Fatemeh Fazel

Random Access Compressed Sensing for Energy-Efficient Underwater Sensor Networks

Inspired by the theory of compressed sensing and employing random channel access, we propose a distributed energy-efficient sensor network scheme denoted by Random Access Compressed Sensing (RACS). The proposed scheme is suitable for long-term deployment of large underwater networks, in which saving energy and bandwidth is of crucial importance. During each frame, a randomly chosen subset of nodes participate in the sensing process, then share the channel using random access. Due to the nature of random access, packets may collide at the fusion center. To account for the packet loss that occurs due to collisions, the network design employs the concept of sufficient sensing probability. With this probability, sufficiently many data packets - as required for field reconstruction based on compressed sensing - are to be received. The RACS scheme prolongs network life-time while employing a simple and distributed scheme which eliminates the need for scheduling.

Random Access Compressed Sensing over Fading and Noisy Communication Channels

Random Access Compressed Sensing (RACS) is an efficient method for data gathering from a network of distributed sensors with limited resources. RACS relies on integrating random sensing with the communication architecture, and achieves overall efficiency in terms of the energy per bit of information successfully delivered. To address realistic deployment conditions, we consider data gathering over a fading and noisy communication channel. We provide a framework for system design under various fading conditions, and quantify the bandwidth and energy requirements of RACS in fading. We show that for most practical values of the signal to noise ratio, energy utilization is higher in a fading channel than it is in a non-fading channel, while the minimum required bandwidth is lower. Finally, we demonstrate the savings in the overall energy and the bandwidth requirements of RACS compared to a conventional TDMA scheme. We show that considerable gains in energy -on the order of 10 dB- are achievable, as well as a reduction in the required bandwidth, e.g., 2.5-fold decrease in the bandwidth for a network of 4000 nodes.

Random access sensor networks: Field reconstruction from incomplete data

We address efficient data gathering from a network of distributed sensors deployed in a challenging field environment with limited power and bandwidth. Utilizing the low-rank property of the sensing field, we leverage results from the matrix completion theory to integrate the sensing procedure with a simple and robust communication scheme based on random channel access. Results show that the space-time map of the sensing field can be recovered efficiently, using only a small subset of sensor measurements, collected over a fading random access channel.

Compressed sensing in random access networks with applications to underwater monitoring

Abstract For networks that are deployed for long-term monitoring of environmental phenomena, it is of crucial importance to design an efficient data gathering scheme that prolongs the life-time of the network. To this end, we consider a Random Access Compressed Sensing (RACS) scheme that considerably reduces the power and bandwidth usage of a large network. Motivated by underwater applications, we propose a continuous-time RACS that eliminates the need for synchronization and scheduling which are difficult to achieve in a distributed acoustic network. We provide an analytical framework for system design that ensures fast recovery and power-efficiency. Through analysis and examples, we demonstrate that recovery of the field can be attained using only a fraction of the resources used by a conventional TDMA network, while employing a scheme which is simple to implement.

Collision Tolerant and Collision Free Packet Scheduling for Underwater Acoustic Localization

This article considers the joint problem of packet scheduling and self-localization in an underwater acoustic sensor network with randomly distributed nodes. In terms of packet scheduling, our goal is to minimize the localization time, and to do so we consider two packet transmission schemes, namely a collision-free scheme (CFS), and a collision-tolerant scheme (CTS). The required localization time is formulated for these schemes, and through analytical results and numerical examples their performances are shown to be dependent on the circumstances. When the packet duration is short (as is the case for a localization packet), the operating area is large (above 3 km in at least one dimension), and the average probability of packet-loss is not close to zero, the collision-tolerant scheme is found to require a shorter localization time. At the same time, its implementation complexity is lower than that of the collision-free scheme, because in CTS, the anchors work independently. CTS consumes slightly more energy to make up for packet collisions, but it is shown to provide a better localization accuracy. An iterative Gauss-Newton algorithm is employed by each sensor node for self-localization, and the Cramér Rao lower bound is evaluated as a benchmark.

