![Directed Diffusion Naming Task descriptions are named by a list of attribute value pairs that describe a task eg: type=wheeled vehicle // detect vehicle location interval=20ms // send events every 20 ms duration=10s // for the next 10s rect=[-100,100,200,400]// from sensors within rectangle Interests and Gradients Interest is usually injected to the network from sink For each active task, sink periodically broadcasts an interest message to each of its neighbors Initial interest contains the specified rect and duration attributes but larger interval attribute Interests tries to determine if there are any sensor nodes that detect the wheeled vehicle(exploratory).](https://image.slidesharecdn.com/directeddiffusionforwirelesssensornetworking-130704095551-phpapp02/75/Directed-diffusion-for-wireless-sensor-networking-11-2048.jpg)
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Directed diffusion for wireless sensor networking
This document summarizes the key ideas of the "Directed Diffusion for Wireless Sensor Networking" paper. It introduces directed diffusion as a data-centric paradigm for wireless sensor networks that is designed for robustness, scalability, and energy efficiency. The core concepts of directed diffusion are interests, data, gradients, and reinforcement, which work together to efficiently route queries to sensor data in the network. Through localized interactions and data aggregation, directed diffusion is shown to significantly reduce energy consumption compared to flooding-based approaches in wireless sensor networks.
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Introduction to wireless sensor networks, discussing architectures, properties, and deployment.
Key problems in sensor networks, introducing directed diffusion as a solution focusing on data-centric approaches.
Overview of directed diffusion components like interests, data, and gradients for efficient communication.
How sensor nodes manage interest packets and return data, including tasks for efficient sampling.
Discussion on exploratory data versus full data pathways, reinforcement methods, and repair techniques.
Design considerations and simulation environments used to test directed diffusion with various metrics.
Metrics evaluation including average energy dissipation, packet delay, and delivery ratio in various scenarios.
Pros and cons of directed diffusion in sensor networks and its resilience, noting areas for further study.
Final thoughts on directed diffusion, emphasizing energy efficiency and the need for improved MAC protocols.
Directed diffusion for wireless sensor networking
- 1. Directed Diffusion forWireless Sensor Networking Authors: Chalermek Intanagonwiwat, Ramesh Govindan, Deborah Estrin, John Heidemann, and Fabio Silva Presented by: Md. Habibur Rahman (AIUB) Course: Sensor Networks and Wireless Computing Instructor: Md. Saidur Rahman (AIUB)
- 2. Wireless Networks Variety ofarchitectures Single hop networks Multi-hop networks
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- 4. Motivation Properties of SensorNetworks Data centric approach: communication based named data, not named nodes No central authority Resource constrained like limited power, computation capacities and memory Nodes are tied to physical locations Nodes may not know the topology due to rapidly changes of topology Nodes are generally stationary Q: How can we get data from the sensors?
- 5. Introduction(1/2) A sensor networkis composed of a large number of sensor nodes, which are densely deployed either inside the phenomenon or very close to it. Random deployment Cooperative capabilities Sensor nodes scattered in a sensor field Multi-hop communication is expected Motivating factors for emergence Applications Advances in wireless technology
- 6. Introduction(2/2) A region requiresevent- monitoring Deploy sensors forming a distributed network Wireless networking Energy-limited nodes On event, sensed and/or processed information delivered to the inquiring destination
- 7. The Problem Whereshould the data be stored? How should queries be routed to the stored data? How should queries for sensor networks be expressed? Where and how should aggregation be performed? EventEvent Sources Sink Node Directed Diffusion A sensor field
- 8. Directed Diffusion Designed forrobustness, scaling and energy efficiency Data centric Sinks place requests as interests for named data Sources satisfying the interest can be found Intermediate nodes can cache or transform data directly toward sinks Attribute-naming based Data aggregation Interest, data aggregation and data propagation are determined by localized interactions.
- 9. Directed Diffusion Four mainfeatures: Interests, Data, Gradients & Reinforcement Interest: a query or an interrogation which specifies what a user wants. Data: collected or processed information Gradient: direction state created in each node that receives interest. Gradient direction is toward the neighboring node which the interest is received Events start flowing from originators of interests along multiple gradient paths.
