Publications
246 results found
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
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- Citations: 10
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 [18] 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
Holland O, Akhavan M, McCann J, 2015, Some initial results and observations from a series of trials within the Ofcom TV White Spaces pilot, IEEE 81st Vehicular Technology Conference: VTC2015, Publisher: IEEE, ISSN: 1550-2252
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.
Wu D, Arkhipov D, Asmare E, et al., 2015, UbiFlow: Mobility Management in Urban-scale Software Defined IoT, Proceedings of the 34th IEEE Conference on Computer Communications (INFOCOM), Publisher: IEEE, Pages: 208-216, ISSN: 0743-166X
The growth of Internet of Things (IoT) devices with multiple radio interfaces has resulted in a number of urban-scale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist (e.g. WiFi, Bluetooth, Cellular). Managing the multinetworks for seamless IoT access and handover, especially in mobile environments, is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick and easy configuration of network devices, but its application in urban-scale multinetworks requiring heterogeneous and frequent IoT access is not well studied. In this paper we present UbiFlow, the first software-defined IoT system for combined ubiquitous flow control and mobility management in urban heterogeneous networks. UbiFlow adopts multiple controllers to divide urban-scale SDN into different geographic partitions and achieve distributed control of IoT flows. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, the relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully examined and studied. The UbiFlow controller differentiates flow scheduling based on per-device requirements and whole-partition capabilities. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performancefor each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks; e.g. 67.21% throughput improvement, 72.99% reduced delay, and 69.59% jitter improvements, compared with alternative SDN systems.
Kartakis S, Abraham E, McCann J, 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
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 simulationof 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 infrastructureenables 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.
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.
Yu W, McCann JA, 2015, High quality SimRank-based similarity search, Departmental Technical Report: 15/3, Publisher: Department of Computing, Imperial College London, 15/3
SimRank is an influential link-based similarity measure thathas been used in many fields of Web search and sociometry.The best-of-breed method by Kusumoto et al. [7], however,does not always deliver high-quality results, since it fails toaccurately 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 oftenincurs a decrease in score s(a, b). The best-known solution,SimRank++ [1], cannot resolve this problem, since a revisedscore 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 timeof [7] from quadratic to linear in the number of iterations.(2) We design a “kernel-based”model to improve the qualityof SimRank, and circumvent the “connectivity trait” issue.(3) We give mathematical insights to the semantic differencebetween SimRank and its variant, and correct an argumentin [7]: “if D is replaced by a scaled identity matrix (1−γ)I,top-K rankings will not be affected much”. The experimentsconfirm that SR# can accurately extract high-quality scores,and is much faster than the state-of-the-art competitors.
Yu W, McCann J, 2015, Efficient partial-pairs SimRank search on large graphs, Proceedings of the VLDB Endowment International Conference on Very Large Data Bases, Vol: 8, Pages: 569-580, ISSN: 2150-8097
The assessment of node-to-node similarities based on graph topology arises in a myriad of applications, e.g., web search. SimRank is a notable measure of this type, with the intuition that "two nodes are similar if their in-neighbors are similar". While most existing work retrieving SimRank only considers all-pairs SimRank s(*, *) and single-source SimRank s(*, j) (scores between every node and query j), there are appealing applications for partial-pairs SimRank, e.g., similarity join. Given two node subsets A and B in a graph, partial-pairs SimRank assessment aims to retrieve only {s(a, b)}∀aεA,∀bεB. However, the best-known solution appears not self-contained since it hinges on the premise that the SimRank scores with node-pairs in an h-go cover set must be given beforehand.This paper focuses on efficient assessment of partial-pairs SimRank in a self-contained manner. (1) We devise a novel "seed germination" model that computes partial-pairs SimRank in O(k|E| min{|A|, |B|}) time and O(|E| + k|V|) memory for k iterations on a graph of |V| nodes and |E| edges. (2) We further eliminate unnecessary edge access to improve the time of partial-pairs SimRank to O(m min{|A|, |B|}), where m ≤ min{k|E|, Δ2k}, and Δ is the maximum degree. (3) We show that our partial-pairs SimRank model also can handle the computations of all-pairs and single-source SimRanks. (4) We empirically verify that our algorithms are (a) 38x faster than the best-known competitors, and (b) memory-efficient, allowing scores to be assessed accurately on graphs with tens of millions of links.
