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12
HEED: A Hybrid, EnergyEfficient, Distributed Clustering Approach for Ad Hoc Sensor Networks
 IEEE Trans. Mobile Computing
, 2004
"... Abstract—Topology control in a sensor network balances load on sensor nodes and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. In this paper, we propose a novel distributed clustering approach for longlived ad hoc sensor networks. Our ..."
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Cited by 266 (1 self)
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Abstract—Topology control in a sensor network balances load on sensor nodes and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. In this paper, we propose a novel distributed clustering approach for longlived ad hoc sensor networks. Our proposed approach does not make any assumptions about the presence of infrastructure or about node capabilities, other than the availability of multiple power levels in sensor nodes. We present a protocol, HEED (Hybrid EnergyEfficient Distributed clustering), that periodically selects cluster heads according to a hybrid of the node residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED terminates in Oð1Þ iterations, incurs low message overhead, and achieves fairly uniform cluster head distribution across the network. We prove that, with appropriate bounds on node density and intracluster and intercluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks. Simulation results demonstrate that our proposed approach is effective in prolonging the network lifetime and supporting scalable data aggregation. Index Terms—Sensor networks, clustering, network lifetime, energy efficiency, fault tolerance. æ 1
Growth codes: Maximizing sensor network data persistence
 in Proc. ACM SIGCOMM
"... Sensor networks are especially useful in catastrophic or emergency scenarios such as floods, fires, terrorist attacks or earthquakes where human participation may be too dangerous. However, such disaster scenarios pose an interesting design challenge since the sensor nodes used to collect and commun ..."
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Cited by 58 (0 self)
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Sensor networks are especially useful in catastrophic or emergency scenarios such as floods, fires, terrorist attacks or earthquakes where human participation may be too dangerous. However, such disaster scenarios pose an interesting design challenge since the sensor nodes used to collect and communicate data may themselves fail suddenly and unpredictably, resulting in the loss of valuable data. Furthermore, because these networks are often expected to be deployed in response to a disaster, or because of sudden configuration changes due to failure, these networks are often expected to operate in a “zeroconfiguration ” paradigm, where data collection and transmission must be initiated immediately, before the nodes have a chance to assess the current network topology. In this paper, we design and analyze techniques to increase “persistence ” of sensed data, so that data is more likely to reach a data sink, even as network nodes fail. This is done by replicating data compactly at neighboring nodes using novel “Growth Codes ” that increase in efficiency as data accumulates at the sink. We show that Growth Codes preserve more data in the presence of node failures than previously proposed erasure resilient techniques.
Decentralized Sparse Signal Recovery for Compressive Sleeping Wireless Sensor Networks
"... Abstract—This paper develops an optimal decentralized algorithm for sparse signal recovery and demonstrates its application in monitoring localized phenomena using energyconstrained largescale wireless sensor networks. Capitalizing on the spatial sparsity of localized phenomena, compressive data c ..."
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Cited by 7 (0 self)
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Abstract—This paper develops an optimal decentralized algorithm for sparse signal recovery and demonstrates its application in monitoring localized phenomena using energyconstrained largescale wireless sensor networks. Capitalizing on the spatial sparsity of localized phenomena, compressive data collection is enforced by turning off a fraction of sensors using a simple random node sleeping strategy, which conserves sensing energy and prolongs network lifetime. In the absence of a fusion center, sparse signal recovery via decentralized innetwork processing is developed, based on a consensus optimization formulation and the alternating direction method of multipliers. In the proposed algorithm, each active sensor monitors and recovers its local region only, collaborates with its neighboring active sensors through lowpower onehop communication, and iteratively improves the local estimates until reaching the global optimum. Because each sensor monitors the local region rather than the entire large field, the iterative algorithm converges fast, in addition to being scalable in terms of transmission and computation costs. Further, through collaboration, the sensing performance is globally optimal and attains a high spatial resolution commensurate with the node density of the original network containing both active and inactive sensors. Simulations demonstrate the performance of the proposed approach. Index Terms—Alternating direction method of multipliers, compressive sensing, consensus optimization, decentralized sparse signal recovery, Wireless sensor networks.
