Applications in Medical Care

Overview of Wireless Sensor Networks

Presenter: Professor Carlos Pomalaza-RáezDecember 11, 2007

The Second International Symposium on Medical Information and Communication Technology

ISMICT 07

Topics

11

1

2

345

6

7

8910

11

12

13

14

16

15

A

B

C

D

EF

17

18

19

20

Overview of Medical Applications Sensor Node Architecture IEEE 802.15.4

Routing Coverage Localization

Stage Model of the Medical Practice

New and better medical devices are continuously introduced to detect vital signals and present them in a suitable format for healthcare givers

The interpretation can be regarded as a data compression and data conformity process

The physicians make a treatment prescription based on the patient’s medical history and current clinical reports by consulting the evidence-based database, pharmaceutical handbook and other resources

Y. Shieh, et al., “Mobile Healthcare: Opportunities and Challenges,” Proceedings of Sixth International Conference on the Management of Mobile Business, July 9-11, 2007, Toronto, Ontario, Canada

Healthcare Wireless Network Expansion

Each day more and more equipment is going “wireless” from pulse-oximeters to more complex patient vital signs monitors and ventilators

Environments must scale from a few clients to 100’s on a single subnet

External factors such as nearby TV and radio stations can affect overall performance.

Interoperability profiles and standards are required to ensure plug-and-play operation in heterogeneous environments

E. Sloane, et al., “Safety First! Safe and Successful Digital Network Wireless Medical Device Systems,” 2006 HIMSS Annual Conference February, San Diego

IP Convergence

RFID reader

Access point

RFID « Tag »

Bedside PC

PC

PDA

Telephone IP

GSM

ScannerBiomedical equipment

Application servers

Wireless phone

Camera Video

PACSServer

W-Fi

Wi-Fi

Laptop

WiMax

Integration of data, voice, image , video on a single traffic network based on the Internet protocol Eliminates the maintenance of a parallel voice network Decreases considerably the expenses on phone calls and fax transmissions Interoperability of networks, applications and devices used in information technology Allows the reuse of the existing data-processing infrastructure

Mobile Devices

Wired networkHospital systems

Laptop

Cellular phones

PDA (Personnel digital assistant)

Wireless router (Wi-Fi)

Computer on wheels

PACSRadiology

LabPharmacyEtc.

Patientrecord

Tablet PC

Facilitates the mobility of doctors, practitioners and caregivers Allows access to patient information at any moment, everywhere and on real time Improves automatic data gathering through barcode or RFID reading Allows the immediate sharing of patient information and results Improves the internal communication within the caregiver team and with the support staff Helps to reduce paper

Room Topology

D. Cypher, et al., “Prevailing over Wires in Healthcare Environments: Benefits and Challenges,” IEEE Communications Magazine, pp. 56-63, April 2006

Medical information collected by sensors on the patient’s body (WPAN) is displayed on a bedside monitor

This information is also transmitted to another hospital location for remote monitoring, e.g., a nurses’ station)

In case of emergency, when the patient is moved from his/her room to the intensive care unit, these communications need to be maintained

Radio frequency identification

Patient Identification and tracking

Equipment localization and tracking

Pharmaceutical product management

Medical and chirurgical equipment tracking

Mobile reader

Wall-mounted reader

RFIDServer

Fixed reader

Access point

Bracelet

Facilitates the management of assets (wheel chairs, scanners, ambulatory equipment, etc)

Improves patient localization and helps caregivers to provide services without delays

Enhances the process of drug administration (identification, distribution, localization, returns and disposal)

Facilitates the automatic data capture and the follow-up of blood and biological samples

Telemedicine

Specialist

DoctorPatient

Researcher

Utilization of different assets independent of their geographical location Multidisciplinary collaboration Facilitates the dissemination of medical knowledge to practicing doctors and medical students Allows doctors in remote and rural areas to consult with specialists in urban areas

Remote monitoring

Data capture Mobile device

GSMGPRSWiMax

Real-time patient monitoring

Mobile monitoring platform

Internet

Reduce the number of patients transferred to urban hospitals

Allows tele-consultation and tele-diagnosis including the option of obtaining opinions of distant experts

Facilitates the patient remote monitoring with instantaneous data transmission for analyses and follow-ups

Allows remote handling of medical equipment (tele-surgery) and direct action of the expert on the patient

Improves coordination of first-responders workers during in the event of catastrophes or emergency cases

Wireless Body Area Network

E. Jovanov, et al., “A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation,” Journal of NeuroEngineering and Rehabilitation, 2005, 2:6

Wireless Body Area Network

The personal server can be implemented on an Internet-enabled PDA or a 3G mobile phone, or a regular laptop of desktop computer. It can communicate with remote upper-level services in a hierarchical type architecture. Its tasks include:

Initialization, configuration, and synchronization of WBAN nodes

Control and monitor operation of WBAN nodes

Collection of sensor readings from physiological sensors

Processing and integration of data from the sensors

Secure communication with remote healthcare provider

Wearable Monitoring Systems

Fabric electrodes have been used to monitor EKG and respiratory activity

M. Pacelli, et al., “Sensing Fabrics for Monitoring Physiological and Biomechanical Variables: E-textile solutions,” 3rd IEEE-EMBS International Summer School and Symposium on Medical Devices and Biosensors, Boston, Sept.4-6, 2006

Biomedical Measurements

S. Arnon, et al., “A Comparative Study of Wireless Communication Network Configurations for Medical Applications,” IEEE Wireless Communications, pp. 56-61, February 2003

Wireless Technologies

Clinical Data vs Wireless Technologies

Biomedical Data Type Typical File Size

EKG recording Electrical signal 100 kB

Electronic Stethoscope Audio 100 kB

X-Ray Still image 1 MB

30s of ultrasound image Moving image 10 MB

Technology Data Rate Frequency Spectrum

GSM 9.6 kbps 900/1800/1900 MHz

GPRS 171.2 kbps 900/1800/1900 MHz

EDGE 384 kbps 900/1800/1900 MHz

3G/UMTS 2 Mbps 1885 MHz – 2200 Mhz

M. Fadlee, et al., “Bluetooth Telemedicine Processor for Multichannel Biomedical Signal Transmission via Mobile Cellular Networks,” IEEE Transactions on Information Technology in Biomedicine, pp. 35-43, March 2005

Framework for Medical Image AnalysisThe remote medical image repositories communicate through different types of network connections with the central computing site that coordinates the distributed analysis.

V, Megalooikonomou, et al., “Medical Data Fusion for Telemedicine,” IEEE Engineering in Medicine and Biology Magazine, pp. 36-42, September/October 2007

DICOMThe Digital Imaging and Communications in Medicine (DICOM) standard is created by the National Electrical Manufacturers Association (NEMA) to aid the distribution and viewing of medical images. DICOM is the most common standard for receiving scans from a hospital.

A single DICOM file contains both a header (which stores information about the patient’s name, the type of scan, image dimensions, etc), and all of the image data

DICOM images can be compressed both by the common lossy JPEG compression scheme as well as a lossless JPEG scheme

A single 500-slice MRI can produce a 68 MB image file

Transmission of DICOM ImagesThe time values represent the total time, i.e. computing time (compression algorithm on each side of the communication link) plus the transmission time

H. Lufei, et al., “Communication Optimization for Image Transmission in Computer-Assisted Surgery,” Proceedings of 2004 Congress of Neurological Surgeons, October 16-21, San Francisco, California

Activity Sensors

They can be useful in monitoring patients undergoing physical rehabilitation such as after a stroke

The Pluto custom wearable designed at Harvard incorporates the TI MSP430 microprocessor and ChipCon CC 2420 radio

Pluto can run continuously for almost 5 hours on a rechargeable 120 mAh lithium battery

It has a Mini-B USB connector for programming and to recharge the battery

The software runs under TinyOS

V. Shnayder, et al., “Sensor Networks for Medical Care,” Technical Report TR-08-05, Division of Engineering and Applied Sciences, Harvard University, 2005.

