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An error beacon filtering algorithm based on particle swarm optimization for underwater wireless sensor networks

An error beacon filtering algorithm based on particle swarm ...

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IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB)

作者： Jingli Du Linfeng Liu Yue Ling Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China Ministry of Education Laboratory of Computer Network and Information Integration (Southeast University) Nanjing China

ISBN: (纸本)9781509013180

Due to the sophisticated environmental variations of water current, underwater creature touch, and strong electromagnetic interference, some beacon nodes tend to move or be damaged, which thus results in larger positioning errors of ordinary nodes. In order to solve the error beacon problem, this paper proposes an error beacon filtering algorithm based on particle swarm optimization (PSO), which can differentiate the beacon accurately. Firstly, several location errors of beacons are calculated by an improved Trilateration, and then the beacon with the maximum positioning error is filtered out through PSO. The remaining beacons are filtered out consecutively until the location error of each beacon does not exceed a preset threshold. Simulation results indicate that the proposed algorithm can detect almost all error beacons effectively with smaller variances.

关键词： Filtering algorithms Particle swarm optimization Filtering Classification algorithms Clustering algorithms Time complexity Wireless sensor networks

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Temporally or spatially dispersed joint RFID estimation using snapshots of variable lengths 15

Temporally or spatially dispersed joint RFID estimation usin...

引用

16th ACM International Symposium on Mobile Ad Hoc networking and Computing, MobiHoc 2015

作者： Xiao, Qingjun Chen, Min Chen, Shigang Zhou, Yian Key Lab of Computer Network and Information Integration Southeast University Ministry of Education China Department of Computer and Information Science and Engineering University of Florida GainesvilleFL United States

ISBN: (纸本)9781450334891

Radio-frequency identification (RFID) technology has been widely used in applications such as inventory control, object tracking, supply chain management. An important research is to estimate the number of tags in a certain area covered by readers. This paper extends the research in both temporal and spatial dimensions to provide much richer information for monitoring the dynamics of distributed RFID systems. More specifically, we are interested in estimating the joint properties of any two snapshots taken at arbitrary locations and arbitrary times in a system. With many practical applications, there is however little prior work on this problem. We propose a joint RFID estimation protocol based on a simple yet versatile snapshot construction. Given the snapshots of any two tag sets, although their sizes may be very different, we design a way to combine their information and more importantly derive formulas to extract the joint properties of the two tag sets from the combined information, with an accuracy that can be arbitrarily set. Through formal analysis, we determine the optimal system parameters that minimize the execution time of taking snapshots, under the constraints of a given accuracy requirement. Our simulation results show that the proposed protocol can reduce the execution time by multifold when comparing with the best alternative approach in the literature. Copyright © 2015 ACM.

关键词： Inventory control

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A Personalized Service Recommendation Algorithm for Service Functionality 2

A Personalized Service Recommendation Algorithm for Service ...

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2nd International Conference on Advanced Cloud and Big Data, CBD 2014

作者： Li, Wei Hu, Di Luo, Junzhou School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Southeast University Nanjing211189 China

ISBN: (纸本)9781479980857

With the increasing number of services on the Internet, it has become a great challenge to help users find services according to their demands. Personalized recommendation technology is an effective way to solve the problem. Existing service recommendation approaches make recommendations among services with same or similar functionalities to meet the non-functional requirements of users, while the functional requirements for service are seldom taken into account and new services that satisfy the needs of users are difficult to be recommended. Therefore, in this paper, we introduce social tags to the process of service recommendation and build service functionality oriented social tags model to describe user preference for service functionality, then we present a personalized service recommendation algorithm for service functionality (PSR-SF). The proposed algorithm first discovers the neighbors of a target user according to services use frequency of users, and then clusters services that have been used by the target user and his neighborhoods by using the functional characteristic vector of services which based on service functionality oriented social tags model. Finally, target user preference for service classes are generated by using service functionality tags use information of users to make recommendations. The experiment results show that the performance of PSR-SF algorithm is better than those existing recommendation algorithms in terms of service recommendation precision, recall and F values. © 2014 IEEE.

