204 results found
Martins PMN, McCann JA, 2017, Network-Wide Programming Challenges in Cyber-Physical Systems, Cyber-Physical Systems: Foundations, Principles and Applications, Pages: 103-113, ISBN: 9780128038017
© 2017 Elsevier Inc. All rights reserved. The worldwide proliferation of mobile connected sensing, processing, and physical actuation devices has brought about a revolution in the way we live, and will inevitably guide the way in which we design applications for these networks. In this chapter we will show how the scalable development of applications for highly distributed, heterogenous large networks requires a shift from the current device-centric programming model to a network-centric semantic model, whereby individual devices are abstracted away and identified by the semantic descriptions of the services they provide. This requires the development of primitives that have network-wide semantics. The emphasis must also be shifted from manipulating individual points of data to manipulating streams of data to enable real-time processing and reasoning. This requires that the programming models not only take into account semantic descriptions of the streams rather than individual devices and data points, but also the various modalities of computing that are possible in this scenario; a computing continuum from in-network processing to cloud computing spanning a range of devices from cloud to edge.
In recent years, the evolution of urban environments, jointly with the progress of theInformation and Communication sector, have enabled the rapid adoption of new solutions thatcontribute to the growth in popularity of Smart Cities. Currently, the majority of the world populationlives in cities encouraging different stakeholders within these innovative ecosystems to seek newsolutions 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 citiescan be utilized to co-create in the OrganiCity project, where key actors like citizens, researchers andother 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 theexperimentation 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 componentsand enablers of the OrganiCity platform are presented and discussed in detail and include, amongothers, 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-scaleopportunistic data collection service operating as an alternative to traditional communications.
Kartakis S, Choudhary B, Gluhak A, et al., 2016, Demystifying Low-Power Wide-Area Communications for City IoT Applications, ACM WiNTECH 2016 Workshop, MobiCom, Publisher: ACM, Pages: 2-8
Low Power Wide Area (LPWA) communication technologieshave the potential to provide a step change in the enablementof cost-effective and energy efficient Internet ofThings (IoT) applications. With an increase in the numberof offerings available the real performance of these emergingtechnologies remain unclear. That is, each technologycomes with its own advantages and limitations; yet there isa lack of comparative studies that examine their trade-offsbased on empirical evidence. This poses a major challengeto IoT solution architects and developers in selecting an appropriatetechnology for an envisioned IoT application in agiven deployment context.In this paper, we look beyond data sheets and white papersof LPWA communication technologies and provide insightsinto the performance of three emerging LPWA solutionsbased on real world experiments with different traf-fic loads and in different urban deployment contexts. Underthe context of this study, specialized hardware was createdto incorporate the different technologies and provide scientificquantitative and qualitative information related to datarates, success rates, transmission mode energy and powerconsumption, and communication ranges. The results of experimentationhighlight the practicalities of placing LPWAtechnologies in real spaces and provide guidelines to IoT solutiondevelopers in terms of LPWA technology selection.Overall aim is to facilitate the design of new LPWA technologiesand adaptive communication strategies that informfuture IoT platforms.
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: 16, Pages: 2008-2022, ISSN: 1536-1233
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.
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.
Yang S, Adeel U, Tahir Y, et al., 2016, Practical opportunistic data collection in wireless sensor networks with mobile sinks, IEEE Transactions on Mobile Computing, Vol: 16, Pages: 1420-1433, ISSN: 1558-0660
Wireless Sensor Networks with Mobile Sinks (WSN-MSs) are considered a viable alternative to the heavy cost ofdeployment of traditional wireless sensing infrastructures at scale. However, current state-of-the-art approaches perform poorly inpractice due to their requirement of mobility prediction and specific assumptions on network topology. In this paper, we focus on lowdelayand high-throughput opportunistic data collection in WSN-MSs with general network topologies and arbitrary numbers of mobilesinks. We first propose a novel routing metric, Contact-Aware ETX (CA-ETX), to estimate the packet transmission delay caused byboth packet retransmissions and intermittent connectivity. By implementing CA-ETX in the defacto TinyOS routing standard CTP andthe IETF IPv6 routing protocol RPL, we demonstrate that CA-ETX can work seamlessly with ETX. This means that current ETXbasedrouting 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 BackpressureCollection (OBC). Both CA-ETX and OBC are lightweight, easy to implement, and require no mobility prediction. Through test-bedexperiments and extensive simulations, we show that the proposed schemes significantly outperform current approaches in terms ofpacket transmission delay, communication overhead, storage overheads, reliability, and scalability.
