Not just a pipe dream!

US water facilities and those around the world are faced with mounting operational and maintenance costs as a result of aging pipeline infrastructures. The ability to monitor and control the infrastructure is no longer a pipe dream but is on its way to becoming reality thanks to wireless sensor networks. PipeNet, is a system designed by researchers at Imperial College in London and CSAIL at MIT in conjunction with Intel Research, to collect hydraulic and acoustic/vibration data at high sampling rates as well as use algorithms for analyzing this data to detect and locate leaks. A study by the EPA (Environmental Protection Agency) estimates that water utilities will need $277 billion over the next 20 years (2003-2023) to install, upgrade, and replace infrastructure. Unfortunately, identifying the high priority areas is a non-trivial task because of the scale and age of the pipeline infrastructures. Failures of large diameter bulk-water transmission pipelines are of greatest concern as these are supply critical systems. When these failures do occur, there are dire consequences including loss of life, severe interruptions in service, degraded fire fighting ability, damage to adjacent infrastructure and buildings, and of course the multi-million dollar repair bills.

PipeNet is a system based on wireless sensor networks which aims to detect, localize and quantify bursts and leaks and other anomalies in water transmission pipelines such as blockages and malfunctioning control valves. The system was based on Intel Motes (the 1st generation of the Imote2 devices available today) to provide remote monitoring in near real-time with support for high data rate time synchronized data collection from multiple locations. This is a significant change from current data acquisition practices of using portable data loggers with a low duty cycle and limited remote monitoring stations that do not have the capability to do local processing or high-bandwidth transmission.

The Motes were responsible for the data collection, local signal processing and relaying of data to the second tier consisting of the Stargate platform that was to relay the data back to the backend server via a GPRS modem. A special sensor board was also designed to interface to the Mote to accommodate the various analog sensors used in PipeNet. The clusters contained pressure sensors and pH monitoring sensors combined with water level monitors and pressure monitors. The mote could be configured to trigger data acquisition on a channel remotely when a monitored channel exceeded some threshold. Acoustic and vibration data analysis was used to detect and locate small leaks which are difficult to identify with hydraulic data. Vibration signals collected showed differences in the mode of wave propagation if a leak was detected. Leaks also manifested themselves in the acoustic signal which propagates uniformly in both directions away from the leak by the escaping water flowing through the rupture in the pipeline.

The Motes communicated from the manhole to the Stargate based gateway deployed on a nearby lamppost. The Stargate, the GPRS modem and 802.11 radio were powered from the power lines at the lamp post. The Intel Mote was connected to the Stargate through its UART interface acting as a bridge between the Stargate and the motes on the pipes. This cluster head was responsible for forming the sensor network, converting the configuration data coming from the Stargate and passing it to the correct sensor node as well as delivering the data collected via the reliable transport protocol to the Stargate where it was converted back into data files. These files were periodically sent to the backend server running in the lab via the GPRS link. The Stargate was equipped with an 802.11 link to facilitate drive-by access for on-site configuration and debugging as well. Data transfer was handled via standard Linux tools, and the data files were then loaded in a Postgres database that stored the individual sensor readings. This gave users the ability to browse these sensor readings by connecting to an Apache Web server running on the server. The web site used Google Maps/Google Earth to allow users to select and browse the sensor locations of interest. Once users selected a sensor location, they could retrieve data corresponding to a user-specified date / time range and sensor type to visualize the data.

Using the leak localization algorithms they developed, the research team was able to localize leaks to within 30 cm. The long term monitoring of pressure and acoustic signals in particular pipes will also facilitate the development of more accurate pattern recognition and classification models in the future. The next revision of the PipeNet system is using the Imote2 platform which integrates many essential components to enable high performance and energy efficient data processing. The XScale processor on the Imote2 has dynamic voltage and frequency scaling capability to allow applications to balance performance and energy needs by selecting speeds between 13 and 624 MHz. In addition, the processor includes a DSP co-processor to accelerate common data analysis primitives (e.g FFT, compression) thereby greatly improving performance and energy efficiency. This performance advantage will allow users to carry out the analysis and data reduction in real-time, thus reducing storage and power. Finally, the Imote2 includes 32 MB of SDRAM and Flash enabling the decoupling of data collection and communication with a richer peripheral support that will provide higher data acquisition rates and improve sensor integration.

