4. APPLICATIONSWSN applications can be classi?ed into two categories: monitoring and tracking.
Monitoring applications include indoor/outdoor environmental monitoring, health and wellness monitoring, power monitoring, inventory location monitoring, factory and process automation, and seismic and structural monitoring. Tracking applications include tracking objects, animals, humans, and vehicles. There are many different applications, in the following examples we describe a few applications that have been deployed and tested in the real environment.PinPtr 2 is an experimental counter-sniper system developed to detect and locate shooters.
The system utilizes a dense deployment of sensors to detect and measure the time of arrival of muzzle blasts and shock waves from a shot. Sensors route their measurements to a base station (e.g., a laptop or PDA) to compute the shooter’s location.
Sensors in the PinPtr system are second-generation Mica2 motes connected to a multi-purpose acoustic sensor board. Each multi-purpose acoustic sensor board is designed with three acoustic channels and a Xilinx Spartan II FPGA. Mica2 motes run on a TinyOS 21 operating system platform that handles task scheduling, radio communication, time, I/O processing, etc. Middleware services developed on TinyOS that are exploited in this application include time synchronization, message routing with data aggregation, and localization.Macroscope of redwood 22 is a case study of a WSN that monitors and records the redwood trees in Sonoma, California. Each sensor node measures air temperature, relative humidity, and photo-synthetically-active solar radiation.
Sensor nodes are placed at different heights of the tree. Plant biologists track changes of spatial gradients in the microclimate around a redwood tree and validate their biological theories.Semiconductor plants and oil tanker application reported in 23 focus on preventive equipment maintenance using vibration signatures gathered by sensors to predict equipment failure. Based on application requirements and site survey, the architecture of the network is developed to meet application data needs. Two experiments were carried out: the ?rst was in a semiconductor fabrication plant and the second on an onboard oil tanker in the North Sea. The goal was to reliably validate the requirements for industrial environments and evaluate the effect of the sensor network architecture. The study also analyzed the impact of platform characteristics on the architecture and performance of real deployment.Underwater monitoring study in 24 developed a platform for underwater sensor networks to be used for longterm monitoring of coral reefs and ?sheries.
The sensor network consists of static and mobile underwater sensor nodes. The nodes communicate via point-to-point links using high speed optical communications. Nodes broadcast using an acoustic protocol integrated in the TinyOS protocol stack. They have a variety of sensing devices, including temperature and pressure sensing devices and cameras. Mobile nodes can locate and move above the static nodes to collect data and perform network maintenance functions for deployment, re-location, and recovery. The challenges of deploying sensors in an underwater environment were some key lessons from this study.MAX 25 is a system for human-centric search of the physical world. MAX allows people to search and locate physical objects when they are needed.
It provides location information reference to identi?able landmarks rather than clear and detailed coordinates. MAX was designed with the objectives of privacy, efficient search of a tagged object, and human-centric operation. MAX uses a hierarchical architecture that requires objects to be tagged, sub-stations as landmarks, and base-station computers to locate the object. Tags on objects can be marked as private or public which is searchable by the public or owner only. MAX is designed for low energy and minimal-delay queries. The implementation of MAX was demonstrated using Crossbow motes where trials were conducted in a room of physical objects.
Connection-less sensor-based tracking system using witness (CenWits) 26 is a search-and-rescue system designed, implemented and evaluated using Berkeley Mica2 sensor motes. The system uses several small radio frequencies (RF)-based sensors and a small number of storage and processing devices. CenWits is not a continuously-connected network.
It is designed for intermittent network connectivity. It is comprised of mobile sensors worn by subjects (people), access points that collect information from these sensors and GPS receivers, and location points to provide location information to the sensors. A subject will use the GPS receivers and location points to determine its current location. The key concept is the use of witnesses to convey a subject’s movement and location information to the outside world. The goal of CenWits is to determine an approximate small area where search-and-rescue efforts can be concentrated.Cyclops 27 is a small camera device that bridges the gap between computationally-constrained sensor nodes and complimentary metal-oxide semiconductor (CMOS) imagers. This work provides sensor technology with CMOS imaging.
With CMOS imaging, humans can (1) exploit a different perspective of the physical world which cannot be seen by human vision, and (2) identify their importance. Cyclops attempts to interface between a camera module and a lightweight sensor node. Cyclops contains programmable logic and memory circuits with high speed data transfer. It contains a micro-controller to interface with the outside world. Cyclops is useful in a number of applications that require high speed processing or high resolution images. WSN in a petroleum facility 28 can reduce cost and improve efficiency. The design of this network is focused on the data rate and latency requirement of the plant.
The network consists of four sensor node and an actuator node. The sensor nodes are based on T-mote sky devices 29. Two AGN1200 pre-802.11N Series MIMO access points 30 are used to create an 802.11b 2.4 GHz wireless local area network. In this multi-hop WSN, the T-mote sky devices send their radio packets to the base station which is forwarded to a crossbow stargate gateway. The crossbow stargate gateway translates the radio packets and sends it along the Ethernet MIMO to a single board TS-3300 computer 31.
The single board TS-3300 computer outputs the sensor data to the distributed control system. The distributed control system can also submit changes to the actuator. In this study, results of network performance, RSSI and LQI measurement and noise were gathered. Results show that the effect of latency and environmental noise can signi?cantly affect the performance of a WSN placed in an industrial environment.
