Most of the traditional wireless networks occur over fixed infrastructure
Most of the traditional wireless networks occur over fixed infrastructure
Access points
Many wireless protocols (heterogeneity problem)
Bluetooth, WiFi, WiMax
We need Seamless network
Connects everyone from their home to work,..
Disasters may be a drive force for such networks
Wireless Cellular Networks
Wireless Cellular Networks
First , Second, 2.5 , third, and 4th generations
Wireless Ad Hoc Networks
Nodes function as host and router
Dynamic topology
Nodes may departure
Requires efficient routing protocols
Mobile Ad Hoc Networks (MANET)
Wireless Sensor Networks (WSN)
Sensing:
Sensing:
Is a technique used to gather information about a physical object or process, including the occurrence of events (i.e., changes in state such as a drop in temperature or pressure).
Sensor:
An object performing such a sensing task
Converts energy of the physical worlds into electrical signal.
Sometimes named “Transducer” converts energy from one form to another.
Examples on remote sensors:
Nose, ears, and eyes They do not need to touch the monitored objects to gather information
PIR is a differential sensor: detects target as it crosses the “beams” produced by the optic
PIR is a differential sensor: detects target as it crosses the “beams” produced by the optic
Data Collection
Data Collection
In-Network Analysis
Data Fusion
Decision Making
Types of Measured Phenomena
Types of Measured Phenomena
Defense Advanced Research Projects Agency (DARPA) organized the Distributed Sensor Nets Workshop (DAR 1978).
Defense Advanced Research Projects Agency (DARPA) organized the Distributed Sensor Nets Workshop (DAR 1978).
DARPA also operated the Distributed Sensor Networks (DSN) program in the early 1980s,
Rockwell Science Center, the University of California at Los Angeles proposed the concept of Wireless Integrated Network Sensors or WINS.
Rockwell Science Center, the University of California at Los Angeles proposed the concept of Wireless Integrated Network Sensors or WINS.
One outcome of the WINS project was the Low Power Wireless Integrated Microsensor (LWIM), produced in 1996
The Smart Dust project at the University of California at Berkeley focused on the design of extremely small sensor nodes called motes. (year of 2000).
The Smart Dust project at the University of California at Berkeley focused on the design of extremely small sensor nodes called motes. (year of 2000).
The goal of this project was to demonstrate that a complete sensor system can be integrated into tiny devices, possibly the size of a grain of sand or even a dust particle.
Crossbow (www.xbow.com),
Crossbow (www.xbow.com),
Sensoria (www.sensoria.com),
Worldsens (http://worldsens.citi.insa-lyon.fr),
Dust Networks (http://www.dustnetworks.com ), and
Ember Corporation (http://www.ember.com ).
Energy
Energy
Sensors powered through batteries sometimes impossible to do.
Mission time may depend on the type of application (e.g. battlefield monitoring – hours or days)
Node’s layers must be designed carefully.
Responsible for providing sensor nodes with access to the wireless channel.
Responsible for providing sensor nodes with access to the wireless channel.
Responsible of Contention free Transmission .
MAC protocols have to be contention free as well as energy efficient.
Contention free requires listening to the wireless channel all the time
Energy efficient requires turning off the radio
Responsible for finding routes from a sensor node to the base station
Responsible for finding routes from a sensor node to the base station
Route characteristics such as length (e.g., in terms of number of hops), required transmission power, and available energy on relay nodes
Determine the energy overheads of multi-hop communication and try to avoid it.
Energy affects the O.S. design :
Energy affects the O.S. design :
Small memory footprint,
Efficient switching between tasks
security mechanisms
Self-Management
Self-Management
Sensors usually deployed in harsh environment.
There is no pre-infrastructure setup.
Once deployed, must operate without human intervention
Sensor nodes must be self-managing in that
They configure themselves,
Operate and collaborate with other nodes,
Adapt to failures, changes in the environment,
Self-organization
Self-organization
A network’s ability to adapt configuration parameters based on system and Environmental state.
Self-optimization
A device’s ability to monitor and optimize the use of its own system resources
Self-protection
Allows a device to recognize and protect itself from intrusions and attacks
Self-healing
Allows sensor nodes to discover, identify, and react to network disruptions.
