Building Efficient Wireless Networks with Low-Level Naming CSCI 599 IES Spring 2002: Building Efficient Wireless Networks with Low-Level Naming Presented By: - Jayman Dalal

CSCI IES Spring 2002, USC Authors The 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 IES Spring 2002, USC Presentation Outline Motivation Introduction & Related Work Architecture Implementations Application Techniques Experiment Results & Evaluation Summary and Critique

CSCI 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 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 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 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 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 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 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 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 IES Spring 2002, USC Architecture Communication Architecture Components Directed Diffusion Attribute Tuples and Matching Rules Filters

CSCI 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 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 IES Spring 2002, USC Directed Diffusion Source Event Interests Sink Gradients

CSCI IES Spring 2002, USC Directed Diffusion Intermediary data is cached as it propagates from source to sink. Reinforcement Negative Reinforcement

CSCI 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 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 B for each attribute a in A where a.op is a formal { matched = false for each attribute b in B where a.key = b.key and b.op is an actual if 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 IES Spring 2002, USC Interactions of Diffusion & Matching Consider an example user query {class IS interest type IS four-legged-animal-search interval IS 20ms duration IS 10s x GE -100 x LE 200 y GE 100 y LE 400} Sensor detects something it responds with {class IS data type IS four-legged-animal-search instance IS elephant x IS 125 y IS 220 intensity IS 0.6 confidence IS 0.85 timestamp IS 1:20}

CSCI 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 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 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 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 IES Spring 2002, USC Application Techniques for Sensor Networks In-network data aggregation Nested Queries

CSCI 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 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 IES Spring 2002, USC Evaluation Aggregation Benefits Nested query Benefits

CSCI IES Spring 2002, USC Node positions in sensor testbed at ISI

CSCI IES Spring 2002, USC Aggregation Benefits Sink – 28; Source – 13, 16, 22, 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 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 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 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 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 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 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?

CSCI IES Spring 2002, USC Questions

CSCI IES Spring 2002, USC