145 results found
Jackson G, Kartakis S, McCann J, Accurate models of energy harvesting for smart environments, IEEE International Conference on Smart Computing (SMARTCOMP 2017), Publisher: IEEE
Over the last decade, the energy optimization ofresource constrained sensor nodes constitutes a major researchtopic in smart environments. However, state of the art energyoptimization algorithms make strong and unrealistic assumptionsof energy models, both in simulations and during the operation ofsmart systems. For instance, simplistic energy models for energyharvesting leads to inaccurate representation and prediction ofthe true dynamics of energy. Consequently, systems for smartenvironments are unable to meet expected performance criteria.In this paper, we propose innovative models to overcome thedrawbacks of simplistic energy representations in smart environments.We provide the insights of how to generate preciselightweight energy models. Using the physical properties of solarand flow energy harvesting as case studies, the trade-off betweenenergy harvesting inference and real-time measurement of energygeneration is explored. To evaluate our proposed energy modelsagainst the simplistic versions, we use real measured data fromour environmental micro-climate monitoring deployment in anurban park and a 103% improvement is seen. Additionally,to define the trade-offs between inferred and measured energygeneration, experiments are conducted utilizing solar and smartwater testbeds.
Jackson G, Qin Z, mccann J, Long term sensing via battery health adaptation, IEEE International Conference on Distributed Computing Systems (ICDCS 2017), Publisher: IEEE
Energy Neutral Operation (ENO) has created theability to continuously operate wireless sensor networks inareas such as environmental monitoring, hazard detection andindustrial IoT applications. Current ENO approaches utilisetechniques such as sample rate control, adaptive duty cycling anddata reduction methods to balance energy generation, storage andconsumption. However, the state of the art approaches makes astrong and unrealistic assumption that battery capacity is fixedthroughout the deployment time of an application. This resultsin scenarios where ENO systems over allocate sensing tasks,therefore as battery capacity degrades it causes the system tono longer be energy neutral and then fail unexpectedly. In thispaper, we formulate the problem to maximise the quality-ofservicein terms of duty cycle and the battery capacity to extendthe deployment lifetime of a sensing application. In addition, wedevelop a lightweight algorithm to solve the formulated problem.Moreover, we evaluate the proposed method using real sensorenergy consumption data captured from micro-climate sensorsdeployed in Queen Elizabeth Olympic Park, London. Resultsshow that a 307% extension of deployment lifetime can beachieved when compared to a traditional ENO solution withouta reduction in the duty cycle of the sensor.
Kolcun R, Boyle D, McCann J, 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.
Tahir YS, McCann, Yang S, BRPL: Backpressure RPL for High-throughput and Mobile IoTs, IEEE Transactions on Mobile Computing, ISSN: 1558-0660
RPL, an IPv6 routing protocol for Low power Lossy Networks (LLNs), is considered to be the de facto routing standard for the Internet of Things (IoT). However, more and more experimental results demonstrate that RPL performs poorly when it comes to throughput and adaptability to network dynamics. This significantly limits the application of RPL in many practical IoT scenarios, such as an LLN with high-speed sensor data streams and mobile sensing devices. To address this issue, we develop BRPL, an extension of RPL, providing a practical approach that allows users to smoothly combine any RPL Object Function (OF) with backpressure routing. BRPL uses two novel algorithms, QuickTheta and QuickBeta, to support time-varying data traffic loads and node mobility respectively. We implement BRPL on Contiki OS, an open-source operating system for the Internet of Things. We conduct an extensive evaluation using both real-world experiments based on the FIT IoT-LAB testbed and large-scale simulations using Cooja over 18 virtual servers on the Cloud. The evaluation results demonstrate that BRPL not only is fully backward compatible with RPL (i.e. devices running RPL and BRPL can work together seamlessly), but also significantly improves network throughput and adaptability to changes in network topologies and data traffic loads. The observed packet loss reduction in mobile networks is, at a minimum, 60% and up to 1000% can be seen in extreme cases.
