Wireless Sensor Networks

Life Under Your Feet

LUYF is a joint project with Katalin Szlavecz from JHU Department of Earth and Planetary Sciences and Alex Szalay from JHU Department of Physics & Astronomy. The purpose of the project is to use WSNs to study the environmental parameters of the soil on various spatial and temporal scales at a much finer granularity than before. As part of the project we did several deployments using both MicaZ and TelosB/Sky and gather data spanning more than 23 months. We are now moving to a bigger scale and plan to deploy around 200 nodes.

You can learn more about our current deployment status from the deployment's blog.

MEDiSN: Medical Emergency Detection in Sensor Networks

Staff shortages and an increasingly aging population are straining the ability of emergency departments to provide high-quality care. Additionally, there is a growing concern about the ability of hospitals and EMS responders to provide effective care during disaster events. To automate the patient monitoring process and improve efficiency, quality of care, and the volume of patients treated, we have developed MEDiSN, a wireless sensor network for monitoring patients’ vital signs in hospitals and disaster events. The MEDiSN system is undergoing multiple tests at a number of hospitals in the Baltimore-Washington area. Recently, MEDiSN has been featured in the Discovery Channel Tech.

Koala

Many sensor network applications belong to a class refered to as simple data collection, and these applications have some interesting characteristics. For example, the data does not have rigid real-time latency requirement, nor does it require in-network processing. In fact, an efficient data collection protocol can really help extending the network lifetime.

Koala is a reliable data retrieval system designed to operate at permille (0.1%) duty cycles. It achieves this goal by keeping the network's node in deep sleep most of the time and reviving them through an efficient network-wide wake-up mechanism. Koala uses the Flexible Control Protocol (FCP), a multi-hop data download protocol. A base station uses neighborhood connecitivity information of each node to calculate the download path. We have successfully integrated Koala with Life Under Your Feet (LUYF) projects, and we are currently evaluating field performance.

DC Genome

This is an umbrella project at Microsoft Research (MSR). The goal of the project is to use data-driven and feedback control approaches to monitor, analyze, and improve data center operation efficiencies to minimize their environmental impacts. We have developed several data collection protocols for large-scale data center deployments.

Target Localization Using Radar Sensor Networks

Target localization in WSN(Wireless Sensor Networks) has been an active research area for its importance and application. Most of the works so far, however, have been focused on cooperative system approach, which requires targets of helping infrastructure nodes localize. Rather, we take non-cooperative approach requiring zero per cent help from targets. For this purpose, we use Radar Sensor Network (RSN), a group of Pulsed Doppler radars interfacing with Tmotes for target tracking problem.Find more

Spatial Characteristics of the Gray Region for 802.15.4 Radios

Packet loss and energy consumption in sensor networks depend critically on the quality of the network's wireless links. In turn, link quality depends on the environment in which the RF signals propagate and the locations of the link's endpoints. Experimental results have shown that a low-power wireless link can be in one of three states or 'regions', as the inter-node distance increases: connected, transitional (gray), and disconnected. The gray region is characterized by extreme variability, whereby small differences in distance or endpoint locations can lead to pronounced differences in loss rates. However, not all is lost. This work investigates the spatial characteristics of the gray region and experimentally shows that one can efficiently identify links with low loss rates within the radio's gray region. One of the possible applications of this finding is in the design of sparse, yet low-loss network deployments. Download PDF

Deluge T2

To serve our own needs for over-the-air reprogramming and as a service to the WSN community we ported the Deluge2 to TinyOS 2. The code was included in the official distribution starting with version TinyOS 2.0.2.

tinyos.py

tinyos.py is a Python implementation of the T2 serial communication. An earlier version was included in the tools package of TinyOS 2.0.2 as part of the Deluge T2. In Nov 2007 the code was committed to the tinyos-2.x-contrib CVS repository.