Bandlimited field reconstruction for wireless sensor networks,” technical report, [27
 IEEE Trans. Inform. Theory
, 2002
"... Wireless sensor networks are often used for environmental monitoring applications. In this context sampling and reconstruction of a physical field is one of the most important problems to solve. We focus on a bandlimited field and find under which conditions on the network topology the reconstructio ..."
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Cited by 6 (4 self)
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Wireless sensor networks are often used for environmental monitoring applications. In this context sampling and reconstruction of a physical field is one of the most important problems to solve. We focus on a bandlimited field and find under which conditions on the network topology the reconstruction of the field is successful, with a given probability. We review irregular sampling theory, and analyze the problem using random matrix theory. We show that even a very irregular spatial distribution of sensors may lead to a successful signal reconstruction, provided that the number of collected samples is large enough with respect to the field bandwidth. Furthermore, we give the basis to analytically determine the probability of successful field reconstruction.
Performance of Linear Field Reconstruction Techniques With Noise and Uncertain Sensor Locations
"... Abstract—We consider a wireless sensor network, sampling a bandlimited field, described by a limited number of harmonics. Sensor nodes are irregularly deployed over the area of interest or subject to random displacement; in addition sensors measurements are affected by noise. Our goal is to obtain a ..."
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Cited by 5 (5 self)
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Abstract—We consider a wireless sensor network, sampling a bandlimited field, described by a limited number of harmonics. Sensor nodes are irregularly deployed over the area of interest or subject to random displacement; in addition sensors measurements are affected by noise. Our goal is to obtain a high quality reconstruction of the field, with the mean square error (MSE) of the estimate as performance metric. In particular, we analytically derive the performance of several reconstruction/estimation techniques based on linear filtering. For each technique, we obtain the MSE, as well as its asymptotic expression in the case where the number of fieldharmonics and the number of sensors grow to infinity, while their ratio is kept constant. Through numerical simulations, we show the validity of the asymptotic analysis, even for a small number of sensors. We provide some novel guidelines for the design of sensor networks when many parameters, such as field bandwidth, number of sensors, reconstruction quality, and sensor displacement characteristics, to be traded off. Index Terms—Irregular sampling, linear filtering, sensor networks. I.
Quality of field reconstruction in sensor networks
 IEEE Infocom, Anchorage, AK
, 2007
"... Abstract — We consider the problem of obtaining a high quality estimates of bandlimited sensor fields when sensor measurements are noisy and the nodes are irregularly deployed and subject to random motion. We consider the mean square error (MSE) of the estimate and we analytically derive the perfor ..."
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Cited by 5 (4 self)
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Abstract — We consider the problem of obtaining a high quality estimates of bandlimited sensor fields when sensor measurements are noisy and the nodes are irregularly deployed and subject to random motion. We consider the mean square error (MSE) of the estimate and we analytically derive the performance of several reconstruction/estimation techniques based on linear filtering. For each technique, we obtain the mean value of the MSE, as well as its asymptotic expression in the case where the field bandwidth and the number of sensors grow to infinity, while their ratio is kept constant. Our results provide useful guidelines for the design of sensor networks when many system parameters have to be traded off. I.
Reconstruction of multidimensional signals from irregular noisy samples
 IEEE TRANS. SIGNAL PROCESS
, 2008
"... We focus on a multidimensional field with uncorrelated spectrum and study the quality of the reconstructed signal when the field samples are irregularly spaced and affected by independent and identically distributed noise. More specifically, we apply linear reconstruction techniques and take the me ..."
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Cited by 5 (2 self)
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We focus on a multidimensional field with uncorrelated spectrum and study the quality of the reconstructed signal when the field samples are irregularly spaced and affected by independent and identically distributed noise. More specifically, we apply linear reconstruction techniques and take the meansquare error (MSE) of the field estimate as a metric to evaluate the signal reconstruction quality. We find that the MSE analysis could be carried out by using the closedform expression of the eigenvalue distribution of the matrix representing the sampling system. Unfortunately, such distribution is still unknown. Thus, we first derive a closedform expression of the distribution moments, and we find that the eigenvalue distribution tends to the Marčenko–Pastur distribution as the field dimension goes to infinity. Finally, by using our approach, we derive a tight approximation to the MSE of the reconstructed field.
