Network and Systems Laboratory nslab.ee.ntu.edu.tw Copyright © Wireless Sensor Networks: Classic Protocols Polly Huang Department of Electrical Engineering National Taiwan University

Network and Systems Laboratory nslab.ee.ntu.edu.tw Classic Protocols Designed for outdoor sensor networks Directed diffusion S-MAC Copyright ©

Network and Systems Laboratory nslab.ee.ntu.edu.tw Directed Diffusion Largely based on slides from Chalermek Intanagonwiwat & Deborah Estrin Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw In Short A data dissemination mechanism fitting into the data-centric communication paradigm for sensor networks Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Sensor Networks Or another One way Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Applications Scientific: eco-physiology, biocomplexity mapping Infrastructure: contaminant flow monitoring (and modeling) Engineering: monitoring (and modeling) structures Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw The Real Need Specialized communication in a wild wide space Specialized: application dependent Wild: little or no infrastructure Wide: expensive to build/use communication infrastructure Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Applications: A Longer List Science: monitoring temperature change on a volcanic island Engineering: monitoring power use of industrial district Infrastructure: monitoring passenger traffic at MRT stations Military: tracking enemy migration in a dessert Disaster: emergency relief after Gozzila taking a short tour of Tokyo Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Common Vision Embed numerous distributed devices to monitor and interact with physical world Exploit spatially and temporally dense, in situation, sensing and actuation Network these devices so that they can coordinate to perform higher-level tasks Requires robust distributed systems of hundreds or thousands of devices Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Challenges Tight coupling to the physical world and embedded in unattended systems Different from traditional Internet, PDA, Mobility applications that interface primarily and directly with human users But solutions might be applicable to the Internet, PDA, Mobility applications as well Untethered, small form-factor, nodes present stringent energy constraints Living with small, finite, energy source is different from traditional fixed but reusable resources such as BW, CPU, Storage Communications is primary consumer of energy in this environment R 4 drop off dictates exploiting localized communication and in- network processing whenever possible

