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15
On the lifetime of wireless sensor networks
 TOSN
"... Network lifetime has become the key characteristic for evaluating sensor networks in an applicationspecific way. Especially the availability of nodes, the sensor coverage, and the connectivity have been included in discussions on network lifetime. Even quality of service measures can be reduced to ..."
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Cited by 34 (8 self)
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Network lifetime has become the key characteristic for evaluating sensor networks in an applicationspecific way. Especially the availability of nodes, the sensor coverage, and the connectivity have been included in discussions on network lifetime. Even quality of service measures can be reduced to lifetime considerations. A great number of algorithms and methods were proposed to increase the lifetime of a sensor network—while their evaluations were always based on a particular definition of network lifetime. Motivated by the great differences in existing definitions of sensor network lifetime that are used in relevant publications, we reviewed the state of the art in lifetime definitions, their differences, advantages, and limitations. This survey was the starting point for our work towards a generic definition of sensor network lifetime for use in analytic evaluations as well as in simulation models—focusing on a formal and concise definition of accumulated network lifetime and total network lifetime. Our definition incorporates the components of existing lifetime definitions, and introduces some additional measures. One new concept is the ability to express the service disruption tolerance of a network. Another new concept is the notion of timeintegration: in many cases, it is sufficient if a requirement is fulfilled over a certain period of time, instead of at every point in time. In addition, we combine coverage and connectivity to
2009. Analytic evaluation of target detection in heterogeneous wireless sensor networks
 TOSN
"... In this article, we address the problem of target detection in Wireless Sensor Networks (WSN). We formulate the target detection problem as a lineset intersection problem and use integral geometry to analytically characterize the probability of target detection for both stochastic and deterministic ..."
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Cited by 8 (0 self)
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In this article, we address the problem of target detection in Wireless Sensor Networks (WSN). We formulate the target detection problem as a lineset intersection problem and use integral geometry to analytically characterize the probability of target detection for both stochastic and deterministic deployments. Compared to previous work, we analyze WSNs where sensors have heterogeneous sensing capabilities. For the stochastic case, we evaluate the probability that the target is detected by at least k sensors and compute the free path until the target is first detected. For the deterministic case, we show an analogy between the target detection problem and the problem of minimizing the average symbol error probability in 2dimensional digital modulation schemes. Motivated by this analogy, we propose a heuristic sensor placement algorithm called DATE, that makes use of well known signal constellations for determining good WSN constellations. We also propose a heuristic called CDATE for connected WSN constellations, that yields high target detection probability.
Acoustic sensor network design for position estimation
, 2007
"... In this paper, we develop tractable mathematical models and approximate solution algorithms for a class of integer optimization problems with probabilistic and deterministic constraints, with applications to the design of distributed sensor networks that have limited connectivity. For a given deploy ..."
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Cited by 7 (4 self)
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In this paper, we develop tractable mathematical models and approximate solution algorithms for a class of integer optimization problems with probabilistic and deterministic constraints, with applications to the design of distributed sensor networks that have limited connectivity. For a given deployment region size, we calculate the Pareto frontier of the sensor network utility at the desired probabilities for dconnectivity and kcoverage. As a result of our analysis, we determine (i) the number of sensors of different types to deploy from a sensor pool, which offers a cost vs. performance tradeoff for each type of sensor, (ii) the minimum required radio transmission ranges of the sensors to ensure connectivity, and (iii) the lifetime of the sensor network. For generality, we consider randomly deployed sensor networks and formulate constrained optimization techniques to obtain the localization performance. The approach is guided and validated using an unattended acoustic sensor network design. Finally, approximations of the complete statistical characterization of the acoustic sensor networks are given, which enable average network performance predictions of any combination of acoustic sensors. Categories and Subject Descriptors: C.2.1 [ComputerCommunication Networks]: Distributed networks; G.1.6 [Numerical Analysis]: Optimization—Constrained optimization, convex programming, integer programming, nonlinear programming; G.3 [Probability and Statistics]: Experimental design
Engineering of SoftwareIntensive Systems: State of the Art and Research Challenges
"... Abstract. Softwareintensive systems become more and more important in our everyday lives. But their increasing complexity makes it difficult to develop and maintain them. This chapter gives an overview of the state of the art of building softwareintensive systems and outlines research challenges t ..."
