146 results found
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.
Verhoef AV, Choudhary BDC, Morris PJM, et al., A high-density wireless underground sensor network (WUSN) to quantify hydro-ecological interactions for a UK floodplain; project background and initial results, EGU General Assembly 2012, held 22-27 April, 2012 in Vienna, Austria., p.6346
Jackson G, Kartakis S, McCann J, et al., 2017, Accurate Models of Energy Harvesting for Smart Environments, IEEE International Conference on Smart Computing (SMARTCOMP 2017), Publisher: IEEE
© 2017 IEEE. Over the last decade, the energy optimization of resource constrained sensor nodes constitutes a major research topic in smart environments. However, state of the art energy optimization algorithms make strong and unrealistic assumptions of energy models, both in simulations and during the operation of smart systems. For instance, simplistic energy models for energy harvesting leads to inaccurate representation and prediction of the true dynamics of energy. Consequently, systems for smart environments are unable to meet expected performance criteria. In this paper, we propose innovative models to overcome the drawbacks of simplistic energy representations in smart environments. We provide the insights of how to generate precise lightweight energy models. Using the physical properties of solar and flow energy harvesting as case studies, the trade-off between energy harvesting inference and real-time measurement of energy generation is explored. To evaluate our proposed energy models against the simplistic versions, we use real measured data from our environmental micro-climate monitoring deployment in an urban park and a 103% improvement is seen. Additionally, to define the trade-offs between inferred and measured energy generation, experiments are conducted utilizing solar and smart water testbeds.
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, 2016 IEEE International Conference on Computer and Information Technology (CIT), Publisher: IEEE, 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.
Tahir Y, Yang S, McCann J, et al., 2017, BRPL: Backpressure RPL for High-throughput and Mobile IoTs, IEEE Transactions on Mobile Computing, Pages: 1-1, ISSN: 1536-1233
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.
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
© 2016 IEEE. We present ADDSEN middleware as a holistic solution for Adaptive Data processing and dissemination for Drone swarms in urban SENsing. To efficiently process se nsed data in the middleware, we have proposed a cyber-physical sensing framework using partially ordered knowledge sharing for distributed knowledge management in drone swarms. A reinforcement learning dissemination strategy is implemented in the framework. ADDSEN uses online learning techniques to adaptively balance the broadcast rate and knowledge loss rate periodically. The learned broadcast rate is adapted by executing state transitions during the process of online learning. A strategy function guides state transitions, incorporating a set of variables to reflect changes in link status. In addition, we design a cooperative dissemination method for the task of balancing storage and energy allocation in drone swarms. We implemented ADDSEN in our cyber-physical sensing framework, and evaluation results show that it can achieve both maximal adaptive data processing and dissemination performance, presenting better results than other commonly used dissemination protocols such as periodic, uniform and neighbor protocols in both single-swarm and multi-swarm cases.
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
© 2002-2012 IEEE. Wireless Sensor Networks with Mobile Sinks (WSN-MSs) are considered a viable alternative to the heavy cost of deployment of traditional wireless sensing infrastructures at scale. However, current state-of-the-art approaches perform poorly in practice due to their requirement of mobility prediction and specific assumptions on network topology. In this paper, we focus on low-delay and high-throughput opportunistic data collection in WSN-MSs with general network topologies and arbitrary numbers of mobile sinks. We first propose a novel routing metric, Contact-Aware ETX (CA-ETX), to estimate the packet transmission delay caused by both packet retransmissions and intermittent connectivity. By implementing CA-ETX in the defacto TinyOS routing standard CTP and the IETF IPv6 routing protocol RPL, we demonstrate that CA-ETX can work seamlessly with ETX. This means that current ETX-based routing protocols for static WSNs can be easily extended to WSN-MSs with minimal modification by using CA-ETX. Further, by combing CA-ETX with the dynamic backpressure routing, we present a throughput-optimal scheme Opportunistic Backpressure Collection (OBC). Both CA-ETX and OBC are lightweight, easy to implement, and require no mobility prediction. Through test-bed experiments and extensive simulations, we show that the proposed schemes significantly outperform current approaches in terms of packet transmission delay, communication overhead, storage overheads, reliability, and scalability.
Zhao C, Yang S, Yang X, et al., 2017, Rapid, User-Transparent, and Trustworthy Device Pairing for D2D-Enabled Mobile Crowdsourcing, IEEE TRANSACTIONS ON MOBILE COMPUTING, Vol: 16, Pages: 2008-2022, 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.
