Dragon: Data discovery and collection architecture for distributed IoT

IEEE Trans. Automat. Sci. Eng.

McCann

2016

Self Cite

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...

Section: Introductionmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Distributed Query Processing

IEEE Trans. Automat. Sci. Eng.

McCann

2016

Self Cite

“…A snapshot query is executed only once and therefore offers very small room for query execution optimisation. The optimisation is focused on inexpensive identification of relevant nodes and retrieving data from them as the query optimisation overhead could easily exceed the gain of this optimisation [7]. On the other hand, continuous queries allow much wider query optimisation possibilities as the query is executed many times over a specified period of time (possibly indefinitely).…”

Section: Rationale and Related Workmentioning

confidence: 99%

“…Our platform for in-network processing of continuous queries extends our previous work on DRAGON [7]. We briefly review the main features of DRAGON and explain its functionality.…”

Section: Computational Platformmentioning

confidence: 99%

Optimal processing node discovery algorithm for distributed computing in IoT

2015 5th International Conference on the Internet of Things (IOT)

McCann

2015

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Abstract-The number of Internet-connected sensing and control devices is growing. Some anticipate them to number in excess of 212 billion by 2020. Inherently, these devices generate continuous data streams, many of which need to be stored and processed. Traditional approaches, whereby all data are shipped to the cloud, may not continue to be effective as cloud infrastructure may not be able to handle myriads of data streams and their associated storage and processing needs. Using cloud infrastructure alone for data processing significantly increases latency, and contributes to unnecessary energy inefficiencies, including potentially unnecessary data transmission in constrained wireless networks, and on cloud computing facilities increasingly known to be significant consumers of energy. In this paper we present a distributed platform for wireless sensor networks which allows computation to be shifted from the cloud into the network. This reduces the traffic in the sensor network, intermediate networks, and cloud infrastructure. The platform is fully distributed, allowing every node in a homogeneous network to accept continuous queries from a user, find all nodes satisfying the user's query, find an optimal node (Fermat-Weber point) in the network upon which to process the query, and provide the result to the user. Our results show that the number of required messages can be decreased up to 49% and processing latency by 42% in comparison with state-of-the-art approaches, including Innet.

Towards Precision Control in Constrained Wireless Cyber-Physical Systems

Internet of Things. IoT Infrastructures

Yeatman

2016

Self Cite

Abstract. This paper introduces the problem of high precision control in constrained wireless cyber-physical systems. We argue that balancing conflicting performance objectives, namely energy e ciency, high reliability and low latency, whilst concurrently enabling data collection and targeted message dissemination, are critical to the success of future applications of cyber-physical systems that leverage networks of constrained wireless devices. We describe the contemporary art in practical collection and dissemination techniques, and select the most applicable for evaluation. A comprehensive simulation study is presented, the results of which show that the current art falls significantly short of desirable performance when inter-packet intervals decrease to those required for precision control. It follows that there is a significant need for further study and new solutions to solve this emerging problem.