PipeNet is the future of pipeline monitoring providing the capability to automatically detect leaks and bursts of water in the transmission pipelines; real-time operation with few false alarms; inexpensive to produce, install, and maintain; high-frequency data collection; the ability to differentiate between sensor and system faults; and a flexible reusable data-flow based programming environment. This system will not only improve our ability to monitor large scale water supply systems, but to conserve our natural resources and use them efficiently.

Heated up about your Energy Bill? Motes to the Rescue!

by Ralph Kling, Chief Architect, Crossbow Technology, Inc.

When I opened up my Gas and Electric bill from our local utility (PG&E here in Northern California) I was shocked: it was over $100 more than I expected. Some further checking confirmed that it was also substantially higher than during the same time last year, by about that amount. So what happened? Sure, energy prices likely have increased since last year and maybe it was a bit colder here the last month. But it still didn’t add up… 

A few years ago I had installed a shiny new programmable digital thermostat to replace an old mechanical gizmo that must have been around since the house was built in the 1950s. And I actually did read the manual (well, some of it anyways) and spent quite a bit of time programming it with different cycles for days and nights and weekends and so on. And, though I was very proud of that accomplishment at the time, the promised energy savings that I had hoped for actually never really materialized. My utility bills stayed pretty much the same before and after the thermostat installation. 

So after receiving the last bill, I investigated the thermostat but it seemed to work just fine and none of the settings had been changed since I had originally made them. After contemplating this for a bit, I had an idea. Since I am working at Crossbow, the leader in Wireless Sensing technologies I have access to the essential tools needed to solve this mystery: I took home a Wireless Sensor Starter Kit which consists of two sensor nodes and a base station. I also added a Stargate Netbridge gateway to record and visualize the data from the sensor nodes without the need to install anything on my PC (or even have it running and consuming energy during the measurements). 

The next day, I installed the “Sensor Network” in my house. It was really simple, just plug the gateway into my home router, place the sensor nodes in the rooms I wanted to monitor and turn them on. All networking and data gathering happen automatically from then on. The sensor nodes are battery powered thus they can be placed pretty much anywhere eliminating the search for outlets. I placed the nodes in the kids’ rooms in the center and back of the house. The nodes have all sorts of sensors including light, humidity, atmospheric pressure etc. but I was particularly interested in the temperature readings. 

The next day, I pulled up the temperature profile gathered so far and I was shocked:

 

The Thermostat was set to 70°F, (about 21°C) during the morning and evening and to 65°F, (18°C) during the rest of the day and at night. The measurements show an average of 24°C during the high and 20°C during the low period, a full 2-3 degrees more than expected. Why were the readings so far off from the preset values at the thermostat? And then it dawned on me: the thermostat was in the living room at the other end of the house. And I remembered that last year in the summer I had closed the heating air vents in that room to prevent the kids’ Lego pieces from falling into the shafts (they are really hard to retrieve from there). Of course I had forgotten about that in the fall when I restarted the heater. 

So the warm air had to make its way through half the house before the living room (and the thermostat) warmed up thus raising the temperature in the other rooms way to high (as well as the heating bill). But this was still a theory that needed to be proven. So I opened up the living room vents all the way, let the sensors collect more data and waited impatiently for the data from the next day. The results were nothing short of astounding:

 

The temperatures in the kids’ rooms now averaged 21°C during the high and 19°C during the low periods, much closer to the desired thermostat settings. Beyond that, the data also shows that one room warms up more poorly than the other (it has more outside walls with poor insulation). Adjusting the vents in those rooms as well could further equalize the temperature readings. 