Deterministic Vs. Ad Hoc Deployment
Deterministic Vs. Ad Hoc Deployment
Wireless Networking
Wireless Networking
Transmission Media
Sensors use wireless medium
Suffer from the same problems that wireless networks suffer from
Fading
High error rate
Wireless Networking
Wireless Networking
Communication range
Communication ranges are always short
It is required for the network to be highly connected
Routing paths will be long
What about critical applications where delay is not acceptable
QoS will be an issue
Wireless Networking
Wireless Networking
Sensing Range
Very small
Nodes might be close to each other
Data Redundancy
Decentralized Management
Decentralized Management
Requires Distributed Algorithms
Overhead might be imposed
Security
Exposed to malicious intrusions and attacks due to unattendance characteristics.
denial-of-service
jamming attack
Dense Node Deployment
Dense Node Deployment
Battery-Powered Sensors
Sever Energy , Computation , and Storage Constraints
Self Configurable
Application Specific
Unreliable Sensor Nodes
Frequent Topology Change
No Global Identifications
Many-to-One Traffic pattern ( multiple sources to a single Sink node)
Data Redundancy
Fault Tolerance
Fault Tolerance
Large number of nodes already deployed or
Nodes do the same job. If one fails , the network still working because its neighbor monitors the same phenomenon .
Mobility
Helps nodes to reorganize themselves in case of a failure of any of the nodes
Attribute-Based Addressing
Addresses are composed of group of attribute-value pairs
Ex. < temp > 35, location = area A>
Location Awareness
Location Awareness
Nodes’ data reporting is associated with location
Priority Based Reporting
Nodes should adapt to the drastic changes in the environment
Query Handling
The sink node / user should be able to query the network
The response should be routed to the originator
We might have multiple sinks in the network
Micro-Electro-Mechanical Systems
Micro-Electro-Mechanical Systems
(MEMS) is a core technology that:
Leverages IC fabrication technology
Builds ultra-miniaturized components
Enables radical new system applications
Augmented General Purpose PCs
Augmented General Purpose PCs
Embedded PCs (PC104), PDAs, etc..
Usually have O.S like Linux and wireless device such as Bluetooth.
Dedicated Sensor Nodes
Commercially off the shelf components (e.g. Berkeley Motes)
System-on-chip Sensor
Platform like Smart dust, BWRC PicoNode
Operating Systems and Language Platforms
Operating Systems and Language Platforms
Typical Platforms are:
TinyOS, nesC, TinyGALS, and Mote
TinyOS
Event Driven O.S.
Requires 178 bytes of memory
Supports Multitasking and code Modularity
Has no file system – only static memory allocation
Simple task scheduler
nesC – extension of C language for TinyOS- set of language constructs
TinyGALS - language for TinyOS for event triggered concurrent execution .
Mote’ - Virtual machine for Berkeley Mote
IEEE 802.15.4 Standard
IEEE 802.15.4 Standard
Specifies the physical and MAC Layers for low-rate WPANs
Data rates of 250 kbps, 40 kbps, and 20 kbps.
Two addressing modes: 16 - bit short and 64 - bit IEEE addressing.
Support for critical latency devices, for example, joysticks.
The CSMA - CA channel access.
Automatic network establishment by the coordinator.
Fully handshaking protocol for transfer reliability.
Power management to ensure low - power consumption.
Some 16 channels in the 2.4 - GHz ISM band, 10 channels in the 915 – MHz band, and 1 channel in the 868 - MHz band.
IEEE 802.15.4 Standard
IEEE 802.15.4 Standard
The physical layer is compatible with current wireless standards such as Bluetooth
MAC layer implements synchronization , time slot management , and basic security mechanisms.
Border Monitoring:
Border Monitoring:
Detect movement where none should exist ,
Decide target classes, e.g., foot traffic to tanks
Ideal when combined with towers, tethered balloons, or UAVs
Sensors HW and Software
Sensors HW and Software
Deployment
Physical , MAC, Routing, Applications
Data Aggregation and Data Mining
Artificial Intelligence and data handling
Self Healing
Web Integration
Heterogeneity
Security
Software Engineering (Simulators )
Cloud Computing and Sensor Networks
Mobility Issues and Localization
Your assignment is to read one sensor network application, as reported in a published paper. Surf the web to find material complementary to my pointers.