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, Pages: 738-738, ISSN: 1424-8220
Water monitoring in households is important to ensure the sustainability of fresh water reserves on our planet. It provides stakeholders with the statistics required to formulate optimal strategies in residential water management. However, this should not be prohibitive and appliance-level water monitoring cannot practically be achieved by deploying sensors on every faucet or water-consuming device of interest due to the higher hardware costs and complexity, not to mention the risk of accidental leakages that can derive from the extra plumbing needed. Machine learning and data mining techniques are promising techniques to analyse monitored data to obtain non-intrusive water usage disaggregation. This is because they can discern water usage from the aggregated data acquired from a single point of observation. This paper provides an overview of water usage disaggregation systems and related techniques adopted for water event classification. The state-of-the art of algorithms and testbeds used for fixture recognition are reviewed and a discussion on the prominent challenges and future research are also included.
In recent years, the evolution of urban environments, jointly with the progress of the Information and Communication sector, have enabled the rapid adoption of new solutions that contribute to the growth in popularity of Smart Cities. Currently, the majority of the world population lives in cities encouraging different stakeholders within these innovative ecosystems to seek new solutions guaranteeing the sustainability and efficiency of such complex environments. In this work, it is discussed how the experimentation with IoT technologies and other data sources form the cities can be utilized to co-create in the OrganiCity project, where key actors like citizens, researchers and other stakeholders shape smart city services and applications in a collaborative fashion. Furthermore, a novel architecture is proposed that enables this organic growth of the future cities, facilitating the experimentation that tailors the adoption of new technologies and services for a better quality of life, as well as agile and dynamic mechanisms for managing cities. In this work, the different components and enablers of the OrganiCity platform are presented and discussed in detail and include, among others, a portal to manage the experiment life cycle, an Urban Data Observatory to explore data assets, and an annotations component to indicate quality of data, with a particular focus on the city-scale opportunistic data collection service operating as an alternative to traditional communications.
Kartakis S, Choudhary BD, Gluhak AD, et al., 2016, Demystifying low-power wide-area communications for city IoT applications, ACM WiNTECH 2016 Workshop, MobiCom, Publisher: ACM, 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
© 2016 IEEE. Contemporary water distribution networks exploit Internet of Things (IoT) technologies to monitor and control the behavior of water network assets. Smart meters/sensor and actuator nodes have been used to transfer information from the water network to data centers for further analysis. Due to the underground position of water assets, many water companies tend to deploy battery driven nodes which last beyond the 10-year mark. This prohibits the use of high-sample rate sensing therefore limiting the knowledge we can obtain from the recorder data. To alleviate this problem, efficient data compression enables high-rate sampling, whilst reducing significantly the required storage and bandwidth resources without sacrificing the meaningful information content. This paper introduces a novel algorithm which combines the accuracy of standard lossless compression with the efficiency of a compressive sensing framework. Our method balances the tradeoffs of each technique and optimally selects the best compression mode by minimizing reconstruction errors, given the sensor node battery state. To evaluate our algorithm, real high-sample rate water pressure data of over 170 days and 25 sensor nodes of our real world large scale testbed was used. The experimental results reveal that our algorithm can reduce communication around 66% and extend battery life by 46% compared to traditional periodic communication techniques.
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
© 2016 IEEE. Over the last decade, there has been a trend where water utility companies aim to make water distribution networks more intelligent in order to improve their quality of service, reduce water waste, minimize maintenance costs etc., by incorporating IoT technologies. Current state of the art solutions use expensive power hungry deployments to monitor and transmit water network states periodically in order to detect anomalous behaviors such as water leakage and bursts. However, more than 97% of water network assets are remote away from power and are often in geographically remote underpopulated areas; facts that make current approaches unsuitable for next generation more dynamic adaptive water networks. Battery-driven wireless sensor/actuator based solutions are theoretically the perfect choice to support next generation water distribution. In this paper, we present an end-to-end water leak localization system, which exploits edge processing and enables the use of battery-driven sensor nodes. Our system combines a lightweight edge anomaly detection algorithm based on compression rates and an efficient localization algorithm based on graph theory. The edge anomaly detection and localization elements of the systems produce a timely and accurate localization result and reduce the communication by 99% compared to the traditional periodic communication. We evaluated our schemes by deploying non-intrusive sensors measuring vibrational data on a real-world water test rig that have had controlled leakage and burst scenarios implemented.
© 2016 IEEE. 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.
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
© 2016 IEEE. Urban IoT data collection is challenging due to the limitations of the fixed sensing infrastructures. Instead of transmitting data directly through expensive cellular networks, citizen-centric data collection scheme through opportunistic network takes advantage of human mobility as well as cheap WiFi and D2D communication. In this paper, we present OppNet, which implements a context aware data forwarding algorithm and fills the gap between theoretical modelling of opportunistic networking and real deployment of citizen-centric data collection system. According to the results from a 3-day real-life experiment, OppNet shows consistent performance in terms of number of hops and time delay. Moreover, the underlying social structure can be clearly identified by analysing social contact data collected through OppNet.
