ORCID Profile
0000-0002-2725-2529
Current Organisation
The Hong Kong Polytechnic University
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Publisher: IEEE
Date: 09-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 2003
Publisher: Springer Science and Business Media LLC
Date: 08-2004
Publisher: IEEE Comput. Soc
Date: 2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
DOI: 10.1109/TC.2013.41
Publisher: IEEE
Date: 10-2013
DOI: 10.1109/MASS.2013.74
Publisher: Elsevier BV
Date: 04-2006
Publisher: Elsevier BV
Date: 11-2009
Publisher: Association for Computing Machinery (ACM)
Date: 06-2014
DOI: 10.1145/2533669
Abstract: Structural health monitoring (SHM) refers to the process of implementing a damage detection and characterization strategy for engineering structures. Its objective is to monitor the integrity of structures and detect and pinpoint the locations of possible damages. Although wired network systems still dominate in SHM applications, it is commonly believed that wireless sensor network (WSN) systems will be deployed for SHM in the near future, due to their intrinsic advantages. However, the constraints (e.g., communication, fault tolerance, energy) of WSNs must be considered before their deployment on structures. In this article, we study the methodology of sensor placement optimization for WSN-based SHM. Sensor placement plays a vital role in SHM applications, where sensor nodes are placed on critical locations that are of civil/structural engineering importance. We design a three-phase sensor placement approach, named TPSP, aiming to achieve the following objectives: finding a high-quality placement for a given set of sensors that satisfies the engineering requirements, ensuring communication efficiency and reliability and low placement complexity, and reducing the probability of failures in a WSN. Along with the sensor placement, we enable sensor nodes to develop “connectivity trees” in such a way that maintaining structural health state and network connectivity, for ex le, in case of a sensor fault, can be done in a distributed manner. The trees are constructed once (unlike dynamic clusters or trees) and do not incur additional communication costs for the WSN. We optimize the performance of TPSP by considering multiple objectives: low communication cost, fault tolerance, and lifetime prolongation. We validate the effectiveness and performance of TPSP through both simulations using real datasets and a proof-of-concept system on a physical structure.
Publisher: China Science Publishing & Media Ltd.
Date: 2007
DOI: 10.1360/JOS180996
Publisher: Springer Science and Business Media LLC
Date: 05-12-2022
DOI: 10.1038/S41598-022-25452-3
Abstract: Recurrent incidents of economically motivated adulteration have long-lasting and devastating effects on public health, economy, and society. With the current food authentication methods being target-oriented, the lack of an effective methodology to detect unencountered adulterants can lead to the next melamine-like outbreak. In this study, an ensemble machine-learning model that can help detect unprecedented adulteration without looking for specific substances, that is, in a non-targeted approach, is proposed. Using raw milk as an ex le, the proposed model achieved an accuracy and F1 score of 0.9924 and 0. 0.9913, respectively, when the same type of adulterants was presented in the training data. Cross-validation with spiked contaminants not routinely tested in the food industry and blinded from the training data provided an F1 score of 0.8657. This is the first study that demonstrates the feasibility of non-targeted detection with no a priori knowledge of the presence of certain adulterants using data from standard industrial testing as input. By uncovering discriminative profiling patterns, the ensemble machine-learning model can monitor and flag suspicious s les this technique can potentially be extended to other food commodities and thus become an important contributor to public food safety.
Publisher: IEEE
Date: 09-2008
DOI: 10.1109/ICPP.2008.10
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: IEEE
Date: 06-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Publisher: IEEE
Date: 05-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: Elsevier BV
Date: 06-2010
Publisher: Elsevier BV
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-08-2023
Publisher: IEEE
Date: 04-2013
Publisher: IEEE
Date: 09-2008
DOI: 10.1109/ICPP.2008.21
Publisher: Association for Computing Machinery (ACM)
Date: 22-03-2023
DOI: 10.1145/3558005
Abstract: Personalized federated learning (PFL) has emerged as a paradigm to provide a personalized model that can fit the local data distribution of each client. One natural choice for PFL is to leverage the fast adaptation capability of meta-learning, where it first obtains a single global model, and each client achieves a personalized model by fine-tuning the global one with its local data. However, existing meta-learning-based approaches implicitly assume that the data distribution among different clients is similar, which may not be applicable due to the property of data heterogeneity in federated learning. In this work, we propose a Group-based Federated Meta-Learning framework, called G-FML , which adaptively ides the clients into groups based on the similarity of their data distribution, and the personalized models are obtained with meta-learning within each group. In particular, we develop a simple yet effective grouping mechanism to adaptively partition the clients into multiple groups. Our mechanism ensures that each group is formed by the clients with similar data distribution such that the group-wise meta-model can achieve “personalization” at large. By doing so, our framework can be generalized to a highly heterogeneous environment. We evaluate the effectiveness of our proposed G-FML framework on three heterogeneous benchmarking datasets. The experimental results show that our framework improves the model accuracy by up to 13.15% relative to the state-of-the-art federated meta-learning.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: IEEE
Date: 04-2014
Publisher: IEEE
Date: 05-2015
Publisher: Springer International Publishing
Date: 2023
Publisher: Association for Computing Machinery (ACM)
Date: 21-12-2022
DOI: 10.1145/3569496
Abstract: Vibration measurement is vital for fault diagnosis of structures (e.g., machines and civil structures). Different structure components undergo distinct vibration patterns, which jointly determine the structure's health condition, thus demanding simultaneous multi-point vibration monitoring. Existing solutions deploy multiple accelerometers along with their power supplies or laser vibrometers on the monitored object to measure multi-point vibration, which is inconvenient and costly. Cameras provide a less expensive solution while heavily relying on good lighting conditions. To overcome these limitations, we propose a cost-effective and passive system, called Multi-Vib, for precise multi-point vibration monitoring. Multi-Vib is implemented using a single mmWave radar to remotely and separately sense the vibration displacement of multiple points via signal reflection. However, simultaneously detecting and monitoring multiple points on a single object is a daunting task. This is because most radar signals are scattered away from vibration points due to their tilted locations and shapes by nature, causing an extremely weak reflected signal to the radar. To solve this issue, we dedicatedly design a physical marker placed on the target point, which can force the direction of the reflected signal towards the radar and significantly increase the reflected signal strength. Another practical issue is that the reflected signal from each point endures interferences and noises from the surroundings. Thus, we develop a series of effective signal processing methods to denoise the signal for accurate vibration frequency and displacement estimation. Extensive experimental results show that the average errors in multi-point vibration frequency and displacement estimation are around 0.16Hz and 14μm, respectively.