Your assignment is to read one sensor network application, as reported in a published paper. Surf the web to find material complementary to my pointers.
Prepare a presentation for only 15 minutes ; use the model of this power point presentation presentApp.ppt.
Before next class, you'll need to email me your presentation.
Your presentation will let other students know about some sensor network application, so they have an overview without having to read the paper in as much detail as you did.
To prepare the presentation, you likely need’nt master all the details of the paper. Often, though, it can help to find backup technical reports and presentations by the researchers, to help you prepare. Overall, you should spend about four to six hours on this task.
Your presentation will be posted on the website to be read by others and it is part of our class . Be ready for some questions from your classmates or from the instructor
Hospital Epidemiology: Wireless Applications for Hospital Epidemiology [ref]
Hospital Epidemiology: Wireless Applications for Hospital Epidemiology [ref]
Nericell: Rich Monitoring of Road and Traffic Conditions using Mobile Smartphones [ref]
Participatory sensing in commerce: Using mobile camera phones to rack market price dispersion [ref]
The BikeNet Mobile Sensing System for Cyclist Experience Mapping [ref]
Model-Based Monitoring for Early Warning Flood Detection [ref]
NAWMS: Nonintrusive Autonomous Water Monitoring System [ref]
Luster: Wireless Sensor Network for Environmental Research [ref]
Hybrid sensor network for cane-toad monitoring [ref]
SensorFlock: An Airborne Wireless Sensor Network of Micro-Air Vehicles [ref]
Identification of Low-Level Point Radiation Sources Using a Sensor Network [ref]
Mobile Sensor/Actuator Network for Autonomous Animal Control [ref]
Mobile Sensor/Actuator Network for Autonomous Animal Control [ref]
Detecting Walking Gait Impairment with an Ear-worn Sensor [ref]
Textiles Digital Sensors for Detecting Breathing Frequency [ref]
Recognizing Soldier Activities in the Field [ref]
Physical Activity Monitoring for Assisted Living at Home [ref]
PipeNet: Wireless sensor network for pipeline monitoring [ref]
Turtles At Risk [ref]
Cyclists' cellphones help monitor air pollution [ref]
Clinical monitoring using sensor network technology [ref]
CargoNet: low-cost micropower sensor node exploiting quasi-passive wakeup for adaptive asychronous monitoring of exceptional events [ref]
Monitoring persons with parkinson's disease with application to a wireless wearable sensor system [ref]
BriMon: a sensor network system for railway bridge monitoring [ref]
Monitoring Heritage Buildings [ref]
PermaDAQ: gathering real-time environmental data for high-mountain permafrost [ref]
Firewxnet: a multi-tiered portable wireless for monitoring weather conditions in wildland fire environments [ref]
Development of an in-vivo active pressure monitoring system [ref]
Personal assistive system for neuropathy [ref]
Smart jacket design for neonatal monitoring with wearable sensors [ref]
Condition Monitoring in Intel Hillsboro Fabrication Plant
Condition Monitoring in Intel Hillsboro Fabrication Plant
or BP’s Loch Rannoch Oil Tanker [ref]
Other BP applications (safety, corrosion detection, empty propane tanks)
Volcano Monitoring
Seismic Monitoring
Landslide Detection
Water Distribution Monitoring and Control (agricultural and sewer)
Water Quality
Water Sense
Lake (Aquatic organism) Monitoring
Cane Toad Monitoring
Neptune Ocean Observatory [ref]
Atmospheric Observatory [ref]
Neon (scope and canonical experiments)
SensorScope
SensorScope
SenseWeb
CarTel [ref]
Odor Source Localization
CodeBlue (Health care)
Activity Recognition [ref]
Assisted Living [ref]
Wearable wireless body area networks (Health care)
Adaptive house
PlaceLab and House_n projects
Participatory Sensing
Responsive Environments (Uberbadge)
Lover’s cup context aware
SensorWebs in the Wild
SensorWebs in the Wild
Dynamic Virtual Fences for Controlling Cows
Hardware design experiences in ZebraNet
Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with ZebraNet (see also additional background & Zebranet Web Site)
Sensor/actuator networks in an agricultural application (you'll need to search for more on this topic)