Pulse Oximeter

Non-invasive technology used to measure the heart rate (HR) and blood oxygen saturation (SpO2)

The technology used is to project infrared and near-infrared light through blood vessels near the skin

By detecting the amount of light absorbed by hemoglobin in the blood at two different wavelengths the level of oxygen can be measured

The heart rate can also be measured since blood vessels contract and expand with the patient’s pulse which affects the pattern of light absorbed over time

Computation of HR and SpO2 from the light transmission waveforms can be performed using standard DSP algorithms

Pulse Oximeter

Smiths Micro Power Oximeter Board Length: 39 mm

Width: 20 mmHeight: 5.6 mm

6.6 mA at 3.3 V, typical power:22 mW Pulse range: 30-254 bpm

SpO2: 0 to 99%

Data is transmitted from the oximeter board at a rate of 60 packets per second (5 bytes per packet)

Minolta Pulsox-2 Size: W69xH60xD28 mm Weight: approx. 70g (with 2 AAA

batteries)

Electrocardiograph (EKG)

The most common type of EKG involves the connection of several leads to a patient’s chest, arms, and leg via adhesive foam pads. The device records a short sampling, e.g. 30 seconds, of the heart’s electric activity between different pairs of electrodes

When there is need to detect intermittent cardiac conditions a continuous EKG measurement is used. This involve the use of a two- or three-electrode EKG to evaluate the patient’s cardiac activity for an extended period

The EKG signal is small (~ 1mV peak-to-peak). Before the signal is digitized it has to be amplified (gain > 1000) using low noise amplifiers and filtered to remove noise

Electrocardiograph

The P wave is associated with the contractions of the atria (the two chambers in the heart that receive blood from outside)

The QRS is a series of waves associated with ventricular contractions (the ventricles are the two major pumping chambers in the heart)

The T and U waves follow the ventricular contractions

Electrocardiograph

IMEC has recently developed a wireless, flexible, stretchable EKG patch for continuous cardiac monitoring

Placed on the arm or on the leg the same system can be used to monitor muscle activity (EMG)

The patch includes a microprocessor, a 2.4 GHz radio link and a miniaturized rechargeable lithium-ion battery

The total size is 60x20 mm2

Data is sampled between 250 and 1000 Hz an continuously transmitted

The battery has a capacity of 175 mAh which provides for continuous monitoring from one day to several days

EKG Signals

Various sampling rates and quantization levels are used when EKG signals are digitized

In practice sampling frequencies between 128 Hz and 256 Hz are used

The higher sampling rates and bit rates, e.g. 16 bits, are used to characterize EKG in sufficient detail

Interoperability

Products to link medical equipment and personal communication devices exist as well

However, virtually all of these are specialized applications—custom interfaces unique to the two devices being linked

To address the medical device plug-and-play interoperability problem, a single communications standard is needed.

There is need for intercommunication among medical devices and clinical information systems. This has been accomplished with a number of medical products. Infusion pumps and ventilators commonly have RS-232 ports, and these devices can communicate with many physiological monitoring instruments.

IEEE 1073.3.5 Project

Transport standards associated with wireless data transport from IEEE 1073 point-of-care medical devices (POC) using personal area (WPAN), local area (WLAN), wide area (WWAN), and other networks

It will make specific recommendations on the use of WPAN, WLAN, and WWAN wireless networks to facilitate medical data transport in various healthcare settings

Specifically, technology protocols will be recommended to facilitate plug-and-play compatibility between (POC) medical devices and wireless networks to an end server or attending healthcare professional

Medical data may range from non-critical to critical parameters, and expected quality of service (i.e., data throughput, latency, fidelity, network coverage) and acceptable performance parameters

MEDICAL INFORMATION BUS (MIB)

MIB is published by the IEEE as the IEEE 1073 standard and follows the ISO OSI seven-layer communications model

MIB is the name for a series of standards of connectivity between critical care bedside medical devices and hospital computer equipment

Examples of these devices are: ventilators, pulse oximeters, patient monitors

The heart of the MIB is the interface between the bedside communications controller (BCC) and one or more device communication controller (DCC)

A medical device can function as both as BCC and DCC, i.e. a bedside monitor can be a BCC connected to a ventilator and an infusion pump DCCs, while at the same time it can be a DCC connected to a clinical information system acting as BCC

MIB

MIB – Logical Interface

ACSE: Association control service

ROSE: Remote operation service element

CMDISE: Common medical device information service element

MDIB: Medical data information base

HL7

Health Level 7 (HL7) standard is designed to enable different health care applications to exchange clinical and administrative data

The most recent version of the HL7 specification uses XML messaging as its foundation

HL7 also allows the use of trigger events, i.e. when a patient’s EKG waveform is available causes a request for that observation data to be sent to another information system

CodeBlue Infrastructure

An ad hoc WSN Infrastructure for emergency medical care It is based on a publish/subscribe model for data delivery Designed to scale across a wide range of network densities an to operate

on a range of wireless devices

D. Malan, et al., “CodeBlue: An Ad Hoc Sensor Network Infrastructure for Emergency Medical Care,” Intl. Workshop on Wearable and Implantable Body Sensor Networks, April 2004.

Architecture of a Sensor Node

Sensors Everywhere

Some current issues:

There are already many deployed sensors

– Mobile phones

– Surveillance cameras

– GPS receivers

– Motion and light sensors

How to organize them in networks

How to retrieve, store, and index data from sensors

Change the attention from “network” to “data”

Combine data processing with data delivery

The Traditional WSN Myth

The wireless sensor network paradigm was a myth from the late 1990s

Usual “assumptions”:

– 1000s of homogeneous “sensing only” nodes

– Mesh routing all nodes

This market is marginal Sink

Luckily, the ideas and algorithms that were developed can be applied to ubiquitous wireless applications

Huge research and market potential

Convergent WSNs

Convergent WSNs have real potential

Hierarchical part of other networks such as B3G

Ubiquitous embedded devices go wireless– Control & sensing– Ubiquitous services

Convergent WSNs

How do we integrate the Internet and Intranets with sensor networks?

– Where is the intelligence?

– Heterogeneous protocol interfaces?

– Scalability and security are important issues

– Mobility support

Gateways play an important role, as they communicate with TCP/IP and sensor networks

– A proxy application often used to translate and shield one level from another

Convergent WSNs

Looking at the sales of uCs, there is a potential for billions of networked devices – much larger than the Internet itself

Huge impact also on the core Internet

– IPv6 will be key to supporting convergent sensor networks

– Intelligent data processing to reduce the network traffic

Embedded Meets Wireless

Microcontrollers are everywhere in embedded systems

– appliances, watches, toys, cameras, industrial control, mobile phones, sensors, cars, automation

Microcontroller vs. microprocessor market

– 15 x more microcontroller units sold yearly (8 billion)

– 20 billion vs 43 billion USD market by 2009

Embedded Meets Wireless

Possibilities of wireless applications are endless

– Projected sales of 802.15.4 chips are 150 million units by 2009

Embedded systems have special characteristics

Academic community very computer science and protocol driven, often ignoring

– Physical layer realities

– Embedded system operation

– Real-time capabilities

Device Architecture

Microcontroller and program code Power supply

– Power management– Renewable energy?

Memory (RAM, FLASH) Sensors Actuators Communication Input/output Part of larger system?