Wu D, Arkhipov DI, Kim M, et al., 2016, ADDSEN: Adaptive Data Processing and Dissemination for Drone Swarms in Urban Sensing, IEEE Transactions on Computers, Vol: 66, Pages: 183-198, ISSN: 0018-9340
We present ADDSEN middleware as a holistic solution for Adaptive Data processing and dissemination for Drone swarms in urban SENsing. To efficiently process sensed 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.
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: 1424-8239
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.
Kartakis S, Yu W, Akhavan M, et al., 2016, Adaptive edge analytics for distributed networked control of water systems, 2016 IEEE First International Conference on Internet-of-Things Design and Implementation, Publisher: IEEE, Pages: 72-82
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.
Kartakis S, Milojevic Jevric M, Tzagkarakis G, et al., 2016, Energy-based Adaptive Compression in Water Network Control Systems, CySWater2016, CPSWeek, Publisher: CPS
Contemporary water distribution networks exploitInternet of Things (IoT) technologies to monitor and controlthe behavior of water network assets. Smart meters/sensorand actuator nodes have been used to transfer informationfrom the water network to data centers for further analysis.Due to the underground position of water assets, many watercompanies tend to deploy battery driven nodes which lastbeyond the 10-year mark. This prohibits the use of high-samplerate sensing therefore limiting the knowledge we can obtainfrom the recorder data. To alleviate this problem, efficientdata compression enables high-rate sampling, whilst reducingsignificantly the required storage and bandwidth resourceswithout sacrificing the meaningful information content. Thispaper introduces a novel algorithm which combines the accuracyof standard lossless compression with the efficiencyof a compressive sensing framework. Our method balancesthe tradeoffs of each technique and optimally selects the bestcompression mode by minimizing reconstruction errors, giventhe sensor node battery state. To evaluate our algorithm, realhigh-sample rate water pressure data of over 170 days and 25sensor nodes of our real world large scale testbed was used.The experimental results reveal that our algorithm can reducecommunication around 66% and extend battery life by 46%compared to traditional periodic communication techniques.
Open WiFi access points (APs) are demonstratingthat they can provide opportunistic data services to movingvehicles. We present CrowdWiFi, a novel system to lookup roadside WiFi APs located outdoors or inside buildings.CrowdWiFi consists of two components: online compressivesensing (CS) and offline crowdsourcing. Online CS presents anefficient framework for the coarse-grained estimation of nearbyAPs along the driving route, where received signal strength (RSS)values are recorded at runtime, and the number and location ofthe APs are recovered immediately based on limited RSS readingsand adaptive CS operations. Offline crowdsourcing assigns theonline CS tasks to crowd-vehicles and aggregates answers on abipartite graphical model. Crowd-server also iteratively infersthe reliability of each crowd-vehicle from the aggregated sensingresults, and then refines the estimation of the APs using weightedcentroid processing. Extensive simulation results and real testbedexperiments confirm that CrowdWiFi can successfully reducethe computation cost and energy consumption of roadside WiFilookup, while maintaining satisfactory localization accuracy.
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.
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
Tahir, Yang, Koliousis, et al., 2015, UDRF: Multi-resource Fairness for Complex Jobs with Placement Constraints, IEEE GLOBECOM 2015, Publisher: IEEE, Pages: 1-7
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.
Tahir Y, Yang S, Koliousis A, et al., 2015, UDRF: Multi-Resource Fairness for Complex Jobs with Placement Constraints, 2015 IEEE Global Communications Conference (GLOBECOM), Publisher: 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.
Yang S, Tahir Y, Chen P, et al., Distributed Optimization in Energy Harvesting Sensor Networks with Dynamic In-network Data Processing, IEEE INFOCOM 2016, Publisher: Institute of Electrical and Electronics Engineers (IEEE), ISSN: 0743-166X
Energy Harvesting Wireless Sensor Networks (EH- WSNs) have been attracting increasing interest in recent years. Most current EH-WSN approaches focus on sensing and net- working 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 sens- ing 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 eval- uate 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.
Yu W, Mccann, 2015, Effectively Positioning Water Loss Event in Smart Water Networks, 2nd International Electronic Conference on Sensors and Applications, Publisher: MDPI, ISSN: 1424-8220
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.
