CS 851 Wireless Sensor Networks Introductory Lecture Professor Jack Stankovic Department of Computer Science University of Virginia September 2003

Purpose of this Lecture Get you to think differently –Regardless of whether you are new to WSN or have been working with them Introduce the basic key issues and their implications Reduce work to its essence

The field is exploding

Smart Spaces Smart School Smart City Smart Factory Other Applications Battlefields/Surveillance Earthquake areas Environmental Monitoring Airport security Emergency Response Location Services

More Applications Interface with the Internet Handheld PDAs/laptops Element in pervasive computing From your reading did you find interesting applications or ideas about applications that were Surprising?

Ad Hoc Wireless Sensor Networks Sensors Actuators CPUs/Memory Radio

Research Questions What are the correct HW elements to make solutions at the OS/middleware/application levels easier? –Current motes are only 1 possible platform How about DSPs? Special security HW? –What capacities (cpu speed, memory, bandwidth, power, etc.) and their fundamental limitations, have if any, on solutions

Sensor/Actuator Clouds Heterogeneous Homogeneous Resource management, team formation, networking, … Severe constraints power, memory, bandwidth, cpu, cost,...

Background: Challenge fundamental assumptions underlying distributed systems technology –How the problems change Key Areas to be Addressed –Routing –Power Management –Localization –Security –Paradigms –Theory –Other Issues Examples: key research problems/solutions –Spatial-Temporal Routing –Application Independent Data Aggregation –Localization Realities

How the Problems Change Environment –connect to physical environment (large numbers, dense, real-time) –massively parallel interfaces (sometimes) –faulty, highly dynamic, non-deterministic –wireless (indirect impact on remote entity) –power management critical Network –structure is dynamically changing –sporadic connectivity –new resources entering/leaving –large amounts of redundancy –self-configure/re-configure –individual nodes are unimportant - route/query to AREA

How the Problems Change OS/Middleware –manage aggregate performance control the system to achieve required emerging behavior How do we know it works? –self-organizing (self-*) –fuzzy membership and team formation –manage power/mobility/real-time/security tradeoffs –geographical/location based (spatial) –real-time/real world (temporal) –data centric

Examples Can you give me examples of simple decentralized algorithms that exhibit aggregate behavior?

Implications Fundamental Assumptions underlying distributed systems technology has changed –wired => wireless (limited range, high error rates) –unlimited power => minimize power –Non-real-time => real-time –fixed set of resources => resources being added/deleted –each node important => aggregate performance New solutions necessary

Example: Resource Management Measure communication errors –if too many increase communication power or if a mobile node it might move closer to the destination

Example: Consensus Classical consensus: all correct processes agree on one value –No power constraints –No real-time constraints –Does not scale well to dense networks –Approximate agreement (some work here) - on sets of values (physical quantities) New Solutions ?

New Concept of Consensus Termination: every correct processor eventually decides some value Uniform Agreement: no two processors decide differently Group Membership: join/leave - everyone knows who is in the group Termination: “at least n” correct processors decide some value by time t Group Agreement: at least n processors decide the same value within epsilon Area/Function Membership: join/leave an area or by function Classical New Definitions

Example: Group Management (Tracking) Base Station

Group Management - API –Create_Group(name,function,criterion,atleast,acc uracy) - implicit and explicit –Destroy_Group(name) –Join() –Leave() –Move_COG() –Expand() -- to gain sensing confidence –Shrink() -- to save power –Commit(grp_ID) - to synchronize group re- configurations

What’s Hard Multiple targets Crossing targets False Alarms –Depends on (changing) environment, sensors, confidence tradeoffs, noise, lost messages, …) Speed of targets Uniqueness of targets Classify targets Proper abstractions Save power/min. commun.

The Essence Power Other limited resources (BW, CPU, …) Extreme Scale Changing “everything” / uncertainty Aggregation –unimportant individual nodes –decentralized, very simple algorithms What I do impacts you (collisions) – mutual exclusion

Six Themes Routing Power Localization Security Paradigms Theory Are there others? Yes…..