Microcontroller

Main processing units of embedded devices Special purpose and highly integrated

– Integrated RAM, ROM, I/O, peripherals

– Extremely good power to performance ratio

– Cheap, typically 0.25 - 10.00 USD Executes programs including embedded system control,

measurement & communications– Usually time-critical requiring guarantees deadlines

– Real-time performance a must in most applications• Pre-emptive scheduled tasks• Queues and semaphores

MSP430 Texas Instruments

mixed-signal uC 16-bit RISC ROM: 1-60 kB RAM: Up to 10 kB Analogue

– 12 bit ADC & DAC

– LCD driver Digital

– USART x 2

– DMA controller

– Timers

ATmega AVR

Atmel AVR family 8-bit RISC RAM: Up to 4 kB ROM: Up to 128 kB Analogue

– ADC– PWM

Digital– USARTs– Timers

ATmega AVRCurrent consumption

VCC = 3 V is: The current consumption is a function of several factors such as: operating voltage operating frequency loading of I/O pins switching rate of I/O pins code executed ambient temperature

The dominating factors are operating voltage and frequency

Mode Current

Active (4 MHz) 5.5 mA

Idle (4 MHz) 2.5 mA

Power-downWDT enabled 25 µA

Power-downWDT disabled 10 µA

WDT = Watchdog Timer

In the past the static power has been assumed as very small when compared with . This is no longer possible as the CMOS technology is moving in the deep sub-micron range, e.g. 0.15 μm and smaller. These devices have large leakage currents which increases the amount of

Power Management

Power dissipation in CMOS systems modeled as

dynP

Ptotal Pdyn Pstat

Pstat V 2

Pdyn f V 2Dynamic power depends on the switching behavior and the frequency of the circuit

Static power (also called leakage component) depends only on the operational voltage

statP

Dynamic voltage scaling (DVS) is a standard technique for managing the power consumption of a system. In particular for CMOS circuits the power consumption, P, is proportional to the core voltage V and the frequency f,

Power Management

2VfPdyn

The number of clock cycles needed to complete a computation is independent of the core frequency which means that the execution time is inversely proportional to the frequency. The total energy, E, is then proportional to the square of the voltage,

2VEdyn

Dynamic Voltage Scaling

Implementing an effective DVS system requires:

A variable power supply capable of a high voltage transition rate and minimum transition losses

A wide operational voltage range

A power scheduler that effectively computes the appropriate frequency and voltage levels needed to execute the various tasks

Dynamic Voltage Scaling

The scheduler responsibilities include deciding when the processor can reduce its power and by how much.

Its implementation assumes a preemptive operating system. This is not possible or difficult to implement in the small operating systems (OS) developed for microcontrollers used in WSN applications.

These small OSs operate on an interrupt-driven policy and no “overseeing” program knows what other parts are doing.

The implementation becomes even more complicated when the application requires the use of a real-time operating system.

Dynamic Voltage Scaling

Experimental work on the effectiveness of DVS shows that:

Static power consumption is a significant contributor, in particular for the case of the controller memory

The relationship between of power and the voltage and frequency is not as simple as the equations above imply

The best way to set the proper frequency and voltage is to take measurements at run-time, e.g. while the application is running

Since an alternate way to manage the energy consumption is to change the clock frequency. Typical applications do not require a constant workload; they are event driven and are supposed to switch between active and idle modes. Microcontrollers such as the TI MSP 430 have a flexible clock system which can facilitate energy saving policies. This clock system provides for:

Dynamic Clock

Low clock frequency for energy conservation modes such as when in an idle state and for time keeping

High clock frequency for fast reaction to events and fast burst processing capability

2VfPdyn

The low clock frequency is available via a low-power 32,768-Hz watch crystal, the high clock frequency signal can be available from the on-chip digitally controlled oscillator (DCO) or from a crystal.

Memory Random access memory (RAM)

– Included on-board in microcontrollers– Often the most valuable resource

Read-only memory (ROM)– Usually actually implemented with NOR flash memory

Flash– Erasable programmable memory – Can be read/written in blocks– Slow during the write process– Consumes power of course!

External memory– External memory supported by some microcontrollers– Serial flash always supported

Flash Memory

Flash memory is very suitable to WSN applications because of its low-energy consumption, small size, and capacity. There are different types of flash memory depending on the underlying cell technology:

NOR – The read-only mode of NOR memories is similar to reading from a common memory. NOR flash memories can be used as execute-in-place memory (XIP). They are less dense that the NAND memories and use more energy for erase and programming.

NAND – Cannot provide execute-in-place. They are accessed much like block devices such as hard disks or memory cards. Associated with each block are a few bytes that should be used for storage of an error detection and correction block checksum. They are less reliable than NOR memories thus the need of error correcting codes (ECC)

Flash MemoryAfter writing a memory location in a flash memory it must be reset or erased before it can be written again. This process is relatively slow and energy consuming and can only be performed on fixed-sized regions known as erase blocks.

Energy per bit (μJ)

Read Write Erase Atmel NOR AT45DB041B (512 KB) 0.26 4.30 2.36 Hitachi MMC (NAND) HB28D032MM2 (32 MB) 0.06 0.58 0.47 From the perspective of energy consumption the number of write and erase operations should be minimized and properly managed. The proper choice of the flash technology plays an important role, NAND technology is substantially more efficient that the NOR technology.

Common Interfaces Digital and analogue I/O

– Accessed by port and pin number (e.g. P1.3)– Some pins are also connected to interrupts

UART– Asynchronous serial bus – After level translation it is an RS232 bus– Usually kbps up to 1 mbps

SPI (serial peripheral interface)– Synchronous serial bus– Reliable with speeds of several Mbps

I2C (inter-integrated circuit) bus– 2-wire bus with data and clock

Parallel bus– Implemented with X-bit width– X-bit address and clock signals

Transceivers Modern embedded communications chips are transceivers: they

combine half-duplex transmission and reception. Transceivers integrate varying functionality, from a bare analogue

interface to the whole digital baseband and key MAC functions.

Relevant Transceiver Characteristics

Level of digital integration Power consumption and efficiency

– Transition speeds and consumption

– Levels of sleep Carrier frequency and data rate Modulation Error coding capabilities Noise figure and receiver sensitivity Received signal strength indicator (RSSI) Support for upper layers Data and control interface characteristics

RFM TR1000

Proprietary radio at 916 MHz OOK and ASK modulation 30 kbps (OOK) or 115.2 kbps (ASK) operation Signal strength indicator Provides bit interface Not included:

– Synchronization– Framing– Encoding– Decoding

Sleep Tx Rx

0.7 uA 12 mA 3.8 mA

Current Consumption CharacteristicsOperating voltage: 2.2 – 3.7 Vdc

CC2420 IEEE 802.15.4 compliant radio 2.4 GHz band using DSSS at 250 kbps Integrated voltage regulator Integrated digital baseband and MAC functions

– Clear channel assessment– Energy detection (RSSI)– Synchronization– Framing– Encryption/authentication– Retransmission (CSMA)

Current Consumption CharacteristicsOperating voltage: 2.1 – 3.6 Vdc

Sleep Idle Tx Rx

20 μA 426 μA 8.5 – 17.4 mA 18.8 mA

Power Consumption To arrive to an energy efficient design all the components of a

WSNs have to be taken into account, in particular how they interact with each other. An isolated energy optimization in a subsystem might not yield the overall expected savings.

Power output level (transceiver)

– Limited savings effect

– Optimal power difficult

– Must be considered globally

Transition times

– Each transition costs

– Power equal to RX mode

– Should be accounted for

Power Consumption

These numerical values suggest that in order to minimize the energy consumption there is not need of a fine power control mechanism since the power used does not change significantly for a large range of RF output power levels.

Output power control however cannot be completely ignored. In a multiple Tx and Rx scenario the power of the transmitted signal has substantial effect on the network topology and consequently in related issues such as multiuser interference and end-to-end delay and throughput.

Power Consumption

Transition times between different transceiver modes need also to be taken into account. During wake-up time and the turn-around times (from Tx to Rx and vice versa), the radio consumes as much power as during a receive mode.

