Wireless Sensor Network (WSN)Computer Networking

Definition• Wireless network consists of spatially distributed autonomous devices• Using sensors to monitor physical or environment conditions• Originally motivated by military applications• Used in many civilian application areas• Each node equipped with radio transceiver• Small microcontroller• Sensor network constitutes a wireless ad-hoc network

▫ Support multi-hop routing algorithm

Constraints and Challenges• On-board battery power• Limited network communication bandwidth• Microprocessor and small amount of memory• Fault tolerance• Transmission media• Hostile environment • Changing of topology • Costs• Scalability• Challenges faced:

▫ Limited hardware▫ Limited support for networking▫ Limited support for software development

Basic view of WSN

External Power Supply

Use base node to

communicate with WSN

Connected to base station

Processing capability

Receive/Send radio signal

Link between Host Computer

and Base Station

Communicate with WSN

Sensor devices

Multi-hop communication

• Improve signal-to-noise-ratio (SNR)▫ Reducing average distances from sensor to source

• Increase energy efficiency due to multi-hop technology▫ Additional relevant information▫ Aggregated through multi-hopping

• Improved robustness against individual sensor node or link failure• Improved scalability

▫ Decentralized algorithms • Energy advantage

▫ By multi-hop RF technology▫ Balancing between 2 factors

Overall cost Energy efficiency

• Detection advantage▫ Limited sensing range▫ Increase sensor density

Advantages of Sensor Networks

r

Psend(Nr)

Psend(Nr)

PreceiveNr

Research Challenges• Sensor Node System

▫ High performance Ability to sense Ability to communicate Ability to process data

▫ Low manufacture cost Millions can be used

▫ Low deployment cost Tether-free self- configuration

▫ Low maintenance cost Long battery life Fault tolerant, self

diagnosis▫ Low disposition cost

Environment friendly

• Sensor Network Information Processing▫ Low power

Sampling frequency Data aggregation and routing

▫ Distributive, collaborative processing Decentralized, decision, estimation Correlated source communication,

coding Distributive data representation Overlay data movement with

underlying network routing▫ Robust and secure operation

Handle missing, faulty, delayed data Privacy protection, intrusion resistant

The Nature of WSN Architecture• Highest level

▫ Decomposes a problem domain into set of services▫ Set of interfaces to its service

• Lowest level▫ Specify its protocol

Packet formats Communication exchange State machine

• Collaborative Processing▫ Process data cooperatively▫ Combine information from multiple sources▫ Data transferred from every sensor node to some other nodes▫ Number of sensor node increase, throughput goes to zero▫ Data can be processed locally to remove redundancy before shipping to remote

node▫ Node can be more selective▫ Collaborative signal and information processing (CSIP)

Sensor Middleware

Internet-based sensor network

WSN Abstract Tuple G• Defined as a complete set of information• G = <V, E, PV, PE>

▫ Network graph with nodes V and links E▫ PV – characterizes the properties of each node in V

Location Computational capability Sensing modality

Acoustic Seismic Magnetic IR Temperature Light

Sensor output type Signal amplitude Source direction-of-arrival (DOA) Target range

Energy reserve▫ PE – properties for each link

Link capability Link quality

Theory of tracking• Constrained optimization problem <G, T, W, Q, J, C>

▫ G is the sensor network▫ T is a set of targets

Locations Shape Signal source type

▫ W is signal model Signal propagate Attenuate in physical medium

▫ Q is a set of user queries Instances and entry points

▫ J specifies an objective function Task requirements

▫ C specifies set of constraints Limitation of time, energy

Tracking Scenario

Query processing:Query enter the

network

Discovery:Detect a user, initialize

tracking

Collaborative processing:

Estimates target location

Communication:Hand off information to next

node

Reporting:Summarize tracking data and send back to query

node

Collaborative localization• Amplitude measurement

▫ Minimum of 3 measurements▫ TDOA (time difference of arrival)

• Performance issues▫ Detectability

How reliably and timely Detect object stimulus

▫ Accuracy System detection, localization

▫ Scalability Specific property of system

▫ Survivability Node or link failure

▫ Resource Usage Amount of resources consumes

Routing in WSN• Utilizes the radio links

▫ Spared and used only when needed▫ Node talks directly to its immediate neighbors

Assume radio range a disk with radius r Unit distance graph (UDG)

• Network deployment is ad-hoc▫ Unstable links▫ Node failures▫ Network disconnections

• Operate wirelessly with limited power▫ Multi-path provides energy efficiency

• Limited or no mobility▫ Dynamic topology (sleeping, falling)

• Nodes know their geographical position• Sensor network generate different MAC – specialized

▫ Sensor network are collaborative systems▫ Most sensor nodes are idle much of the time▫ Improve bandwidth utilization▫ Lack of mobility taken into account▫ Issues of energy efficiency, scalability, robustness

MAC protocol• Wireless cellular networks

▫ TDMA (time division multiple access)▫ FDMA (frequency division multiple access)▫ CDMA (code division multiple access)

• Ethernet and WLAN▫ CSMA (carrier sense multiple access)

• S-MAC (for WSN)▫ Reduce energy waste

Idle listening, collisions, overhearing▫ Major components

Periodic listen and sleep Collision avoidance Overhearing avoidance Message passing