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 09-2007
DOI: 10.1109/ICPP.2007.58
Publisher: Elsevier BV
Date: 11-2010
Publisher: IEEE
Date: 06-2016
Publisher: IEEE
Date: 06-2012
Publisher: Elsevier BV
Date: 03-2013
Publisher: Springer Science and Business Media LLC
Date: 07-2006
Publisher: Wiley
Date: 22-12-2013
DOI: 10.1002/ETT.2775
Abstract: Cognitive radio techniques provide potential solutions to the spectrum crisis problem. In cognitive radio networks, a secondary user (SU) opportunistically accesses the authorised spectrum of a primary user (PU) to transmit its data. In order to avoid collisions with the PU, the SU needs to frequently detect the status of the channel before accessing the spectrum. However, frequent detection of channel status consumes a lot of energy, which is unaffordable for mobile SUs. In this paper, we investigate how to preserve the limited energy of SUs in mobile cognitive networks. Besides the sensing and transmitting actions used in traditional spectrum access policies, we introduce a new sleeping action with which mobile SUs can better exploit the trade‐off between their consumption and data transmission throughput. We define a utility function to characterise the effect of sleeping actions, in which correct sleeping actions are rewarded for preserving energy, and incorrect sleeping actions are penalised for wasting data transmission opportunities. We prove that, in the meaning of maximising the mean benefit, the utility function exhibits a threshold‐based structure. Based on this structure, we design a new spectrum access strategy with which mobile SUs can chose their optimal actions to achieve the best trade‐off between energy consumption and throughput. Simulation results show that our spectrum access strategy greatly reduces energy consumption of mobile SUs by up to 85% and, meanwhile, incurs nearly the same PU collision rate and throughput as in state‐of‐the‐art solutions. Copyright © 2013 John Wiley & Sons, Ltd.
Publisher: Elsevier BV
Date: 06-2013
Publisher: Association for Computing Machinery (ACM)
Date: 07-09-2016
DOI: 10.1145/2968450
Abstract: Traditional tracking solutions in wireless sensor networks based on fixed sensors have several critical problems. First, due to the mobility of targets, a lot of sensors have to keep being active to track targets in all potential directions, which causes excessive energy consumption. Second, when there are holes in the deployment area, targets may fail to be detected when moving into holes. Third, when targets stay at certain positions for a long time, sensors surrounding them have to suffer heavier work pressure than do others, which leads to a bottleneck for the entire network. To solve these problems, a few mobile sensors are introduced to follow targets directly for tracking because the energy capacity of mobile sensors is less constrained and they can detect targets closely with high tracking quality. Based on a realistic detection model, a solution of scheduling mobile sensors and fixed sensors for target tracking is proposed. Moreover, the movement path of mobile sensors has a provable performance bound compared to the optimal solution. Results of extensive simulations show that mobile sensors can improve tracking quality even if holes exist in the area and can reduce energy consumption of sensors effectively.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 02-2012
DOI: 10.1109/PDP.2012.51
Publisher: Hindawi Limited
Date: 2015
DOI: 10.1155/2015/745472
Publisher: SPIE-Intl Soc Optical Eng
Date: 04-2010
DOI: 10.1117/1.3381182
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2010
Publisher: IEEE
Date: 2006
Publisher: IEEE Comput. Soc
Date: 2003
Publisher: Informa UK Limited
Date: 07-2007
Publisher: IEEE
Date: 03-2012
Publisher: IEEE
Date: 2006
DOI: 10.1109/SKG.2006.33
Publisher: Elsevier BV
Date: 07-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: IEEE
Date: 08-2008
Publisher: Inderscience Publishers
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Publisher: Springer Science and Business Media LLC
Date: 05-2008
Publisher: IEEE
Date: 2009
DOI: 10.1109/MSN.2009.13
Publisher: IEEE
Date: 06-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2007
DOI: 10.1109/TC.2007.1053
Publisher: Elsevier BV
Date: 02-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Publisher: Informa UK Limited
Date: 09-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2010
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 06-2014
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 09-2009
DOI: 10.1109/ICPP.2009.13
Publisher: IEEE
Date: 09-2009
DOI: 10.1109/ICPP.2009.16
Publisher: Inderscience Publishers
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2010
Publisher: Springer US
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
DOI: 10.1109/MC.2017.37
Publisher: Wiley
Date: 13-09-2005
DOI: 10.1002/CPE.934
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 04-2015
Publisher: IEEE
Date: 12-2011
DOI: 10.1109/MSN.2011.22
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 11-2000
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2007
Publisher: Elsevier BV
Date: 06-2012
Publisher: Association for Computing Machinery (ACM)
Date: 23-07-2015
DOI: 10.1145/2753763
Abstract: Multihop broadcasting in low-duty-cycle Wireless Sensor Networks (WSNs) is a very challenging problem, since every node has its own working schedule. Existing solutions usually use unicast instead of broadcast to forward packets from a node to its neighbors according to their working schedules, which is, however, not energy efficient. In this article, we propose to exploit the broadcast nature of wireless media to further save energy for low-duty-cycle networks, by adopting a novel broadcasting communication model. The key idea is to let some early wake-up nodes postpone their wake-up slots to overhear broadcasting messages from its neighbors. This model utilizes the spatiotemporal locality of broadcast to reduce the total energy consumption, which can be essentially characterized by the total number of broadcasting message transmissions. Based on such model, we aim at minimizing the total number of broadcasting message transmissions of a broadcast for low-duty-cycle WSNs, subject to the constraint that the broadcasting latency is optimal. We prove that it is NP-hard to find the optimal solution, and design an approximation algorithm that can achieve a polylogarithmic approximation ratio. Extensive simulation results show that our algorithm outperforms the traditional solutions in terms of energy efficiency.