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Cited by 3 (0 self)
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Abstract. Softwareintensive systems become more and more important in our everyday lives. But their increasing complexity makes it difficult to develop and maintain them. This chapter gives an overview of the state of the art of building softwareintensive systems and outlines research challenges that have been identified by the InterLink working group “softwareintensive systems and new computing paradigms”. 1
Looking around first: Localized potentialbased clustering in spontaneous networks
 IEEE Communications Letters
, 2007
"... Abstract — We propose a new budgetbased clustering algorithm for selforganizing networks. The basic idea behind our solution is that nodes first sense the environment using inherent Hello packets before starting forming clusters. In contrast with previous solutions that blindly distribute the budg ..."
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Cited by 2 (1 self)
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Abstract — We propose a new budgetbased clustering algorithm for selforganizing networks. The basic idea behind our solution is that nodes first sense the environment using inherent Hello packets before starting forming clusters. In contrast with previous solutions that blindly distribute the budget to nearby nodes, our PotentialBased Clustering algorithm applies a proportional budget distribution based on the connectivity degree of the nodes. This approach matches the principle that nodes in real networks are not uniformly distributed. Our simulation results show that the proposed approach outperforms previous ones. Index Terms — Networks, distributed algorithms, clustering methods.
An Efficient Key Distribution Scheme for Heterogeneous Sensor Networks ABSTRACT
"... Key distribution refers to the problem of establishing shared secrets on sensor nodes such that secret symmetric keys for communication privacy, integrity and authenticity can be generated. In a wireless sensor network, predistribution of secret keys is possibly the most practical approach to prote ..."
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Key distribution refers to the problem of establishing shared secrets on sensor nodes such that secret symmetric keys for communication privacy, integrity and authenticity can be generated. In a wireless sensor network, predistribution of secret keys is possibly the most practical approach to protect network communications but it is difficult due to the ad hoc nature, intermittent connectivity, and resource limitations of the sensor networks. In this paper, we propose a key distribution scheme based on random key predistribution for heterogeneous sensor network (HSN) to achieve better performance and security as compared to homogeneous network which suffer from high communication overhead, computation overhead, and/or high storage requirements. In a key generation process, instead of generating a large pool of random keys, a key pool is represented by a small number of generation keys. For a given generation key and publicly known seed value, a oneway hash function generates a key chain, and these key chains collectively make a key pool. Each sensor node is assigned a small number of randomly selected generation keys. The proposed scheme reduces the storage requirements while maintaining the same security strength.
Pareto Frontiers of Sensor Networks for Localization
"... We develop a theory to predict the localization performance of randomly distributed sensor networks consisting of various sensor modalities when only a constant active subset of sensors that minimize localization error is used for estimation. The characteristics of the modalities include measurement ..."
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Cited by 1 (1 self)
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We develop a theory to predict the localization performance of randomly distributed sensor networks consisting of various sensor modalities when only a constant active subset of sensors that minimize localization error is used for estimation. The characteristics of the modalities include measurement type (bearing or range) and error, sensor reliability, FOV, sensing range, and mobility. We show that the localization performance of a sensor network is a function of a weighted sum of the total number of each sensor modality. We also show that optimization of this weighted sum is independent of how the sensor management strategy chooses the active sensors. We combine the utility objective with other objectives, such as lifetime, coverage and reliability to determine the best mix of sensors for an optimal sensor network design. The Pareto efficient frontier of the multi objectives are obtained with a dynamic program, which also accommodates additional convex constraints. 1
EnergyEfficient Protocol for Deterministic and Probabilistic Coverage in Sensor Networks
"... Abstract—Various sensor types, e.g., temperature, humidity, and acoustic, sense physical phenomena in different ways, and thus are expected to have different sensing models. Even for the same sensor type, the sensing model may need to be changed in different environments. Designing and testing a dif ..."