Design of a random access network for compressed sensing

For networks that are deployed for long-term monitoring of environmental phenomena, it is of crucial importance to design an efficient data gathering scheme that prolongs the lifetime of the network. To this end, we exploit the sparse nature of the monitored field and consider a Random Access Compressed Sensing (RACS) scheme in which the sensors transmit at random to a fusion center which reconstructs the field. We provide an analytical framework for system design that captures packet collisions due to random access as well as packet errors due to communication noise. Through analysis and examples, we demonstrate that recovery of the field can be attained using only a fraction of the resources used by a conventional TDMA network, while employing a scheme which is simple to implement and requires no synchronization.

Target localization and tracking in a random access sensor network

We consider tracking of multiple objects using a wireless sensor network where distributed nodes transmit to a fusion center using random access. During an initialization phase, targets are identified on a discrete set of locations using a sparse identification method. Tracking then proceeds to update the target locations and amplitudes explicitly, using a gradient algorithm to solve the underlying non-linear optimization problem. Updating continues at the pace dictated by the average sensing/transmission rate, which can be adjusted to suit an expected target velocity. By focusing explicitly on the target locations, as opposed to continuing with sparse identification over a quantized space whose size may be much greater than the number of targets, the goal is to reduce the computational complexity, improve the performance, and eliminate the spatial quantization effects.

Impact of fading on random access compressed sensing

We address the design of a large-scale sensor network, deployed for long-term environmental monitoring. Taking into account the sparse nature of the monitored field, we integrate the sensing and the communication aspects of the network into an efficient Random Access Compressed Sensing (RACS) scheme. RACS is inspired by the theory of compressed sensing and employs random channel access to collect a sufficient number of observations at the fusion center to reconstruct the field. In this paper, we study the impact of fading on RACS. We provide a framework for system design that specifically targets a Rayleigh fading channel. Moreover, we quantify the energy and bandwidth requirements and provide analytical results demonstrating the robustness of RACS in the presence of fading.

Packet scheduling for underwater acoustic sensor network localization

This article considers the problem of packet scheduling for localization in an underwater acoustic sensor network where sensor nodes are distributed randomly in an operating area. Our goal is to minimize the localization time, and to do so we consider two packet transmission schemes, namely collision-free, and collision-tolerant. Through analytical results and numerical examples the performances of these schemes are shown to be comparable. In general, for small packet length (as is the case for a localization packet) and large operating area (above 3km in at least one dimension), the performances of the collision-tolerant protocol is superior to its collision-free counterpart. At the same time, the anchors work independently of each other, and this feature simplifies the implementation process.

Random access compressed sensing in underwater sensor networks

In this paper, we propose a power-efficient underwater sensor network scheme employing compressed sensing and random channel access. The proposed scheme is suitable for applications where a large number of sensor nodes are deployed uniformly over a certain area to measure a physical phenomenon. The underlying assumption is that most physical phenomena have sparse representations in the frequency domain. The network is assumed to have a Fusion Center (FC) that collects the observations of sensor nodes and reconstructs the measured field based on the obtained measurements. The proposed method is completely decentralized, i.e., sensor nodes act independently without the need for coordination with each other or with the FC. During each frame, a Bernoulli random generator at each node determines whether the node participates in sampling or stays inactive during that sampling period. If selected, it measures the physical quantity of interest, e.g. temperature. A second random generator with a uniform distribution then picks a (random) delay for the node to send its data to the FC. The proposed network scheme, referred to as Random Access Compressed Sensing (RACS), results in a simple power-efficient design, for: a) it eliminates the need for duplexing, which requires coordination from the FC; b) there is no need for acknowledgment packets and retransmissions in case packets collide; and moreover, c) it is efficient in terms of the communication resources used (only a small fraction of nodes sample and transmit in each sampling period).