Johnson M, McCann J, Santer M, et al., 2017, On orbit validation of solar sailing control laws with thin-film spacecraft, The Fourth International Symposium on Solar Sailing, Publisher: Japan Space Forum
Many innovative approaches to solar sail mission and trajectory design have been proposed over the years, but very few ever have the opportunity to be validated on orbit with real spacecraft. Thin-Film Spacecraft/Lander/Rovers (TF-SLRs) are a new class of very low cost, low mass space vehicle which are ideal for inexpensively and quickly testing in flight new approaches to solar sailing. This paper describes using TF-SLR based micro solar sails to implement a generic solar sail test bed on orbit. TF-SLRs are high area-to-mass ratio (A/m) spacecraft developed for very low cost consumer and scientific deep space missions. Typically based on a 5 μm or thinner metalised substrate, they include an integrated avionics and payload system-on-chip (SoC) die bonded to the substrate with passive components and solar cells printed or deposited by Metal Organic Chemical Vapour Deposition (MOCVD). The avionics include UHF/S-band transceivers, processors, storage, sensors and attitude control provided by integrated magnetorquers and reflectivity control devices. Resulting spacecraft have a typical thickness of less than 50 μm, are 80 mm in diameter, and have a mass of less than 100 mg resulting in sail loads of less than 20 g/m2. TF-SLRs are currently designed for direct dispensing in swarms from free flying 0.5U Interplanetary CubeSats or dispensers attached to launch vehicles. Larger 160 mm, 320 mm and 640 mm diameter TF-SLRs utilizing a CubeSat compatible TWIST deployment mechanism that maintains the high A/m ratio are also under development. We are developing a mission to demonstrate the utility of these devices as a test bed for experimenting with a variety of mission designs and control laws. Batches of up to one hundred TF-SLRs will be released on earth escape trajectories, with each batch executing a heterogeneous or homogenous mixture of control laws and experiments. Up to four releases at different points in orbit are currently envisaged with experiments currently
Ren X, Yu CM, Yu W, et al., 2017, High-dimensional crowdsourced data distribution estimation with local privacy, Pages: 226-233
© 2016 IEEE.High-dimensional crowdsourced data collected from a large number of users may produc3 rich knowledge for our society but also bring unprecedented privacy threats to participants. Recently differential privacy has been proposed as an effective means to mitigate privacy concerns. However, existing work on differential privacy suffers from the 'curse of high-dimensionality' (data with multiple attributes) and high scalability (data with large scale records). Moreover, traditional methods of differential privacy were achieved via aggregation results, which cannot guarantee local privacy for distributed users in crowdsourced systems. To deal with these issues, in this paper we propose a novel scheme that can efficiently estimate multivariate joint distribution for high-dimensional data with local privacy. On the client side, we employ randomized response techniques to locally transform data from distributed users into privacy-preserving bit strings, which can prevent potential inside privacy attacks in crowdsourced systems. On the server side, the crowdsourced bit strings are aggregated for multivariate distribution estimation. Specifically, we first propose a multivariate version of the expectation maximization (EM) based algorithm to estimate the joint distribution of high dimensional data. To speed up the performance, unlike the EM-based method that needs to scan each user's bit string, we propose to use Lasso regression to obtain the distribution estimation from the aggregation information only once, which can significantly reduce the computation time for multivariate distribution estimation. Extensive experiments on real-world datasets demonstrate the efficiency of our multivariate distribution estimation scheme over existing estimation schemes.
Wu D, Arkhipov DI, Kim M, et al., 2017, ADDSEN: Adaptive Data Processing and Dissemination for Drone Swarms in Urban Sensing, IEEE TRANSACTIONS ON COMPUTERS, Vol: 66, Pages: 183-198, ISSN: 0018-9340
Yang S, Adeel U, Tahir Y, et al., 2017, Practical Opportunistic Data Collection in Wireless Sensor Networks with Mobile Sinks, IEEE TRANSACTIONS ON MOBILE COMPUTING, Vol: 16, Pages: 1420-1433, ISSN: 1536-1233
Carboni D, Gluhak A, McCann JA, et al., 2016, Contextualising Water Use in Residential Settings: A Survey of Non-Intrusive Techniques and Approaches, SENSORS, Vol: 16, ISSN: 1424-8220
Kartakis S, Choudhary BD, Gluhak AD, et al., 2016, Demystifying low-power wide-area communications for city IoT applications, Pages: 2-8
© 2016 ACM.Low Power Wide Area (LPWA) communication technologies have the potential to provide a step change in the enablement of cost-effective and energy efficient Internet of Things (IoT) applications. With an increase in the number of offerings available the real performance of these emerging technologies remain unclear. That is, each technology comes with its own advantages and limitations; yet there is a lack of comparative studies that examine their trade-offs based on empirical evidence. This poses a major challenge to IoT solution architects and developers in selecting an appropriate technology for an envisioned IoT application in a given deployment context. In this paper, we look beyond data sheets and white papers of LPWA communication technologies and provide insights into the performance of three emerging LPWA solutions based on real world experiments with different traffic loads and in different urban deployment contexts. Under the context of this study, specialized hardware was created to incorporate the different technologies and provide scientific quantitative and qualitative information related to data rates, success rates, transmission mode energy and power consumption, and communication ranges. The results of experimentation highlight the practicalities of placing LPWA technologies in real spaces and provide guidelines to IoT solution developers in terms of LPWA technology selection. Overall aim is to facilitate the design of new LPWA technologies and adaptive communication strategies that inform future IoT platforms.
