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20
Universal Rigidity and Edge Sparsification for Sensor Network Localization
, 2009
"... Owing to their high accuracy and ease of formulation, there has been great interest in applying convex optimization techniques, particularly that of semidefinite programming (SDP) relaxation, to tackle the sensor network localization problem in recent years. However, a drawback of such techniques is ..."
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Owing to their high accuracy and ease of formulation, there has been great interest in applying convex optimization techniques, particularly that of semidefinite programming (SDP) relaxation, to tackle the sensor network localization problem in recent years. However, a drawback of such techniques is that the resulting convex program is often expensive to solve. In order to speed up computation, various edge sparsification heuristics have been proposed, whose aim is to reduce the number of edges in the input graph. Although these heuristics do reduce the size of the convex program and hence making it faster to solve, they are often ad hoc in nature and do not preserve the localization properties of the input. As such, one often has to face a tradeoff between solution accuracy and computational effort. In this paper we propose a novel edge sparsification heuristic that can provably preserve the localization properties of the original input. At the heart of our heuristic is a graph decomposition procedure, which allows us to identify certain sparse generically universally rigid subgraphs of the input graph. Our computational results show that the proposed approach can significantly reduce the computational and memory complexities of SDP–based algorithms for solving the sensor network localization problem. Moreover, it compares favorably with existing speedup approaches, both in terms of accuracy and solution time. 1
SpiderBat: Augmenting Wireless Sensor Networks with Distance and Angle Information
, 2011
"... Having access to accurate position information is a key requirement for many wireless sensor network applications. We present the design, implementation and evaluation of SpiderBat, an ultrasoundbased ranging platform designed to augment existing sensor nodes with distance and angle information. Sp ..."
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Having access to accurate position information is a key requirement for many wireless sensor network applications. We present the design, implementation and evaluation of SpiderBat, an ultrasoundbased ranging platform designed to augment existing sensor nodes with distance and angle information. SpiderBat features independently controllable ultrasound transmitters and receivers, in all directions of the compass. Using a digital compass, nodes can learn about their orientation, and combine this information with distance and angle measurements using ultrasound. To the best of our knowledge, SpiderBat is the first ultrasoundbased sensor node platform that can measure absolute angles between sensor nodes accurately. The availability of angle information enables us to estimate node positions with a precision in the order of a few centimeters. Moreover, our system allows to position nodes in multihop networks where pure distancebased algorithms must fail, in particular in sparse networks, with only a single anchor node. Furthermore, information on absolute node orientations makes it possible to detect whether two nodes are in lineofsight. Consequently, we can detect the presence of obstacles and walls by looking at patterns in the received ultrasound signal.
Multiple round random ball placement: Power of second chance
 in COCOON ’09, 2009
"... In a pioneering work, Gupta and Kumar [8] studied the critical transmission range needed for the connectivity of random wireless networks. Their result implies that, given a square region of n×√ n, the asymptotic number of random nodes (each with transmission range 1) needed to form a connected net ..."
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In a pioneering work, Gupta and Kumar [8] studied the critical transmission range needed for the connectivity of random wireless networks. Their result implies that, given a square region of n×√ n, the asymptotic number of random nodes (each with transmission range 1) needed to form a connected network is Θ(n lnn) with high probability. This result has been used as cornerstones in deriving a number of asymptotic bounds for random multihop wireless networks, such as network capacity [7, 10, 11, 14]. In this paper we show that the asymptotic number of nodes needed for connectivity can be significantly reduced to Θ(n ln lnn) if we are given a “second chance ” to deploy nodes. More generally, under some deployment assumption, if we can deploy nodes in k rounds (for a constant k) and the deployment of the ith round can utilize the information gathered from the previous i − 1 rounds, we show that the number of nodes needed to provide a connected network with high probability isΘ(n ln(k) n). (See Eq (1) for the definition of ln(k) n.) Similar results hold when we need deploy sensors such that the sensing regions of all sensors cover the region of interest.
Collaborative Location Certification for Sensor Networks
"... Location information is of essential importance in sensor networks deployed for generating locationspecific event reports. When such networks operate in hostile environments, it becomes imperative to guarantee the correctness of event location claims. In this paper we address the problem of assessi ..."