So, with a very simple setup (it took literally 5 minutes to set up) I was able to gather a lot of valuable data and hopefully significantly reduce my heating bill (we will see next month). And all that without lowering the temperature settings on the thermostat! Beyond that there are lots more insights that can be gathered from the data: The slope between the day and night settings shows how fast the house cools down and how well it is insulated. Other data like the humidity measurements can point to potential problems with mold if the readings are too high. Do your kids always “forget” to turn off the lights – well a plot of the data can very convincingly show how much energy is wasted. 

I am sure there are lots more good ideas – reader’s suggestions are welcome!

Dr. Ralph Kling is currently the Chief Architect of the Wireless Business Unit at Crossbow Technology. Ralph is leading the Wireless engineering team at Crossbow and is responsible for new product strategies, technical directions and Standards activities.

Previously, Ralph was Principal Architect and Director of Sensor Network Operation at Intel Corporate Research. In this capacity, he was responsible for ground-breaking research in the area of Wireless Sensor Network Platforms that resulted in such novel designs as the Intel Mote and Imote2. Before joining Intel Research, Ralph's previous assignments include managing the Itanium® Processor Family microarchitecture/ performance group and the Microprocessor Research Lab (MRL).

Ralph obtained his Master's and Ph.D. degrees from the University of Illinois at Urbana-Champaign. His thesis research focused on Simulated Evolution, a new global optimization method for integrated circuit designs. Prior to coming to the US on a Fulbright scholarship, Ralph studied Electrical Engineering in his hometown of Hanover in Germany. He enjoys skiing in the winter and beaches in the summer.

Motes in Antarctica!

Can you imagine a place where the temperature can get to -82°C? A place without sunlight for half the year. Imagine a frozen desert with no permanent human population - the coldest, driest and windiest continent on earth... It is in this place that research scientists from the Institute of Remote Sensing Applications at the Chinese Academy of Sciences have deployed a wireless sensing system using Crossbow's Mote platforms. Being a resident of sunny California I can barely fathom such an environment. If the temperature drops below 50°F (10°C), I want a cup of hot chocolate and a fire to cuddle in front of. Imagine being in a place where the climate is so harsh - there are no plants or animals. Talk about extreme sensing!

China News reported that the wireless sensor network called the 'Unmanned Wireless Intelligent Snow and Ice Observation System in Extreme Environments' has been successfully set up around the Dome-A area near the South Pole, which is the southernmost point on Earth's surface. Dome-A (Dome Argus) is the highest and possibly coldest place in Antarctica and perhaps the coldest naturally occurring place on Earth. It is the highest ice feature in Antarctica, comprising a dome or eminence of 4.093 m elevation.  The system was co-developed by Crossbow Technology and the Chinese Academy of Science to develop a solution that can consistently work under extreme environmental conditions such as a 4-month polar-night, -82°C temperature lows and an annual average temperature of -55°C. The wireless system deployed by CAS scientists is designed to overcome the low-temperature, high-altitude and soft snow-surface.

The deployed system consists of two base stations and four nodes. Each node is powered by two low-temperature resistant batteries which are expected to last for one year. The communication capability between each node can achieve ranges of up to 1000m. Each node samples environmental data every 15 minutes, including temperature (weather temperature, snow temperature and the snow temperature below 1 meter), humidity, sunlight, and air pressure. The collected data is sent wirelessly to the central base station that collects and sends the data to Beijing every day. Meanwhile, the other remote base station is used to store the data locally to be collected later by an expedition team. The two base stations ensure that no data is lost in the communication.

The Antarctic ice sheet where Dr. Xiao and his scientific expedition team ventured is the highest point on the continent. The group hopes to gather vital information on the environment and observing conditions around Dome-A to determine whether or not it is a viable location to expand the observatories in the region. Dome A claims the best astronomical sky conditions in the world, as it is devoid of clouds and boasting steady air that makes for clear viewing. Imagine...Motes in Antarctica!