Publisher: Elsevier BV
Date: 04-2005
DOI: 10.1016/J.BIOSYSTEMS.2004.10.003
Abstract: Cook's Theorem [Cormen, T.H., Leiserson, C.E., Rivest, R.L., 2001. Introduction to Algorithms, second ed., The MIT Press Garey, M.R., Johnson, D.S., 1979. Computer and Intractability, Freeman, San Fransico, CA] is that if one algorithm for an NP-complete or an NP-hard problem will be developed, then other problems will be solved by means of reduction to that problem. Cook's Theorem has been demonstrated to be correct in a general digital electronic computer. In this paper, we first propose a DNA algorithm for solving the vertex-cover problem. Then, we demonstrate that if the size of a reduced NP-complete or NP-hard problem is equal to or less than that of the vertex-cover problem, then the proposed algorithm can be directly used for solving the reduced NP-complete or NP-hard problem and Cook's Theorem is correct on DNA-based computing. Otherwise, a new DNA algorithm for optimal solution of a reduced NP-complete problem or a reduced NP-hard problem should be developed from the characteristic of NP-complete problems or NP-hard problems.
Publisher: Informa UK Limited
Date: 10-2013
Publisher: IEEE
Date: 08-2012
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-1999
Publisher: IEEE
Date: 12-2010
DOI: 10.1109/EUC.2010.5
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2014
DOI: 10.1109/TPDS.2013.91
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2011
Publisher: IEEE
Date: 2003
Publisher: Elsevier BV
Date: 06-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: IEEE
Date: 09-2010
DOI: 10.1109/ICPP.2010.28
Publisher: IEEE
Date: 2006
DOI: 10.1109/GCC.2006.35
Publisher: IEEE
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2010
Publisher: IEEE
Date: 10-2013
DOI: 10.1109/MASS.2013.47
Publisher: IEEE
Date: 10-2014
DOI: 10.1109/MASS.2014.15
Publisher: Elsevier BV
Date: 2016
Publisher: ACM
Date: 28-06-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: IEEE
Date: 2006
DOI: 10.1109/CIT.2006.45
Publisher: IEEE
Date: 03-2012
Publisher: IEEE
Date: 03-2013
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11576235_94
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
Publisher: Wiley
Date: 02-09-2005
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 2003
Publisher: Springer Science and Business Media LLC
Date: 12-2010
DOI: 10.1155/2010/439890
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2013
Publisher: IEEE
Date: 04-2013
Publisher: IEEE
Date: 12-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2006
Publisher: IEEE
Date: 12-2011
DOI: 10.1109/APSCC.2011.4
Publisher: Springer Science and Business Media LLC
Date: 10-08-2012
Publisher: IEEE
Date: 12-2008
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11564621_28
Publisher: IEEE
Date: 03-2012
Publisher: Elsevier BV
Date: 11-2003
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 10-2009
Publisher: Elsevier BV
Date: 09-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: IEEE
Date: 10-2009
Publisher: IEEE Comput. Soc
Date: 2003
Publisher: IEEE Comput. Soc
Date: 2002
Publisher: Elsevier BV
Date: 04-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Publisher: Elsevier BV
Date: 04-2011
Publisher: IEEE
Date: 03-2011
Publisher: IEEE
Date: 08-2015
Publisher: World Scientific Pub Co Pte Lt
Date: 06-2004
DOI: 10.1142/S0129054104002583
Abstract: We study hierarchical configuration of distributed systems for achieving optimized system performance. A distributed system consists of a collection of local processes which are distributed over a network of processors, and work in a cooperative manner to fulfill various tasks. A hierarchical approach is to group and organize the distributed processes into a logical hierarchy of multiple levels, so as to coordinate the local computation/control activities to improve the overall system performance. It has been proposed as an effective way to solve various problems in distributed computing, such as distributed monitoring, resource scheduling, and network routing. The optimization problem considered in this paper is concerned with finding an optimal hierarchical partition of the processors, so that the total traffic flow over the network is minimized. The problem in its general form has been known to be NP-hard. Therefore, we just focus on distributed computing jobs which require collecting and processing information from all processors. By limiting levels of the hierarchy to two, we will establish the analytically optimal hierarchical configurations for two popular interconnection networks: mesh and hypercube. Based on analytical results, partitioning algorithms are proposed to achieve minimal communication cost (network traffic flow). We will also present and discuss heuristic algorithms for multiple-level hierarchical partitions.