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Location information is of essential importance in sensor networks deployed for generating locationspecific event reports. When such networks operate in hostile environments, it becomes imperative to guarantee the correctness of event location claims. In this paper we address the problem of assessing location claims of untrusted (potentially compromised) nodes. The mechanisms introduced here prevent a compromised node from generating illicit event reports for locations other than its own. This is important because by compromising “easy target” sensors (say, sensors on the perimeter of the field that’s easier to access), the adversary should not be able to impact data flows associated with other (“premium target”) regions of the network. To achieve this goal, in a process we call location certification, data routed through the network is “tagged ” by participating nodes with “belief ” ratings, collaboratively assessing the probability that the claimed source location is indeed correct. The effectiveness of our solution relies on the joint knowledge of participating nodes to assess the truthfulness of claimed locations. By collaboratively generating and propagating a set of “belief ” ratings with transmitted data and event reports, the network allows authorized parties (e.g. final data sinks) to evaluate a metric of trust for the claimed location of such reports. Belief ratings are derived from a data model of
Multiscale Anchorfree Distributed Positioning in Sensor Networks
"... Abstract—Positioning is one of the most fundamental problems in sensor networks: Given the network’s connectivity graph and some additional local information on measured distances and/or angles, the goal is to recover the nodes ’ positions. Varying the assumptions regarding the nature and the qualit ..."
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Abstract—Positioning is one of the most fundamental problems in sensor networks: Given the network’s connectivity graph and some additional local information on measured distances and/or angles, the goal is to recover the nodes ’ positions. Varying the assumptions regarding the nature and the quality of the measurements, there has been extensive research for both hardness results and practical, distributed, positioning schemes. This paper addresses these issues for a setting that appears to be most likely in realworld scenarios in the future – nodes can roughly measure distances and relative angles. We will show that this problem is N Phard like most positioning problems even for arbitrarily small errors. We will also propose an algorithm combining robustness to erroneous measurements and scalability in a completely distributed fashion and provide simulation results for networks of up to 128k nodes with varying errors. I.
Local Distributed Algorithms for MultiRobot Systems
, 2012
"... The field of swarm robotics focuses on controlling large populations of simple robots to accomplish tasks more effectively than what is possible using a single robot. This thesis develops distributed algorithms tailored for multirobot systems with large populations. Specifically we focus on local d ..."
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The field of swarm robotics focuses on controlling large populations of simple robots to accomplish tasks more effectively than what is possible using a single robot. This thesis develops distributed algorithms tailored for multirobot systems with large populations. Specifically we focus on local distributed algorithms since their performance depends primarily on local parameters on the system and are guaranteed to scale with the number of robots in the system. The first part of this thesis considers and solves the problem of finding a trajectory for each robot which is guaranteed to preserve the connectivity of the communication graph, and when feasible it also guarantees the robots advance towards a goal defined by an arbitrary motion planner. We also describe how to extend our proposed approach to preserve the kconnectivity of the communication graph. Finally, we show how our connectivitypreserving algorithm can be combined with standard averaging procedures to yield a provably correct flocking algorithm. The second part of this thesis considers and solves the problem of having each
A Finegrained Hopcount Based Localization Algorithm for Wireless Sensor Networks
"... Abstract — In recent years, many localization algorithms have been proposed for wireless sensor networks, in which the hopcount based localization schemes are attractive due to the advantage of low cost. However, these approaches usually utilize discrete integers to calculate the hopcounts between ..."