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, Pages: 921-926, ISSN: 2159-4228
© 2016 IEEE. Users of mobile devices require ubiquitous Internet connectivity especially when located in urban areas. However, when using a city's underground metro system there are often situations where there is no signal coverage for a considerable time period. One large city facing this issue is London whose underground metro system includes a significant number of lines using deep level tunnels. To facilitate mobile Internet connectivity for travellers using these lines we designed DeepOpp, a context-aware system that enables offline access to social media content. This access is achieved by prefetching and caching content when connectivity opportunities (such as urban 3G or station WiFi) arise. DeepOpp can measure signal characteristics (strength, bandwidth and latency) and based on current and historical information make signal coverage predictions in order to activate data prefetching of social media content. An Android-based implementation of DeepOpp has enabled us to carry out real tests on the London underground and demonstrate its benefits which promote it as a viable solution for cities with similar metro systems such as New York, Paris and Shanghai.
© 1993-2012 IEEE. Open WiFi access points (APs) are demonstrating that they can provide opportunistic data services to moving vehicles. We present CrowdWiFi, a novel system to look up roadside WiFi APs located outdoors or inside buildings. CrowdWiFi consists of two components: online compressive sensing (CS) and offline crowdsourcing. Online CS presents an efficient framework for the coarse-grained estimation of nearby APs along the driving route, where received signal strength (RSS) values are recorded at runtime, and the number and location of the APs are recovered immediately based on limited RSS readings and adaptive CS operations. Offline crowdsourcing assigns the online CS tasks to crowd-vehicles and aggregates answers on a bipartite graphical model. Crowd-server also iteratively infers the reliability of each crowd-vehicle from the aggregated sensing results, and then refines the estimation of the APs using weighted centroid processing. Extensive simulation results and real testbed experiments confirm that CrowdWiFi can successfully reduce the computation cost and energy consumption of roadside WiFi lookup, while maintaining satisfactory localization accuracy.
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, ISSN: 0743-166X
© 2016 IEEE. Energy Harvesting Wireless Sensor Networks (EH-WSNs) have been attracting increasing interest in recent years. Most current EH-WSN approaches focus on sensing and networking algorithm design, and therefore only consider the energy consumed by sensors and wireless transceivers for sensing and data transmissions respectively. In this paper, we incorporate CPU-intensive edge operations that constitute in-network data processing (e.g. data aggregation/fusion/compression) with sensing and networking; to jointly optimize their performance, while ensuring sustainable network operation (i.e. no sensor node runs out of energy). Based on realistic energy and network models, we formulate a stochastic optimization problem, and propose a lightweight on-line algorithm, namely Recycling Wasted Energy (RWE), to solve it. Through rigorous theoretical analysis, we prove that RWE achieves asymptotical optimality, bounded data queue size, and sustainable network operation. We implement RWE on a popular IoT operating system, Contiki OS, and evaluate its performance using both real-world experiments based on the FIT IoT-LAB testbed, and extensive trace-driven simulations using Cooja. The evaluation results verify our theoretical analysis, and demonstrate that RWE can recycle more than 90% wasted energy caused by battery overflow, and achieve around 300% network utility gain in practical EH-WSNs.
© 2016 IEEE. Random Walk with Restart (RWR) is an appealing measure of proximity between nodes based on graph structures. Since real graphs are often large and subject to minor changes, it is prohibitively expensive to recompute proximities from scratch. Previous methods use LU decomposition and degree reordering heuristics, entailing O(|V | 3 ) time and O(|V | 2 ) memory to compute all (|V | 2 ) pairs of node proximities in a static graph. In this paper, a dynamic scheme to assess RWR proximities is proposed: (1) For unit update, we characterize the changes to all-pairs proximities as the outer product of two vectors. We notice that the multiplication of an RWR matrix and its transition matrix, unlike traditional matrix multiplications, is commutative. This can greatly reduce the computation of all-pairs proximities from O(|V | 3 ) to O(|Δ|) time for each update without loss of accuracy, where |Δ| (≪ |V | 2 ) is the number of affected proximities. (2) To avoid O(|V | 2 ) memory for all pairs of outputs, we also devise efficient partitioning techniques for our dynamic model, which can compute all pairs of proximities segment-wisely within O(l|V |) memory and O(⌈ |V | l ⌉) I/O costs, where 1 ≤ l ≤ |V | is a user-controlled trade-off between memory and I/O costs. (3) For bulk updates, we also devise aggregation and hashing methods, which can discard many unnecessary updates further and handle chunks of unit updates simultaneously. Our experimental results on various datasets demonstrate that our methods can be 1-2 orders of magnitude faster than other competitors while securing scalability and exactness.
