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Device-Agent Based Middleware Approach for Mixed Mode Environments.
Abstract
In this paper we introduce a new agent-based approach for middleware in mixed mode environments. Mixed mode environments have different dimensions of heterogeneity: devices with widely differing capabilities, different soft- ware, and different communication technologies. To deal with these, each node in the network is seen as an inde- pendent entity: a device agent. These device agents are abstractions of the network nodes and they offer services corresponding to nodes' capabilities and also use services offered by other agents. Interoperability is achieved through an agent interaction interface. For the description of device types, capabilities and services a predefined but extensible ontology is used. This approach combines and adopts re- sults from the areas of multi agent systems and heteroge- neous ad hoc networks.
... • A3ME (Agent-based Middleware Approach for Mixed Mode Environments) ontology is a basic classification for self-description and discovery of devices [45], [44]. Devices are classified as tag, mote, mobile, workstation, Server, vehicle and multimedia. ...
... Nevertheless, several NFPs are defined, providing more or less details on IoT devices or services. Several ontologies have been defined for sensors and actuators , such as FIPA device ontology, CSIRO SENSOR ontology [68], ONTOSENSOR [79], SWAMO ontology [92], [99] SDO [26], [27], MMI device ontology Ontology [38], SENSORML processes [9], [77], CESN ontology [10], WISNO [46], A3ME ontology [45], [44], ONTONYM -SENSOR [24], SSN ontology. As synthetized in table 2.5 , some of them propose Non Functional Property (NFP) descriptions. ...
To fit the renewed globalized economic environment, enterprises, and mostly SMEs, have to develop new networked and collaborative strategies, focusing on networked value creation (instead of the classical value chain vision), fitting the blue ocean context for innovative products and service development. Even if collaborative organizations have been studied for decades, the closer connection of information systems involved by the so-called “Industry 4.0” developed by leading industries in Europe, US and Asia requires to set new IT models to support agile and evolving collaborative Business Process (BP) enactment, integrating both traditional Information Systems (IS) and production control processes. By now, these product/service ecosystems are mostly supported by software services, which span multiple organizations and providers, and on multiple cloud-based execution environments, increasing the call for openness, agility, interoperability and trust for both production and Information System organization. These requirements are well supported by SOA, Web 2.0 and XaaS technologies for Information Systems. Taking advantage of IoT, services and Cloud technologies, the development of Cloud of Things (CoT) changes the way control application are engineered and developed moving from a dedicated design and development of control applications to a Control as a Service vision. This vision requires developing a new architecture to connect physical and logical objects as well as integrating basic control patterns to organize a consistent control service orchestration. To fit this challenge, we propose a multi-layer Control as a Service architecture to describe control systems in a holistic way. Our Control service model is built according to an event-driven orchestration strategy. Thanks to the integration of a context manager, analyzing continuously the system environment as well as the control system behavior, these context-aware control services can be deployed
... Ontologies are particularly fitting to handle the challenges arising from the large volume and variety of transportation related data (e.g., survey, sensor data) [6][7]. Majority of the ontologies developed in transportation research domain focus on trip planning [1,8], trip disruption [9], traffic management [10][11], service monitoring [12], and urban freight transport problems [13,14]. Despite the extensive use of ontologies in transportation research, to the best of the authors' knowledge, there is no scholarly work available in literature that directly focuses on an integrated ontology for transportation infrastructure planning. ...
... SWAMO [141] describes physical devices and process models and tasks in a distributed and intelligent software agents environment. And A3ME ontology [142] was developed to classify devices and their capabilities in a heterogeneous network. The ISTAR [143] ontology was developed as part of a system to automatically select sensors for tasks based on their fitness for the task description. ...