Power Consumption sleepsleepidleidleRxRxTxTxupRxRxupTxTxRxupwkRx

Favg TPTPTPTPTNTNPTP

TP )(

1

Wakeup Time Effects

The amount of time and power needed to wake-up (start-up) a radio is not negligible

When start-up energy consumption is taken into account the energy per bit requirements can be significantly higher for the transmission of short packets than for longer ones

TR 1000 (115kbps)

0

10

20

30

40

50

60

10 100 1000 10000

Packet Size (bits)

Eb

it ( p

J )

Sensors & Actuators

Sensors measure real-world phenomena and convert them to electrical form

– Analogue sensors require an ADC

– Digital sensors use e.g. I2C or SPI interfaces

– Human interface can also be a sensor (button)

IEEE 1451 standard becoming important

– Transducer interface to networks, systems, and instruments

– Defines standard interfaces and autoconfiguration

– Also some protocol specifications

Actuators convert an electrical signal to some action

– Analogue and digital interfaces both common

– A motor servo is a good example

Real-time Operating Systems

Operating system manages hardware and software computer resources

Embedded systems have pre-defined tasks

– Designed to optimize size, cost, efficiency etc. Real-time

– Real-time OS provides tools to meet deadlines

– Pre-emptive, queues, semaphores Concurrency

– Execution flows (tasks) able to run simultaneously

– Threads and processes Sockets and APIs

Real-time Issues

Wireless embedded systems usually are real-time

– Watch, robot, building sensor, control node A RTOS only facilitates real-time system creation

– Still requires correct software development RTOS is not necessarily high performance

– Can meet general system deadlines (soft real-time)

– or deterministically (hard real-time) Deadlines can be met using

– Specialized pre-emptive scheduling algorithms

– Proper inter-task design & communication

– Semaphores and queues to avoid racing

Real-time Issues

Task1

Task2

Task3

T1 T2 TN

T1 T2 TN

Task1

Task2

Task3 time

Task1

Task2

Task3

Lock resource A

Try access resource A

Unlock resource A

Concurrency

Concurrency occurs when two or more execution flows run simultaneously

It introduces many problems such as – Race conditions from shared resources– Deadlock and starvation

OS needs to coordinate between tasks– Data exchange, memory, execution, resources

There are two main techniques– Process based

• CPU time split between execution tasks

– Event based

Concurrency

Process based

Event based

Programming Interfaces

Application Programming Interface– Need to be well planned, especially in embedded systems– APIs to hide hardware specifics– API to the protocol stack– API to middleware components

Sockets– Software construct allowing communications between hosts or

processes– The BSD Socket API the most common network programming

construct– Used in NanoStack for accessing the protocol stack

OS Examples

Example embedded operating systems– Contiki (www.sics.se/~adam/contiki)– FreeRTOS (www.freertos.org)– TinyOS (www.tinyos.org)– Ambient RT (www.ambientsystems.com)– and thousands of others...

For higher powered MCUs (e.g. ARMs)– VX Works– Microcontroller Linux– Windows CE– Symbian

IEEE 802.15.4

IEEE 802.15.4

Standard for home networking, industrial control and building automation. It defines the physical layer (PHY) and the MAC sublayer specifications for supporting simple devices that consume minimal power and operate in the personal operating space of 10 meters.

Main characteristics

Data rates of 250 Kb/s, 40 Kb/s, and 20 Kb/s

Star or peer-to-peer operation

Support for low latency devices

CSMA-CA channel access

Low power consumption

IEEE 802.15.4Operating Frequency Bands

IEEE 802.15.4Parameters

Transmit Power At least 1 mW

Transmit Center Frequency Tolerance ±40 ppm

Receiver Sensitivity -85 dBm @ 2.4 GHz band -92 dBm @ 868/915 band

RSSI Measurements Packet strength indication Clear channel assessment

IEEE 802.15.4Frequency Bands and Data Rates

WLANS and WPANS

ZigBee uses IEEE 802.15.4 services and adds network construction (star networks, peer-to-peer networks, cluster tree networks), security, application services, etc.

Full function device (FFD)– Any topology– Network coordinator capable– Talks to any other device

Reduced function device (RFD)– Limited to star topology– Cannot become a network coordinator– Talks only to a network coordinator– Very simple implementation

IEEE 802.15.4

Device Classes

Full function device

Reduced function device

Communications flow

Master/slave

PANCoordinator

IEEE 802.15.4 Star Topology

Full function device Communications flow

Point to point Cluster tree

IEEE 802.15.4 Peer-to-Peer Topology

Full function device

Reduced function device

Communications flow

Clustered stars - for example,cluster nodes exist between roomsof a hotel and each room has a star network for control.

Combined Topology

IEEE 802.15.4 Optional Superframe Structure

4 Types of MAC Frames:

• Data Frame

• Beacon Frame

• Acknowledgment Frame

• MAC Command Frame

IEEE 802.15.4 MAC Frame Structure

Routing

Network Layer

Network Layer

Physical

Data Link

Network

Transport

ApplicationBasic issues: Power efficiency Data centric – The nature of the data determines the traffic

flow Publish/subscribe paradigm Data aggregation – To manage the potential implosion of

traffic because of the data centric routing Rather than conventional node addresses, use attribute-

based addressing, e.g. “region where humidity is below 5%”

Localization systems, i.e. ability of the nodes to establish position information

Internetworking with external networks via gateway or proxy nodes

Data Centric

In WSNs applications the identity of the node is not important compared with the information being reported

The same event could be sensed and reported by several nodes, from the application point of view is of no concern which of these nodes is reporting the data

The fact that the data is the center of attention is what makes for a data-centric networking

Data centrality brings in different networking architectures such as data-centric addressing and data fusion and aggregation

These architectures also allowed for improved energy efficiency since they scale better by being implementable using mainly local information about the direct neighbors

Data Centric Sensor nodes advertise sensed data and wait for a request from the

interested nodes

Flow of the advertisement

Data Aggregation

Data coming from multiple sensor nodes are aggregated when they reach a common routing or relaying node on their way to the sink

Aggregation takes place if the data arriving the common node have same attributes of the phenomenon being sensed

Phenomenon being sensed

Routing

Multihop routing is common due to limited transmission range

Phenomenonbeing sensed

Sink

• Limited node mobility• Power aware• Irregular topology• MAC aware• Limited buffer space

Some routing issues in WSNs

Data aggregationtakes place here

Publish/Subscribe

A node interested in a given kind of data can subscribe to it Any node can publish data, along with information about it Upon publication of some type of data all subscribers to that kind of

data are notified An very important issue is the use of appropriate “data descriptors”

that are used to match publications and subscriptions The content-based publish/subscribe approach is a naming scheme

that allows to formulate the matching conditions between subscriptions and publications

Meets the need to be able to express the need for certain data and the delivery of the data

RoutingProblem – How to efficiently route:

Data from the sensors (or publishers) towards the sink (or subscribers) and,

Queries and control packets from the sink (or subscribers) towards the sensor nodes (or publishers)

Flooding

Flooding is a very simple technique that can be used to disseminate information across a network. Each node sends an incoming packet to all its neighbors and thus the packet is sure to arrive its intended destination. It has severe drawbacks such as,

Implosion – Duplicated messages are sent to the same node

Overlap – Two or more nodes share the same observing region, they may sense the same stimuli at the same time. As a result, neighbor nodes receive duplicated messages

Resource blindness – Not take into account the available energy resources. A promiscuous routing technique such as flooding wastes energy unnecessarily

Gossiping

A variation of flooding attempting to correct some of its drawbacks

Nodes do not indiscriminately broadcast but instead send a packet to a randomly selected neighbor who once it receives the packet it repeats the process

It is not as simple to implement as the flooding mechanism and it takes longer for the propagation of messages across the network

Flooding and Gossiping are variations of routing methods that try to avoid the use of the routing tables that have been used extensively for routing in conventional Ad-Hoc networks

Randomized Forwarding

A key parameter in gossiping-based routing algorithms is the probability with which a node retransmits a newly incoming message. It has been shown(†) that there is a critical probability value below which gossip is not effective and the message reaches a small number of nodes.

