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Mobile Ad-hoc and Sensor Networks (MSN), 2011 Seventh International Conference on

Date 16-18 Dec. 2011

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  • [Front cover]

    Publication Year: 2011 , Page(s): C1
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  • [Title page i]

    Publication Year: 2011 , Page(s): i
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  • [Title page iii]

    Publication Year: 2011 , Page(s): iii
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  • [Copyright notice]

    Publication Year: 2011 , Page(s): iv
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  • Table of contents

    Publication Year: 2011 , Page(s): v - xi
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  • Preface

    Publication Year: 2011 , Page(s): xii - xiii
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  • Organization Committee

    Publication Year: 2011 , Page(s): xiv
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  • Technical Program Committee

    Publication Year: 2011 , Page(s): xv - xvii
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  • Reviewers

    Publication Year: 2011 , Page(s): xviii
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  • Keynote Abstracts

    Publication Year: 2011 , Page(s): xix - xxi
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    These keynote abstracts discuss the following: beyond stability: open problems in multi-hop wireless networks; and why are femtocells invading wireless technologies?. View full abstract»

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  • Nonparametric Copula Density Estimation in Sensor Networks

    Publication Year: 2011 , Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (604 KB) |  | HTML iconHTML  

    Statistical and machine learning is a fundamental task in sensor networks. Real world data almost always exhibit dependence among different features. Copulas are full measures of statistical dependence among random variables. Estimating the underlying copula density function from distributed data is an important aspect of statistical learning in sensor networks. With limited communication capacities or privacy concerns, centralization of the data is often impossible. By only collecting the ranks of the data observed by different sensors, we estimate and evaluate the copula density on an equally spaced grid after binning the standardized ranks at the fusion center. Without assuming any parametric forms of copula densities, we estimate them nonparametrically by maximum penalized likelihood estimation (MPLE) method with a Total Variation (TV) penalty. Linear equality and positivity constraints arise naturally as a consequence of marginal uniform densities of any copulas. Through local quadratic approximation to the likelihood function, the constrained TV-MPLE problem is cast as a sequence of corresponding quadratic optimization problems. A fast gradient based algorithm solves the constrained TV penalized quadratic optimization problem. Numerical experiments show that our algorithm can estimate the underlying copula density accurately. View full abstract»

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  • A Constant-Approximation for Maximum Weight Independent Set of Links under the SINR Model

    Publication Year: 2011 , Page(s): 9 - 14
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (337 KB) |  | HTML iconHTML  

    In this paper we study the following optimization problem in a plane multihop wireless network under the physical interference model. Given a multihop wireless network and a positive link weight (or demand) function, select a set of independent links whose total weight is maximized. This problem is known to be NP-hard. The best known approximation algorithm for this problem achieves logarithmic factor approximation with power control. In this work, we present a constant-approximation algorithm for the problem with the power assignment specified in this paper when the link weight-to-length ratio is bounded. Moreover, our constant-approximation bound is valid regardless of the value of the noise power and the lengths of the communication links. View full abstract»

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  • An Incomplete Coverage Control Based on Target Tracking Wireless Sensor Network

    Publication Year: 2011 , Page(s): 15 - 19
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (396 KB) |  | HTML iconHTML  

    Coverage control is one of the most important technologies in Wireless Sensor Network (WSN). In the precondition of better coverage quality, how to format optimal coverage with least sensors is a significant problem to be solved. A new incomplete coverage control based on target tracking sensor network which called mobile-constrained optimal target tracking coverage algorithm (MCOTT) is presented. In our approach, static sensors will be pre-deployed, collaborating with mobile sensors to achieve an optimal coverage which based on a target trajectory prediction model. Simulation results show that, MCOTT has more advantages like good robustness, high level of target coverage, low energy consumption. The algorithm can save the number of sensors and prolong the network lifetime effectively. View full abstract»

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  • Localized Topologies with Bounded Node Degree for Three Dimensional Wireless Sensor Networks

    Publication Year: 2011 , Page(s): 20 - 27
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (380 KB) |  | HTML iconHTML  