Kartakis S, Jevric MM, Tzagkarakis G, et al., 2016, Energy-based Adaptive Compression in Water Network Control Systems, International Workshop on Cyber-Physical Systems for Smart Water Networks (CySWater), Publisher: IEEE, Pages: 43-48
Kartakis S, Yu W, Akhavan R, et al., 2016, Adaptive Edge Analytics for Distributed Networked Control of Water Systems, IEEE 1st International Conference on Internet-of-Things Design and Implementation (IoTDI), Publisher: IEEE, Pages: 72-82
Kolcun R, Boyle DE, McCann JA, 2016, Efficient Distributed Query Processing, IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, Vol: 13, Pages: 1230-1246, ISSN: 1545-5955
Shi F, Adeel U, Theodoridis E, et al., 2016, OppNet: Enabling Citizen-Centric Urban IoT Data Collection Through Opportunistic Connectivity Service, IEEE 3rd World Forum on Internet of Things (WF-IoT), Publisher: IEEE, Pages: 723-728
Wu D, Lambrinos L, Przepiorka T, et al., 2016, Facilitating Mobile Access to Social Media Content on Urban Underground Metro Systems, IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Publisher: IEEE, ISSN: 2159-4228
Yang S, Tahir Y, Chen P-Y, et al., 2016, Distributed Optimization in Energy Harvesting Sensor Networks with Dynamic In-network Data Processing, 35th IEEE Annual International Conference on Computer Communications (IEEE INFOCOM), Publisher: IEEE
Yu W, McCann J, 2016, Random Walk with Restart over Dynamic Graphs, 16th IEEE International Conference on Data Mining (ICDM), Publisher: IEEE, Pages: 589-598, ISSN: 1550-4786
Zhao C, Yang S, Yang X, et al., 2016, Rapid, User-Transparent, and Trustworthy Device Pairing for D2D-Enabled Mobile Crowdsourcing, IEEE Transactions on Mobile Computing, Vol: PP, ISSN: 1536-1233
© 2016 IEEE.Mobile Crowdsourcing is a promising service paradigm utilizing ubiquitous mobile devices to facilitate large-scale crowdsourcing tasks (e.g. urban sensing and collaborative computing). Many applications in this domain require Device-to-Device (D2D) communications between participating devices for interactive operations such as task collaborations and file transmissions. Considering the private participating devices and their opportunistic encountering behaviors, it is highly desired to establish secure and trustworthy D2D connections in a fast and autonomous way, which is vital for implementing practical Mobile Crowdsourcing Systems (MCSs). In this paper, we develop an efficient scheme, Trustworthy Device Pairing (TDP), which achieves user-transparent secure D2D connections and reliable peer device selections for trustworthy D2D communications. Through rigorous analysis, we demonstrate the effectiveness and security intensity of TDP in theory. The performance of TDP is evaluated based on both real-world prototype experiments and extensive trace-driven simulations. Evaluation results verify our theoretical analysis and show that TDP significantly outperforms existing approaches in terms of pairing speed, stability, and security.