For larger probability values that the critical threshold the message reaches most if not all of the nodes in the network.

Gossiping then has a bimodal behavior with the critical threshold value between 0.6 and 0.8. By exploiting this behavior the gossip protocol uses 35% fewer messages than flooding.

(†) Z. Haas, J. Halpern, and L. Li, “Gossip-Based Ad Hoc Routing”, IEEE/ACM Transactions on Networking, Vol. 14, No. 3, pp. 479-491, June 2006

Behavior on a 20x50 grid

Behavior on a 20x50 grid

Behavior on a 20x50 grid

Routing

In addition to the concepts of data aggregation and data centrality, it is important to identify the nature of the WSN traffic, which will depend on the application.

Assuming a uniform density of nodes, the number of transmissions can be used as a metric for energy consumption.

Since receiving a packet consumes almost as much energy as transmitting a packet it is also important that the MAC protocol limits the number of listening neighbors in order to conserve energy.

Routing

If N is the number of nodes, Q the number of queries, and E the number of events, and some type of flooding mechanism is being used then:

If the number of events is much higher than the number of queries it is better to use some type of query flooding since the number of transmissions is proportional to N*Q which is much less than N*E

If the number of events is low compared with the number of queries it is better to use some type of event flooding since now N*E is much less than N*Q

In both cases it is assumed that the “return path” (for the events or the queries) is built during the flooding process

Other underlying routing mechanisms are recommended if the number of events and queries are of the same order

A Data-Centric Routing

SPIN – Sensor Protocols for Information via Negotiation(†) Attempts to correct the major deficiencies of classical flooding, in particular the indiscriminate flow of packets with the related energy waste

SPIN messages ADV- advertise data REQ- request specific data DATA- requested data

Resource management Nodes decide their capability

of participation in data transmissions

A B

A B

A B

ADV

REQ

DATA

(†) W. Heinzelman, J. Kulik, and H. Balakrishnan, “Adaptive Protocols for Information Dissemination in Wireless Sensor Networks,” Proc. 5th ACM/IEEE Mobicom Conference, Seattle, WA, August, 1999.

SPINA mechanism developed for the case where the number of queries is higher than the number of events.

Use information descriptors or meta-data for negotiation prior to transmission of the data

Each node has its own energy resource manager which is used to adjust its transmission activity

The family of SPIN protocols are:

SPIN-PP – For point-to-point communication

SPIN-EC – Similar to SPIN-PP but with energy conservation heuristics added to it

SPIN-BC – Designed for broadcast networks. Nodes set random timers after receiving ADV and before sending REQ to wait for someone else to send the REQ

SPIN-RL – Similar to SPIN-BC but with added reliability. Each node keeps track of whether it receives requested data within the time limit, if not, data is re-requested

A node senses something “interesting”Neighbor sends a REQ listing all of the data it would like to acquireSensor broadcasts dataNeighbors aggregate data and broadcast(advertise) meta-data

SPIN-BCThe process repeats itself across the network

DATAREQADV

It sends meta-data to neighbors

SPIN-BC

I am tired I need to sleep …

Advertise meta-data

Request data

Send dataAdvertise

Advertise

Nodes do need not to participate in the process

Request data

Send data

Send data

Advertise meta-data

Request data

Send data

SPIN

Pros– Energy – More efficient than classic flooding – Latency – Converges quickly– Scalability – Local interactions only– Robust – Immune to node failures

Cons– Nodes always participating– Need of and adequate MAC layer to support an efficient

implementation. The simulation analysis uses a modified 802.11 MAC protocol

Directed Diffusion(†)

A mechanism developed for the case where it is expected that the number of events is higher than the number of queries

Is data-centric in nature

The sink propagates its queries or “interests” in the form of attribute-value pairs

The interests are injected by the sink and disseminated throughout the network. During this process, “gradients” are set at each sensor that receives an interest pointing towards the sensor from which the interest was received

(†) C. Intanagonwiwat, R. Govindan, D. Estrin, and J. Heidemann “Directed Diffusion for Wireless Sensor Networking,” IEEE/ACM Trans. on Networking, February 2003

Directed Diffusion

This process can create, at each node, multiple gradients towards the sink. To avoid excessive traffic along multiple paths a “reinforcement” mechanism is used at each node after receiving data, e.g. reinforce:

Neighbor from whom new events are received

Neighbor who is consistently performing better than others

Neighbor from whom most events received

There is also a mechanism of “negative reinforcement” to degrade the importance of a particular path

Gradient represents both direction towards data matching and status of demand with desired update rate

Probability 1/energy costThe choice of path is made locally at every node for every packet

Uses application-aware communication primitivesexpressed in terms of named data

Consumer of data initiates interest in data with certain attributes

Nodes diffuse the interest towards producers via a sequence of local interactions

This process sets up gradients in the network to draw events matching the interest

Collect energy metrics along the wayEvery route has a probability of being chosen

Directed Diffusion

Sink

Source

Four-leggedanimal

Reinforcement and negative reinforcement used to converge to efficient distribution

Has built-in tolerance to nodes moving out of range or dying

Source

Sink

Directed Diffusion

Pros– Energy – Much less traffic than flooding. For a network of size

N the total cost of transmissions and receptions is whereas for flooding the order is

– Latency – Transmits data along the best path– Scalability – Local interactions only– Robust – Retransmissions of interests

Cons– The set up phase of the gradients is expensive– Need of and adequate MAC layer to support an efficient

implementation. The simulation analysis uses a modified 802.11 MAC protocol

Directed Diffusion

)( NnO)(nNO

Geographic Routing

Geographic routing protocols assume:

All nodes know their geographic location

Each node knows its 1-hop neighbors

Destination is a node with a given location

Each packet can hold a limited amount of information as to where it has been in the network

Geographic ForwardingGreedy Approach

Select the neighbor geographically closest to the destination and forward the data to that neighbor

Geographic ForwardingGreedy Approach

Problem: It can get stuck in a local minima

Geographic Forwarding

When the connectivity between the nodes can be modeled as unit graphs, i.e. a node is always connected to nodes within a fixed nominal range and never connected to nodes outside this range, then algorithms have been developed to escape from a local minima†.

† B. Karp , H. T. Kung, “GPSR: greedy perimeter stateless routing for wireless networks,” Proceedings of the 6th annual international conference on Mobile computing and networking, p.243-254, August 06-11, 2000, Boston, Massachusetts, USA

Geographic ForwardingFor the case of a unit disk graph the greedy algorithm failure can be illustrated in the following example:

A is closer to Y than B or C and thus it will not forward the packet to either of them

† B. Karp , H. T. Kung, “GPSR: greedy perimeter stateless routing for wireless networks,” Proceedings of the 6th annual international conference on Mobile computing and networking, p.243-254, August 06-11, 2000, Boston, Massachusetts, USA

Geographic ForwardingA solution is to have a “perimeter” forwarding which attempts to route the packet around the “void”.

To guarantee perimeter forwarding it is necessary that the graph G that represents the network is “planar”, i.e., no two edges should intersect each other

Planarized Graphs

Remove some edges so that the remaining graph is a connected planar graph

Planarized Graphs

Relative Neighborhood Graph (RNG) – The edge XY is introduced if the intersection of circles centered at X and Y with radius the distance d(x,y) is

free of other nodes

Gabriel Graph (GG) – The edge XY is introduced if no other node is present within the circle whose diameter is d(x,y)

RNG and Gabriel graphs can be found in a distributed fashion

Routing Without Location Information

Location information is not available, e.g. too expensive

What are then the options to still have some type of geographic routing?

