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 long-lived 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 Energy-Efficient 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 O(1) 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 intra-cluster and inter-cluster 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.

Node clustering in sensor networks increases scalability, robustness, and energy-efficiency. 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-Efficient Distributed clustering), for clustering sensors deployed in hostile environments in an interleaved manner with low complexity. Our primary objective is to construct a k-faulttolerant (i.e., k-connected) 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 vertex-disjoint 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 re-clustering the network. We prove that REED asymptotically achieves k-connectivity if certain conditions on node density are met. We also discuss inter-cluster routing and MAC layer considerations, and investigate REED clustering properties via extensive simulations.

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 10-20 % 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 Robust-Soliton. 1 I.

Abstract—This paper develops an optimal decentralized algorithm for sparse signal recovery and demonstrates its application in monitoring localized phenomena using energy-constrained large-scale 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 in-network 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 low-power one-hop 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.

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.

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 multi-network 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 well-defined 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 NP-Hard, 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.

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