Nowadays, the adoption of the Internet of Things (IoT) has received a considerable interest from both academia and industry. It provides enhancements in quality of life, business growth and efficiency in multiple domains. However, the heterogeneity of the "Things" that can be connected in such environments makes interoperability among them a challenging problem. Moreover, the observations produced by these "Things" are made available with heterogeneous vocabularies and data formats. This heterogeneity prevents generic solutions from being adopted on a global scale and makes difficult to share and reuse data for other purposes than those for which they were originally set up. In this thesis, we address these challenges in the context of healthcare applications considering how we transform raw data to cognitive knowledge and ontology-based information shared between IoT system components. With respect to heterogeneity and integration challenges, our main contribution is an ontology-based IoT architecture allowing the deployment of semantic IoT applications. This approach allows sharing of sensors observations, contextualization of data and reusability of knowledge and processed information. Specific contributions include: * Design of the Cognitive Semantic Sensor Network ontology (CoSSN) ontology: CoSSN aims at overcoming the semantic interoperability challenges introduced by the variety of sensors potentially used. It also aims at describing expert knowledge related to a specific domain. * Design and implementation of SeMoM: SeMoM is a flexible IoT architecture built on top of CoSSN ontology. It relies on a message oriented middleware (MoM) following the publish/subscribe paradigm for a loosely coupled communication between system components that can exchange semantic observation data in a flexible way. From the applicative perspective, we focus on healthcare applications. Indeed, specific approaches and individual prototypes are preeminent solutions in healthcare which straighten the need of an interoperable solution especially for patients with multiple affections. With respect to these challenges, we elaborated two case studies 1) bedsore risk detection and 2) Activities of Daily Living (ADL) detection as follows: * We developed extensions of CoSSN to describe each domain concepts and we developed specific applications through SeMoM implementing expert knowledge rules and assessments of bedsore and human activities. * We implemented and evaluated the SeMoM framework in order to provide a proof of concept of our approach. Two experimentations have been realized for that target. The first is based on a deployment of a system targeting the detection of ADL activities in a real smart platform. The other one is based on ADLSim, a simulator of activities for ambient assisted living that can generate a massive amount of data related to the activities of a monitored person.
... Prior to SSN ontology, several sensor ontologies had been established. Compton et al. [28] and Wang et al. [29] reviewed the typical sensor ontologies such as the CSIRO sensor ontology [30], which describes sensors, observations and scientific models for use in workflows, the CESN ontology [31], which considers a coastal environmental sensor with instrument, deployment, location and physical properties for inferring domain knowledge from coastal observation data, the A3ME ontology [32], which was developed to classify devices and their capabilities in a heterogeneous network, with a focus on making the ontology usable on resource constrained devices, the SWAMO ontology [33], which describes physical devices, process models and tasks for intelligent software agents, the MMI ontology [34], which is intended for marine equipment interoperability and focuses on system structure and observational processes and results, the OntoSensor ontology [35], which was created to build a knowledge base of sensors for querying and reasoning by setting sensors, capabilities descriptions and measurements as key framework concepts. These ontologies were built for their specific scope of application and have no universality. ...
Observation schedules depend upon the accurate understanding of a single sensor’s observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no study has described the dynamic associations among the observation capabilities of multiple sensors used for integrated observational planning. This limitation results in a failure to realize effective sensor selection. This paper develops a sensor observation capability association (SOCA) ontology model that is resolved around the task-sensor-observation capability (TSOC) ontology pattern. The pattern is developed considering the stimulus-sensor-observation (SSO) ontology design pattern, which focuses on facilitating sensor selection for one observation task. The core aim of the SOCA ontology model is to achieve an observation capability semantic association. A prototype system called SemOCAssociation was developed, and an experiment was conducted for flood observations in the Jinsha River basin in China. The results of this experiment verified that the SOCA ontology based association method can help sensor planners intuitively and accurately make evidence-based sensor selection decisions for a given flood observation task, which facilitates efficient and effective observational planning for flood satellite sensors.
... The Proteus project [MP08] which supports scientific knowledge transfer between different robotics communities of France, by representing robots and scenarios in an ontology to guaranty a commune and standardize use. The A3ME ontology [HJB08] defines heterogeneous mobile devices for crisis situations application, to allow communication interoperability between them, independently to the hardware platform, to the operating system or to the communication system. The SWAMO NASA project [WSS + 08, UPWS11] uses ontology as a prototyping method to provide standard interfaces to access different mission resources (sensors, agent capabilities...) of spacecrafts. ...