One option is to assign “virtual” coordinates† to each node and then apply a standard geographic routing algorithm

The virtual coordinates do not need to be an accurate representation of the actual location of the nodes, however they should preserve the existent connectivity among the nodes

It is also desirable to be able to compute the virtual coordinates using only local connectivity information, i.e., each node knows about its neighbors

† A. Rao, C. Papadimitriou , S. Shenker, I. Stoica, “Geographic routing without location information,” Proceedings of the 9th annual international conference on Mobile computing and networking, September 14-19, 2003, San Diego, CA, USA

Routing: Virtual Coordinates

Case when the perimeter nodes and their location is known

All non-perimeter nodes can determine their coordinates through an iterative relaxation procedure

Nodes will tend to move towards the perimeter nodes

Routing: Virtual Coordinates Perimeter nodes do not change their coordinates

Non-perimeter nodes update their coordinates through multiple iterations

In each iteration, a node takes its coordinates as the average coordinates of its neighbors

)(i iterations10 )(ii iterations100 )(iii iterations1000

Routing without Embedding the Link Connectivity in a Plane†

Still use virtual coordinates Divide the problem into computing a global topology and a local

routing mechanism Global topology estimation

– Provides information about connected components and holes in the network

– The whole field is partitioned into tiles

– The assumed stable topology allows for proactive routing Within each tile the sensor distribution is expected to be “uniform”

and greedy forwarding using local coordinates can be used Local routing uses reactive protocols based on local connectivity

† Q. Fang, J. Gao, L.J. Guibas, V. Silva, and L. Zhang, “GLIDER: Gradient Landmark-based Distributed Routing for Sensor Networks,” INFOCOM 2005.

GLIDER

Select a set of landmarksConstruct Landmark Voronoi

Complex (LVC)Construct Combinatorial

Delaunay Triangulation (CDT) graph on landmarks

Voronoi Diagrams

Let P be a set of n distinct points in the plane

The Voronoi diagram of P is the subdivision of the plane into n cells, one for each point

A point q lies in the cell corresponding to a point pi P if and only ifEuclidean_Distance(q, pi ) < Euclidean_Distance(q, pj ), for each pi P, j i

Delauney Triangulation

It is the dual graph of the Voronoi diagram

If one draws a line between any two points whose Voronoi domains touch

A set of triangles is obtained, known as the Delaunay Triangulation

No point in P is inside the circumcircle of any triangle in a Delauney Triangulation

GLIDER: Routing Global

The Combinatorial Delaunay graph D(L) encodes global connectivity information that is accessible to every node for proactive route planning on tiles

LocalHigh-level routes on tiles are realized as actual paths in the network by using reactive protocols

Transport Layer

Transport Layer

Physical

Data Link

Network

Transport

ApplicationTCP variants developed for traditional wireless networks are not suitable for WSNs where the notion of end-to-end reliability has to be reinterpreted,

Many more senders (the sensors) than destinations

There is still need of end-to-end reliability between the sink and individual nodes for situations such as retasking or reprogramming

The protocols developed should be energy aware and simple enough to be implemented for the low-end type of hardware and software of many WSN applications

For the same event there is a high level of redundancy or correlation in the data collected by the sensors, i.e. there is no need for end-to-end reliability between individual sensors and the destination, but instead between the event and the destination

Transport LayerCoverage

An important issue in sensor networks is the one of reliability. An aspect of this issue is the one of detection reliability, i.e. whether the events that the network is supposed to detect can be detected.

This means that the locations where events can occur are within the sensing range of at least one node. The coverage problem deals with the required node density and related topics.

Once the event has been detected the corresponding data must be reliable reported to one or more sink nodes, which can be more than one hop away. This translates into the need of a reliable transport protocol.

CoverageThe Art Gallery Problem

Place a (the minimum?) number of cameras such that every point in the art gallery is monitored by at least one camera

k-Coverage

Given a set of sensors S={s1, s2,…, sn} in a 2-D area A. Each sensor has a sensing range ri, i.e., it can monitor any point that is within a distance

ri from si . A location in A is said to be k-covered it is within at least k sensors’ sensing ranges.

Worst Case CoverageMaximal Breach Path

It is a path through a sensor network that has the largest minimum distance to any sensor node.

Given a sensor field for which the location of each sensor node si is known, and given the location of the initial (I) and final (F) points the problem is then to identify a path between I and F with the lowest operability, i.e., the maximal breach path. For any point on this path, the distance to the closest sensor is maximized.

By construction, the Voronoi diagram contains this path since the line segments in the Voronoi diagram maximize the distance from the closest nodes.

Worst Case CoverageMaximal Breach Path Algorithm†

Create a node for each vertex in the Voronoi diagram Create an edge for each line segment in the Voronoi diagram Assign the edge with its minimal distance from the closest sensor as

its weight Perform a Binary-Search and Bread-First-Search During each step of the Binary Search, check to see if a path exists

using only edges with weights larger than the specified search criteria (breach_weight)

If a path exists:• Increase breach_weight, and repeat the search

If no path exists:• Reduce breach_weight to consider edges with lower weights

(†) S. Meguerdichian, F. Koushanfar, M. Potkonjak, M.B. Srivastava, “Coverage Problems In Wireless Ad-hoc Sensor Networks”, Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies, 2001.

Worst Case CoverageMaximal Breach Path

I F

Best Case CoverageMaximal Support Path

It is a path through a sensor network that has the smallest maximum distance to the sensor set.

Given a sensor field for which the location of each sensor node si is known, and given the location of the initial (I) and final (F) points the problem is then to identify a path between I and F with of maximal support. For any point on this path, the distance to the closest sensor is minimized.

By construction, the Delauney triangulation produces triangles that have minimal edge length among all possible triangulations, therefore the maximal support path must lie on the lines of the Delauney triangulation of the sensors in S

Worst Case CoverageMaximal Support Path Algorithm†

The Voronoi diagram is replaced by the Delaunay triangulation as the underlying geometric structure

The edges in graph G are assigned weights equal to the length of the corresponding line segments in the Delaunay triangulation

The search parameter breach_weight is replaced by the new parameter support_weight

support_weight is now an upper bound on all the edge weights that lie on the maximal support path, and there must exist at least one edge with weight equal to support weight

(†) S. Meguerdichian, F. Koushanfar, M. Potkonjak, M.B. Srivastava, “Coverage Problems In Wireless Ad-hoc Sensor Networks”, Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies, 2001.

The algorithm used is exactly the same as for Maximal breach path, with the following changes:

Best Case CoverageMaximal Support Path

I F

Localization

Localization

Location necessary in order for sensed data to be meaningful e.g. forest fire detection

Location information is taken for granted in many network designs, e.g. geographic routing

Equipping each node with GPS is not always feasible due to power constraints and other limitations inherent to sensor networks

Localize using inter-node distances

Nodes can often measure their distances to nearby nodes, e.g. ultra-wideband ranging

The network localization problem is to determine the positions of all the nodes

Anchors are nodes whose positions are known

The distances between some nodes are known

??

??

?

The network is localizable if there exists exactly one position in the plane corresponding to each non-anchor node so that all known inter-node distances are satisfied

A network in the plane

?

A node is localizable if its position is uniquely determined by the known inter-node distances and anchor positions

Anchor positions from GPS or manual configuration.

