A semantic middleware for autonomic wireless sensor networks

Rocha, Atslands Rego da; Delicato, Flávia C.; Souza, José Neuman de; Gomes, Danielo G.; Pìrmez, Luci

doi:10.1145/1551693.1551697

Cited by 20 publications

(9 citation statements)

References 9 publications

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“…Another type of middleware is semantic wireless sensor middleware described in [da Rocha et al 2009]; it is a rule-and knowledge-based middleware. It facilitates analysis and controlling of complex tasks in monitoring applications.…”

Section: Gateway Model and Resource Discoverymentioning

confidence: 99%

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

2018

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Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber world. The Internet of Things (IoT) consists of the network-enabled devices and communication technologies that allow connectivity and integration of physical objects (Things) into the digital world (Internet). Enormous amounts of dynamic IoT data are collected from Internet-connected devices. IoT data is usually multi-variant streams that are heterogeneous, sporadic, multi-modal and spatio-temporal. IoT data can be disseminated with di erent granularities and have diverse structures, types and qualities. Dealing with the data deluge from heterogeneous IoT resources and services imposes new challenges on indexing, discovery and ranking mechanisms that will allow building applications that require on-line access and retrieval of ad-hoc IoT data. However, the existing IoT data indexing and discovery approaches are complex or centralised which hinders their scalability. The primary objective of this paper is to provide a holistic overview of the state-of-the-art on indexing, discovery and ranking of IoT data. The paper aims to pave the way for researchers to design, develop, implement and evaluate techniques and approaches for on-line large-scale distributed IoT applications and services.

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Section: Gateway Model and Resource Discoverymentioning

confidence: 99%

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

2018

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“…The platform introduced by da Rocha et al, for instance, applies rule-based reasoning and fuzzy logic to ontologies. [18] III. DISTRIBUTED REASONING The distributed application whose tasks we will be scheduling allows for monitoring and management of a wireless sensor network: by collecting and analyzing data from the sensors, the application detects various issues in the network.…”

Section: Related Workmentioning

confidence: 99%

Resource-Aware Scheduling of Distributed Ontological Reasoning Tasks in Wireless Sensor Networks

Pauw

Verstichel

Volckaert

et al. 2010

2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing

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Abstract-As the number of wireless sensor network applications continues to grow, the need for specialized task scheduling mechanisms, aware of the sensor devices' capabilities and realtime resource availability, is becoming more and more apparent. In this paper, we therefore propose a generic model for task scheduling in heterogeneous networks, which we subsequently use to schedule distributed reasoning tasks, originating from a real-world WSN monitoring and management application. By means of simulation, we evaluate several developed scheduling heuristics and compare the results to an optimal solution of the same WSN task scheduling problem, obtained using ILP. Experiments show that our heuristics produce acceptable task schedules while maintaining a low resource footprint.

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“…There are also works whose primary goal is to achieve self-star properties in wireless networks [22][23][24][25]. In [22], for example, requirements to implement self-configuration and self-optimization are briefly discussed.…”

Section: Related Workmentioning

confidence: 99%

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

et al. 2014

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Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks.

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A semantic middleware for autonomic wireless sensor networks

Cited by 20 publications

References 9 publications

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

Resource-Aware Scheduling of Distributed Ontological Reasoning Tasks in Wireless Sensor Networks

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

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