Optimal processing node discovery algorithm for distributed computing in IoT

Kolcun, Roman; Boyle, David; McCann, Julie A.

doi:10.1109/iot.2015.7356550

Cited by 10 publications

(12 citation statements)

References 14 publications

(22 reference statements)

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“…regularly adding and removing of nodes to the network, may be more effectively supported. We have extended DRAGON towards supporting in-network processing of continuous queries in homogeneous constrained networks [30] and our ongoing work includes implementing support for heterogeneous constrained networks. Since c. Message about the failure is propagated further into the network.…”

Section: Discussionmentioning

confidence: 99%

Efficient Distributed Query Processing

Kolcun

Boyle

McCann

2016

IEEE Trans. Automat. Sci. Eng.

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Abstract-A variety of wireless networks, including applications of Wireless Sensor Networks, Internet of Things, Cyberphysical Systems, etc., increasingly pervade our homes, retail, transportation systems and manufacturing processes. Traditional approaches communicate data from all sensors to a central system, and users (humans or machines) query this central point for results, typically via the web. As the number of deployed sensors, thus generated data streams, is increasing exponentially, this traditional approach may no longer be sustainable, or desirable in some application contexts. Therefore, new approaches are required to allow users to directly interact with the network, for example requesting data directly from sensor nodes. This is difficult, as it requires every node to be capable of point-to-point routing, in addition to identifying a subset of nodes that can fulfil a user's query. This paper presents DRAGON, a platform that allows any node in the network to identify all nodes that satisfy user queries, i.e. request data from nodes, and relay the result to the user. The DRAGON platform achieves this in a fully distributed way. No central orchestration is required, network overheads are low, and latency is improved over existing comparable methods. DRAGON is evaluated on networks of various topologies and different network densities. It is compared to the state-of-the-art algorithms based on summary trees, like Innet and SENS-Join. DRAGON is shown to outperform these approaches up to 88% in terms of network traffic required, also a proxy for energy efficiency, and 84% in terms of processing delay. Note to Practitioners:Abstract-This work is motivated by the continuing deluge of constrained, wirelessly connected sensing and control devices. Networks of communicable sensors and actuators are finding increased applicability across a range of industries and application scenarios. They are often thought of as a subset of the Internet of Things. However, due to the inherent difficulty in building theses systems, technically and in terms of balancing the trade-offs between (economic) cost and performance (energy, latency, reliability, determinism), uptake has been slow. The community is relatively small, and therefore has not overcome all of the problems that present themselves considering required functionality of industrial applications. There is a need to find find new ways to interact with these devices, particularly those with heterogeneous attributes. There is also clear motivation to progress from traditional system architectures, whereby all data sensed are transmitted to centralised storage and management platform, to decentralised means of interrogation and control. This work proposes a solution to this problem, describing and evaluating a novel framework to query constrained networked devices based on two key improvements over the current art. The first is construction and management of a dynamic routing mechanism that facilitates the second; a method to store static attributes in a distributed m...

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Section: Discussionmentioning

confidence: 99%

Efficient Distributed Query Processing

Kolcun

Boyle

McCann

2016

IEEE Trans. Automat. Sci. Eng.

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“…These parts are distributed in such way that every node has access to the full copy of the DSAT by communicating with nodes nearby. Kolcun et al (2015) [9] extended the framework to support continuous queries. For that purpose, a Processing Node Discovery (PND) algorithm was proposed, allowing any node in the network to accept and process a continuous query.…”

Section: Background and Processing Platformmentioning

confidence: 99%

“…The Mixed algorithm combines the algorithm for homogeneous networks proposed in [9] with the Request algorithm. First, the algorithm for homogeneous networks finds a node with the lowest cost amongst all nodes (i.e.…”

Section: Mixed Algorithmmentioning

confidence: 99%

“…The algorithms were evaluated in the TinyOS simulator TOSSIM [14]; chosen for its popularity within the research community, its reasonable quality in simulating low-powered wireless communication, and to build upon DRAGON [9], also implemented in TinyOS. The in-built radio and noise models were used, with synchronised nodes operating at 15% duty cycle, and the packet size fixed to 45 bytes.…”

Section: Evaluation Experiments Setupmentioning

confidence: 99%

“…Request permissions from permissions@acm.org. known as 'edge processing' [9,17]. These approaches assume that the nodes have enough memory and a powerful enough CPU to process several data streams.…”

Section: Introductionmentioning

confidence: 99%

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Efficient In-Network Processing for a Hardware-Heterogeneous IoT

Kolcun

Boyle

McCann

2016

Proceedings of the 6th International Conference on the Internet of Things

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As the number of small, battery-operated, wireless-enabled devices deployed in various applications of Internet of Things (IoT), Wireless Sensor Networks (WSN), and Cyber-physical Systems (CPS) is rapidly increasing, so is the number of data streams that must be processed. In cases where data do not need to be archived, centrally processed, or federated, innetwork data processing is becoming more common. For this purpose, various platforms like DRAGON, Innet, and CJF were proposed. However, these platforms assume that all nodes in the network are the same, i.e. the network is homogeneous. As Moore's law still applies, nodes are becoming smaller, more powerful, and more energy efficient each year; which will continue for the foreseeable future. Therefore, we can expect that as sensor networks are extended and updated, hardware heterogeneity will soon be common in networks -the same trend as can be seen in cloud computing infrastructures. This heterogeneity introduces new challenges in terms of choosing an in-network data processing node, as not only its location, but also its capabilities, must be considered. This paper introduces a new methodology to tackle this challenge, comprising three new algorithms -Request, Traverse, and Mixed -for efficiently locating an in-network data processing node, while taking into account not only position within the network but also hardware capabilities. The proposed algorithms are evaluated against a naïve approach and achieve up to 90% reduction in network traffic during long-term data processing, while spending a similar amount time in the discovery phase.

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Role of Intelligent IOT Applications in Fog paradigm: Issues, Challenges and Future Opportunities

Kumar

Goel

2020

Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications

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Optimal processing node discovery algorithm for distributed computing in IoT

Cited by 10 publications

References 14 publications

Efficient Distributed Query Processing

Efficient Distributed Query Processing

Efficient In-Network Processing for a Hardware-Heterogeneous IoT

Role of Intelligent IOT Applications in Fog paradigm: Issues, Challenges and Future Opportunities

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