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Abstract—Various sensor types, e.g., temperature, humidity, and acoustic, sense physical phenomena in different ways, and thus are expected to have different sensing models. Even for the same sensor type, the sensing model may need to be changed in different environments. Designing and testing a different coverage protocol for each sensing model is indeed a costly task. To address this challenging task, we propose a new probabilistic coverage protocol (denoted by PCP) that could employ different sensing models. We show that PCP works with the common disk sensing model as well as probabilistic sensing models, with minimal changes. We analyze the complexity of PCP and prove its correctness. In addition, we conduct an extensive simulation study of largescale sensor networks to rigorously evaluate PCP and compare it against other deterministic and probabilistic protocols in the literature. Our simulation demonstrates that PCP is robust, and it can function correctly in presence of random node failures, inaccuracies in node locations, and imperfect time synchronization of nodes. Our comparisons with other protocols indicate that PCP outperforms them in several aspects, including number of activated sensors, total energy consumed, and network lifetime.
Optimal surface deployment problem in wireless sensor networks
 in Proc. of the 31st Annual IEEE Conference on Computer Communications (INFOCOM’12
, 2012
"... Abstract—Sensor deployment is a fundamental issue in a wireless sensor network, which often dictates the overall network performance. Previous studies on sensor deployment mainly focused on sensor networks on 2D plane or in 3D volume. In this paper, we tackle the problem of optimal sensor deployment ..."
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Abstract—Sensor deployment is a fundamental issue in a wireless sensor network, which often dictates the overall network performance. Previous studies on sensor deployment mainly focused on sensor networks on 2D plane or in 3D volume. In this paper, we tackle the problem of optimal sensor deployment on 3D surfaces, aiming to achieve the highest overall sensing quality. In general, the reading of a sensor node exhibits unreliability, which often depends on the distance between the sensor and the target to be sensed, as observed in a wide range of applications. Therefore, with a given set of sensors, a sensor network offers different accuracy in data acquisition when the sensors are deployed in different ways in the Field of Interest (FoI). We formulate this optimal surface deployment problem in terms of sensing quality by introducing a general function to measure the unreliability of monitored data in the entire sensor network. We present its optimal solution and propose a series of algorithms for practical implementation. Extensive simulations are conducted on various 3D mountain surface models to demonstrate the effectiveness of the proposed algorithms. I.
1 Limit Laws for kCoverage of Paths by a MarkovBoolean Model
, 706
"... Let P: = {Xi}i≥1 be a stationary point process in ℜ d, {Ci}i≥1 be a sequence of i.i.d random sets in ℜ d, and {Y t i; t ≥ 0}i≥1 be i.i.d. {0, 1}valued continuous time stationary Markov chains. We define the MarkovBoolean model Ct: = {Y t i (Xi + Ci), i ≥ 1}. Ct represents the coverage process at t ..."
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Let P: = {Xi}i≥1 be a stationary point process in ℜ d, {Ci}i≥1 be a sequence of i.i.d random sets in ℜ d, and {Y t i; t ≥ 0}i≥1 be i.i.d. {0, 1}valued continuous time stationary Markov chains. We define the MarkovBoolean model Ct: = {Y t i (Xi + Ci), i ≥ 1}. Ct represents the coverage process at time t. We first obtain limit laws for kcoverage of an area at an arbitrary instant. We then derive limit laws for the kcoverage induced on a onedimensional path at an arbitrary instant. Finally, we obtain the limit laws for the kcoverage seen by a particle as it moves along a onedimensional path.