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CSCI 599 IES Spring 2002:
Building Efficient Building Efficient Wireless Networks with Wireless Networks with
Low-Level NamingLow-Level Naming Presented By: -
Jayman DalalJayman [email protected]
CSCI 599 - IES Spring 2002, USC
AuthorsThe authors are researchers at USC/Information Sciences Institute. Work carried out as a part of SCADDS project.
• John Heidemann
• Ramesh Govindan
• Deborah Estrin
• Fabio Silva
• Chermek Intangonwiwat
• Deepak Ganesan
CSCI 599 - IES Spring 2002, USC
Presentation Outline• Motivation
• Introduction & Related Work
• Architecture
• Implementations
• Application Techniques
• Experiment Results & Evaluation
• Summary and Critique
CSCI 599 - IES Spring 2002, USC
Motivation
• Emergence of Sensor Networks
• Lack of a concrete software architecture supporting following in sensor networks:– named data
– in-network processing
– nested queries
• Unavailability of operational testbed
CSCI 599 - IES Spring 2002, USC
Introduction & Related Work
• Comparison of conventional and sensor networks.
• Key contributions of the research work
• Related attribute based naming systems
• Related systems using in-network processing
• Comparable sensor-network-specific systems
CSCI 599 - IES Spring 2002, USC
Comparison of Conventional & Sensor Networks
Conventional Networks
• High Bandwidth
• Low Delay
• Throughput is primary constraint
Sensor Networks
• Plentiful processing time
• Bandwidth primary constraint
CSCI 599 - IES Spring 2002, USC
Key Contributions of this Research Work
• Attribute-based naming scheme with flexible matching rules.
• Showing how this approach to naming enables application specific, in-network processing
CSCI 599 - IES Spring 2002, USC
Related Attribute based naming systems• Attribute based naming systems based on top of general
purpose networks
– Univers and Yellow-pages naming at UA
– X.500 and LDAP
• Attribute based communication for structuring distributed systems
– Structuring distributed programs using CPUs
– ISIS and Information Bus
• Using attribute based primitives for specific problems
– Reliable Multicast communication using named data
– Distributed Whiteboard
CSCI 599 - IES Spring 2002, USC
Related Systems using In-Network Processing
• Active Services
– Assumes roughly equivalent distances between all service providing nodes
– This work assumes communication cost between nodes vary greatly
• Active Networks
– Target internet-like domains
– This work targets sensor network domains
• Adaptive Web Caching and Peer-to-Peer file processing
CSCI 599 - IES Spring 2002, USC
Comparable Sensor-Network-Specific Systems• Internet Ad-Hoc Routing
– Does not support in-network processing
• Jini & Ninja Service Discovery Service– Distribute processing to user nodes– Suitable for LAN – High bandwidth
• Piconet– Tiered architecture and energy conserving– Lacks attribute based naming and in-network processing
• SPIN– Globally unique identifiers for individual sensors– Does not consider application-specific in-network processing
CSCI 599 - IES Spring 2002, USC
Comparable Sensor-Network-Specific Systems• Intentional Naming Systems
– Very similar in use of attributes and dynamically location of devices
– Uses an overlay network over IP-based Internet
• LEACH – Use of in-network data compression
• DataSpace– Use of IPv6 multicast addresses corresponding to geographic
locations.
• COUGAR– Use of centralized query translation and distributed processing
• Declarative Routing– Very similar to this research work
CSCI 599 - IES Spring 2002, USC
Architecture
Communication Architecture Components
• Directed Diffusion
• Attribute Tuples and Matching Rules
• Filters
CSCI 599 - IES Spring 2002, USC
Directed Diffusion
• A data-centric communication paradigm for sensor networks
• Goal is to establish an efficient n-way communication mechanism
CSCI 599 - IES Spring 2002, USC
Directed Diffusion - Definitions
• Interest: A list of attribute-value pairs describing a task
• Sink: Node originating the interest
• Source: Sensor node that matches the interest, collects data and sends it back
• Gradient: Direction towards which data matching and interest flows and status of demand
CSCI 599 - IES Spring 2002, USC
Directed Diffusion
• Intermediary data is cached as it propagates from source to sink.
• Reinforcement
• Negative Reinforcement
CSCI 599 - IES Spring 2002, USC
Attribute Tuples & Matching Rules
• Interests and data messages are composed of attribute-value-operation tuples.
• Keys: Identify attributes. Drawn from central authority.
• Operations: Define interactions of data messages and interests. e.g. GT, LT, GE, LE, EQ, EQ_ANY, IS.
• Actual: Literal or bound value
• Formal: Comparison or unbound value.