Chen P-Y, Yang S, McCann JA, 2015, Distributed Real-Time Anomaly Detection in Networked Industrial Sensing Systems, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, Vol: 62, Pages: 3832-3842, ISSN: 0278-0046
Chen P-Y, Yang S, McCann JA, et al., 2015, Detection of False Data Injection Attacks in Smart-Grid Systems, IEEE Communications Magazine, Vol: 53, Pages: 206-213, ISSN: 1558-1896
Smart grids are essentially electric grids that use information and communication technology to provide reliable, efficient electricity transmission and distribution. Security and trust are of paramount importance. Among various emerging security issues, FDI attacks are one of the most substantial ones, which can significantly increase the cost of the energy distribution process. However, most current research focuses on countermeasures to FDIs for traditional power grids rather smart grid infrastructures. We propose an efficient and real-time scheme to detect FDI attacks in smart grids by exploiting spatial-temporal correlations between grid components. Through realistic simulations based on the US smart grid, we demonstrate that the proposed scheme provides an accurate and reliable solution.
Holland O, Ping S, Sastry N, et al., 2015, Some Initial Results and Observations from a Series of Trials within the Ofcom TV White Spaces Pilot, 81st IEEE Vehicular Technology Conference (VTC Spring), Publisher: IEEE, ISSN: 1550-2252
Kartakis S, Abraham E, McCann JA, 2015, WaterBox: A testbed for monitoring and controlling smart water networks
Copyright 2015 ACM.Smart water distribution networks are a good example of a large scale Cyber-Physical System that requires monitoring for precise data analysis and network control. Due to the critical nature of water distribution, an extensive simulation of decision making and control algorithms are required before their deployment. Although some aspects of water network behaviour can be simulated in software such as hydraulic responses in valve changes, software simulators are unable to include dynamic events such as leakages or bursts in physical models. Furthermore, due to safety concerns, contemporary large-scale testbeds are limited to the monitoring processes or control methods with well established safety guarantees. Sophisticated algorithms for dynamic and optimal water network reconfiguration are not yet widespread. This paper presents a small-scale testbed, WaterBox, which allows the simulation of emerging/advanced monitoring and control algorithms in a fail-safe environment. The flexible hydraulic, hardware, and software infrastructure enables a substantial number of experiments. On-going experiments are related to in-node data processing and decision making, energy optimization, event-driven communication, and automatic control.
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 (IOT), Publisher: IEEE, Pages: 72-79
Lalanda P, McCann JA, Hamon C, 2015, Demo Abstract: Teaching Pervasing Computing with an integrated environment, IEEE International Conference on Pervasive Computing and Communication Workshops PerCom Workshops, Publisher: IEEE, Pages: 205-207
Martins PMN, McCann JA, 2015, The Programmable City, 6th International Conference on Ambient Systems, Networks and Technologies (ANT) / 5th International Conference on Sustainable Energy Information Technology (SEIT), Publisher: ELSEVIER SCIENCE BV, Pages: 334-341, ISSN: 1877-0509
Tahir Y, Yang S, Adeel U, et al., 2015, Symbiot: Congestion-driven Multi-resource Fairness for Multi-User Sensor Networks, 2015 IEEE 17th International Conference on High Performance Computing and Communications (HPCC), Publisher: IEEE, Pages: 654-659
Tahir Y, Yang S, Koliousis A, et al., 2015, UDRF: Multi-resource Fairness for Complex Jobs with Placement Constraints, IEEE Global Communications Conference (GLOBECOM), Publisher: IEEE, ISSN: 2334-0983
Tahir Y, Yang S, Koliousis A, et al., 2015, UDRF: Multi-resource fairness for complex jobs with placement constraints
© 2015 IEEE.In this paper, we study the problem of multi-resource fairness in systems with multiple users. Each user requires to run one or more complex jobs that consist of multiple interconnected tasks. A job is considered finished when all its corresponding tasks have been executed in the system. Tasks can have different resource requirements. Because of special demands on particular hardware or software, tasks can have placement constraints limiting the type of machines they can run on. We develop User-Dependence Dominant Resource Fairness (UDRF), a generalized version of max-min fairness that combines graph theory and the notion of dominant resource shares to ensure multi- resource fairness between users with complex jobs. UDRF satisfies several desirable properties including strategy proofness, which ensures that users do not benefit from misreporting their true resource demands. We propose an offline algorithm that computes optimal UDRF allocation while the scheduling process can be to be decentralize across multiple schedulers. But optimality comes at a cost, especially for systems where schedulers need to make thousands of online scheduling decisions per second. Therefore, we develop a lightweight online algorithm that closely approximates UDRF. Large-scale simulations driven by Google cluster- usage traces show that UDRF achieves better resource utilization and throughput compared to the current state-of-the-art in multi-resource fair allocation.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.