Publisher: SPIE-Intl Soc Optical Eng
Date: 11-2009
DOI: 10.1117/1.3258347
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Informa UK Limited
Date: 06-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Publisher: IEEE
Date: 06-2012
Publisher: Institute of Electronics, Information and Communications Engineers (IEICE)
Date: 2010
Publisher: Elsevier BV
Date: 11-2007
Publisher: Institute of Electronics, Information and Communications Engineers (IEICE)
Date: 2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
Publisher: IEEE
Date: 12-2011
Publisher: Elsevier BV
Date: 06-2005
Publisher: ACM
Date: 28-06-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 04-2013
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2018
Publisher: IEEE
Date: 12-2010
Publisher: IEEE
Date: 03-2010
Publisher: IEEE
Date: 2005
DOI: 10.1109/ICPP.2005.25
Publisher: Elsevier BV
Date: 08-2007
Publisher: IEEE
Date: 12-2010
Publisher: IEEE
Date: 04-2012
Publisher: ICST
Date: 2008
Publisher: IEEE
Date: 04-2014
Publisher: IEEE
Date: 08-2016
DOI: 10.1109/ICPP.2016.43
Publisher: IEEE
Date: 2005
Publisher: Elsevier BV
Date: 03-2003
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2002
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11576235_102
Publisher: ACM
Date: 21-05-2013
Publisher: Elsevier BV
Date: 2001
Publisher: Elsevier BV
Date: 05-2004
Publisher: IEEE
Date: 10-2014
DOI: 10.1109/SRDS.2014.22
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2015
Publisher: Elsevier BV
Date: 05-2013
Publisher: Elsevier BV
Date: 02-2011
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 11-2010
Publisher: Elsevier BV
Date: 12-2016
Publisher: IEEE
Date: 03-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-07-2022
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 09-2010
Publisher: Association for Computing Machinery (ACM)
Date: 10-08-2023
DOI: 10.1145/3614437
Publisher: IEEE
Date: 03-2012
DOI: 10.1109/AINA.2012.81
Publisher: Springer Science and Business Media LLC
Date: 23-06-2010
Publisher: IEEE
Date: 2009
DOI: 10.1109/CSE.2009.201
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-12-2022
Publisher: IEEE Comput. Soc
Date: 2001
Publisher: IEEE
Date: 2006
DOI: 10.1109/ICPP.2006.16
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2007
Publisher: IEEE
Date: 12-2012
DOI: 10.1109/RTSS.2012.60
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2020
Publisher: IEEE
Date: 1999
Publisher: Elsevier BV
Date: 02-2016
Publisher: IEEE
Date: 03-2011
Publisher: IEEE
Date: 12-2013
DOI: 10.1109/RTSS.2013.35
Publisher: IEEE
Date: 2010
Publisher: ACM Press
Date: 2006
Publisher: IEEE
Date: 03-2013
DOI: 10.1109/AINA.2013.27
Publisher: IEEE
Date: 11-2012
Publisher: Elsevier BV
Date: 10-2007
Publisher: IEEE
Date: 10-2016
Publisher: IEEE
Date: 09-2012
Publisher: Inderscience Publishers
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2016
Publisher: IEEE
Date: 03-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 11-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Publisher: IEEE
Date: 04-2013
Publisher: IEEE
Date: 05-2016
Publisher: IEEE
Date: 2006
Publisher: Elsevier BV
Date: 07-2008
Publisher: Springer Science and Business Media LLC
Date: 03-1970
Publisher: Elsevier BV
Date: 12-2005
Publisher: Elsevier BV
Date: 06-2017
Publisher: Inderscience Publishers
Date: 2009
Publisher: IEEE
Date: 11-2009
Publisher: Elsevier BV
Date: 04-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 12-2013
DOI: 10.1109/RTSS.2013.19
Publisher: Springer Science and Business Media LLC
Date: 17-03-2015
Publisher: Springer Science and Business Media LLC
Date: 03-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2010
DOI: 10.1109/TMC.2010.129
Publisher: Wiley
Date: 13-07-2012
Publisher: IEEE
Date: 06-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: IEEE
Date: 05-2014
Publisher: IEEE
Date: 10-2009
Publisher: IEEE Comput. Soc
Date: 2001
Publisher: IEEE
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: IEEE
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Publisher: IEEE
Date: 12-2007
Publisher: IEEE
Date: 10-2007
DOI: 10.1109/SKG.2007.153
Publisher: IEEE
Date: 11-2009
Publisher: IEEE
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: No publisher found
Date: 2003
Publisher: IEEE
Date: 2008
DOI: 10.1109/ICC.2008.83
Publisher: IEEE
Date: 03-2009
Publisher: IEEE
Date: 10-2007
Publisher: IEEE
Date: 10-2011
DOI: 10.1109/MASS.2011.17
Publisher: Springer Science and Business Media LLC
Date: 14-07-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: IEEE
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Wiley
Date: 11-10-2007
DOI: 10.1002/CPE.1270
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2004
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: IEEE
Date: 03-2016
Publisher: IEEE
Date: 06-2016
Publisher: Elsevier BV
Date: 12-2010
Publisher: Springer Science and Business Media LLC
Date: 16-01-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 10-2006
Publisher: Springer Science and Business Media LLC
Date: 03-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2007
DOI: 10.1109/MC.2007.123
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: IEEE
Date: 05-2011
Publisher: IEEE
Date: 10-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: China Science Publishing & Media Ltd.