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Abstract — In recent years, many localization algorithms have been proposed for wireless sensor networks, in which the hopcount based localization schemes are attractive due to the advantage of low cost. However, these approaches usually utilize discrete integers to calculate the hopcounts between nodes. Such coarsegrained hopcounts make no distinction among onehop nodes. More seriously, as the hopcounts between nodes increase, the cumulative deviation of hopcounts would become unacceptable. In order to solve this problem, we propose the concept of finegrained hopcount. It is a kind of floattype hopcount, which refines the coarsegrained one close to the actual distance between nodes. Based on this idea, we propose a finegrained hopcount based localization algorithm (AFLA). In AFLA, we first refine the hopcount information to obtain finegrained hopcounts, then use the Apollonius circle method to achieve initial position estimations, and finally further improve the localization precision through confidence spring model (CSM). We conduct the comprehensive simulations to demonstrate that AFLA can achieve 30 % higher average accuracy than the existing hopcount based algorithm in most scenarios and converge much faster than the traditional massspring model based scheme. Furthermore, AFLA is robust to achieve an approximate 35 % accuracy even in noisy environment with a DOI of 0.4. Besides, we also construct a Testbed that consists of 17 MICAz motes to verify the performance of AFLA in real environment. Index Terms — Finegrained, Localization, Apollonius circle, Confidence spring model, Wireless sensor networks
Distributed Localization and Clustering Using Data Correlation and the Occam’s Razor Principle
"... Abstract—We present a distributed algorithm for computing a combined solution to three problems in sensor networks: localization, clustering, and sensor suspension. Assuming that initially only a rough approximation of the sensor positions is known, we show how one can use sensor measurements to ref ..."
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Abstract—We present a distributed algorithm for computing a combined solution to three problems in sensor networks: localization, clustering, and sensor suspension. Assuming that initially only a rough approximation of the sensor positions is known, we show how one can use sensor measurements to refine the set of possible sensor locations, to group the sensors into clusters with linearly correlated measurements, and to decide which sensors may suspend transmission without jeopardizing the consistency of the collected data. Our algorithm applies the “Occam’s razor principle ” by computing a “simplest ” explanation for the data gathered from the network. We also present centralized algorithms, as well as efficient heuristics. I.
AnchorFree Localization in Mixed . . .
, 2008
"... Recent technological advances have fostered the emergence of Wireless Sensor Networks (WSNs), which consist of tiny, wireless, batterypowered nodes that are expected to revolutionize the ways in which we understand and construct complex physical systems. A fundamental property needed to use and mai ..."
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Recent technological advances have fostered the emergence of Wireless Sensor Networks (WSNs), which consist of tiny, wireless, batterypowered nodes that are expected to revolutionize the ways in which we understand and construct complex physical systems. A fundamental property needed to use and maintain these WSNs is âlocalizationâ, which allows the establishment of spatial relationships among nodes over time. This dissertation presents a series of Geographic Distributed Localization (GDL) algorithms for mixed WSNs, in which both static and mobile nodes can coexist. The GDL algorithms provide a series of useful methods for localization in mixed WSNs. First, GDL provides an approximation called âhopcoordinatesâ, which improves the accuracy of both hopcounting and connectivitybased measurement techniques. Second, GDL utilizes a distributed algorithm to compute the locations of all nodes in static networks with the help of the hopcoordinates approximation. Third, GDL integrates a sensor component into this localization paradigm for possible mobility and as a result allows for a more complex deployment of WSNs as well as lower costs. In addition, the development of GDL incorporated the possibility of manipulated communications, such as wormhole attacks. Simulations
Research Statement
"... As people rely more and more on computers, building and maintaining a secure computing environment becomes one of the most important research topics. However, many computer programs remain vulnerable, which makes intrusions to a computer or a network of computers easy. The number of vulnerabilities ..."
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As people rely more and more on computers, building and maintaining a secure computing environment becomes one of the most important research topics. However, many computer programs remain vulnerable, which makes intrusions to a computer or a network of computers easy. The number of vulnerabilities reported to CERT 1 grows rapidly from 1090 in the year of 2000 to 5990 in 2005. According to SecurityTracker 2, buffer overflows top the list at more than half of the vulnerabilities reported in 2004. Vulnerabilities like buffer overflows may permit an attacker to inject attack code, and cause the vulnerable machine to run the attacker’s program, instead. Detecting such intrusions is critical in securing a computer system. I have been working on hostbased intrusion detections for my Ph.D. thesis, and my research interests are in computer security and network security. 0.1 Recent Research Results Intrusion detection systems can be categorized into signaturebased and anomalybased systems. Signaturebased systems maintain an uptodate database of intrusion signatures, and look for behavior that matches the signatures. Anomalybased systems, on the other hand, maintain a normal behavior model and look for deviations from that model. Although signaturebased techniques are