The drive toward the implementation and massive deployment of wireless sensor networks calls for ultralow-cost and low-power nodes. While the digital subsystems of the nodes are still following Moore's Law, there is no such trend regarding the performance of analog components. This work proposes a fully integrated architecture of both digital and analog components (including local oscillator) that offers significant reduction in cost, size, and overall power consumption of the node. Even though such a radical architecture cannot offer the reliable tuning of standard designs, it is shown that by using random network coding, a dense network of such nodes can achieve throughput linear in the number of channels available for communication. Moreover, the ratio of the achievable throughput of the untuned network to the throughput of a tuned network with perfect coordination is shown to be close to 1/e. This work uses network coding to leverage the fact that throughput equal to the max-flow in a graph is achievable even if the topology is not know a priori. However, the challenge here is finding the max-flow of the random graph corresponding to the network.

In a sensor network, data routing is tightly coupled to the needs of a sensing task, and hence the application semantics. This paper introduces the novel idea of information-directed routing, in which routing is formulated as a joint optimization of data transport and information aggregation. The routing objective is to minimize communication cost, while maximizing information gain, differing from routing considerations for more general ad hoc networks. The paper uses the concrete problem of locating and tracking possibly moving signal sources as an example of information generation process, and considers two common information extraction patterns in a sensor network: routing a user query from an arbitrary entry node to the vicinity of signal sources and back, or to a prespecified exit node, maximizing information accumulated along the path. We derive information constraints from realistic signal models, and present several routing algorithms that find near-optimal solutions for the joint optimization problem. Simulation results have demonstrated that information-directed routing is a significant improvement over a previously reported greedy algorithm, as measured by sensing quality such as localization and tracking accuracy and communication quality such as success rate in routing around sensor holes.

This paper presents an adaptive approach for conserving energy in high-density sensor networks. The proposed method allows sensor nodes to sleep while ensuring that application performance constraints are met. Unlike deterministic algorithms that assume static connectivity, the approach uses a randomized algorithm to provide robustness to the variations in network connectivity. These variations are due to fading channels, depletion or addition of nodes, node mobility, and the sleeping of nodes. The algorithm developed in the paper is extremely lightweight and does not require nodes to keep any state information about their individual neighbors. Based on local observations, each node independently decides when to sleep and wakeup. The algorithm also ensures an evenly distributed workload among nodes, and achieves energy savings proportional to the density of nodes.

Sensor tasking is essential to many sensing applications in resource-constrained wireless ad hoc sensor networks. In this paper, we present a multi-step lookahead algorithm for sensor selection and information routing. The algorithm is based on the information-driven sensor querying (IDSQ) that uses mutual information as a utility measure for potential information contribution of individual sensors, and extend it to prediction of information gain over a finite horizon while balancing cost such as the number of communication hops. Simulation results on target tracking problems have shown that the multi-step lookahead algorithm significantly improves the tracking performance compared to the original greedy algorithm, when "sensor holes" are present in a sensor network.

We propose a novel approach to reducing energy consumption in sensor networks using a distributed adaptive signal processing framework and efficient algorithm. While the topic of energy-aware routing to alleviate energy consumption in sensor networks has received attention recently (C. Toh, 2001; R. Shah et al., 2002), in this paper, we propose an orthogonal approach to previous methods. Specifically, we propose a distributed way of continuously exploiting existing correlations in sensor data based on adaptive signal processing and distributed source coding principles. Our approach enables sensor nodes to blindly compress their readings with respect to one another without the need for explicit and energy-expensive intersensor communication to effect this compression. Furthermore, the distributed algorithm used by each sensor node is extremely low in complexity and easy to implement (i.e., one modulo operation), while an adaptive filtering framework is used at the data gathering unit to continuously learn the relevant correlation structures in the sensor data. Our simulations show the power of our proposed algorithms, revealing their potential to effect significant energy savings (from 10%-65%) for typical sensor data corresponding to a multitude of sensor modalities.

The list Viterbi algorithm (LVA) with an arithmetic coding based continuous error detection (CED) scheme is applied to high-order partial-response magnetic recording channels. Commonly used magnetic recording systems employ distance-enhancing codes along with parity-check post-processors to correct most dominant error events. The system presented here inserts a CED encoder between the outer Reed-Solomon (RS) code and the channel, and utilizes LVA along with CED's error detection capabilities to improve the performance of the Viterbi decoder. Simulations show that this system results in a 2 dB improvement over MTR encoded EEPRML decoding at a BER of 2/spl times/10/sup -6/ in additive white Gaussian noise and localizes error occurrences to the end of the sector.