关键词： Social networking (online)

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Maximum margin partial label learning 7

Maximum margin partial label learning

引用

7th Asian Conference on Machine Learning, ACML 2015

作者： Yu, Fei Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

Partial label learning deals with the problem that each training example is associated with a set of candidate labels, and only one among the set is the ground-truth label. The basic strategy to learn from partial label examples is disambiguation, i.e. by trying to recover the ground-truth labeling information from the candidate label set. As one of the major machine learning techniques, maximum margin criterion has been employed to solve the partial label learning problem. Therein, disambiguation is performed by optimizing the margin between the maximum modeling output from candidate labels and that from non-candidate labels. However, in this formulation the margin between the ground-truth label and other candidate labels is not differentiated. In this paper, a new maximum margin formulation for partial label learning is proposed which aims to directly maximize the margin between the ground-truth label and all other labels. Specifically, an alternating optimization procedure is utilized to coordinate ground-truth label identification and margin maximization. Extensive experiments show that the derived partial label learning approach achieves competitive performance against other state-of-the-art comparing approaches. © 2015 F. Yu & M.-L. Zhang.

关键词： Learning systems

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Analytic Latency Model for Message Dissemination in Opportunistic networks

Analytic Latency Model for Message Dissemination in Opportun...

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IEEE Wireless Communications and networking Conference

作者： Qi Wang Sanfeng Zhang Key Laboratory of Computer Network and Information Integration in Southeast University Ministry of Education School of Computer Science and Engineering Southeast University

ISBN: (纸本)9781467398152

In opportunistic networks, messages are delivered based on a store-forward-carry paradigm. network topology varies with time and dynamism of the links between nodes has a primary effect on the message delay, one of the network performance indicators. To get a precise estimation of the network latency with less complexity, a rigorous framework modeling the information propagation process is developed. The model is based on a sophisticated 2~(N-1)-state Markov chain and yields a closed-form expression under non-homogenous assumption, as well as an asymptotic formula under homogenous assumption. Finally, to assess our model's scalability and reliability, analytical results are validated by simulations on a real-life human mobility trace and two standard mobility models. The results demonstrate that the model predicts the routing performance accurately for networks of different size and mobility models.

关键词： network delay propagation process dynamism network Closed-form solutions Markov chain Dissemination mobility Latency Ganglia performance indicators

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Hybrid resource provisioning for workflow scheduling in cloud computing 1

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2nd International Conference on Human Centered Computing, HCC 2016

作者： Chen, Long Guo, Yucheng Li, Xiaoping Ruiz, Rubén School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing211189 China Grupo de Sistemas de Optimización Aplicada Instituto Tecnológico de Informática Ciudad Politécnica de la Innovación Universitat Politécnica de Valéncia Edifico 8G Acc. B. Camino de Vera s/n Valéncia46021 Spain

ISBN: (数字)9783319318547

ISBN: (纸本)9783319318530

In cloud computing, cloud service providers always provide two resource provisioning manners to cloud consumers, reservation and on-demand. Costs can be reduced using these two manners. In this paper, we consider deadline constrained cloud workflow scheduling problem with total resource renting cost minimization by integrating the two manners. An integer programming model of the problem is constructed. A malleable earliest and finish time heuristic is proposed for the problem under study. Experimental results verify the effectiveness of proposed algorithm on instances with different scales and resources with different discounts. © Springer International Publishing Switzerland 2016.

关键词： Cloud computing

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Reliable task allocation with load balancing in multiplex networks

Reliable task allocation with load balancing in multiplex ne...

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作者： Jiang, Yichuan Zhou, Yifeng Li, Yunpeng School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing211189 China

In multiplex networks, agents are connected by multiple types of links;a multiplex network can be split into more than one network layer that is composed of the same type of links and involved agents. Each network link type has a bias for communicating different types of resources;thus, the task's access to the required resources in multiplex networks is strongly related to the network link types. However, traditional task allocation and load balancing methods only considered the situations of agents themselves and did not address the effects of network link types in multiplex networks. To solve this problem, this article considers both link types and agents, and substantially extends the existing work by highlighting the effect of network layers on task allocation and load balancing. Two multiplex network-adapted models of task allocation with load balancing are presented: network layer-oriented allocation and agent-oriented allocation. This article also addresses the unreliability in multiplex networks, which includes the unreliable links and agents, and implements a reliable task allocation based on a negotiation reputation and reward mechanism. Our findings show that both of our presented models can effectively and robustly satisfy the task allocation objectives in unreliable multiplex networks;the experiments prove that they can significantly reduce the time costs and improve the success rate of tasks for multiplex networks over the traditional simplex network-adapted task allocation model. Lastly, we find that our presented network layer-oriented allocation performs much better in terms of reliability and allocation time compared to our presented agent-oriented allocation, which further explains the importance of network layers in multiplex networks. © 2015 ACM.