In prospective planetary missions, heterogeneous vehicles such as orbiter, lander, rover, blimp will have to cooperate in-situ in order to increase the overall exploration capabilities. This paper is aimed at presenting the current status of modelling and methodological developments to support the definition and implementation of an In Situ Interaction Service for any space vehicle subject to cooperate in such missions. The main objective of this work is to increase the overall system's autonomy by allowing local direct interactions between vehicles, with minimal intervention from the ground control. The knowledge modeling and sharing , using ontology as formalization support, as well as the implementation of associated interactions mechanisms will be the the basis to support an efficient cooperation. A simulation environment based on ROS, is currently under development to test and validate these concepts .
The fast development of the so-called shared economy as well as collaborative networked organization increases the call for responsive and strongly interconnected production and delivery systems. The closer connection of information systems and control systems has to be achieved, as “Industry 4.0” developed by leading industries in Europe, USA and Asia requires to set new IT models in order to support agile collaborative business processes and production control processes. Whereas the service-based organizations and technologies, such as Service-Oriented Architecture, Web 2.0, Cloud and Everything as a Service technologies as well as Business models, have been successfully deployed to support agile systems relying on service selection, composition and orchestration, only few works have addressed cloud-based control systems. Despite the flexibility provided by the Internet of Thing, the integration of Cyber-Physical systems (CPS) in production control system is limited due to their lack of interoperability. To overcome this limit and provide a smooth integration of CPS systems in collaborative production process control, we propose a service-based control model integrating sensors, controllers and actuators. To support the Industry 4.0 digital transformation, including collaborative production process design and management, our Control as a Service model relies on control ontology. Our control ontology gathers IoT devices (sensors and actuators…) and services, including their functional and non-functional properties description. Then, a Control as a Service architecture is designed to facilitate the development of cloud control system via two steps: service pre-selection/pre-composition, event-driven and context-aware service orchestration. The measurement of three core components, data manager, event manager and context manager, shows the feasibility of application of our Control as a Service model/architecture in a supply chain temperature control use case.
Frailty is a common clinical syndrome in older adults; it carries an increased risk of negative health events and outcomes including falls, incident disability, hospitalization, and mortality. Therefore, it is critical to identify high-risk subsets of the elderly population and explore new arenas for frailty prevention and treatment. This chapter will provide an overview of the current state of assessment models for frailty syndrome in the elderly and will describe a new assessment tool based on mobile technology, which takes account and advantage of the ways in which elderly people interact with a touchscreen. While healthcare providers and researchers in the field of aging have long been aware of the changing characteristics and needs of older people living in the community, there has not been any marked change in frailty syndrome assessment models until now. In the twenty-first century world with its technological advancements, the elderly require new, special physical skills combining perceptual, motor, and cognitive abilities for their functional daily activities and for maintaining their independence and quality of life. We believe a conceptual model of frailty can be expanded to incorporate new aspects related to the usage of technology by the elderly, better covering the complexity and multidimensionality of modern life. In addition, in our vision, that the expanded conceptual model can be operationalized and translated into an assessment tool. In the last part of this chapter, we will present the “Touchscreen Assessment Tool” (TATOO), an assessment tool based on this expanded conceptual model. This novel tool assesses elderly people’s frailty and functioning using a touchscreen, a representative technology required for several activities involved in daily functioning in the modern world. Most importantly, we present the TATOO prototype, which we plan to develop in the future as a continuous monitoring instrument for activities performed in daily life, combined with advanced sensor-based measuring and big data analytics algorithms.