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
Wu D, Liu Q, Zhang Y, et al., 2014, CrowdWiFi: efficient crowdsensing of roadside WiFi networks, Proceedings of the 15th ACM/IFIP/USENIX Middleware Conference, Pages: 229-240
In this paper, we present CrowdWiFi, a novel vehicular middleware to identify and localize roadside WiFi APs that are located outside or inside buildings. Our work is motivated by the recent surge in availability of open WiFi access points (APs) that are enabling opportunistic data services to moving vehicles. Two key elements of CrowdWiFi that provide vehicles with opportunistic WiFi access include (a) an online compressive sensing component and (b) an offline crowdsourcing module. Online compressive sensing (CS) techniques are primarily used to for the coarse-grained estimation of nearby APs along the driving route; here, the received signal strength (RSS) values are recorded at runtime, and the number and locations of APs are recovered immediately based on limited RSS readings. The offline crowdsourcing mechanism assigns the online CS tasks to crowd-vehicles and aggregates answers using a bipartite graphical model. This offline crowdsourcing executes at a crowd-server that iteratively infers the reliability of each crowd-vehicle from the aggregated sensing results and refines the estimation of APs using weighted centroid processing. Extensive simulation results and real testbed experiments confirm that CrowdWiFi can successfully reduce the number of measurements needed for AP recovery, while maintaining satisfactory counting and localization accuracy. In addition, the impact of CrowdWiFi middleware on WiFi handoff and data transmission applications is examined.
Yang S, McCann JA, 2014, Distributed Optimal Lexicographic Max-Min Rate Allocation in Solar-Powered Wireless Sensor Networks, ACM TRANSACTIONS ON SENSOR NETWORKS, Vol: 11, ISSN: 1550-4859
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- Citations: 27
Kolcun R, McCann JA, 2014, Dragon: Data Discovery and Collection Architecture for Distributed IoT, Internet of Things 2014 - The 4th International Conference on the Internet of Things (IoT 2014), Pages: 91-96
Wireless Low-powered Sensing Systems (WLSS) are becoming more prevalent, taking the form of Wireless Sensor/Actuator Networks, Internet of Things, Phones etc. As node and network capabilities of such systems improve, there is more motivation to push computation into the network as it saves energy, prolongs system lifetime, and enables timely responses to events or control activities. Another advantage of such edge-processing is that these networks can become autonomous in the sense that users can directly query the network via any node in the network and are not required to connect to gateways or retrieve data via long range communications. Dragon is a scheme that efficiently identifies nodes that can reply to user requests based on static criteria that either describes that node or its data and provides the ability to near-optimally route queries or actuation control messages to those nodes. Dragon is scalable and agile as it does not require any central point orchestrating the search. In this paper we demonstrate significant performance improvements compared with state-of-the-art approaches in terms of numbers of messages required (up to 93% less) and its ability to scale to 100s of nodes.
Yang S, Sheng Z, McCann JA, et al., 2014, Distributed Stochastic Cross-layer Optimization for Multi-hop Wireless Networks with Cooperative Communications, IEEE TRANSACTIONS ON MOBILE COMPUTING, Vol: 13, Pages: 2269-2282, ISSN: 1536-1233
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- Citations: 12
Adeel U, Yang S, McCann J, 2014, Self-Optimizing Citizen-Centric Mobile Urban Sensing Systems, 11th International Conference on Autonomic Computing, Pages: 161-167
In this paper, we develop a novel networking scheme that supports both real-time and delay-tolerant urban sensing applications. This maintains optimality through self-adapting its communications strategy using either inexpensive short-range opportunistic transmissions or reliable long-range cellular radios. Core to this scheme is the trading of mobile sensor data in a virtual market where we demonstrate that our scheme can incentivize phone users to participate. We show that the scheme can optimise network throughput while minimising total phone costs, in terms of 3G and battery costs.
Kartakis S, McCann J, 2014, Real-time Edge Analytics for Cyber Physical Systems using Compression Rates, 11th International Conference on Autonomic Computing (ICAC'14) - USENIX 2014
There is a movement in many practical applications of Cyber-Physical Systems to push processing to the edge. This is particularly important were the CPS is carrying out monitoring and control, where the latency between the decision making and control message reception should be minimal. However, CPS are limited by the capabilities of the typically battery powered low resourced devices. In this paper we present a self-adaptive scheme that both reduces the amount of resources required to store high sample rate data at the edge and at the same time carries out initial data analytics. Using out Smart Water datasets, plus a selection from other real world CPS applications, we show that our algorithm reduces computation by 98%; data volumes by 55%; while requiring only 11KB of memory at runtime (including the compression algorithm). In addition we show that our system supports self-tuning and automatic reconfiguration which means that manual tuning is alleviated and the scheme can be both applied to any kind of raw data automatically and is able self-optimize as the nature of the incoming data changes over time.