Routing Solutions must be –Power aware –Robust to lost messages, dead motes, voids –Real-time –Communication range variations –Moving end points –Amount of state information –Extreme Scale –Secure

Power Example Algorithms –AFECA – power up and power down with time proportional to the number of neighbors –GAF – create grid and keep at least one mote alive in each grid (rotate among them in the grid) –SBPM – no grids; non-deterministic; minimize connectivity; decentralized; complete sensing coverage (60% savings over no power management) –Differentiated Surveillance 50% less energy than “best” other solution

Power Other power savings: –Vary transmission power –Turn off devices not needed On – all devices on Off – microprocessor in low power state so that registers/memory are not lost and clock interrupt can occur –Checking – microprocessor and radio are on –Choose routes that minimize power –Aggregate messages to save power

Localization Space (localization) and Time (clock sync) Basis –Environmental monitoring – where and when events occurred Localization is a function of –Hardware available, cost requirement, signal propagation model, timing and energy requirements, network makeup, nature of environment, node and beacon density, time sync, communication costs, error requirements, device mobility, …

Security What is the single most important issue that could prevent WSNs from wide scale deployment? –Security –2 nd issue -> Privacy At application level –Authenticity and integrity Security of each service (examples) –Routing: non-secure if a single node is captured! Eavesdrop or change message Flood Insidious unintended consequences of collecting data –Monitor oceans for fish migration (data mine location of submarine fleet)

Security Localization –Attacker can report he is close to everyone –Chirp then wait then transmit to give false location (normally chirp and transmit simultaneously – measure signals difference) Network Discovery –Provide false messages to create false topology –Prevent convergence

Paradigms Virtual Machines SQL and data services models EnviroTrack Tie to physical systems/physics Swarm computing Biological metaphors

Theory Theory of computation for WSN Emerging behavior of local/decentralized algorithms New graph theory New spatial-temporal analysis Aggregate control theory Utilization Equivalent Bounds Modeling and Analysis What are the fundamental scientific questions

Other Key Issues (1) Sensing/communication range ratio Sensing/communication/power tradeoffs Sensing Range Communication Range What if the opposite?

Other Key Issues (2) Reality programming –Robust to faults –Sensor realities Don’t believe one reading Hysteresis Sensor fusion Activation delays Avoid false alarms Self-Calibration

Other Key Issues (3) Limited capacities Rapid dynamics Scaling factors and implications on behaviors –Extreme scaling Insidious interactions –High density with many motes off to enable long system lifetime; turn on when activity happens then too many with many collisions and poor response

Other Key Issues (4) Architecture – hierarchy of control/capability/functionality Size of targets/events (point/area) Fire X Explosion

Middleware Services Non-traditional –Configuration service –Automatic calibration –Network programming –Reset services –Management services

Middleware Services Real-Time Routing –SPEED – spatial-temporal concept Application Independent Data Aggregation –AIDA – feedback control Localization –APIT – realities of wireless world

Sensor Net Routing End-to-end Real-time Collisions Congestion Destination Source Assumption: Nodes know location

SPEED USE VELOCITY

Application Independent Data Aggregation Expensive to acquire the “channel” Small data packets Group data packets into 1 MAC packet Works in addition to other data aggregation techniques which are based on semantics

Major Architectural Difference

FIXED SCHEME Accumulate N packets N: degree of aggregation –FIXED –On Demand –Adaptive/FC T: Time out for old packets when accumulation rate is slow

DYNAMIC/Adaptive FC Adaptive choice of N Take into account the output Queue delay Delay is used to adjust the output queue push rate and degree of aggregation

Localization Determine the geographic location of each node with a high degree of accuracy (necessary for application) –Applications search and rescue disaster relief target tracking –Protocols location aware routing guaranteeing sensing coverage location directory services Fundamental and Enabling Service

Radio Model in Evaluation Radio Model DOI = Degree of Irregularity DOI = 0.05 DOI = 0.2

X Known: Signal strength is not good indicator of distance over the entire region Hypothesis: Signal strength IS accurate enough for nodes very close to each other!

Testing Hypothesis

Summary (Much) Current Distributed Systems Technology –wired networks, powerful nodes, highly reliable nodes, interaction with users, fixed numbers of resources/team members, unlimited power,... Embedded (Large Scale) Distributed Systems –wireless, simple nodes, unreliable nodes, interaction with the environment, resources being added and deleted continuously, power management needed, …