This chapter presents an overview of many wearable devices of different types that have been proven in medical and home environments as being helpful in Quality of Life enhancement of elder adults. The recent advances in electronics and microelectronics allow the development of low-cost devices that are widely used by many people as monitoring tools for well-being or preventive purposes. Remote healthcare monitoring, which is based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers efficient solutions that allows people to live in their comfortable home environment, being somehow protected. Furthermore, the expensive healthcare facilities are getting free to be used for intensive care patients as the preventive measures are getting at home. The remote systems can monitor very important physiological parameters of the patients in real time, observe health conditions, assessing them, and most important, provide feedback. Sensors are used in electronics medical and non-medical equipment and convert various forms of vital signs into electrical signals. Sensors can be used for life-supporting implants, preventive measures, long-term monitoring of disabled or ill patients. Healthcare organizations like insurance companies need real-time, reliable, and accurate diagnostic results provided by sensor systems that can be monitored remotely, whether the patient is in a hospital, clinic, or at home.
Raumautomationssysteme (RA-Systeme) können einen ganz wesentlichen Beitrag zur Energieeinsparung und CO2-Reduktion von Gebäuden leisten. Die hohen Investitionskosten für ihre Errichtung erweisen sich jedoch als Hemmnis für eine weitere Verbreitung, sodass sie ihr Potenzial nicht umfassend entfalten können. Einen wichtigen Ansatz zur zukünftigen Verbesserung dieser Situation bietet die Automatisierung des Entwurfs von RA-Systemen, wodurch sowohl die Entwurfskosten als auch die Gesamtkosten der Systeme gesenkt werden können.
Ein fundamentales Defizit verhinderte jedoch bislang einen automatisierten Entwurf, nämlich das Fehlen von maschinenverarbeitbarem Wissen über die Gebäudeautomationsgeräte und ihre Interoperabilität, das für die rechnerbasierte Lösung der Entwurfsaufgabe unverzichtbar ist. Denn nach aktuellem Stand der Technik existiert kein geeigneter elektronischer Gerätebeschreibungsansatz, der die erforderlichen Informationen beschreibt und einen automatisierten Entwurf ermöglicht.
Das Ergebnis dieser Arbeit bildet daher ein neuartiger semantischer Gerätebeschreibungsansatz, der für Gebäudeautomationsgeräte verschiedener Technologien, Hersteller, Gewerke und Komplexität geeignet ist. Er basiert auf einem erweiterbaren Geräte- und Funktionsprofilmodell, das mit der Ontologiesprache OWL formalisiert ist. Der semantische Gerätebeschreibungsansatz unterstützt die Spezifikation von Geräteanforderungen, die rechnerbasierte Suche geeigneter Geräte, ihre grundlegende Parametrierung und automatische logische Komposition zu funktionierenden, interoperablen Gesamtsystemen. Eine wesentliche Besonderheit besteht darin, dass die Geräte und ihre Funktionsprofile nicht mehr allein technisch und syntaktisch, sondern auch semantisch präzise spezifiziert werden können. Durch die Einführung eines semantischen Interoperabilitätsmodells können hiermit automatisiert wesentlich zuverlässigere Interoperabilitätsaussagen getroffen werden, als es herkömmliche elektronische Gerätebeschreibungen ermöglichen.
Durch die Verwendung von OWL bilden die semantischen Gerätebeschreibungen ein wissensbasiertes Modell mit großer Ausdrucksstärke, das die Definition komplexer logischer Ausdrücke und Regeln und ihre Verarbeitung durch Reasoning ermöglicht. Dies erweist sich als entscheidender Vorteil, um eine hohe Qualität der Gerätebeschreibungen und der hiermit erzeugten Entwürfe mittels wissensbasierter Konformitätsprüfung in generischer Weise sicherzustellen.
Zusammenfassend betrachtet stellt der semantische Gerätebeschreibungsansatz alle entwurfsrelevanten Informationen erstmalig in maschinenverarbeitbarer Form zur Verfügung. Er kann damit das bestehende fundamentale Defizit technisch beheben und den automatisierten Entwurf von interoperablen Multivendor-Raumautomationssystemen verschiedener Technologien durch geeignete Entwurfsalgorithmen ermöglichen.
Wireless sensor networks (WSNs) were made possible around the late 1990s by industry scale availability of small and energy efficient microcontrollers and radio interfaces. Application areas for WSNs range from agriculture to health care and emergency response scenarios. Depending on the scenario a sensor network can span from some rooms to an area of several square miles in size and so the number of sensor nodes can vary from a fistful of nodes to hundreds or thousands. Sensor nodes are composed from a set of building blocks: processing, communication, sensing/actuating and a power supply. The power supply is usually a battery pack. Especially these limited energy resources make it tremendously important to save resources to achieve a long lifetime.