Date: 2007
DOI: 10.1360/JOS180146
Publisher: IEEE
Date: 2006
DOI: 10.1109/PDP.2006.11
Publisher: IEEE
Date: 2003
Publisher: IEEE
Date: 2003
Publisher: Wiley
Date: 13-07-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: IEEE
Date: 08-2008
Publisher: IEEE
Date: 12-2008
Publisher: Elsevier BV
Date: 02-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
Publisher: Springer Science and Business Media LLC
Date: 30-01-2008
Publisher: Elsevier BV
Date: 07-2007
Publisher: Wiley
Date: 23-06-2009
DOI: 10.1002/WCM.819
Publisher: IEEE
Date: 10-2012
Publisher: IEEE
Date: 10-2012
DOI: 10.1109/SRDS.2012.26
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-03-2021
Publisher: Wiley
Date: 2010
DOI: 10.1002/ETT.1409
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2012
DOI: 10.1109/TMC.2011.191
Publisher: Springer Science and Business Media LLC
Date: 04-09-2009
Publisher: Springer International Publishing
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2015
Publisher: IEEE
Date: 2006
DOI: 10.1109/ICPP.2006.69
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Elsevier BV
Date: 05-2008
Publisher: Wiley
Date: 26-07-2012
Publisher: IEEE Comput. Soc
Date: 2001
Publisher: Association for Computing Machinery (ACM)
Date: 29-04-2013
Abstract: The contribution of cloud computing and mobile computing technologies lead to the newly emerging mobile cloud computing paradigm. Three major approaches have been proposed for mobile cloud applications: 1) extending the access to cloud services to mobile devices 2) enabling mobile devices to work collaboratively as cloud resource providers 3) augmenting the execution of mobile applications on portable devices using cloud resources. In this paper, we focus on the third approach in supporting mobile data stream applications. More specifically, we study how to optimize the computation partitioning of a data stream application between mobile and cloud to achieve maximum speed/throughput in processing the streaming data. To the best of our knowledge, it is the first work to study the partitioning problem for mobile data stream applications, where the optimization is placed on achieving high throughput of processing the streaming data rather than minimizing the makespan of executions as in other applications. We first propose a framework to provide runtime support for the dynamic computation partitioning and execution of the application. Different from existing works, the framework not only allows the dynamic partitioning for a single user but also supports the sharing of computation instances among multiple users in the cloud to achieve efficient utilization of the underlying cloud resources. Meanwhile, the framework has better scalability because it is designed on the elastic cloud fabrics. Based on the framework, we design a genetic algorithm for optimal computation partition. Both numerical evaluation and real world experiment have been performed, and the results show that the partitioned application can achieve at least two times better performance in terms of throughput than the application without partitioning.
Publisher: IEEE
Date: 12-2014
DOI: 10.1109/RTSS.2014.30
Publisher: Elsevier BV
Date: 02-2008
Publisher: Wiley
Date: 17-01-2006
DOI: 10.1002/CPE.1026
Publisher: Elsevier BV
Date: 12-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2013
Publisher: IEEE
Date: 04-2014
Publisher: IEEE
Date: 03-2016
Publisher: Wiley
Date: 03-06-2011
Publisher: China Science Publishing & Media Ltd.
Date: 2007
DOI: 10.1360/JOS180127
Publisher: IEEE
Date: 06-2012
DOI: 10.1109/HPCC.2012.99
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 12-2012
Publisher: Elsevier BV
Date: 05-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2012
Publisher: IEEE
Date: 12-2012
Publisher: Inderscience Publishers
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2015
Publisher: IEEE
Date: 10-2010
Publisher: Springer Science and Business Media LLC
Date: 12-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Publisher: Elsevier BV
Date: 10-2010
Publisher: IEEE
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2008
Publisher: ACM
Date: 25-06-2012
Publisher: Inderscience Publishers
Date: 2005
Publisher: Springer Science and Business Media LLC
Date: 2013
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 02-2010
Publisher: IEEE
Date: 05-2010
Publisher: IEEE
Date: 12-2011
Publisher: IEEE
Date: 04-2011
Publisher: IEEE
Date: 06-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
Publisher: Elsevier BV
Date: 04-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Publisher: IEEE
Date: 12-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: IEEE
Date: 12-2008
DOI: 10.1109/EUC.2008.163
Publisher: Wiley
Date: 06-2009
DOI: 10.1002/WCM.627
Publisher: MDPI AG
Date: 13-01-2009
DOI: 10.3390/S90100445
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: IEEE
Date: 08-2016
Publisher: Springer International Publishing
Date: 2016
Publisher: Association for Computing Machinery (ACM)
Date: 22-02-2023
DOI: 10.1145/3570953
Abstract: Federated learning is a privacy-preserving machine learning technique that trains models across multiple devices holding local data s les without exchanging them. There are many challenging issues in federated learning, such as coordinating participants’ activities, arbitrating their benefits, and aggregating models. Most existing solutions employ a centralized approach, in which a trustworthy central authority is needed for coordination. Such an approach incurs many disadvantages, including vulnerability to attacks, lack of credibility, and difficulty in calculating rewards. Recently, blockchain was identified as a potential solution for addressing the abovementioned issues. Extensive research has been conducted, and many approaches, methods, and techniques have been proposed. There is a need for a systematic survey to examine how blockchain can empower federated learning. Although there are many surveys on federated learning, few of them cover blockchain as an enabling technology. This work comprehensively surveys challenges, solutions, and future directions for blockchain-empowered federated learning (BlockFed). First, we identify the critical issues in federated learning and explain why blockchain provides a potential approach to addressing these issues. Second, we categorize existing system models into three classes: decoupled, coupled, and overlapped, according to how the federated learning and blockchain functions are integrated. Then we compare the advantages and disadvantages of these three system models, regard the disadvantages as challenging issues in BlockFed, and investigate corresponding solutions. Finally, we identify and discuss the future directions, including open problems in BlockFed.
