alone or via a DSL, on a self-reconfigurable robot system. These features include transparent socket communication, module identification, easy software transfer and reliable module-to-module communication. The end result is a software platform for modular robots that where appropriate builds on existing work in operating systems, virtual machines, middleware and high-level languages.Comment: Presented at DSLRob 2011 (arXiv:1212.3308

Proximal business intelligence on the semantic web

This is the post-print version of this article. The official version can be accessed from the link below - Copyright @ 2010 Springer.Ubiquitous information systems (UBIS) extend current Information System thinking to explicitly differentiate technology between devices and software components with relation to people and process. Adapting business data and management information to support specific user actions in context is an ongoing topic of research. Approaches typically focus on providing mechanisms to improve specific information access and transcoding but not on how the information can be accessed in a mobile, dynamic and ad-hoc manner. Although web ontology has been used to facilitate the loading of data warehouses, less research has been carried out on ontology based mobile reporting. This paper explores how business data can be modeled and accessed using the web ontology language and then re-used to provide the invisibility of pervasive access; uncovering more effective architectural models for adaptive information system strategies of this type. This exploratory work is guided in part by a vision of business intelligence that is highly distributed, mobile and fluid, adapting to sensory understanding of the underlying environment in which it operates. A proof-of concept mobile and ambient data access architecture is developed in order to further test the viability of such an approach. The paper concludes with an ontology engineering framework for systems of this type – named UBIS-ONTO

Engineering Resilient Collective Adaptive Systems by Self-Stabilisation

Collective adaptive systems are an emerging class of networked computational systems, particularly suited in application domains such as smart cities, complex sensor networks, and the Internet of Things. These systems tend to feature large scale, heterogeneity of communication model (including opportunistic peer-to-peer wireless interaction), and require inherent self-adaptiveness properties to address unforeseen changes in operating conditions. In this context, it is extremely difficult (if not seemingly intractable) to engineer reusable pieces of distributed behaviour so as to make them provably correct and smoothly composable. Building on the field calculus, a computational model (and associated toolchain) capturing the notion of aggregate network-level computation, we address this problem with an engineering methodology coupling formal theory and computer simulation. On the one hand, functional properties are addressed by identifying the largest-to-date field calculus fragment generating self-stabilising behaviour, guaranteed to eventually attain a correct and stable final state despite any transient perturbation in state or topology, and including highly reusable building blocks for information spreading, aggregation, and time evolution. On the other hand, dynamical properties are addressed by simulation, empirically evaluating the different performances that can be obtained by switching between implementations of building blocks with provably equivalent functional properties. Overall, our methodology sheds light on how to identify core building blocks of collective behaviour, and how to select implementations that improve system performance while leaving overall system function and resiliency properties unchanged.Comment: To appear on ACM Transactions on Modeling and Computer Simulatio