Yu W, McCann JA, 2015, Gauging Correct Relative Rankings For Similarity Search, 24th ACM International on Conference on Information and Knowledge Management (CIKM '15), Publisher: Association for Computing Machinery, Pages: 1791-1794
One of the important tasks in link analysis is to quantify the similarity between two objects based on hyperlink structure. SimRank is an attractive similarity measure of this type. Existing work mainly focuses on absolute SimRank scores, and often harnesses an iterative paradigm to compute them. While these iterative scores converge to exact ones with the increasing number of iterations, it is still notoriously difficult to determine how well the relative orders of these iterative scores can be preserved for a given iteration. In this paper, we propose efficient ranking criteria that can secure correct relative orders of node-pairs with respect to SimRank scores when they are computed in an iterative fashion. Moreover, we show the superiority of our criteria in harvesting top-K SimRank scores and bucket orders from a full ranking list. Finally, viable empirical studies verify the usefulness of our techniques for SimRank top-K ranking and bucket ordering.
Zhao C, Shi F, Huang R, et al., 2015, Trustworthy device pairing for opportunistic device-to-device communications in mobile crowdsourcing systems, S3 Wireless of the Students, by the Students, for the Students, Publisher: ACM, Pages: 4-6
Mobile Crowdsourcing System is an emerging service paradigm base on numerous personal smart devices, where the Deviceto- Device communication among opportunistically encountered participating devices is an indispensable part of task allocation, file transmission and data collaboration. Considering that participating devices are privately held and opportunistically encountered, we design the Trustworthy Device Pairing (TDP) scheme that realizes user-transparent sharing secret key negotiation and reliable peer device determination for trustworthy spontaneous D2D transactions. TDP is demonstrated to be effective based on our proof-of-concept implementation, and a further evaluation on efficiency will be conducted.
Yang X, Zhao C, Yang S, et al., 2015, A Systematic Key Management Mechanism for Practical Body Sensor Networks, IEEE International Conference on Communications (ICC), Publisher: IEEE, Pages: 7310-7315, ISSN: 1550-3607
Security plays a vital role in promoting the practicality of Wireless Body Sensor Networks (BSNs), which provides a promising solution to precise human physiological status monitoring. A fundamental security issue in BSN is key management, including establishment and maintenance of the key system. However, current BSN key management solutions are either designed for specific phases of a BSN's life-time or restricted to strong assumptions such as homogeneous BSN composition, pre-deployed key materials, and existing secure path, which limits their applications in real-world BSNs. In this paper, we develop the Systematic Key Management (SKM) for practical BSNs, where basic human interactions are conducted for non-predeployed secure BSN initialization, and authenticated key agreement is achieved using lightweight non-pairing certificateless public key cryptography. We construct a BSN prototype consisting of self-designed motes and Android phones to evaluate the real-world performance of SKM. Through extensive simulations and test-bed experiments, we demonstrate that our lightweight SKM scheme manages to provide high security guarantee while outperforming state-of-the-art approaches in terms of both computation and storage efficiency.
Yang X, Ren X, Yang S, et al., 2015, A novel temporal perturbation based privacy-preserving scheme for real-time monitoring systems, COMPUTER NETWORKS, Vol: 88, Pages: 72-88, ISSN: 1389-1286
Tahir, Yang, Shusen, et al., 2015, Symbiot: Congestion-driven Multi-resource Fairness for Multi-User Sensor Networks, 17TH IEEE International Conference on High Performance Computing and Communications, Publisher: IEEE, Pages: 654-659
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 (IoTs) in which users require to run a serious of sensing tasks with various resource requirements. 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 utilizing resources and reducing average completion times.
Yang S, Adeel U, McCann J, 2015, Backpressure Meets Taxes: Faithful Data Collection in Stochastic Mobile Phone Sensing Systems, The 34th Annual IEEE International Conference on Computer Communications (INFOCOM 2015), Publisher: IEEE, ISSN: 0743-166X
The use of sensor-enabled smart phones is considered to be a promising solution to large-scale urban data collection. In current approaches to mobile phone sensing systems (MPSS), phones directly transmit their sensor readings through cellular radios to the server. However, this simple solution suffers from not only significant costs in terms of energy and mobile data usage, but also produces heavy traffic loads on bandwidth-limited cellular networks. To address this issue, this paper investigates cost-effective data collection solutions for MPSS using hybrid cellular and opportunistic short-range communications. We first develop an adaptive and distribute algorithm OptMPSS to maximize phone user financial rewards accounting for their costs across the MPSS. To incentivize phone users to participate, while not subverting the behavior of OptMPSS, we then propose BMT, the first algorithm that merges stochastic Lyapunov optimization with mechanism design theory. We show that our proven incentive compatible approaches achieve an asymptotically optimal gross profit for all phone users. Experiments with Android phones and trace-driven simulations verify our theoretical analysis and demonstrate that our approach manages to improve the system performance significantly (around 100\%) while confirming that our system achieves incentive compatibility, individual rationality, and server profitability.