CC2420 Security

To effectively provide security to the packets generated by the well used TI/Chipcon CC2420 radios, we have provided interfaces in TinyOS 2 to enable the CC2420 radio's in-line security features. Specifically we use the CC2420 security for the data transmitted in the MEDiSN project to protect packets containing patient's physiological data. The code will be included in the official distribution of TinyOS 2.1.1. Additional information on its usage can be found in the official TinyOS tutorial webpage.

Adaptive Data Collection in Environmental Monitoring Networks

The size of typical environmental monitoring networks is growing at a rapid rate. Traditional strategies of collecting data fail to cope up with the power constraints at such high scales. We note that environmental modalities tend to exhibit strong correlations in time and space. One can exploit these correlations to collect data during more informative (or transient) periods, and from more informative locations. We explore a principal component analysis (PCA) based approach towards an adaptive data collection framework that captures the spatial and heterogeneity in typical environment monitoring networks. This work is motivated by the practical challenges encountered in the LUYF project. However, our methodology is general enough that it applies to a wide class of environmental monitoring networks.

Multiple Radio Modalities in Environmental Monitoring Networks


The environmental characteristics of environments such as temperate forests exhibit a great deal of heterogeneity. The soil in an old-growth area may be quite different from those of a recently-cultivated area in the same forest, for instance. This leads us to create sensor deployments that are characterized by small, dense patches spread across a wide region-- each patch gives us a snapshot of one subtype of the environment, and the diversity of the patches covered gives us a view of the entire environment under observation.

Most WSN protocols assume that each node is the same, but this is not appropriate for such a network. We are exploring data-collection protocols that balance the costs and benefits associated with using radio amplifiers and low-frequency radios to make efficient networks with highly variable node densities. Creating reliable, energy-efficient systems that take advantage of these different modalities will allow domain scientists to accurately characterize large environments without the cost and effort overheads of deploying extra relay nodes.

Typhoon

Reliable large-object dissemination protocols have mostly used in in-network reprogramming. Typhoon aims to reduce dissemination completion time, which reduces the extend of service interruption in the network. In addition, it reduces motes' idle listening time, a major consumer of energy during dissemination. Typhoon sends unicast packets in order for the sender to receive packet acknowledgements quickly. In addition, nodes can snoop packets to reduce the number of total requests. To allow multiple data transfers, every transfer takes place on a channel that is differet from each other and from the common channel. Another benefit of channel diversity is in improving pipelining performance. For more information, please refer to our EWSN 2008 publication.

Past Projects


Botnets are one of the major threats to the Internet today. This project aims to shed light on this phenomenon. Towards this goal, we established a scalable distributed data collection infrastructure to capture and track large collections of Botnets. Currently, our system is tailored to capture Botnets that use IRC to disseminate the Botmaster commands to his Bot armies. Our infrastructure collects Malware using a distributed Honeynet. The collected Malware is then analyzed to discover new IRC Botnets and extract their features to enable infiltration and longitudinal lightweight tracking. For more information, please visit the project's web page.

  • Mobile Malware

In recent years, there has been widespread adoption of wireless networks, as a medium for communication e.g. metro-area WiFi networks, campus/enterprise wireless deployments etc. These developments bring with themselves, their own set of security maladies. Mobility of nodes can be exploited to spread malware among wireless nodes moving across network domains. Propagation of malware across network boundaries occurs trivially because, nodes can easily traverse firewalls and other such perimeter defenses. This project aims to explore the space of worm modeling, detection and containment of such mobile malware.


We describe the design and implementation of solutions for localization problems in multi-modal wireless sensor networks. The problem of network self-localization, namely determining the positions of the nodes that comprise the network, is addressed optically using a set of pan-tilt-zoom (PTZ) cameras to search for a small light-source attached to each of the sensor nodes. Once the locations and headings of the network's nodes are estimated by the cameras, the network can be used to detect and estimate the location of objects traveling through it. Target localization is performed within the network, using information from magnetometers connected to the sensor nodes. We evaluate the performance of the proposed target localization algorithms through simulations and an implementation running on MicaZ motes. Find more.