Today’s WSNs are usually planned and developed to satisfy only one application, and they are controlled by a single user. But, with the Internet of Things approaching, more and more sensor networks will be used for multiple tasks simultaneously and are reaching larger sizes. As sensor networks grow it becomes mandatory to localize traffic, both for energy conservation as well as security. Additionally, the broadcast medium of the wireless channel of WSNs allows an adversary all sorts of attacks, like eavesdropping, replaying messages, and denial of service attacks. In large or unattended networks it is even possible to physically attack the hardware of a sensor node to gain access to its firmware and cryptographic keys.
In this work we propose the Scopes Framework and the security enhancement Sec- Scopes. The Scopes Framework introduces dynamic partitioning of a WSN with support for multiple in-network tasks. SecScopes enables secure access control, key exchange and communication.
The partitioning is done by a scoping mechanism which allows the dynamic defini- tion of subsets of sensor nodes. The Scopes Framework supports in-network tasks by managing network connections for each task, and allowing the selection of efficient routing algorithms. To allows access control on a partition of the network we introduce attribute-based encryption in sensor networks. Secure key exchange is also based on this encryption scheme. To secure communication more efficient symmetric cryptography is employed.
With the Scopes Framework we provide a modular and flexible architecture that can be adjusted to the needs of different scenarios. We present a detailed evaluation of the performance of the framework and compare and discuss the results for the different stages of the framework. The results of the evaluation show the general feasibility of the approach, in spite of the adverse resource constraints.
Semantic sensor networks use declarative descriptions of sensors promote reuse and integration, and to help solve the difficulties of installing, querying and maintaining complex, heterogeneous sensor networks. This paper reviews the state of the art for the semantic specification of sensors, one of the fundamental technologies in the semantic sensor network vision. Twelve sensor ontologies are reviewed and analysed for the range and expressive power of their concepts. The reasoning and search technology developed in conjunction with these ontologies is also reviewed, as is technology for annotating OGC standards with links to ontologies. Sensor concepts that cannot be expressed accurately by current sensor ontologies are also discussed.
The Sensor Web is a coordinated observation infrastructure composed of distributed resources that can behave as a single, autonomous, task-able, reconfigurable observing system that provides observed and derived data along with the associated metadata by using a set of standards-based service oriented interfaces. But these resources, including sensor, data, platform etc., with different characteristics are hard to be fused well. This paper analyzes concepts of various types of sensor web resources for atmospheric observing, focusing on how to access different types of resources expediently, abstract essential features of these sensor web resources, and construct a Sensor Web Resources Ontology for Atmospheric Observation (SWROAO) represented by Web Ontology Language. The SWROAO could serve as a knowledge repository of sensor web resources for the research and application community in atmospheric science.
This technical report introduces goals and early findings of a multi-year multi-party project MUNDO. MUNDO is a pool of services and enablers for mobile and ubiquitous computing. It emphasizes easy global evolutionary deployment as a migra- tion path for the Internet as a whole towards a nomadic computing economy. The no- madic users access the network via personal devices and associated public and private appliances. The network is liberated from common constraints such as central opera- tors and fixed home bases of users. It hosts services which are by themselves migrat- able and highly adaptive with respect to region and context-awareness, quality varia- tions, and terminal characteristics. Migration and adaptation may be realized by dif- ferent implementations of the same service. An underlying general cost model helps to mediate user desires and service offerings. A number of scenarios and correspond- ing MUNDO application pools are described in order to motivate the features and scope of MUNDO. We describe a "radio on steroids" application pool called DACAR in more detail. We outline the architecture of MUNDO and elaborate its system services.