    Three dimensional (3D) wireless sensor networks have attracted a lot of attention due to its great potential usages in both commercial and civilian applications. Topology control in 3D sensor networks has been studied recently. Different 3D geometric topologies were proposed to be the underlying network topologies to achieve the sparseness of the communication networks. However, most of the proposed 3D topologies cannot bound the node degree, i.e., some nodes may need to maintain large number of neighbors in the constructed topologies, which is not energy efficient and may lead to large interference. In this paper, we extend several existing 3D topologies to a set of new 3D topologies with bounded node degree. We provide theoretical analysis on their power efficiency and node degree and also simulation evaluations over random 3D sensor networks. The simulation results confirm nice performance of these proposed 3D topologies. View full abstract»

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  • An Environment Monitoring System for Precise Agriculture Based on Wireless Sensor Networks

    Publication Year: 2011 , Page(s): 28 - 35
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (500 KB) |  | HTML iconHTML  

    To solve the problems occurring in the traditional precision agriculture such as poor real-time data acquisition, small monitoring coverage area, excessive manpower requirement etc., this paper designs and deploys an environment monitoring system for precise agriculture based on wireless sensor networks in a red bayberry greenhouse located on a hillside. This system can automatically collect the temperature, humidity, illumination, voltage and other parameters of the deployment zone, and transmit the data to the remote server via GPRS in real time. This system also includes a web-based platform integrated with Google Maps to release the greenhouse environmental status and provide real-time voice and SMS alarm service. Since the experimental area is lack of mains supply, the system is powered by solar and storage batteries. The experiment result shows that the low-cost system has strong scalability, and can provide real-time, stable and accurate service for precise agriculture. View full abstract»

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  • Applying Modified Cramér-Rao Bound to Random Sensor Deployment

    Publication Year: 2011 , Page(s): 36 - 44
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1656 KB) |  | HTML iconHTML  

    Careful node placement can be a very efficient optimization means for achieving the desired design goals, as well as good performance in the network application. In this paper, we discuss the sensor deployment problem in the context of target location through the Modified Cramer-RaoBound (MCRB). It provides a minimum variance bound on estimation error giving an idea of the quality in the expected results. Assuming uniformly deployed sensor, Modified Fisher Information Matrix (MFIM) and MCRB expressions have been obtained. They have been derived for different kinds of measurement: Time of Arrival (ToA), Angle of Arrival (AoA)and Received Signal Strength (RSS). In addition we are able to quantify the information increment that the distribution of a new sensor can provide. Finally, all these expressions lead to a methodology, which makes it possible to select the number of deployed sensors and their category. The results point out the behaviour of estimation error when the number of deployed sensors is increased, and which points of the surveillance area are more problematic. View full abstract»

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  • Energy-Efficient Data Gathering in High-Voltage Transmission Line Monitoring System

    Publication Year: 2011 , Page(s): 45 - 51
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (821 KB) |  | HTML iconHTML  

    It is important to protect power supply grid system against various damages for national economics. A high-voltage transmission line monitoring system is an effective way to protect the power supply system. This paper presents an energy-efficient data gathering mechanism for such a system. Our contributions are two-fold: (1) a detailed measurement of the energy consumption for wireless nodes, (2) the collaboration between backbone network nodes and subnets to implement the sleep-wakeup mechanism. To validate the proposed mechanism, we have established an indoor test bed consisting of 8 backbone network nodes. The energy consumption of each node with our mechanism is evaluated and compared to that without sleep. The results show that the energy consumption has decreased by 24% by our scheme. View full abstract»

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  • A Novel Accurate Forest Fire Detection System Using Wireless Sensor Networks

    Publication Year: 2011 , Page(s): 52 - 59
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (382 KB) |  | HTML iconHTML  

    A forest fire has long been a severe threat to the forest resources and human life. The threat could effectively be mitigated by timely and accurate detection. In this paper, we propose a novel accurate forest fire detection system using Wireless Sensor Networks (WSNs). In the proposed system, the detection accuracy is increased by applying the multi-criteria detection that an alarm decision depends on multiple attributes of a forest fire. The multi-criteria detection is implemented by the artificial neural network which fuses sensing data corresponding to multiple attributes of a forest fire into an alarm decision. Due to the utilization of the artificial neural network, the proposed system enjoys low overhead and the self-learning capability. Furthermore, we have developed a prototype consisting TelosB sensor nodes and carried out extensive experiments to study the performance of the proposed system. We have also developed a solar battery in order to persistently power the unattended sensor node deployed in the forest. View full abstract»