Network and Systems Laboratory nslab.ee.ntu.edu.tw Energy the Bottleneck Resource Communication VS Computation Cost [Pottie 2000] E α R 4 10 m: 5000 ops/transmitted bit 100 m: 50,000,000 ops/transmitted bit Avoid communication over long distances Cannot assume global knowledge, cannot pre- configure networks Achieve desired global behavior through localized interactions Empirically adapt to observed environment Can leverage data processing/aggregation inside the network Can leverage data processing/aggregation inside the network Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw In-Network Processing Sensor technology is advancing steadily Situations detected by the sensors can be surprisingly rich For example, all these at once Detecting a speech Inferring the location and identity of the speaker These information can be used to facilitate efficient dissemination of the recorded speech Suppressing speech coming from the same speaker Forwarding towards the likely listeners Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw New Design Themes Long-lived systems that can be untethered and unattended Energy efficient communication Self configuring systems that can be deployed ad hoc Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Approach Leverage data processing inside the network Exploit computation near data to reduce communication Achieve desired global behavior with adaptive localized algorithms (i.e., do not rely on global interaction or information) Dynamic, messy (hard to model), environments preclude pre-configured behavior Can ’ t afford to extract dynamic state information needed for centralized control or even Internet-style distributed control Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Why can ’ t we simply adapt Internet protocols and “ end to end ” architecture? Internet routes data using IP Addresses in Packets and Lookup tables in routers Humans get data by “ naming data ” to a search engine Many levels of indirection between name and IP address Works well for the Internet, and for support of Person- to-Person communication Embedded, energy-constrained (un-tethered, small-form-factor), unattended systems can ’ t tolerate communication overhead of indirection Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Therefore, Directed Diffusion Features Operations Evaluations Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Directed Diffusion Paradigm Data-centric communication Supported with distributed algorithms using localized interactions Application-specific in-network processing Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw IP Communication Organize system based on named nodes Per-node forwarding state Senders need to push data to the node address of sink Bob Alice To Bob My name is Alice. I am a 19-yr old girl… Chris I am Bob Bob there I am Bob Bob there I am Bob To Bob My name is Alice. I am a 19-yr old girl… To Bob My name is Alice. I am a 19-yr old girl… Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Data-Centric Communication Organize system based on named data Per-data diffusion state Sinks need to be specific about what data they’d pull Tell me about girls Tell me about girls Girl info goes there Tell me about girls Girl info goes there Tell me about girls Here’s a 19-yr old girl… Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Directed Diffusion Paradigm Data-centric communication Supported with distributed algorithms using localized interactions Application-specific in-network processing Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Localized Interaction Diffuse requests/interest across network Set up gradients to guide responses/data Diffuse responses/data based on the gradients (Pretty much the same as in the IP routing) Tell me about girls Tell me about girls Girl info goes there Tell me about girls Girl info goes there Tell me about girls Here’s a 19-yr old girl… Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Directed Diffusion Paradigm Data-centric communication Supported with distributed algorithms using localized interactions Application-specific in-network processing Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Without In-Network Processing Data are simply passed on Tell me about girls Tell me about girls Girl info goes there Tell me about girls Girl info goes there Tell me about girls Tell me about girls Here’s a 20-yr old girl… Here’s a 19-yr old girl… Here’s a 20-yr old girl… Here’s a 19-yr old girl… Here’s a 20-yr old girl… Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw With In-Network Processing Data are aggregated and then passed on Girl info goes there Here’re two 19+ yr old girls… Girl info goes there Here’s a 20-yr old girl… Here’s a 19-yr old girl… Here’re two 19+ yr old girls… Here’s a 20-yr old girl… Here’s a 19-yr old girl… Here’re two 19+ yr old girls… Application-specific Aggregation Here! Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Directed Diffusion Paradigm Data-centric communication Supported with distributed algorithms using localized interactions Application-specific in-network processing Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Example: Remote Surveillance Interrogation: e.g., “ Give me periodic reports about animal location in region A every t seconds ” e.g., “ Give me periodic reports about animal location in region A every t seconds ” Interrogation is propagated to sensor nodes in region A Sensor nodes in region A are tasked to collect data Data are sent back to the users every t seconds Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Basic Directed Diffusion Setting up gradients Source Sink Interest = Interrogation Gradient = Who is interested Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Basic Directed Diffusion Source Sink Sending data and Reinforcing the best path Low rate eventReinforcement = Increased interest Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Directed Diffusion and Dynamics Recovering from node failure Source Sink Low rate event High rate event Reinforcement Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Directed Diffusion and Dynamics Source Sink Stable path Low rate event High rate event Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Local Behavior Choices For propagating interests In this example, flood In this example, flood More sophisticated behaviors possible: e.g. based on cached information, GPS For data transmission Multi-path delivery with selective quality along different paths Multi-path delivery with selective quality along different paths probabilistic forwarding single-path delivery, etc. For setting up gradients data-rate gradients are set up towards neighbors who send an interest. data-rate gradients are set up towards neighbors who send an interest. Others possible: probabilistic gradients, energy gradients, etc. For reinforcement reinforce paths, or parts thereof, based on observed delays reinforce paths, or parts thereof, based on observed delays, losses, variances etc. other variants: inhibit certain paths because resource levels are low Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Simulation Study Key metric Average Dissipated Energy per event delivered indicates energy efficiency and network lifetime diffusion Compare diffusion to flooding flooding omniscient multicast centrally computed tree (omniscient multicast) Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Diffusion Simulation Details ns-2 Simulator: ns-2 Network Size: Nodes Transmission Range: 40m Constant Density: 1.95x10 -3 nodes/m 2 (9.8 nodes in radius) MAC: Modified Contention-based MAC Energy Model: Mimic a realistic sensor radio [Pottie 2000] 660 mW in transmission, 395 mW in reception, and 35 mw in idle Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Diffusion Simulation Surveillance application 5 sources are randomly selected within a 70m x 70m corner in the field 5 sinks are randomly selected across the field High data rate is 2 events/sec Low data rate is 0.02 events/sec Event size: 64 bytes Interest size: 36 bytes All sources send the same location estimate for base experiments All sources send the same location estimate for base experiments Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Average Dissipated Energy Average Dissipated Energy (Joules/Node/Received Event) Network Size Diffusion Omniscient Multicast Flooding Diffusion can outperform flooding and even omniscient multicast. WHY ? Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw In-network Processing Average Dissipated Energy (Joules/Node/Received Event) Network Size Diffusion With Suppression Diffusion Without Suppression Application-level suppression allows diffusion to reduce traffic and to surpass omniscient multicast. Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Negative Reinforcement Average Dissipated Energy (Joules/Node/Received Event) Network Size Diffusion With Negative Reinforcement Diffusion Without Negative Reinforcement Reducing high-rate paths in steady state is critical Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Summary of Diffusion Results Under the investigated scenarios, diffusion outperformed omniscient multicast and flooding Application-level data dissemination has the potential to improve energy efficiency significantly Duplicate suppression is only one simple example out of many possible ways. Aggregation (next) All layers have to be carefully designed Not only network layer but also MAC and application level Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Standard Standard energy model) Average Dissipated Energy (Joules/Node/Received Event) Network Size Diffusion Omniscient Multicast Flooding Standard is dominated by idle energy Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Contention-based protocol RTS-CTS-DATA-ACK RTS CTS Sender Receiver DATA ACK [Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specification, IEEE Std edition] Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw S-MAC Contention-based protocol RTS-CTS-DATA-ACK Listen interval Send packets Receive packets [W. Ye et al., “ An energy-efficient MAC protocol for wireless sensor networks ”, in INFOCOM 2002] Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Schedule synchronization Schedules can differ Neighboring nodes have same schedule Node 1 Node 2 sleep listen sleep listen sleep Schedule 2 Schedule 1 Border nodes: two schedules broadcast twice (Borrowed from S-MAC) Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Scheduling in S-MAC Unknown neighbors the same schedule Schedule 2 Schedule 1 Collision 1 Unicast Broadcast Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Questions? Copyright © 2008

Network and Systems Laboratory nslab.ee.ntu.edu.tw Copyright © Questions?