The use of a novel approach, middleware layer, to fully meet the design and implementation challenges of wireless sensor network technologies is discussed. It is suggested that a complete middleware solution should contain a runtime environment that supports and coordinates multiple applications to achieve adaptive and efficient system resources use to prolong the life of the sensor network. The design and development of a successful middleware layer must address many challenges such as managing limited power and resources, scalability, heterogeneity, real-world integration, and quality of service. Middleware using autonomic computing could provide more robustness, reliability, and self-management.
In future computing environments, networked sensors will play an increasingly important role in mediating between the physical and virtual worlds. However, programming sensor networks, and the applications that depend on the data they produce, is extremely challenging. The need for suitable middleware to address this problem is evident. In the last few years, various middleware solutions for sensor networks have emerged. These dier in terms of their mod- els for querying and data aggregation, and their assumptions about the topology and other characteristics of the network. Naturally, the assumptions made for each particular mid- dleware limit its potential applicability. Most of the current solutions provide relatively simple query abstractions, and therefore are not suitable for applications that have sophis- ticated requirements for processing of sensor data in the network. This paper presents a survey and analysis of the current state-of-the art in the field, highlighting the open re- search challenges. It also draws on the authors' experience with developing middleware for context-aware systems - that is, systems that rely on sensor-derived data to intelligently adapt their behaviour - to propose some future directions for the development of middleware for sensor networks.
In the rapidly developing field of sensor networks, bridging the gap between the applications and the hardware presents a major challenge. Although middleware is one solution, it must be specialized to the qualities of sensor networks, especially energy consumption. The work presented here provides two contributions: a new operational setting for sensor networks and a middleware for easing software development in this setting. The operational setting we target removes the usual assumption of a central collection point for sensor data. Instead the sensors are sparsely distributed in an environment, not necessarily able to communicate among themselves, and a set of clients move through space accessing the data of sensors nearby, yielding a system which naturally provides context relevant information to client applications. We further assume the clients are wirelessly networked and share locally accessed data. This scenario is relevant, for example, when relief workers access the information in their zone and share this information with other workers. Our second contribution, the middleware itself is an extension of LlME, our earlier work on middleware for mobile ad hoc networks. The model makes sensor data available through a tuple space interface, providing the illusion of shared memory between applications and sensors. This paper presents both the model and the implementation of our middleware incorporated with the Crossbow Mote sensor platform.
This article presents Agilla, a mobile agent middleware designed to support self-adaptive applications in wireless sensor networks. Agilla provides a programming model in which applications consist of evolving communities of agents that share a wireless sensor network. Coordination among the agents and access to physical resources are supported by a tuple space abstraction. Agents can dynamically enter and exit a network and can autonomously clone and migrate themselves in response to environmental changes. Agilla's ability to support self-adaptive applications in wireless sensor networks has been demonstrated in the context of several applications, including fire detection and tracking, monitoring cargo containers, and robot navigation. Agilla, the first mobile agent system to operate in resource-constrained wireless sensor platforms, was implemented on top of TinyOS. Agilla's feasibility and efficiency was demonstrated by experimental evaluation on two physical testbeds consisting of Mica2 and TelosB nodes.
We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices.
Pervasive computing environments add a multitude of additional devices to our current computing landscapes. Specialized embedded systems provide sensor information about the real world or offer a distinct functionality, e.g. presentation on a "smart wall". Spontaneous networking leads to constantly changing availability of services. This requires middleware support to ease application development. Additionally, we argue that an extensible middleware platform covering small embedded systems to fill-fledged desktop computers is needed. Such a middleware should provide easy-to-use abstractions to access remote services and device-specific capabilities. We present a micro-broker-based approach which meets these requirements by allowing uniform access to device capabilities and services through proxies and the integration of different interoperability protocols. A minimum configuration of the middleware can be executed on embedded systems. Resource-rich execution environments are supported by the extensibility of the middleware.
Composed of tens of thousands of tiny devices with very limited resources ("motes"), sensor networks are subject to novel systems problems and constraints. The large number of motes in a sensor network means that there will often be some failing nodes; networks must be easy to repopulate. Often there is no feasible method to recharge motes, so energy is a precious resource. Once deployed, a network must be reprogrammable although physically unreachable, and this reprogramming can be a significant energy cost.
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