Volcanic monitoring 32 with WSN can help speed up deployment, installation, and maintenance process. WSN equipment are smaller, lighter, and consume less power. The challenges of a WSN application for volcanic data collection include reliable event detection, efficient data collection, high data rates and sparse deployment of nodes. Given these challenges, a network consists of 16 sensor nodes was deployed on VolcanReventador in northern Ecuador. Each sensor node is a T-mote sky device 29 equipped with an external omni-directional antenna, a seismometer, a microphone, and a custom hardware interface board. Of the 16 sensor nodes, 14 sensor nodes are equipped with a single axis Geospace Industrial GS-11 Geophone with corner frequency of 4.5 Hz while the other two sensor nodes carried triaxialGeospace Industries GS-1 seismometers with corner frequencies of 1 Hz.
The custom hardware interface board was designed with four Texas Instruments AD7710 analog-to-digital converters to integrate with the T-mote sky devices. Each sensor node draws power from a pair of alkaline D cell batteries. Sensor nodes are placed approximately 200–400 m apart from each other. Nodes relay data via multi-hop routing to a gateway node. The gateway node connected to a long-distance FreeWave radio modem transmits the collected data to the base station. During network operation, each sensor node samples two or four channels of seismoacoustic data at 100 Hz.
The data is stored in local ?ash memory. When an interesting event occurs, the node will route a message to the base station. If multiple nodes report the same event, then data is collected from the nodes in a round-robin fashion. When data collection is completed, the nodes return to sampling and storing sensor data locally.In the 19 days of deployment, the network observed 230 eruptions and other volcanic events. About 61% of the data was retrieved from the network due to short outages in the network from software component failure and power outage. Overall, the system performed well in this study.
Health monitoring applications 33 using WSN can improve the existing health care and patient monitoring. Five prototype designs have been developed for applications such as infant monitoring, alerting the deaf, blood pressure monitoring and tracking, and ?re-?ghter vital sign monitoring. The prototypes used two types of motes: T-mote sky devices 29 and SHIMMER (Intel Digital Health Group’s Sensing Health with Intelligence, Modularity, Mobility, and Experimental Re-usability).Because many infant die from sudden infant death syndrome (SIDS) each year, Sleep Safe is designed for monitoring an infant while they sleep. It detects the sleeping position of an infant and alerts the parent when the infant is lying on its stomach. Sleep Safe consists of two sensor motes.
One SHIMMER mote is attached to an infant’s clothing while a T-mote is connected to base station computer. The SHIMMER node has a three-axis accelerometer for sensing the infant’s position relative to gravity. The SHIMMER node periodically sends packets to the base station for processing.
Based on the size of the sensing window and the limit set by the user, the data is processed to determine if the infant is on their back.Baby Glove prototype is designed to monitor the important internal organs of the body. Baby Glove is a swaddling baby wrap with sensors that can monitor an infant’s temperature, hydration, and pulse rate. A SHIMMER mote is connected to the swaddling wrap to transmit the data to the T-mote connected to the base station. Like Sleep Safe, an alert is sent to the parent if the analyzed data exceeds the health settings.FireLine is a wireless heart rate sensing system.
It is used to monitor a ?re ?ghter’s heart rate in real-time to detect any abnormality and stress. FireLine consist of a Tmote, a custom made heart rate sensor board, and three re-usable electrodes. All these components are embedded into a shirt that a ?re ?ghter will wear underneath all his protective gears. The readings are taken from the T-mote and is then transfer to another T-mote connected to the base station. If the ?re ?ghter’s heart rate is increasing too high, an alert is sent.
[email protected] is a wireless blood pressure monitor and tracking system. [email protected] uses a SHIMMER mote located inside a wrist cuff which is connected to a pressure sensor. The user’s blood pressure and heart rate is computed using the oscillometric method. The SHIMMER mote records the reading and sends it to the T-mot connected to the user’s computer. A software application processes the data and provides a graph of the user’s blood pressure and heart rate over time.LISTSENse enables the hearing impaired to be informed of the audible information in their environment.
A user carries the base station T-mote with him. The base station T-mote connected to consists of a vibrator and LEDs. Transmitter motes are place near objects (e.
g., smoke alarm and doorbell) that can be heard. Transmitter motes consist of an omni-directional condenser microphone. They periodically sample the microphone signal at a rate of 20 Hz. If the signal is greater than the reference signal, an encrypted activation message is sent to the user. The base station T-mote receiving the message actives the vibrator and its LED lights to warn the user.
The user must press the acknowledge button to deactivate the alert.ZebraNet9 system is a mobile wireless sensor network used to track animal migrations. ZebraNet is composed of sensor nodes built into the zebra’s collar. The node consists of a 16-bit TI microcontroller, 4 Mbits off-chip ?ash memory, a 900 MHz radio, and a GPS unit.
Positional readings are taking using the GPS and sent multi-hop across zebras to the base station. The goal is to accurately log each zebra’s position and use them for analysis. A total of 6–10 zebra collars were deployed at the Sweetwaters game reserve in central Kenya to study the effects and reliability of the collar and to collect movement data. After effective use of the collar, the biologists observed that the collared zebras were affected by the collars. They observed additional head shakes from those zebra in the ?rst week. After the ?rst week, the collared zebra show no difference than the uncollared zebra. A set of movement data was also collected during this study.
From the data, the biologists can better understand the zebra movements during the day and night.