Yu W, McCann JA, 2015, High Quality Graph-Based Similarity Search, 38th International ACM SIGIR Conference on Research and Development in Information (SIGIR '15), Publisher: Association for Computing Machinery, Pages: 83-92
SimRank is an influential link-based similarity measure that has been used in many fields of Web search and sociometry. The best-of-breed method by Kusumoto et. al., however, does not always deliver high-quality results, since it fails to accurately obtain its diagonal correction matrix D. Besides, SimRank is also limited by an unwanted "connectivity trait": increasing the number of paths between nodes a and b often incurs a decrease in score s(a,b). The best-known solution, SimRank++, cannot resolve this problem, since a revised score will be zero if a and b have no common in-neighbors. In this paper, we consider high-quality similarity search. Our scheme, SR#, is efficient and semantically meaningful: (1) We first formulate the exact D, and devise a "varied-D" method to accurately compute SimRank in linear memory. Moreover, by grouping computation, we also reduce the time of from quadratic to linear in the number of iterations. (2) We design a "kernel-based" model to improve the quality of SimRank, and circumvent the "connectivity trait" issue. (3) We give mathematical insights to the semantic difference between SimRank and its variant, and correct an argument: "if D is replaced by a scaled identity matrix, top-K rankings will not be affected much". The experiments confirm that SR# can accurately extract high-quality scores, and is much faster than the state-of-the-art competitors.
Yu W, McCann J, 2015, Co-Simmate: Quick Retrieving All Pairwise Co-Simrank Relevance, The 53rd Annual Meeting of the Association for Computational Linguistics
Co-Simrank is a useful Simrank-like measureof similarity based on graph structure.The existing method iteratively computeseach pair of Co-Simrank score from a dotproduct of two Pagerank vectors, entailingO(log(1/ǫ)n3) time to compute all pairsof Co-Simranks in a graph with n nodes,to attain a desired accuracy ǫ. In this study,we devise a model, Co-Simmate, to speedup the retrieval of all pairs of Co-Simranksto O(log2(log(1/ǫ))n3) time. Moreover,we show the optimality of Co-Simmateamong other hop-(uk) variations, and integrateit with a matrix decomposition basedmethod on singular graphs to attain higherefficiency. The viable experiments verifythe superiority of Co-Simmate to others.
Yu W, McCann J, 2015, High Quality Graph-Based Similarity Retrieval on Large Graphs, The 38th ACM SIGIR International Conference, Publisher: ACM
Martins P, McCann JA, 2015, The Programmable City, 6th International Conference on Ambient Systems, Networks and Technologies (ANT-2015)., Publisher: Elsevier, Pages: 334-341, ISSN: 1877-0509
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.
Yu W, Lin X, Zhang W, et al., 2015, Fast All-Pairs SimRank Assessment on Large Graphs and Bipartite Domains, IEEE Transactions on Knowledge and Data Engineering, Vol: 27, Pages: 1810-1823, ISSN: 1041-4347
SimRank is a powerful model for assessing vertex-pair similarities in a graph. It follows the concept that two vertices are similar if they are referenced by similar vertices. The prior work  exploits partial sums memoization to compute SimRankin O(Kmn) time on a graph of n vertices and m edges, for K iterations. However, the computations among different partial sums may have duplicate redundancy. Besides, to guarantee a given accuracy ϵ, the existing SimRank needs K = ⌈logC ϵ⌉iterations, where C is a damping factor, but the geometric rate of convergence is slow if a high accuracy is expected. In this paper, (1) a novel clustering strategy is proposed to eliminate duplicate computations occurring in partial sums, and an efﬁcient algorithm is then devised to accelerate SimRank computation to O(Kd′n2) time, where d′ is typically much smaller than m n . (2) A new differential SimRank equation is proposed, which can represent the SimRank matrix as an exponential sum of transition matrices, as opposed to the geometric sum of the conventional counterpart. This leads to a further speedup in the convergence rate of SimRank iterations. (3) In bipartite domains, a novel ﬁner-grained partial max clustering method is developed to speed up the computation of the Minimax SimRank variation from O(Kmn) to O(Km′n) time, where m′ (≤ m) is the number of edges ina reduced graph after edge clustering, which can be typically much smaller than m. Using real and synthetic data, we empirically verify that (1) our approach of partial sums sharing outperforms the best known algorithm by up to one order of magnitude; (2) the revised notion of SimRank further achieves a 5X speedup on large graphs while also fairly preserving the relative order of original SimRank scores; (3) our ﬁner-grained partial max memoization for the Minimax SimRank variation in bipartite domains is 0.5–1.2 orders of magnitude faster than the baselines.
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
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