Publisher: IEEE
Date: 11-2015
Publisher: IEEE Comput. Soc
Date: 2002
Publisher: Springer Science and Business Media LLC
Date: 09-11-2010
Publisher: Elsevier BV
Date: 06-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: Elsevier BV
Date: 11-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 2010
Publisher: Institution of Engineering and Technology (IET)
Date: 09-2007
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Science and Business Media LLC
Date: 27-05-2007
DOI: 10.1155/2007/48984
Publisher: IEEE
Date: 04-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2004
Publisher: Emerald
Date: 27-06-2008
DOI: 10.1108/17427370810890256
Abstract: Context‐aware mobile computing extends the horizons of the conventional computing model to a ubiquitous computing environment that serves users at anytime, anywhere. To achieve this, mobile applications need to adapt their behaviors to the changing context. The purpose of this paper is to present a generalized adaptive middleware infrastructure for context‐aware computing. Owing to the vague nature of context and uncertainty in context aggregation for making adaptation decisions, the paper proposes a fuzzy‐based service adaptation model (FSAM) to improve the generality and effectiveness of service adaptation using fuzzy theory. By the means of fuzzification of the context and measuring the fitness degree between the current context and the predefined optimal context, FSAM selects the most suitable policy to adopt for the most appropriate service. The paper evaluates the middleware together with the FSAM inference engine by using a C us Assistant application. The paper is of value in presenting a generalized adaptive middleware infrastructure for context‐aware computing and also comparing the performance of the fuzzy‐based solution with a conventional threshold‐based approach for context‐aware adaptation.
Publisher: Association for Computing Machinery (ACM)
Date: 02-2008
Abstract: Peer-to-peer (P2P) live video streaming has been widely used in distance education applications to deliver the captured video courses to a large number of online students. By allowing peers serving each other in the network, P2P technology overcomes many limitations in the traditional client-server paradigm to achieve user and bandwidth scalabilities. However, existing systems do not perform well when the number of online students increases, and the system performance degrades seriously. One of the reasons is that the construction of the peer overlay in existing P2P systems has not considered the underlying physical network topology and can cause serious topology mismatch between the P2P overlay network and the physical network. The topology mismatch problem brings great link stress (unnecessary traffic) in the Internet infrastructure and greatly degrades the system performance. In this article, we address this problem and propose a locality-aware P2P overlay construction method, called Nearcast , which builds an efficient overlay multicast tree by letting each peer node choose physically closer nodes as its logical children. We have conducted extensive simulations to evaluate the performance of Nearcast in comparison with the existing RTT and NICE protocols. Also, Nearcast has been deployed on a wide-area network testbed to delivery video coursed to about 7200 users distributed across 100 collages in 32 cities in China. The experimental results show that Nearcast leads to lower link stress and shorter end-to-end latencies compared with the RTT and NICE protocols.
Publisher: IEEE
Date: 05-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Elsevier BV
Date: 06-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Publisher: IEEE
Date: 03-2008
Publisher: Elsevier BV
Date: 02-2010
Publisher: Wiley
Date: 13-07-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2019
Publisher: IEEE
Date: 11-2007
Publisher: IEEE
Date: 09-2010
DOI: 10.1109/ICPPW.2010.7
Publisher: IEEE
Date: 11-2015
Publisher: Oxford University Press (OUP)
Date: 03-2001
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2014
Publisher: IEEE
Date: 04-2009
Publisher: Springer Science and Business Media LLC
Date: 06-2006
Publisher: MDPI AG
Date: 30-06-2014
DOI: 10.3390/S140711605
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 04-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Publisher: Elsevier BV
Date: 08-2006
Publisher: Elsevier BV
Date: 06-2007
Publisher: Elsevier BV
Date: 08-2010
Publisher: IEEE Comput. Soc
Date: 2001
Publisher: IEEE
Date: 12-2008
DOI: 10.1109/EUC.2008.129
Publisher: Association for Computing Machinery (ACM)
Date: 03-12-2023
DOI: 10.1145/3530682
Abstract: In the last decade, many studies have significantly pushed the limits of wireless device-free human sensing (WDHS) technology and facilitated various applications, ranging from activity identification to vital sign monitoring. This survey presents a novel taxonomy that classifies the state-of-the-art WDHS systems into 11 categories according to their sensing task type and motion granularity . In particular, existing WDHS systems involve three primary sensing task types. The first type, behavior recognition , is a classification problem of recognizing predefined meaningful behaviors. The second type is movement tracking , monitoring the quantitative values of behavior states integrating with spatiotemporal information. The third type, user identification , leverages the unique features in behaviors to identify who performs the movements. The selected papers in each sensing task type can be further ided into sub-categories according to their motion granularity. Recent advances reveal that WDHS systems within a particular granularity follow similar challenges and design principles. For ex le, fine-grained hand recognition systems target extracting subtle motion-induced signal changes from the noisy signal responses, and their sensing areas are limited to a relatively small range. Coarse-grained activity identification systems need to overcome the interference of other moving objects within the room-level sensing range. A novel research framework is proposed to help to summarize WDHS systems from methodology, evaluation performance, and design goals. Finally, we conclude with several open issues and present the future research directions from the perspectives of data collection , sensing methodology , performance evaluation , and application scenario .
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 06-2007
DOI: 10.1109/ICC.2007.782
Publisher: Elsevier BV
Date: 07-2010
Publisher: IEEE
Date: 2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Publisher: IEEE
Date: 2007
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 12-2008
DOI: 10.1109/EUC.2008.121
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: IEEE
Date: 12-2008
DOI: 10.1109/FGCN.2008.68
Publisher: Elsevier BV
Date: 12-2021
Publisher: Springer Science and Business Media LLC
Date: 15-04-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: IEEE
Date: 12-2008
DOI: 10.1109/EUC.2008.150
Publisher: Wiley
Date: 28-09-2006
DOI: 10.1002/CPE.1096
Publisher: Springer Science and Business Media LLC
Date: 11-2008
Publisher: IEEE Comput. Soc
Date: 2002
Publisher: IEEE Comput. Soc
Date: 2001
Publisher: Elsevier BV
Date: 11-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Publisher: IEEE
Date: 2006
DOI: 10.1109/PDP.2006.9
Publisher: Springer Science and Business Media LLC
Date: 06-04-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2009
DOI: 10.1109/TKDE.2008.99
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 12-2012
DOI: 10.1109/MSN.2015.43
Publisher: China Science Publishing & Media Ltd.