Localization

The network localization problem is NP-Hard

The localization problem is solvable if and only if the network is localizable

Even assuming exact distance measurements, there is currently no algorithm that can localize a large class of localizable networks without requiring high connectivity while giving correctness guarantees

When distance between nodes are used in a localization problem the approach is called lateration; when angles between nodes are used the approach is called angulation

Localization

Multilateration

Base stations advertise their coordinates & transmit a reference signal

Node uses the reference signal to estimate distances to each of the base stations

Distance measurements are noisy

Base Station 1

Base Station 3

Base Station 2

u

Problem Formulation

Need to minimize the sum of squares of the residuals

The objective function is

This a non-linear optimization problem. There are many ways to solve (e.g. gradient descent methods)

22,, )ˆ()ˆ( uiuiiuiu yyxxrf

2,min),( iuuu fyxF

Collaborative Multilateration

All available measurements are used as constraints

Solve for the positions of multiple unknowns simultaneously

This is a non-linear optimization problem!

Known position

Unknown position

Problem Formulation

214

2141,41,4

254

2545,45,4

234

2343,43,4

253

2535,35,3

232

2323,23,2

)ˆ()ˆ(

)ˆ()ˆ(

)ˆˆ()ˆˆ(

)ˆ()ˆ(

)ˆ()ˆ(

yyxxRf

yyxxRf

yyxxRf

yyxxRf

yyxxRf

2,4433 min)ˆ,ˆ,ˆ,ˆ( jifyxyxF

The objective function is

Can be solved using iterative least squares

1

2

34

5

6

Estimating Distances - RSSISince every sensor node has a radio one way to estimate distances is to transmit a signal of known strength and then use the signal strength of the corresponding received signal and the path loss coefficient to estimate distance. In theory the energy of a radio signal diminishes with the square of the distance from the signal’s source. A more realistic assumption is to use path loss

coefficient α and compute the received power as,

d

PcP Tx

Rx α typically varies† between 2 and 5, depending on several parameters such as the nature of obstacles, carrier frequency, etc.

† S.Y. Seidel, T.S. Rappaport, “914 Mhz Path Loss Prediction Models For Indoor Wireless Communications in Multifloored Buildings”, IEEE Transactions on Antennas and Propagation, February 1992.

Rx

Tx

P

cPd

A major drawback of this model is that assumes that the behavior is the same in all directions and thus the connectivity between nodes is of a disk type nature.

Connectivity Over Space†

A single transmitter (RFM TR 1000) is located at (6,6) in a 12x 14 grid of 147 nodes. All nodes are identically oriented on a tennis court with 2-foot spacing.

† C. Whitehouse, “The Design Of Calamari: An Ad-hoc Localization System for Sensor Networks”, Master’s thesis, University of California at Berkeley, 2002.

Radio Hop CountIf two nodes can communicate by radio their distance from each other is less than R (the maximum radio range). This type of local connectivity can be used to compute inter-node distances.

A very simple approximation would be that if the hop count between two nodes si and sj is hij then the distance between si and sj , dij , is less than

R*hij. A better estimate is one takes into account the expected number of

neighbors, nlocal. The distance covered by one radio hop is then†

1

1

1)(cos 21

1 dteeRdttt

nn

hop

local

local

† L. Kleinrock and J.A. Silvester, “Optimum Transmission Radii For Packet Radio Networks or Why Six is a Magic Number”, In IEEE National Telecommunications Conference, December 1978.

and dij ≈ hij*dhop

Examples of Hop Count

hAC = 4, unfortunately, hBD is also four, due to an obstruction in the topology

Estimating Distances

Time of arrival (ToA)

– Use time of transmission, propagation speed, time of arrival to compute distance

– Problem: Exact time synchronization

Time Difference of Arrival (TDoA)

– Use two different signals with different propagation speeds

– Example: ultrasound and radio signal

• Propagation time of radio negligible compared to ultrasound

– Compute difference between arrival times to compute distance

– Problem: Calibration, expensive/energy-intensive hardware

Packet-Level LocalizationAn Experiment†

Motivation: Is it possible to learn about a node’s position by observing the packet statistics?

Imagine an scenario where a mobile sink traverses an area of stationary sensor nodes following different routes. Is it possible to learn about the sink’s position if it traverses the same area many times?

Assume also that the nature of the scene does not change, e.g., the obstacles are the same or the way they behave is similar from one instance to the other.

† T. L. Hemminger, D. R. Loker, and C. Pomalaza-Ráez, “A Neural Method for Identifying Transmission Source Locations”, Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Helsinki, Finland, September 2006.

Experimental Testbed

IEEE 802.11 technology (Cisco Aironet)

Multiple stationary nodes (clients)

One mobile node (server)

Mobile node broadcasts packets

Stationary nodes record signal and packet statistics

TCP network connection between the server and each client

Diagnostic utility software provided with the wireless adapter cards collect packet statistics

Of the 42 statistics provided by this software, it was determined that 13 provided significant variability to be pursued as candidates in determining the server location

Data ReductionTrend analysis of the collected data revealed a large amount of correlation between the variables and a matrix of rank 6, i.e., there was a strong level of dependence between the variables. A eigenvalue, eigenvector statistical analysis was used to reduce the dimensionality of the data and retain the most relevant parameters.

Receive Statistics Transmit Statistics

Bytes Received Bytes Transmitted

Total Packets Received OK Ack Packets Transmitted

MAC CRC Errors Packets Deferred Energy Detect

SNR Packets No Ack Received

Receiver Statistics

Bytes Received The number of bytes of data that were received successfully

Total Packets Received OK The number of all packets that were received successfully

MAC CRC Errors

The number of packets that had a valid 802.11 Physical Layer Convergence Protocol (PLCP) header but contained a CRC error in the data portion of the packet

Signal level → SNR

The signal strength for all received packets

Range: 0 to 100% or -95 to -45 dBm

Transmitter Statistics

Bytes Transmitted The number of bytes of data that were transmitted successfully

Ack Packets Transmitted The number of acknowledgment (Ack) packets that were transmitted in response to successfully received unicast packets

Packets Deferred Energy Detect

The number of packets that were delayed because RF energy was already detected. This condition is usually caused by another radio transmitting a packet or by some other RF source jamming the signal (such as a microwave oven)

Packets No Ack Received The number of transmitted packets that did not have their corresponding Ack packet received successfully

Layout of the Building, Upper Floor

11

1

2

345

6

7

89101112

13

14

16 15A

B

C

D

EF

17

1819

20

Six Pentium-class computers were used as stationary nodes. A laptop computer was configured as a server (adjacent to the access point). Both were placed on a mobile cart. A-F are the location of the clients. 1-16 are initial Tx points. 17-20 are additional Tx points.

Packet Characteristics I

C lient A

0.920

0.930

0.940

0.950

0.960

0.970

0.980

0.990

1.000

1.010

0 5 10 15 20

P osit ion

Byte

s

Receiv

ed

C lient A

0.000

0.200

0.400

0.600

0.800

1.000

1.200

0 2 4 6 8 10 12 14 16 18

Posi ti on

MA

C C

RC

Err

ors

Packet Characteristics II

C lient A

0.000

0.200

0.400

0.600

0.800

1.000

1.200

0 5 10 15 20

P osit ion

Packets

Def.

En

erg

y

Dete

ct

C lient D

0.955

0.960

0.965

0.970

0.975

0.980

0.985

0.990

0.995

1.000

1.005

0 2 4 6 8 10 12 14 16 18

Posi ti on

Byte

s

Receiv

ed

Packet Characteristics III

C lient F

0.88

0.9

0.92

0.94

0.96

0.98

1

1.02

0 2 4 6 8 10 12 14 16 18

Posi ti on

Byte

s

Receiv

ed

C lient D

0.000

0.200

0.400

0.600

0.800

1.000

1.200

0 5 10 15 20

P osit ion

Packets

Def.

En

erg

y

Dete

ct

Data Collection

One mobile server and six clients

Two-minute accumulation

16 transmission positions

Interpolation to 134 points

The interpolated points would correspond to points 61 cm apart

The actual measurements were not used in the training of the NN

Data ProcessingNeural Networks

Packet characteristics and RF SNR are affected in a non-linear manner by the materials and topology of a building. This provides the rationale to explore the possibility of obtaining solutions from a feed-forward neural network.