An Architecture for Robust Sensor Network Communications
, 2005
"... Node clustering in sensor networks increases scalability, robustness, and energyefficiency. In hostile environments, unexpected failures or attacks on cluster heads (through which communication takes place) may partition the network or degrade application performance. We propose REED (Robust Energy ..."
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Cited by 4 (0 self)
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Node clustering in sensor networks increases scalability, robustness, and energyefficiency. In hostile environments, unexpected failures or attacks on cluster heads (through which communication takes place) may partition the network or degrade application performance. We propose REED (Robust EnergyEfficient Distributed clustering), for clustering sensors deployed in hostile environments in an interleaved manner with low complexity. Our primary objective is to construct a kfaulttolerant (i.e., kconnected) clustered network, where k is a constant determined by the application. Fault tolerance is achieved by selecting k independent sets of cluster heads (i.e., cluster head overlays) on top of the physical network, so that each node can quickly switch to other cluster heads in case of failures. The independent cluster head overlays also give multiple vertexdisjoint routing paths for load balancing and security. Network lifetime is prolonged by selecting cluster heads with high residual energy and low communication cost, and periodically reclustering the network. We prove that REED asymptotically achieves kconnectivity if certain conditions on node density are met. We also discuss intercluster routing and MAC layer considerations, and investigate REED clustering properties via extensive simulations.
Data Persistence for ZeroConfiguration Sensor Networks
 Department of Computer Science, Columbia University
, 2006
"... Sensor networks are especially useful in catastrophic or emergency scenarios such as floods, fires, terrorist attacks or earthquakes where human participation may be too dangerous. However, such disaster scenarios pose an interesting design challenge since the sensor nodes used to collect and commun ..."
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Cited by 4 (2 self)
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Sensor networks are especially useful in catastrophic or emergency scenarios such as floods, fires, terrorist attacks or earthquakes where human participation may be too dangerous. However, such disaster scenarios pose an interesting design challenge since the sensor nodes used to collect and communicate data may themselves fail suddenly and unpredictably, resulting in the loss of valuable data. Furthermore, because these networks are often expected to be deployed in response to a disaster, or because of sudden configuration changes due to failure, these networks are often expected to operate in a “zeroconfiguration” paradigm, where data collection and transmission must be initiated immediately, before the nodes have a chance to assess the current network topology. In this paper, we design and analyze techniques to increase “persistence ” of sensed data, so that data is more likely to reach a data sink, even as network nodes fail. This is done by replicating data compactly at neighboring nodes using novel growth codes that increase in efficiency as data accumulates at the sink. We show via simulations that in typical sensor network topologies, our novel protocol can preserve about 1020 % more data than when no coding is done. We also design a dynamically changing codeword degree distribution based on our results and show that it delivers data at a much faster rate compared to other well known degree distributions such as Soliton and RobustSoliton. 1 I.
SEÇMECE: Optimizing Lifetime of Federated Sensor Networks by Exploiting Data and Model Redundancy
, 2007
"... Next generation sensor network deployments are foreseen to be large infrastructures, with multiple concurrent tasks running on the same set of hardware. Applications will need standardized methods to access and integrate data from such heterogeneous sensor networks. Hence, a Federated Sensor Network ..."
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Cited by 1 (0 self)
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Next generation sensor network deployments are foreseen to be large infrastructures, with multiple concurrent tasks running on the same set of hardware. Applications will need standardized methods to access and integrate data from such heterogeneous sensor networks. Hence, a Federated Sensor Network (FSN) model can significantly simplify the development of multinetwork applications by presenting a unified system view, hiding the heterogeneity, and performing optimizations. The optimizations are possible due to redundancies in the system. First, a given task can be accomplished by multiple alternative ways (i.e. models), each of which incorporates different types of sources (model redundancy). Second, sensor readings about the same physical phenomenon at some welldefined spatial locations are correlated (spatial data redundancy), and third, sensor observations do not lose their utility immediately but only over time (temporal data redundancy). Seçmece optimizer makes use of these redundancies to select which models to run, using which sources at which rate, aiming to achieve the required quality with the minimum cost. We first show that even this optimization problem is strongly NPHard, and then extend the problem to maximize the system lifetime, the time the system can continue answering the given queries with enough quality. Finally, we study our solution approaches, and possible modifications, to demonstrate performance gains in both real world scenarios and random generated setups.