Yu W, McCann J, 2014, Sig-SR: SimRank Search over Singular Graphs, The 37th Annual ACM SIGIR Conference, Publisher: Association for Computing Machinery, Pages: 859-862
SimRank is an attractive structural-context measure of similaritybetween two objects in a graph. It recursively followsthe intuition that “two objects are similar if they are referencedby similar objects”. The best known matrix-basedmethod [1] for calculating SimRank, however, implies anassumption that the graph is non-singular, i.e., its adjacencymatrix is invertible. In reality, non-singular graphs are veryrare; such an assumption in [1] is too restrictive in practice.In this paper, we provide a treatment of [1], by supportingsimilarity assessment on non-invertible adjacency matrices.Assume that a singular graph G has n nodes, with r (< n)being the rank of its adjacency matrix. (1) We show thatSimRank matrix S on G has an elegant structure: S can berepresented as a rank r matrix plus a scaled identity matrix.(2) By virtue of this, an efficient algorithm over singulargraphs, Sig-SR, is proposed for calculating all-pairs SimRankin O(r(n2 + Kr2)) time for K iterations. In contrast, theonly known matrix-based algorithm that supports singulargraphs [2] needs O(r4n2) time. The experimental results onreal and synthetic datasets demonstrate the superiority ofSig-SR on singular graphs against its baselines.
McCann JA, Adeel U, Yang S, 2014, Self-Optimizing Citizen-Centric Mobile Urban Sensing Systems, 11th International Conference on Autonomic Computing (ICAC 14)
McCann JA, Kartakis S, 2014, Real-time Edge Analytics for Cyber Physical Systems using Compression Rates, 11th International Conference on Autonomic Computing (ICAC 14)
Lv B, Yu W, Wang L, et al., 2014, Efficient Processing Node Proximity via Random Walk with Restart, The16th Asia-Pacific Web Conference, Publisher: Springer, Pages: 542-549, ISSN: 0302-9743
Gallacher S, Jetter C, Kalnikate V, et al., 2014, Investigating the Challenges of Crowd Sensing: Lessons from Zurich, Structures of Knowledge Co-creation Between Organisations and the Public in 17th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2014), Publisher: ACM
Crowd sensing has the potential to empower urban citizens in the current trend of “Smart City” research and development. In compliment to top-down initiatives tackling infrastructure and resource issues, crowd sensing can support a bottom-up movement where urban citizens have the potential to impact and drive change. However, there are many social and practical issues that must be addressed to expand the current crowd sensing communities beyond sensor and technology experts and into the wider general public. The SenCity workshop [1] explored the use cases and opportunities for crowd sensing in urban environments. It also investigated the various challenges in a hands-on and practical way, moving out of the classroom and into the city to get first-hand experience. In this paper we present the workshop itself and the key observations and outcomes that could influence further work in this area.
Yu W, McCann J, 2014, Efficient PartialPairs SimRank search on large graphs, Departmental Technical Report: 14/11, Publisher: Department of Computing, Imperial College London, 14/11
The assessment of node-to-node similarities based on graphtopology arises in a myriad of applications, e.g., web search.SimRank is a notable measure of this type, with the intuitionthat “two nodes are similar if their in-neighbors are similar”.While most existing work retrieving SimRank only considersall-pairs SimRank s(⋆, ⋆) and single-source SimRank s(⋆, j)(scores between every node and query j), there are appealingapplications for partial-pairs SimRank, e.g., similarity join.Given two node subsets A and B in a graph, partial-pairsSimRank assessment aims to retrieve only {s(a, b)}∀a∈A,∀b∈B.However, the best-known solution [17] is not self-containedsince it hinges on the premise that the SimRank scores withnode-pairs in an h-go cover set must be given beforehand.This paper focuses on efficient assessment of partial-pairsSimRank in a self-contained manner. (1) We devise a novel“seed germination” model that computes partial-pairs Sim-Rank in O(k|E|min{|A|, |B|}) time and O(|E|+k|V |) memoryfor k iterations on a graph of |V | nodes and |E| edges.(2) We further eliminate unnecessary edge access to improvethe time of partial-pairs SimRank to O(mmin{|A|, |B|}),where m ≤ min{k|E|, 2k}, and is the maximum degree.(3) We show that our partial-pairs SimRank model also canhandle the computations of all-pairs and single-source Sim-Ranks, as well as partial-pairs SimRank* (a related notionof SimRank). (4) We empirically verify that our algorithmsare (a) 38x faster than the best-known competitors, and (b)memory-efficient, allowing scores to be assessed accuratelyon graphs with tens of millions of links.
Asmare E, McCann J, 2014, Lightweight Sensing Uncertainty Metric – Incorporating Accuracy and Trust, Sensors Journal, IEEE, Vol: PP, Pages: 1-1, ISSN: 1530-437X
Lalanda P, McCann JA, Diaconescu A, 2014, Self-Managing Pervasive Computing, 2014 IEEE Eighth International Conference on Self Adaptive and Self Organizing Systems Workshops (SASOW 2014), Publisher: IEEE, Pages: 5-5, ISSN: 1949-3673
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