Date: 2007
DOI: 10.1360/JOS182038
Publisher: IEEE
Date: 08-2006
Publisher: IEEE
Date: 12-2007
DOI: 10.1109/PRDC.2007.65
Publisher: Elsevier BV
Date: 06-2004
Publisher: Informa UK Limited
Date: 06-2006
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: ICST
Date: 2008
Publisher: IEEE
Date: 2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2016
Publisher: IEEE
Date: 06-2012
Publisher: Elsevier BV
Date: 07-2015
Publisher: Springer Science and Business Media LLC
Date: 12-2005
Publisher: IEEE
Date: 08-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2007
Publisher: IEEE
Date: 09-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2001
DOI: 10.1109/32.917521
Publisher: IEEE
Date: 09-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: IEEE
Date: 06-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2013
DOI: 10.1109/TC.2011.255
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
Publisher: IEEE Comput. Soc
Date: 2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2008
Publisher: Association for Computing Machinery (ACM)
Date: 20-05-2015
DOI: 10.1145/2746343
Abstract: Position information plays a pivotal role in wireless sensor network (WSN) applications and protocol/algorithm design. In recent years, range-free localization algorithms have drawn much research attention due to their low cost and applicability to large-scale WSNs. However, the application of range-free localization algorithms is restricted because of their dramatic accuracy degradation in practical anisotropic WSNs, which is mainly caused by large error of distance estimation. Distance estimation in the existing range-free algorithms usually relies on a unified per hop length (PHL) metric between nodes. But the PHL between different nodes might be greatly different in anisotropic WSNs, resulting in large error in distance estimation. We find that, although the PHL between different nodes might be greatly different, it exhibits significant locality that is, nearby nodes share a similar PHL to anchors that know their positions in advance. Based on the locality of the PHL, a novel distance estimation approach is proposed in this article. Theoretical analyses show that the error of distance estimation in the proposed approach is only one-fourth of that in the state-of-the-art pattern-driven scheme (PDS). An anchor selection algorithm is also devised to further improve localization accuracy by mitigating the negative effects from the anchors that are poorly distributed in geometry. By combining the locality-based distance estimation and the anchor selection, a range-free localization algorithm named underline S /underline elective underline M /underline ultilateration (SM) is proposed. Simulation results demonstrate that SM achieves localization accuracy higher than 0.3 r , where r is the communication radius of nodes. Compared to the state-of-the-art solution, SM improves the distance estimation accuracy by up to 57% and improves localization accuracy by up to 52% consequently.
Publisher: IEEE
Date: 06-2016
DOI: 10.1109/MDM.2016.21
Publisher: IEEE
Date: 04-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2020
Publisher: IEEE
Date: 12-2015
Publisher: Springer International Publishing
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Publisher: IEEE
Date: 2006
DOI: 10.1109/CIT.2006.116
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2008
Publisher: IEEE
Date: 07-2011
Publisher: Elsevier BV
Date: 02-2015
Publisher: Wiley
Date: 2005
DOI: 10.1002/WCM.341
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2014
DOI: 10.1109/TPDS.2013.50
Publisher: IEEE
Date: 02-2011
DOI: 10.1109/PDP.2011.30
Publisher: IEEE
Date: 03-2007
Publisher: IEEE
Date: 10-2006
Publisher: MDPI AG
Date: 20-03-2014
DOI: 10.3390/S140305573
Publisher: IEEE
Date: 06-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2013
DOI: 10.1109/TC.2012.162
Publisher: Springer Science and Business Media LLC
Date: 27-03-2009
Publisher: IEEE Comput. Soc
Date: 2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2015
Publisher: Elsevier BV
Date: 11-2015
Publisher: Elsevier BV
Date: 08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-08-2023
Publisher: IEEE
Date: 06-2007
DOI: 10.1109/ICC.2007.598
Publisher: ACM Press
Date: 2003
Publisher: IEEE
Date: 09-2008
Publisher: Elsevier BV
Date: 07-2010
Publisher: IEEE
Date: 04-2011
Publisher: Elsevier BV
Date: 07-2015
Publisher: Springer Science and Business Media LLC
Date: 05-2008
Publisher: Elsevier BV
Date: 10-2017
Publisher: IEEE
Date: 06-2012
Publisher: Elsevier BV
Date: 02-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2019
Publisher: China Science Publishing & Media Ltd.
Date: 2005
DOI: 10.1360/JOS161378
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2015
DOI: 10.1109/TC.2013.195
Publisher: IEEE
Date: 09-2008
Publisher: IEEE
Date: 07-2016
DOI: 10.1109/UIC-ATC-SCALCOM-CBDCOM-IOP-SMARTWORLD.2016.0054
Publisher: IEEE
Date: 03-2013
Publisher: Elsevier BV
Date: 03-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: IEEE
Date: 2002
Publisher: IEEE
Date: 04-2009
Publisher: IEEE
Date: 06-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2010
Publisher: Elsevier BV
Date: 08-2004
Publisher: Association for Computing Machinery (ACM)
Date: 29-06-2016
DOI: 10.1145/2926964
Abstract: In recent years, distributed intelligent microelectromechanical systems (DiMEMSs) have appeared as a new form of distributed embedded systems. DiMEMSs contain thousands or millions of removable autonomous devices, which will collaborate with each other to achieve the final target of the whole system. Programming such systems is becoming an extremely difficult problem. The difficulty is due not only to their inherent nature of distributed collaboration, mobility, large scale, and limited resources of their devices (e.g., in terms of energy, memory, communication, and computation) but also to the requirements of real-time control and tolerance for uncertainties such as inaccurate actuation and unreliable communications. As a result, existing programming languages for traditional distributed and embedded systems are not suitable for DiMEMSs. In this article, we first introduce the origin and characteristics of DiMEMSs and then survey typical implementations of DiMEMSs and related research hotspots. Finally, we propose a real-time programming framework that can be used to design new real-time programming languages for DiMEMSs. The framework is composed of three layers: a real-time programming model layer, a compilation layer, and a runtime system layer. The design challenges and requirements of these layers are investigated. The framework is then discussed in further detail and suggestions for future research are given.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: China Science Publishing & Media Ltd.
