Networked Computing in Wireless Sensor Networks for Structural Health Monitoring

This paper studies the problem of distributed computation over a network of wireless sensors. While this problem applies to many emerging applications, to keep our discussion concrete we will focus on sensor networks used for structural health monitoring. Within this context, the heaviest computation is to determine the singular value decomposition (SVD) to extract mode shapes (eigenvectors) of a structure. Compared to collecting raw vibration data and performing SVD at a central location, computing SVD within the network can result in significantly lower energy consumption and delay. Using recent results on decomposing SVD, a well-known centralized operation, into components, we seek to determine a near-optimal communication structure that enables the distribution of this computation and the reassembly of the final results, with the objective of minimizing energy consumption subject to a computational delay constraint. We show that this reduces to a generalized clustering problem; a cluster forms a unit on which a component of the overall computation is performed. We establish that this problem is NP-hard. By relaxing the delay constraint, we derive a lower bound to this problem. We then propose an integer linear program (ILP) to solve the constrained problem exactly as well as an approximate algorithm with a proven approximation ratio. We further present a distributed version of the approximate algorithm. We present both simulation and experimentation results to demonstrate the effectiveness of these algorithms

CapEst: A Measurement-based Approach to Estimating Link Capacity in Wireless Networks

Estimating link capacity in a wireless network is a complex task because the available capacity at a link is a function of not only the current arrival rate at that link, but also of the arrival rate at links which interfere with that link as well as of the nature of interference between these links. Models which accurately characterize this dependence are either too computationally complex to be useful or lack accuracy. Further, they have a high implementation overhead and make restrictive assumptions, which makes them inapplicable to real networks. In this paper, we propose CapEst, a general, simple yet accurate, measurement-based approach to estimating link capacity in a wireless network. To be computationally light, CapEst allows inaccuracy in estimation; however, using measurements, it can correct this inaccuracy in an iterative fashion and converge to the correct estimate. Our evaluation shows that CapEst always converged to within 5% of the correct value in less than 18 iterations. CapEst is model-independent, hence, is applicable to any MAC/PHY layer and works with auto-rate adaptation. Moreover, it has a low implementation overhead, can be used with any application which requires an estimate of residual capacity on a wireless link and can be implemented completely at the network layer without any support from the underlying chipset

IEEE 802.11 is good enough to build wireless multi-hop networks

This work formally establishes that IEEE 802.11 yields exceptionally good performance in the context of wireless multi-hop networks. A common misconception is that existing CSMA-CA random access schemes like IEEE 802.11 yield unfair and inefficient rates in wireless multi-hop networks. This misconception is based on works which study IEEE 802.11-scheduled multi-hop networks with TCP or in saturation conditions both of which grossly underutilize the available capacity that IEEE 802.11 provides, or use topologies which cannot occur in practice due to physical layer limitations. To formally establish our thesis, we will derive worst case performance bounds on IEEE 802.11 in multi-hop networks. We first characterize the achievable rate region for any IEEE 802.11-scheduled multi-hop network. To do so, we first characterize the achievable edge-rate region, that is, the set of edge rates that are achievable on a given topology. This requires a careful consideration of the inter-dependence among edges, since neighboring edges collide with and affect the idle time perceived by the edge under study. We approach this problem in two steps. First, we consider two-edge topologies and study the fundamental ways by which they interact. Then, we consider arbitrary multi-hop topologies, compute the effect that each neighboring edge has on the edge under study in isolation, and combine to get the aggregate effect. We then use the characterization of the set of feasible rates to compare the max-min rate allocation achieved by IEEE 802.11 and optimal, and find that: (i) IEEE 802.11 is never worse than 16% of the optimal when ignoring physical layer constraints, (ii) in any realistic topology with geometric constraints due to the physical layer, IEEE 802.11 is never worse than 30% of the optimal, and (iii) in typical topologies IEEE 802.11 attains more than 55% of the optimal throughput. Considering that the state-of-the-art distributed approximations to optimal scheduling achieve lower worst case bounds than the above, IEEE 802.11 is surprisingly efficient. To ensure that this good performance is achievable with a distributed rate controller, we propose WCP-CAP. It provides explicit and precise rate feedback to sources while exchanging control information only amongst the neighbors. WCP-CAP achieves max-min rates within 15% of the optimal for all the topologies considered in this paper