8 results found
Chen P-Y, Bhatia L, Kolcun R, et al., 2020, Contact-aware opportunistic data forwarding in disconnected LoRaWAN mobile networks, 40th IEEE International Conference on Distributed Computing Systems, Publisher: IEEE
LoRaWAN is one of the leading Low Power WideArea Network (LPWAN) architectures. It was originally designedfor systems consisting of static sensor or Internet of Things (IoT)devices and static gateways. It was recently updated to introducenew features such as nano-second timestamps which open upapplications to enable LoRaWAN to be adopted for mobile devicetracking and localisation. In such mobile scenarios, devices couldtemporarily lose communication with the gateways because ofinterference from obstacles or deep fading, causing throughputreduction and delays in data transmission. To overcome thisproblem, we propose a new data forwarding scheme. Instead ofholding the data until the next contact with gateways, devices canforward their data to nearby devices that have a higher probabil-ity of being in contact with gateways. We propose a new networkmetric called Real-Time Contact-Aware Expected TransmissionCount (RCA-ETX) to model this contact probability in real-time. Without making any assumption on mobility models, thismetric exploits data transmission delays to model complex devicemobility. We also extend RCA-ETX with a throughput-optimalstochastic backpressure routing scheme and propose Real-TimeOpportunistic Backpressure Collection (ROBC), a protocol tocounter the stochastic behaviours resulting from the dynamicsassociated with mobility. To apply our approaches seamlesslyto LoRaWAN-enabled devices, we further propose two newLaRaWAN classes, namely Modified Class-C and Queue-basedClass-A. Both of them are compatible with LoRaWAN Class-Adevices. Our data-driven experiments, based on the London busnetwork, show that our approaches can reduce data transmissiondelays up to25%and provide a53%throughput improvementin data transfer performance.
Ren J, Dubois DJ, Choffnes D, et al., 2019, Information exposure from consumer IoT devices: a multidimensional, network-informed measurement approach, ACM Internet Measurement Conference (IMC), Publisher: ASSOC COMPUTING MACHINERY, Pages: 267-279
Internet of Things (IoT) devices are increasingly found in everyday homes, providing useful functionality for devices such as TVs, smart speakers, and video doorbells. Along with their benefits come potential privacy risks, since these devices can communicate information about their users to other parties over the Internet. However, understanding these risks in depth and at scale is difficult due to heterogeneity in devices' user interfaces, protocols, and functionality.In this work, we conduct a multidimensional analysis of information exposure from 81 devices located in labs in the US and UK. Through a total of 34,586 rigorous automated and manual controlled experiments, we characterize information exposure in terms of destinations of Internet traffic, whether the contents of communication are protected by encryption, what are the IoT-device interactions that can be inferred from such content, and whether there are unexpected exposures of private and/or sensitive information (e.g., video surreptitiously transmitted by a recording device). We highlight regional differences between these results, potentially due to different privacy regulations in the US and UK. Last, we compare our controlled experiments with data gathered from an in situ user study comprising 36 participants.
Boyle D, Kolcun R, Yeatman E, 2016, Towards precision control in constrained wireless cyber-physical systems, 2nd International Summit on Internet of Things - IoT Infrastructures( IoT 360), Publisher: Springer Verlag (Germany), Pages: 292-306, ISSN: 1867-8211
This paper introduces the problem of high precision control in constrained wireless cyber-physical systems. We argue that balancing conflicting performance objectives, namely energy efficiency, high reliability and low latency, whilst concurrently enabling data collection and targeted message dissemination, are critical to the success of future applications of constrained wireless cyber-physical systems. We describe the contemporary art in practical collection and dissemination techniques, and select the most appropriate for evaluation. A comprehensive simulation study is presented and experimentally validated, 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.
Kolcun R, Boyle D, McCann J, 2016, Efficient In-Network Processing for a Hardware-Heterogeneous IoT, IoT2016 - 6th International Conference on the Internet of Things, Publisher: IEEE
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, in-network data processing is becoming more common. For this purpose, various platforms like D RAGON , 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, hardwareheterogeneity 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 roposed 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.
Kolcun R, Boyle DE, McCann JA, 2016, Efficient Distributed Query Processing, IEEE Transactions on Automation Science and Engineering, Vol: 13, Pages: 1230-1246, ISSN: 1558-3783
A variety of wireless networks, including applications of Wireless Sensor Networks, Internet of Things, and Cyber-physical Systems, 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, and 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 with 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.
Kolcun R, Boyle D, McCann JA, 2015, Optimal processing node discovery algorithm for distributed computing in IoT, 5th International Conference on the Internet of Things, Publisher: IEEE, Pages: 72-79
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.
Boyle D, Kolcun R, Yeatman E, 2015, Devices in the internet of things, Journal of the Institute of Telecommunications Professionals, Vol: 9, Pages: 26-31, ISSN: 1755-9278
There are many potential applications in the utilities, critical infrastructure monitoring and control and environmental monitoring. This article charts the device-level technologies used in the creation the IoT, including hardware, software and communications. IoT's emergence coincided with the development of radio frequency identification (RFID) technology offering advantages such as the communicable range, ability to write data to a tag, and the possibility of reading multiple tags more efficiently with a single reader. RFID is now just one of many component IoT technologies. We have arrived at a situation where it is practically trivial to integrate computation and communication into any manufactured thing, and it is equally feasible to connect and technologically perceive natural things using communicable sensors. Furthermore, it is possible to react to, and control the environment using embedded computing devices coupled with actuators. Thorough comprehension of functional and non-functional requirements is necessary to develop an effective, efficient design specification for an IoT device. But given the large design space and complexity, there are numerous barriers to entry. As a result, many types of device have been adopted as practical de facto hardware development platforms across research communities, anc hacker and maker communities. In each case, intermediary 'operating systems', designed to simplify their programming by masking hardware complexity, are typically used. Their technical specifications are often not fully disclosed, but they do rely on well- defined standards to ensure the necessary interoperability. The majority of devices are characterised as single board computers. The final design for a market-ready product will likely be as efficient and cost-effective as possible in terms of design, but include sufficient redundancy to support software updates and potential shifts in standards. Since the early 2000s, the wireless sensor network comm
Kolcun R, McCann JA, 2014, Dragon: Data Discovery and Collection Architecture for Distributed IoT, Internet of Things 2014 - The 4th International Conference on the Internet of Things (IoT 2014), Pages: 91-96
Wireless Low-powered Sensing Systems (WLSS) are becoming more prevalent, taking the form of Wireless Sensor/Actuator Networks, Internet of Things, Phones etc. As node and network capabilities of such systems improve, there is more motivation to push computation into the network as it saves energy, prolongs system lifetime, and enables timely responses to events or control activities. Another advantage of such edge-processing is that these networks can become autonomous in the sense that users can directly query the network via any node in the network and are not required to connect to gateways or retrieve data via long range communications. Dragon is a scheme that efficiently identifies nodes that can reply to user requests based on static criteria that either describes that node or its data and provides the ability to near-optimally route queries or actuation control messages to those nodes. Dragon is scalable and agile as it does not require any central point orchestrating the search. In this paper we demonstrate significant performance improvements compared with state-of-the-art approaches in terms of numbers of messages required (up to 93% less) and its ability to scale to 100s of nodes.
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