Muzammil Hussain

An Underwater Robotic Network for Monitoring Nuclear Waste Storage Pools

Nuclear power provides a significant portion of our current energy demand and is likely to become more wide spread with growing world population. However, the radioactive waste generated in these power plants must be stored for around 60 years in underwater storage pools before permanent disposal. These underwater storage environments must be carefully monitored and controlled to avoid an environmental catastrophe. In this paper, we present an underwater mobile sensor network that is being developed to monitor these waste storage pools. This sensing system will also be used in very old storage pools to build maps of their internal structure which can then be used for waste removal and pool decommissioning. In this paper, we outline the unique challenges of our application scenario which include robot localization in cluttered underwater environments and the effect of location errors on environment mapping. We also list other industrial applications that can benefit from our underwater sensor network.

A Singlehop Collaborative Feedback Primitive for Wireless Sensor Networks

To achieve scalability, energy-efficiency, and timeliness, wireless sensor network deployments increasingly employ in-network processing. In this paper, we identify singlehop feedback collection as a key building block for in-network processing applications, and introduce a basic singlehop primitive, pollcast. The key idea behind this primitive is to exploit the receiver-side collision detection information at the MAC-layer to speed-up collaborative feedback collection. Using pollcast, a node can get an affirmation about the existence of a node-level predicate P in its neighborhood in constant time by asking all nodes where P hold to reply simultaneously. We have implemented pollcast on Tmotes using Chipcon 2420 radio. Our results show that this primitive is indeed lightweight, resilient, and effective. Our paper is also the first time receiver-side collision detection is achieved in a practical manner for Chipcon 2420 radio.

Distributed localization in cluttered underwater environments

Mapping and exploration of closed fluid tanks, such as nuclear waste storage tanks and water sewage treatment ponds, by a swarm of underwater robots requires them to be aware of their positions, as they traverse the depths of the tank. This paper considers the challenges faced in localizing robots using reference-based localization in such clutter-prone environments. Clutter affects localization in two ways: it blocks reference communication signals, effectively preventing them from reaching the more remote regions in the tanks, and, second, it leads to the formation of non-line-of-sight (NLOS) distance measurements. In this paper, we consider the application of distributed localization techniques to cluttered environments and assess their performance. We also investigate how to place robots in a multi-hop network to improve the accuracy of position estimation in the far-fetched regions of the tank, when distributed localization is used.

TRANSACT: A Transactional Framework for Programming Wireless Sensor/Actor Networks

Effectively managing concurrent execution is one of the biggest challenges for future wireless sensor/actor networks (WSANs): for safety reasons concurrency needs to be tamed to prevent unintentional nondeterministic executions, on the other hand, for real-time guarantees concurrency needs to be boosted to achieve timeliness. We propose a transactional, optimistic concurrency control framework for WSANs that enables understanding of a system execution as a single thread of control, while permitting the deployment of actual execution over multiple threads distributed on several nodes. By exploiting the atomicity and broadcast properties of singlehop wireless communication, we provide a lightweight implementation of our transactional framework on the motes platform.

Characterization of non-line-of-sight (NLOS) bias via analysis of clutter topology

Clutter-prone environments are challenging for range-based localization, where distances between anchors and the unlocalised node are estimated using wireless technologies like radio, ultrasound, etc. This is so due to the incidence of Non-Line-Of-Sight (NLOS) distance measurements as the direct path between the two is occluded by the presence of clutter. Thus NLOS distances, having large positive biases, can severely degrade localization accuracy. Till date, NLOS error has been modelled as various distributions including uniform, Gaussian, Poisson and exponential. In this paper, we show that clutter topology itself plays a vital role in the characterization of NLOS bias. We enumerate a feature-set for clutter topologies, including features that can be practically deduced without complete knowledge of the clutter topology. We then analyze the significance of these features, both individually and in combination with each other, in the estimation of the NLOS rate as well as the NLOS bias distribution for arbitrary clutter topologies. We show that we can obtain the NLOS rate with an error of only 0.03 for a given clutter topology using only those clutter topology features that can be practically realized in a real deployment. We show that estimating the NLOS bias distribution is more challenging which give a small number of poor estimations.

Adaptive node placement for improving localization accuracy in clutter-prone environments

Range-based localization techniques can yield unacceptably large errors when used in cluttered environments. Clutter in the environment leads to large distance measurement errors due to non-line-of-sight (NLOS) signal propagation. In this paper, we focus on the localization of a mobile node in clutter using a multi-hop localization method, namely DV-Distance, and suggest that node mobility is key to improving localization accuracy in cluttered environments. We propose APDV, a distributed control algorithm that carefully moves nodes in the monitored area to reduce distance overestimates caused by clutter and network sparsity. We evaluate the performance of APDV in simulated and real network settings. Our technique is shown to outperform existing approaches when a majority of the distance measurements are NLOS in nature.

Dynamic node placement for multi-hop localization in cluttered environments

Accurate localization in wireless sensor actuator networks (WSANs) is crucial since a primary purpose in their deployments is accomplishing tasks based on a spatial dimension in the environment — be it sensor data collection or actuation.

Effect of clutter topology on multi-hop localizer placement

Accuracy in range-based localization systems can degrade rapidly in the presence of clutter in the environment. This is due to the incidence of Non-Line-of-Sight (NLOS) distance measurements between the anchors and an unlocalized node. While a large corpus of research work dealt with the scenario where the NLOS distances form a minority of the total distances to anchors, there has been not much research done to handle the situation where a majority or even all distance measurements to anchors are NLOS in nature. In our previous work, we showed that using localizers in a cluttered environment can improve the localization accuracy of a target node even when all the distance measurements are NLOS. Instead of NLOS bias, these techniques suffer residual multi-hop error, which is caused due to the distance overestimate when a multi-hop chain is used instead of the straight-line distance. In this paper, we analyze the effect of clutter topology on the multi-hop error. We use machine learning techniques to estimate the aggregate forms of the multi-hop error for a given clutter topology when only characteristic features of the clutter topology are provided.