Date: 2007
DOI: 10.1360/JOS181092
Publisher: Elsevier BV
Date: 10-2003
Publisher: IEEE
Date: 12-2010
DOI: 10.1109/EUC.2010.29
Publisher: Institution of Engineering and Technology (IET)
Date: 03-2013
Publisher: Elsevier BV
Date: 03-2011
Publisher: IEEE Comput. Soc
Date: 2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2005
Publisher: Wiley
Date: 2005
DOI: 10.1002/SPE.676
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11590354_106
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Publisher: IEEE
Date: 03-2012
Publisher: IEEE
Date: 2002
Publisher: IEEE
Date: 07-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 07-2010
Publisher: Institution of Engineering and Technology (IET)
Date: 12-08-2011
Publisher: IEEE
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2014
Publisher: IEEE
Date: 12-2009
Publisher: IEEE
Date: 10-2014
Publisher: IEEE
Date: 03-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2019
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 04-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2016
Publisher: IEEE
Date: 2006
Publisher: IEEE
Date: 2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: Association for Computing Machinery (ACM)
Date: 03-2014
DOI: 10.1145/2567925
Abstract: BitTorrent (BT) is one of the most common Peer-to-Peer (P2P) file sharing protocols. Rather than downloading a file from a single source, the protocol allows users to join a swarm of peers to download and upload from each other simultaneously. Worms exploiting information from BT servers or trackers can cause serious damage to participating peers, which unfortunately has been neglected previously. In this article, we first present a new worm, called Adaptive BitTorrent worm (A-BT worm), which finds new victims and propagates sending forged requests to trackers. To reduce its abnormal behavior, the worm estimates the ratio of infected peers and adaptively adjusts its propagation speed. We then build a hybrid model to precisely characterize the propagation behavior of the worm. We also propose a statistical method to automatically detect the worm from the tracker by estimating the variance of the time intervals of requests. To slow down the worm propagation, we design a safe strategy in which the tracker returns secured peers when receives a request. Finally, we evaluate the accuracy of the hybrid model, and the effectiveness of our detection method and containment strategy through simulations.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Publisher: IEEE
Date: 12-2012
Publisher: IEEE
Date: 12-2012
Publisher: IEEE
Date: 12-2012
Publisher: Emerald
Date: 05-2005
DOI: 10.1108/17427370580000117
Abstract: Mobile agent, as a new mobile computing technology, has been applied to various parallel and distributed computing problem solutions. Several mobile agent systems have been built to drive the agents following a platform dependant scheme, and some formal approaches have been proposed to describe mobile agents’ behaviors or properties for respective purposes. However, there remains a lack of a standard approach to describing a mobile agent algorithm and its semantics from the viewpoint of a practical program, which makes it difficult to specify an algorithm unambiguously and verify its correctness formally. This paper proposes a practical approach to overcome that difficulty by defining a script language and associated mechanisms to specify and verify mobile agent algorithms. The language, called SMAL, can describe mobile agent’s behaviors clearly due to its explicitly defined semantics. Based on the semantics, a transformation function for converting the specified algorithm to its equivalent specification in Mobile UNITY, a well‐known mobile computation formal approach for correctness verification, is presented. Formal verification of the algorithms can be accomplished based on the UNITY‐logic rules.
Publisher: Elsevier BV
Date: 08-2014
Publisher: ACM
Date: 17-09-2011
Publisher: IEEE
Date: 06-2015
Publisher: Elsevier BV
Date: 06-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2015
Publisher: IEEE
Date: 04-2016
DOI: 10.1109/IOTDI.2015.5
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2014
Publisher: Elsevier BV
Date: 08-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-07-2021
Publisher: IEEE
Date: 04-2014
Publisher: IEEE
Date: 12-2008
DOI: 10.1109/EUC.2008.26
Publisher: IEEE
Date: 03-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2016
Publisher: IEEE
Date: 03-2015
Publisher: IEEE
Date: 09-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Publisher: Elsevier BV
Date: 10-2015
Publisher: IEEE
Date: 05-2014
Publisher: Springer US
Date: 2008
Publisher: Springer Science and Business Media LLC
Date: 09-2010
Publisher: Elsevier BV
Date: 02-2015
Publisher: Springer Science and Business Media LLC
Date: 11-08-2009
Publisher: IEEE
Date: 06-2009
DOI: 10.1109/MESH.2009.13
Publisher: IEEE
Date: 12-2011
Publisher: ACM
Date: 12-12-2011
Publisher: ACM Press
Date: 2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: IEEE
Date: 07-2013
Publisher: IEEE
Date: 10-2013
DOI: 10.1109/ICPP.2013.39
Publisher: Springer Science and Business Media LLC
Date: 20-02-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2010
DOI: 10.1109/TMC.2010.39
Publisher: IEEE
Date: 2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
Publisher: IEEE
Date: 2005
DOI: 10.1109/PRDC.2005.39
Publisher: IEEE
Date: 03-2016
DOI: 10.1109/SOSE.2016.43
Publisher: Springer International Publishing
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
Publisher: IEEE
Date: 10-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: IEEE Comput. Soc
Date: 2001
No related grants have been discovered for Jiannong Cao.