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  • Real-Time Data Aggregation with High Success Probability in Contention-Based Wireless Sensor Networks

    Publication Year: 2011 , Page(s): 60 - 67
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (394 KB) |  | HTML iconHTML  

    We study the real-time data aggregation in contention-based wireless sensor networks that use CSMA/CA MAC layer protocols as defined in IEEE 802.15.4 or IEEE 802.11 standard. The problem is, for a given data aggregation tree and a delay bound, to maximize the overall transmission success probability of all sensor nodes within the delay bound. In CSMA/CA protocols, the success probability and the expected transmission delay are highly sensitive to node interference, while the node interference is often very high in the large scale sensor networks. We propose a hybrid method that combines the CSMA/CA protocol with TDMA scheduling of transmissions. We divide the child nodes of a parent into groups and schedule the groups into different "time-frames" for transmission. Within the group, the nodes still use the CSMA/CA protocol to compete for data transmission. By doing so, we divide a large collision domain (i.e., all child nodes competing to transmit to their parent) into several small collision domains (i.e., a group of nodes competing for transmission), and the success probability can thus be significantly improved. On the other hand, the "time-frame" used in our method is much larger than the timeslot used in pure TDMA protocols. It only requires loose synchronization of clocks, which is suitable for low-cost sensor networks. We transform our objective of maximizing the overall success probability into minimizing the overall node interference. We then convert our problem to the maximum weight k-cut problem, which is NP-hard. We propose two efficient heuristic algorithms to solve the problem. Simulation results have shown that our proposed method can improve the success probability significantly compared with the method that uses pure CSMA/CA protocols. View full abstract»

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  • Strong Barrier Coverage Using Directional Sensors with Arbitrarily Tunable Orientations

    Publication Year: 2011 , Page(s): 68 - 74
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (438 KB) |  | HTML iconHTML  

    Barrier coverage is an important problem for sensor networks to fulfill some given sensing tasks. Barrier coverage guarantees the detection of events happened crossing a barrier of sensors. In majority study of barrier coverage using sensor networks, sensors are assumed to have an isotropic sensing model. However, in many applications such as monitoring an area using video camera, the sensors have directional sensing model. In this paper, we investigate strong barrier coverage using directional sensors, where sensors have arbitrarily tunable orientations to provide good coverage. We investigate the problem of finding appropriate orientations of directional sensors such that they can provide strong barrier coverage. By exploiting geographical relations among directional sensors and deployment region boundaries, we first introduce the concept of virtual node to reduce the solution space from continuous domain to discrete domain. We then construct a directional barrier graph (DBG) to model this barrier coverage question such that we can quickly answer whether there are directional sensors' orientations that can provide strong barrier coverage over a given belt region. If the belt region is strong barrier covered, we then develop energy-efficient solutions to find strong barrier path(s) that will approximately minimize the total or the maximum rotation angles of all directional sensors. Extensive simulations are conducted to verify the effectiveness of our solution. View full abstract»

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  • Removing Heavily Curved Path: Improved DV-hop Localization in Anisotropic Sensor Networks

    Publication Year: 2011 , Page(s): 75 - 82
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (707 KB) |  | HTML iconHTML  

    In Wireless Sensor Networks (WSNs) a multitude of location-dependent applications have been proposed recently, which is very intriguing for researchers to discover and design more accurate and cost-effective localization algorithms. In an isotropic networks, the Euclidean distance between a pair of nodes may not correlate closely with the hop count between them because the corresponding shortest path may have to curve around intermediate holes, resulting in poor distance estimation. And without the help of a large number of uniformly deployed seed nodes, those schemes fail in an isotropic WSNs. To address this issue and improve the accuracy of localization, we propose the Removing Heavily Curved Path (RHCP) scheme in this paper. RHCP takes advantage of selecting the paths which are not heavily affected by the holes to recalculate the location of each unknown node. Through simulation, the results reveal that RHCP performs better than original DV-Hop in an isotropic networks with different shape of holes. In addition, through iterations of RHCP, the results get improved for different anchor node densities. View full abstract»

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  • Data Collection in Wireless Sensor Networks by Utilizing Multiple Mobile Nodes