CSCI 599 - IES Spring 2002, USC
Attribute Tuples & Matching Rules
• One-way Match: Compares all formal parameters of one attribute set against actuals of another.
given two attribute sets A and Bfor each attribute a in A where a.op is a formal {
matched = falsefor each attribute b in B where a.key =
b.key and b.op is an actualif a.val compares with b.val using a.op, then matched = true
if not matched then return false(no match)}
• Complete Match: One-way match succeeds in both directions
CSCI 599 - IES Spring 2002, USC
Interactions of Diffusion & Matching• Consider an example user query
{class IS interesttype IS four-legged-animal-searchinterval IS 20msduration IS 10sx GE -100x LE 200y GE 100y LE 400}
• Sensor detects something it responds with{class IS datatype IS four-legged-animal-searchinstance IS elephantx IS 125y IS 220intensity IS 0.6confidence IS 0.85timestamp IS 1:20}
CSCI 599 - IES Spring 2002, USC
Filters• A novel concept.
• Application-specific code invoked when data enters a node.
• Influences manipulation of data, caching of data and its further route.
• Can be hard coded at design time or distributed as mobile code packages.
CSCI 599 - IES Spring 2002, USC
Implementations
• SCADDS diffusion version 3 or the macro diffusion (PC/104 Node)
• MIT Lincoln Labs’ Declarative Routing (WINSng 1.0 node)
• Micro Diffusion (UCB Rene Mote
CSCI 599 - IES Spring 2002, USC
Macro Diffusion & Declarative Routing
• Run on Linux on desktop PCs and PC/104-based sensor nodes and on WINSng 1.0 sensor nodes.
• Use of publish/subscribe APIs having event-driver programming style.
• Differences:– Filters
– Routing Mechanism
CSCI 599 - IES Spring 2002, USC
Micro Diffusion
• Scaled down version containing gradients, single tag attributes and limited filters
• Runs on 8 bit CPU and 8KB memory
• Motes and micro diffusion can be used in areas requiring dense sensor distribution.
CSCI 599 - IES Spring 2002, USC
Application Techniques for Sensor Networks
• In-network data aggregation
• Nested Queries
CSCI 599 - IES Spring 2002, USC
In-network Data Aggregation
• Multiple detection of target results in unnecessary communications.
• Energy can be conserved if data is aggregated:
– Binary value – there was a detection.
– An area – there was a detection in quadrant 2.
– An application specific aggregation.
• Caching of data by intermediate sensors. Suppresses propagation of duplicate data.
CSCI 599 - IES Spring 2002, USC
Nested Queries
• Triggering of secondary sensor based on status on primary.
• Advantage: Triggered sensor directly interprets the initial sensor’s data => Reduction in network traffic and latency
• Challenges:– How to robustly match initial
and triggered sensors?
– How to select a good triggered sensor?
Simple Approach
Nested Approach
CSCI 599 - IES Spring 2002, USC
Aggregation Benefits
• Sink – 28; Source – 13, 16, 22, 25. 4 Hops.
• Measures aggregate bytes sent by diffusion for networks with and without data aggregation.
• Suppression is able to reduce network traffic by 42% for four sources.
CSCI 599 - IES Spring 2002, USC
Nested Query Benefits• User – 39; Audio Sensor –
20; Light Sensors – 13, 16, 22, 25
• One hop from light sensor to audio, and two hops from there to user.
• Measures percentage of light change events that successfully result in audio data delivered to user.
• Flat queries suffer more loss than nested queries
CSCI 599 - IES Spring 2002, USC
Summary
• An approach to communication in highly constrained distributed systems like sensor networks focusing on attribute-based naming and in-network processing.
• In-network processing with filters, data aggregation, nested queries help reduce network traffic and conserve energy.
CSCI 599 - IES Spring 2002, USC
Critique – Main Contribution
• Introduces a topology independent attribute-based naming system for low-level communication.
• Uses application-level in-network processing using filters, data aggregation and nested queries.
• This technique provides many advantages for wireless sensor networks by reducing communication overhead and overall energy consumption.
CSCI 599 - IES Spring 2002, USC
Critique – Claims
• Topologically independent low-level naming helps in reducing transmission thereby conserving energy
• In-network processing done using nested queries and filters, close to the place where data is sensed, reduces communication costs.
CSCI 599 - IES Spring 2002, USC
Critique – Assumptions
• Sufficient amount of storage space is available to cache the amount of data generated.
• In-network processing time is minimal and does not affect real-time communication, if required.
CSCI 599 - IES Spring 2002, USC
Critique – Concerns
• No clear-cut definition of “a neighbor”.
• Security Issues: Replacing the node with a malicious one or signal trapping.
• Needs to have more robust communications.
• Issues regarding mobility of nodes. Does the gradients still remain valid?