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- 11. Directed Diffusion Naming Taskdescriptions are named by a list of attribute value pairs that describe a task eg: type=wheeled vehicle // detect vehicle location interval=20ms // send events every 20 ms duration=10s // for the next 10s rect=[-100,100,200,400]// from sensors within rectangle Interests and Gradients Interest is usually injected to the network from sink For each active task, sink periodically broadcasts an interest message to each of its neighbors Initial interest contains the specified rect and duration attributes but larger interval attribute Interests tries to determine if there are any sensor nodes that detect the wheeled vehicle(exploratory).
- 12. Interests Interests: a querywhich specifies what a user wants by naming the data. Sink periodically broadcasts interest messages to each neighbor. Includes the rectangle and duration attributes from the request. Includes a larger interval attribute All nodes maintain an interest cache
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- 14. Sensor Node Receives interestpacket Node is within the rectangle coordinates Task the sensor system to generate samples at the highest rate of all the gradients. Data is sent using unicast
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- 16. Exploratory versus Data Exploratoryuse lower data rates Once the sensor is able to report the data a reinforcement path is created Data gradients used to report high quality/high bandwidth data.
- 17. Positive Reinforcement Sink re-sendsoriginal interest message with smaller interval Neighbor nodes see the high bandwidth request and reinforce at least one neighbor using its data cache This process selects an empirically low-delay path.
- 18. Multiple Sources &Sinks The current rules work for multiple sources and sinks
- 19. Negative Reinforcement Repair canresult in more than one path being reinforced Time out gradients Send negative reinforcement message
- 20. Repair C detects degradation Noticesrate of data significantly lower Gets data from another neighbor that it hasn’t seen To avoid downstream nodes from repairing their paths C must keep sending interpolated location estimates.
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- 22. Simulation Environment NS2, 50nodes in 160x160 sqm., range 40m Random 5 sources in 70x70, random 5 sinks Average node density constant in all simulations Comparison against flooding and omniscient multicast 1.6Mbps 802.11 MAC Not realistic (reliable transmission, RTS/CTS, high power, idle power ~ receive power) Set idle power to 10% of receive power, 5% of transmit power
- 23. Metrics Average dissipated energy pernode energy dissipation / # events seen by sinks Average packet delay latency of event transmission to reception at sink Distinct event delivery # of distinct events received / # of events originally sent Both measured as functions of network size
- 24. Average Dissipated Energy In-networkaggregation reduces DD redundancy Flooding poor because of multiple paths from source to sink flooding DiffusionMulticast Flooding DiffusionMulticast
- 25. Delay DD finds leastdelay paths, as OM – encouraging Flooding incurs latency due to high MAC contention, collision flooding Diffusion Multicast
- 26. Average energy anddelay Average delay is misleading Directed Diffusion is better than Omniscient Multicast!? Omniscient multicast sends duplicate messages over the same paths Topology has little path diversity Why not suppress messages with Omniscient Multicast just as in Directed Diffusion?
- 27. Event Delivery Ratiounder node failures Delivery ratio degrades with higher % node failures Graceful degradation indicates efficient negative reinforcement 0 % 10% 20%
- 28. Analysis Energy gains aredependent on 802.11 energy assumptions Directed Diffusion has lowest average dissipated energy Data aggregation and negative reinforcement enhance performance considerably Differences in power consumption disappear if idle– time power consumption is high Can the network always deliver at the interest’s requested rate? Can diffusion handle overloads? Does reinforcement actually work?
- 29. Continued…. Pros Energy – Muchless traffic than flooding. Latency – Transmits data along the best path Scalability – Local interactions only Robust – Retransmissions of interests Cons The set up phase of the gradients is expensive Need of and adequate MAC layer to support an efficient implementation. The simulation analysis uses a modified 802.11 MAC protocol Design doesn’t deal with congestion or loss Periodic broadcasts of interest reduces network lifetime Nodes within range of human operator may die quickly
- 30. Conclusions Directed diffusion, aparadigm proposed for event monitoring sensor networks Energy efficiency achievable Diffusion mechanism resilient to fault tolerance Conservative negative reinforcements proves useful More thorough performance evaluation is required MAC for sensor networks needs to be designed carefully for further performance gains
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