    Publication Year: 2011 , Page(s): 83 - 90
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (447 KB) |  | HTML iconHTML  

    Data collection is a fundamental and important issue in wireless sensor networks (WSNs). Recent research has shown that using mobile nodes to collect and carry data in WSNs has many advantages over static multi-hop routing. In this paper, we focus on the problem of data collection in WSNs with the minimum mobile nodes. A mobile node can pick up the data cached in a sensor node via one-hop wireless communications when it passes by a point that R meters away from this stationary sensor node. Since the storage capability of sensor node is limited, each mobile node must visit the sensor nodes, that assigned to it, every t seconds to avoid the overflow of sensor data. In order to reduce the cost of mobile nodes, we try to minimize the number of mobile nodes. We formally prove that the problem of minimizing the number of mobile nodes required by periodical data collection in WSNs is NP hard. We propose a path planning algorithm to minimize the number of mobile nodes. Our simulation results show that our approach can notably reduce the number of required mobile nodes as much as 55.6%. View full abstract»

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  • Robust Human Detection with Low Energy Consumption in Visual Sensor Network

    Publication Year: 2011 , Page(s): 91 - 97
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3537 KB) |  | HTML iconHTML  

    In this paper, we try to address the difficult problem of detecting humans robustly with low energy consumption in the visual sensor network. The proposed method contains two parts: one is an ESOBS (Enhanced Self-Organizing Background Subtraction) based foreground segmentation module to obtain active areas in the observed area from the visual sensor; the other is a HOG (Histograms of Oriented Gradients) based detection module to detect the appearance shape from the foreground areas. Moreover, we create a large pedestrian dataset according to the specific scene in visual sensor networks. Numerous experiments are conducted. The experimental results show the effectiveness of our method. View full abstract»

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  • Content Based Pre-diagnosis for Wireless Sensor Networks

    Publication Year: 2011 , Page(s): 98 - 104
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (603 KB) |  | HTML iconHTML  

    The network diagnosis is significant and difficult because the limited resource in WSNs, especially the heavy communication burden for diagnosis. How to reduce the diagnosis overhead is crucial. In this paper, we argue that the sensing data from sensor networks presents another new model called DSD for network diagnosis. We discover that the character of the sensing data reflect the network status in some way, according to considerable experiments in the Green Orbs project. We mine the hints between the sensing data and the failure in the sensor networks, and maintain a failure knowledge library which can update through self-learning. Through this diagnosis mechanism, we deduce the node failures and the network failures without any additional network burden, and the final diagnosis report can give the network administrators good reference for further diagnosis. In this way, we make full use of the sensing data to do the pre-diagnosis according to the characteristics of the sensing data. This approach avoids sending metrics periodically from sensor nodes. Moreover, the failure library can improve the efficiency of diagnosis. We analysis the three months sensing data from the Green Orbs project, analytically and experimentally, we show that the proposed scheme can improve the diagnosis performance with low energy cost. View full abstract»

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  • Collaborative Scheduling in Highly Dynamic Environments Using Error Inference

    Publication Year: 2011 , Page(s): 105 - 114
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (696 KB) |  | HTML iconHTML  

    Energy constraint is a critical hurdle hindering the practical deployment of long-term wireless sensor network applications. Turning off (i.e., duty cycling) sensors could reduce energy consumption, however at the cost of low sensing fidelity due to sensing gaps introduced. Existing techniques have studied how to collaboratively reduce the sensing gap in space and time, however none of them provides a rigorous approach to confine sensing error within desirable bounds. In this work, we propose a collaborative scheme called CIES, based on the novel concept of error inference between collaborative sensor pairs. Within a node, we use a sensing probability bound to control tolerable sensing error. Within a neighborhood, nodes can trigger additional sensing activities of other nodes when inferred sensing error has aggregately exceed the tolerance. We conducted simulations to investigate system performance using historical soil temperature data in Wisconsin-Minnesota area. The simulation results demonstrate that the system error is confined within the specified error tolerance bounds and that a maximum of 60 percent of the energy savings can be achieved, when the CIES is compared to several fixed probability sensing schemes such as eSense. We further validated the simulation and algorithms by constructing a lab test-bench to emulate actual environment monitoring applications. The results show that our approach is effective and efficient in tracking the dramatic temperature shift in highly dynamic environments. View full abstract»

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