Strategies for Routing• Reactive protocols, constructing path on demand only

▫ Dynamic source routing (DSR)▫ Ad hoc on demand distance vector routing (AODV)

• Local stateless algorithms▫ Greedy distance routing

Pick the closet to destination among the neighbors▫ Compass routing

Pick the one that minimizes the angle to destination

Strategies for Routing• Geographical Routing

▫ All nodes know their geographical location▫ Knows its immediate one-hop neighbors▫ Routing destination is specified▫ Each packet hold bounded amount of additional routing information

• Attribute-based Routing▫ Node’s location▫ Type of sensors▫ Certain range of values in a certain type of sensed data▫ Used attribute value pairs to describe the data

Attribute value record Information request record

type = animal type = animal

instance = horse instance = horse

location = [89, 154] rect = [0, 200, 0, 200]

time = 2:45:12

Time Synchronization in WSN• Latency time

▫ Send time▫ Access time▫ Propagation time▫ Receive time

• Three message exchange▫ Delay D▫ Phase difference d▫ Using spanning tree favoring direct connections with reliable delays

I1 = t2 – t1 P1 = t5 – t4

Communication delay d

Different Types of Queries• Queries for relations among set of events

▫ “sound an alarm whenever two sensors within 10 meters of each other simultaneously detect an abnormal temperature”

• Time-dependent query▫ Long-running, continuous queries▫ Snapshot queries▫ Historical queries

• In summary▫ Conditions restricting set of sensors from contributing data▫ Correlate data from different sensors▫ Trigger data collection or signal processing▫ Generate sub-queries▫ Able to use outputs from past queries as inputs for further queries▫ Accurate time synchronization▫ Node geographical location information

Database Organization in WSN• Centralized warehousing approach

▫ Sensor sends data to central server▫ Central server connected to network via access point▫ Nodes near access point become traffic hot spots▫ Sampling rates set to highest

• In-network storage approach▫ Rendezvous (meeting) points between data and queries selected

Overhead to store and access data is minimized Overall load is balance across network

▫ Reduce communication overhead▫ Data can be aggregated▫ TinyDB query processing

SQL-style declarative query interface Sensitive to resource constraints and lossy communication Uses an epoch-based mechanism (divided into time intervals)

▫ Data Centric Storage (DCS) Names and stores data by its (physical) attributes Translate attributes into a node location or ID Distribute storage load across the entire network Geographical Hash Table (GHT) utilized hash functions

Sensor Network Programming• Operating System : TinyOS

▫ Support sensor network application on resource-constrained hardware platforms (Berkeley motes) No file system Support only static memory allocation Simple task model Minimal devise and networking abstractions

▫ OS is compiled with applications Lower layer closer to the hardware Higher layer closer to application

▫ Unique component architecture Provides as a library set of system software components Components include software functionalities Thin wrappers around hardware Wires those components together with other components

▫ Task Scheduler, created by components Maintains a task queue Run to completion, non pre-emptive Invokes new task from queue or put CPU to sleep mode

▫ Events Ultimate sources of triggered executions Execution of interrupt handler Run to completion but can pre-empt tasks and can be pre-empted by other events

Samples of wireless sensor hardware

Sensor Network Programming• Imperative language: nesC

▫ Extension of C to support and reflect design of TinyOS▫ Interfaces

Provides interface Set of method calls exposed to the upper layers

Uses interface Set of method calls hiding the lower layer components

▫ Implementation Modules

Implemented by application code (C-like syntax) Configurations

Connecting interfaces of existing components▫ Asynchronous code (AC)

Code reachable from at least one interrupt handler▫ Synchronous code (SC)

Code reachable only from tasks

Sensor Network Programming• Simulators

▫ Quickly study the performance of algorithms Timing Power Bandwidth Scalability

▫ Sensor node model Software execution platform Sensor host Communication terminal

▫ Communication model Model the communication at physical layer Stimulate RF propagation delay Collision of simultaneous transmission

▫ Physical environment model Key element is physical phenomenon of interest

▫ Statistics and visualization Results collected for analysis

▫ Cycle-driven (CD) Discretizes the continuous notion of real time into ticks

▫ Discrete-event (DE) Assume time is continuous and an event may occur at any time

Applications using WSN• Emerging applications

▫ Build on WLAN or Bluetooth technology▫ ZigBee and Ultra-Wide Band (UWB)

• Asset and warehouse management▫ Monitor and track assets▫ Collect real-time inventory and retail information by using RFID technology

• Automotive applications▫ Dedicated short-range communication (DSRC)▫ Telematics and entertainment, link to databases

• Building monitoring and control▫ Cut down energy costs ▫ Detect biological agents or chemical pollutants▫ Security system

• Environmental monitoring▫ Monitor conditions and movements of wild animals or plants▫ Monitor air quality and track environmental pollutants, biological or chemical hazards

• Healthcare applications▫ Monitor vital signs of patients, remotely connected to other places▫ In-home elderly healthcare system

• Industrial process control▫ Monitor manufacturing process, condition of industrial equipment

• Military battlefield awareness▫ Real-time battlefield intelligence

• Security and surveillance▫ Imagers or video sensors useful in identifying and tracking moving entities

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