Neural networks have become popular in several engineering fields and appear to be a logical approach to this problem since they are particularly well suited in solving non-linear problems.

An 8-tuple input vector:

an output vector:

},,,,,,,{ iiiiiiiii sgfedcbav

},{ iii yxo a – g are the packet parameters described earlier, and s is the SNR, are used to train a NN

, where Ni is the position index,

Neural Network

6 NN networks, one for each client

8 input nodes

2 output nodes (x, y)

9 sigmoids in hidden layer

Levenberg-Marquardt algorithm†

It took 600 epochs to reach the desired mean-squared error of 10-4

† M.T. Hagan and M. Menhaj, "Training feed-forward networks with the Marquardt algorithm," IEEE Transactions on Neural Networks, Vol. 5, No. 6, 1999, pp. 989-993, 1994.

Considerations

Interpolation – The first sixteen set of measurements were used for the interpolation process that yielded 134 training sets, the actual measurements were used to test the NN

Gaussian Weighting - In order to merge the contribution of each

network, it is necessary to combine the output (x-y positions) from each, yet averaging them affords undue influence from clients positioned further from the server, possibly skewing the results. To compensate, a Gaussian weighting function was employed to reduce the effects from distant clients, i.e.,

2/])'()'[( 22iiii yyxx

i ew

where (xi,yi) are the coordinates of the client and are

the output of a specific network.

)','( ii yx

Results

Position predictions for the 16 original locations. Circles indicate true locations and “+” represents weighted centroids from the contributions of all networks.

Additional Test Points

Position predictions of four additional test points.

Circles indicate true locations and “+” indicates system output.

Errors

Range error of system from the original 16 locations using median filter

Original Set (Range Error)

0

0.5

1

1.5

2

2.5

3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

P osit ion

Ran

ge e

rror

in

mete

rs

Original Set (Std. Dev.)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

P osit ion

Std

. d

ev.

of

ran

ge

(m)

Localization Experiment: Summary

Off the shelf technology (hardware and software)

The neural networks are trained off-line and require very little computational or transmission overhead during the consultation phase

The statistics collected are related to the position of the server and the multipath nature of the building

There is need to more precisely determine which of the packet statistics are more relevant and to refine the algorithm

Tutorial Summary

The use of wireless communications technology in Medical Applications is increasing steadily

This technology can have an important contribution in improving lives of patients at the same time as reducing costs

Wireless sensor networks (WSN) are already being deployed in a variety of scenarios including those in the area of medical care

This tutorial focus on those WSN aspects that are deemed to be of interest to medical applications

A turning point in the use of WSN in the medical field will be when the intended users (patients, doctors, nurses, etc.) accept this technology as readily as the current medical equipment and devices

References

S. Arnon, D. Bhastekar, D. Kedar, and A. Tauber, “A Comparative Study of Wireless Communication Network Configurations for Medical Applications,” IEEE Wireless Communications, pp. 56-61, February 2003

D. Cypher, N. Chevrollier, N. Montavont, and N. Golmi, “Prevailing over Wires in Healthcare Environments: Benefits and Challenges,” IEEE Communications Magazine, April 2006

M. Fadlee, A. Rasid, and B. Woodward, “Bluetooth Telemedicine Processor for Multichannel Biomedical Signal Transmission via Mobile Cellular Networks,” IEEE Transactions on Information Technology in Biomedicine, pp. 35-43, March 2005

Q. Fang, J. Gao, L.J. Guibas, V. Silva, and L. Zhang, “GLIDER: Gradient Landmark-based Distributed Routing for Sensor Networks,” INFOCOM 2005.

Z. Haas, J. Halpern, and L. Li, “Gossip-Based Ad Hoc Routing”, IEEE/ACM Transactions on Networking, Vol. 14, No. 3, pp. 479-491, June 2006

M.T. Hagan and M. Menhaj, "Training feed-forward networks with the Marquardt algorithm," IEEE Transactions on Neural Networks, Vol. 5, No. 6, 1999, pp. 989-993, 1994.

W. Heinzelman, J. Kulik, and H. Balakrishnan, “Adaptive Protocols for Information Dissemination in Wireless Sensor Networks,” Proc. 5th ACM/IEEE Mobicom Conference, Seattle, WA, August, 1999.

References

T. L. Hemminger, D. R. Loker, and C. Pomalaza-Ráez, “A Neural Method for Identifying Transmission Source Locations”, Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Helsinki, Finland, September 2006.

E. Jovanov, A. Milenkovic, C. Otto, and P. de Groen, “A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation,” Journal of NeuroEngineering and Rehabilitation, 2005, 2:6

B. Karp , H. T. Kung, “GPSR: greedy perimeter stateless routing for wireless networks,” Proceedings of the 6th annual international conference on Mobile computing and networking, p.243-254, August 06-11, 2000, Boston, Massachusetts, USA

L. Kleinrock and J.A. Silvester, “Optimum Transmission Radii For Packet Radio Networks or Why Six is a Magic Number”, In IEEE National Telecommunications Conference, December 1978.

C. Intanagonwiwat, R. Govindan, D. Estrin, and J. Heidemann “Directed Diffusion for Wireless Sensor Networking,” IEEE/ACM Trans. on Networking, February 2003

H. Lufei, W. Shi, and L. Zamorano, “Communication Optimization for Image Transmission in Computer-Assisted Surgery,” Proceedings of 2004 Congress of Neurological Surgeons, October 16-21, San Francisco, California

References

D. Malan, T. Fulford-Jones, M. Welsh, and S. Moulton., “CodeBlue: An Ad Hoc Sensor Network Infrastructure for Emergency Medical Care,” Intl. Workshop on Wearable and Implantable Body Sensor Networks, April 2004.

S. Meguerdichian, F. Koushanfar, M. Potkonjak, M.B. Srivastava, “Coverage Problems In Wireless Ad-hoc Sensor Networks”, Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies, 2001.

V. Megalooikonomou and D. Kontos, “Medical Data Fusion for Telemedicine,” IEEE Engineering in Medicine and Biology Magazine, pp. 36-42, September/October 2007

M. Pacelli, G. Loriga, N. Taccini, and R. Paradiso, “Sensing Fabrics for Monitoring Physiological and Biomechanical Variables: E-textile solutions,” Proceedings of the 3rd IEEE-EMBS International Summer School and Symposium on Medical Devices and Biosensors, Boston, Sept.4-6, 2006

A. Rao, C. Papadimitriou , S. Shenker, I. Stoica, “Geographic routing without location information,” Proceedings of the 9th annual international conference on Mobile computing and networking, September 14-19, 2003, San Diego, CA, USA

S. Seidel, T.S. Rappaport, “914 Mhz Path Loss Prediction Models For Indoor Wireless Communications in Multifloored Buildings”, IEEE Transactions on Antennas and Propagation, February 1992.

References

Y. Shie, F. Tsai, A. Anavim, M. Wang, C-M Lin, “Mobile Healthcare: Opportunities and Challenges,” Proceedings of Sixth International Conference on the Management of Mobile Business, July 9-11, 2007, Toronto, Ontario, Canada

V. Shnayder, B. Chen, K. Lorincz, T. Fulford-Jones, and Matt Welsh, “Sensor Networks for Medical Care,” Technical Report TR-08-05, Division of Engineering and Applied Sciences, Harvard University, 2005.

E. Sloane and T. Cooper, “Safety First! Safe and Successful Digital Network Wireless Medical Device Systems,” 2006 HIMSS Annual Conference February, San Diego

C. Whitehouse, “The Design Of Calamari: An Ad-hoc Localization System for Sensor Networks”, Master’s thesis, University of California at Berkeley, 2002.