关键词： network layers

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Position Prediction Social-Relationship-Based on Multi-order Markov Model 3

Position Prediction Social-Relationship-Based on Multi-order...

引用

3rd International Conference on Advanced Cloud and Big Data, CBD 2015

作者： Zou, Yue Zhang, Sanfeng College of Software Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration in Southeast University Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9781467385374

Opportunistic network mobile user-nodes location prediction can help to collect the data, improve the dynamic network and help to decrease the problems which include the delay, low success rate, high storage space energy consume and so on, which are the results of the blindness in the process of delivery. In this paper, a mobile node location prediction algorithm base on the Multi-Order Markov prediction model as the initial forecast, and then use social-relationship which is closely linked to the node to optimize the algorithm. Experimental results show that the proposed algorithm is better than the single Multi-Order Markov model. © 2015 IEEE.

关键词： Location

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Hyper-compact virtual estimators for big network data based on register sharing

Hyper-compact virtual estimators for big network data based ...

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ACM SIGMETRICS International Conference on Measurement and Modeling of computer Systems, SIGMETRICS 2015

作者： Xiao, Qingjun Chen, Shigang Chen, Min Ling, Yibei Department of Computer and Information Science and Engineering University of Florida GainesvilleFL United States Key Lab. of Computer Network and Information Integration Southeast University Ministry of Education China Telcordia Technologies and Applied Research Ericsson United States

ISBN: (纸本)9781450334860

Cardinality estimation over big network data consisting of numerous flows is a fundamental problem with many practical applications. Traditionally the research on this problem focused on using a small amount of memory to estimate each flow's cardinality from a large range (up to 109). However, although the memory needed for each flow has been greatly compressed, when there is an extremely large number of flows, the overall memory demand can still be very high, exceeding the availability under some important scenarios, such as implementing online measurement modules in network processors using only on-chip cache memory. In this paper, instead of allocating a separated data structure (called estimator) for each flow, we take a different path by viewing all the flows together as a whole: Each flow is allocated with a virtual estimator, and these virtual estimators share a common memory space. We discover that sharing at the register (multi-bit) level is superior than sharing at the bit level. We propose a framework of virtual estimators that allows us to apply the idea of sharing to an array of cardinality estimation solutions, achieving far better memory efficiency than the best existing work. Our experiment shows that the new solution can work in a tight memory space of less than 1 bit per flow or even one tenth of a bit per flow-a quest that has never been realized before. © Copyright 2015 ACM.

关键词： Cache memory

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Gradient compared p-LMS algorithms for sparse system identification 27

Gradient compared p-LMS algorithms for sparse system identif...

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27th Chinese Control and Decision Conference, CCDC 2015

作者： Feng, Yong Wu, Jiasong Zeng, Rui Luo, Limin Shu, Huazhong School of Biological Science and Medical Engineering Southeast University Nanjing China Laboratory of Image Science and Technology Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

ISBN: (纸本)9781479970179

In this paper, we propose two novel p-norm penalty least mean square (p-LMS) algorithms as supplements of the conventional p-LMS algorithm established for sparse adaptive filtering recently. A gradient comparator is employed to selectively apply the zero attractor of p-norm constraint for only those taps that have the same polarity as that of the gradient of the squared instantaneous error, which leads to the new proposed gradient compared p-norm constraint LMS algorithm (pGC-LMS). We explain that the pGC-LMS can achieve lower mean square error than the standard p-LMS algorithm theoretically and experimentally. To further improve the performance of the filter, the pNGC-LMS algorithm is derived using a new gradient comparator which takes the sign-smoothed version of the previous one. The performance of the pNGC-LMS is superior to that of the pGC-LMS in theory and in simulations. Moreover, these two comparators can be easily applied to other norm constraint LMS algorithms to derive some new approaches for sparse adaptive filtering. The numerical simulation results show that the two proposed algorithms achieve better performance than the standard LMS algorithm and p-LMS algorithm in terms of convergence rate and steady-state behavior in sparse system identification settings. © 2015 IEEE.

关键词： Least Mean Square Algorithm New Gradient Comparator Sparse System Identification p Norm Constraint

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