Chen P-Y, Yang S, McCann JA, et al., 2015, Distributed Real-Time Anomaly Detection in Networked Industrial Sensing Systems, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, Vol: 62, Pages: 3832-3842, ISSN: 0278-0046
© 1982-2012 IEEE. Reliable real-time sensing plays a vital role in ensuring the reliability and safety of industrial cyberphysical systems (CPSs) such as wireless sensor and actuator networks. For many reasons, such as harsh industrial environments, fault-prone sensors, or malicious attacks, sensor readings may be abnormal or faulty. This could lead to serious system performance degradation or even catastrophic failure. Current anomaly detection approaches are either centralized and complicated or restricted due to strict assumptions, which are not suitable for practical large-scale networked industrial sensing systems (NISSs), where sensing devices are connected via digital communications, such as wireless sensor networks or smart grid systems. In this paper, we introduce a fully distributed general anomaly detection (GAD) scheme, which uses graph theory and exploits spatiotemporal correlations of physical processes to carry out real-time anomaly detection for general large-scale NISSs. We formally prove the scalability of our GAD approach and evaluate the performance of GAD for two industrial applications: building structure monitoring and smart grids. Extensive trace-driven simulations validate our theoretical analysis and demonstrate that our approach can significantly outperform state-of-the-art approaches in terms of detection accuracy and efficiency.
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
© 2015 IEEE. TV White Spaces (TVWS) technology allows wireless devices to opportunistically use locally-available TV channels enabled by a geolocation database. The UK regulator Ofcom has initiated a pilot of TVWS technology in the UK. This paper concerns a large- scale series of trials under that pilot. The purposes are to test aspects of white space technology, including the white space device and geolocation database interactions, the validity of the channel availability/powers calculations by the database and associated interference effects on primary services, and the performances of the white space devices, among others. An additional key purpose is to perform research investigations such as on aggregation of TVWS resources with conventional resources and also aggregation solely within TVWS, secondary coexistence issues and means to mitigate such issues, and primary coexistence issues under challenging deployment geometries, among others. This paper provides an update on the trials, giving an overview of their objectives and characteristics, some aspects that have been covered, and some early results and observations.
Kartakis S, Abraham E, McCann JA, et al., 2015, WaterBox: A testbed for monitoring and controlling smart water networks, Cyber-Physical Systems for Smart Water Networks (CysWater), CPS Week 2015, Publisher: Association for Computing Machinery
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, et al., 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
© 2015 IEEE. 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.
Lalanda P, McCann JA, Hamon C, et al., 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
© 2015 IEEE. In this paper, we present a novel learning environment that allows students to develop, execute, and test pervasive applications. This Java-based environment includes an execution platform built on top of Apache Felix OSGi and iPOJO, an Eclipse-based integrated environment, and a smart home simulator. Currently the environment challenges students to build five pervasive applications during the course. This system has been successfully tested by students enrolled in specially designed Masters courses.
Martins PMN, McCann JA, Martins PMN, et al., 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
© 2015 The Authors. Published by Elsevier B.V. The worldwide proliferation of mobile connected devices has brought about a revolution in the way we live, and will inevitably guide the way in which we design the cities of the future. However, designing city-wide systems poses a new set of challenges in terms of scale, manageability and citizen involvement. Solving these challenges is crucial to making sure that the vision of a programmable Internet of Things (IoT) becomes reality. In this article we will analyse these issues and present a novel programming approach to designing scalable systems for the Internet of Things, with an emphasis on smart city applications, that addresses these issues.
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
© 2015 IEEE. In this paper, we study the problem of multi-resource fairness in multi-user sensor networks with heterogeneous and time-varying resources. Particularly we focus on data gathering applications run on Wireless Sensor Networks (WSNs) or Internet of Things (IoT) in which users require to run a serious of sensing operations with various resource requirements. We consider both the resource demands of sensing tasks, and data forwarding tasks needed to establish multi-hop relay communications. By exploiting graph theory, queueing theory and the notion of dominant resource shares, we develop Symbiot, a light-weight, distributed algorithm that ensures multi-resource fairness between these users. With Symbiot, nodes can independently schedule its resources while maintaining network-level resource fairness through observing traffic congestion levels. Large-scale simulations based Contiki OS and Cooja network emulator show the effectiveness of Symbiot in adaptively utilizing available resources and reducing average completion times.
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, Pages: 1-7, ISSN: 2334-0983
In this paper, we study the problem of multi- resource fairness in systems running 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 may 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 re- source shares to ensure multi-resource fairness between complex workflows. 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. 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. Besides that, we propose a simple mechanism to decentralize the UDRF scheduling process across multiple schedulers. 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 fair resource allocation.
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