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Increasing Longevity in Sensor Networks

This presentation discusses the problem of energy consumption in sensor networks and explores existing approaches, such as power-aware routing and adaptive energy-conserving routing, to increase the longevity of the network. Future work and conclusions are also discussed.

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Increasing Longevity in Sensor Networks

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  1. L3 (Live & Let Live)- Increasing Longevity in Sensor Networks EE 228A Professor Walrand Contributors: Tanya Roosta Anshuman Sharma

  2. Outline • Introduction • Problem Definition • Existing Approaches • Our Approach • Future Work • Conclusion • Q&A

  3. Outline • Introduction • Problem Definition • Existing Approaches • Our Approach • Future Work • Conclusion • Q&A

  4. Introduction What are sensor networks • Networks comprised of hundreds to thousand nodes, where each node is a sensor • Examples of use include guidance and control, data collection and aggregation • Sensor nodes are designed to be • Low cost • Non obtrusive • Dynamically reprogrammable

  5. Outline • Introduction • Problem Definition • Existing Approaches • Our Approach • Future Work • Conclusion • Q&A

  6. Problem Definition • Sensors must be lightweight and compact • Limited Power Supply • Replenishing power is not an option • Important to minimize power consumption of each node to maximize battery life and lifetime of entire network • Existing network protocols stress on QoS (high throughput and low delay) and high bandwidth efficiency

  7. Problem Definition (cont…) Energy Consumption • Energy consumption occurs in three domains: sensing, data processing and communication. • In a wireless sensor network, communication is the major consumer of energy • Example For ground to ground transmission, it costs 3J to transmit 1 Kb over a distance of 100m. However, a general-purpose processor with 100 MIPS processing capability executes 300 million instructions for the same amount of energy

  8. Problem Definition (cont…) Design Challenges • Three main classes • Hardware • Wireless Networking • Application

  9. Problem Definition (cont…) Routing in Wireless Networks: Revisited • Direct Communication Protocol: Each sensor sends its data directly to the base station • Multi-hop routing protocol (MTE) Nodes route data destined to the base station through intermediate nodes • At first look it seems that a multi-hop approach would be able conserve more power

  10. Problem Definition (cont…) Multi-hop Routing Protocols • Table-driven (proactive) • Destination-Sequenced Distance-Vector Routing • Cluster Gateway Switch Routing • Wireless Routing Protocol • Source-initiated (Reactive) • Ad Hoc On-Demand Distance Vector Routing • Dynamic Source Routing • Temporally-Based Routing • Signal Stability Routing

  11. Outline • Introduction • Problem Definition • Existing Approaches • Our Approach • Future Work • Conclusion • Q&A

  12. Existing Approaches Power-Aware Routing: Metrics • Minimize energy consumed/packet: Minimizes the total energy consumed over n nodes • Maximize Time to Network Partition: A load balancing problem so that the response time is minimized • Minimize Cost/Packet: Assigns a cost function to each node and minimizes the total cost of routing a packet from that node

  13. Existing Approaches (cont…) Routing in Clustered Multi-hop Networks • Aggregate nodes into clusters controlled by a cluster-head • Clustering on the basis of either lowest-ID distributed clustering algorithm or highest-connectivity algorithm • Within a cluster, a cluster-head controlled token protocol used to allocate channel. • Cluster Routing Protocol

  14. Total system energy dissipated for the 100-node random network

  15. Existing Approaches (cont…) Adaptive Energy-Conserving Routing • BECA • Turn of radio power • Involvement of application layer information • Can increase latency and packet loss • AFECA • All the nodes do not need be involved • Exploiting node density • Can interchange nodes for routing purposes

  16. Existing Approaches (cont…) Adaptive Energy-Conserving Routing (cont…) • BECA • Nodes are in three possible states: sleeping, listening, active. • Start in sleeping state. Radio is off. • After a certain time, transition to listening state • If a node has data to transmit it transitions to active state

  17. Existing Approaches (cont…) Adaptive Energy-Conserving Routing (cont…) • AFECA • Used in densely-populated networks • Each node estimates its neighborhood • Each node increases its sleeping time proportional to the number of nodes in its neighborhood

  18. BECA versus AODV for different values of sleeping time • The latency for unmodified AODV is fixed • The latency grows roughly linearly • The growth is slightly lower at higher traffic rates

  19. Percentage of energy saved is (Er - Es) / Er • Less saving for higher traffic rates since more nodes in active mode • High values of sleeping time give no energy improvement

  20. PE is the loss rate • PE=P/E where P is the size of data delivered and E is the total energy consumed by all nodes • We can use PE to determine an optimal value for the sleeping time

  21. Assumption: Unlimited amount of energy in the nodes As expected AFECA and BECA do worse in terms of latency and packet loss than unmodified AODV

  22. AFECA has a better energy consumption than BECA as expected

  23. AFECA aggressive power savings result in the consistently highest efficiency

  24. Assumption: The nodes have limited amount of power BECA protocol is about 20% longer and AFECA is about 55% longer than unmodified AODV when the energy in the nodes is limited.

  25. Outline • Introduction • Problem Definition • Existing Approaches • Our Approach • Future Work • Conclusion • Q&A

  26. Our Approach Insight • Computation is much cheaper than communication • Use of distributed approach to reduce • Total number of transmissions • Energy dissipated in the network • Application-level/ higher layer feedback is important • Establish trade-offs (complexity vs. performance improvement, etc)

  27. Our Approach (cont…) • Radio Model (First Order) ETx(k,d) =ETx-elec(k) + ETx-amp(k,d) =Eelec*k + amp*k*d2 ERx(k) =ERx-elec(k) =Eelec*k ETx-elec = ERx-elec = Eelec (Energy dissipated to run Rx/Tx) amp (Energy dissipated for amplifying to get good gain) Source: Energy-Efficient Communication Protocol for Wireless Microsensor Networks: MIT

  28. Our Approach (cont…) Additions to Radio Model • Does not consider energy consumption while radios are idle • Inclusion of idle time based on experiments with WaveLAN radios • Most of the time the radio is idle, hence idle time dominates energy consumption • Add term  idle (idle energy expended per unit time)

  29. Our Approach (cont…) • Important to determine critical transmission range • Let there be • n total nodes • k cliques that we intend to form • Use modified Prim algorithm to form cliques of at least 3 nodes • Why the magic number 3?

  30. Our Approach (cont…) • Pick k nodes at random (for each of the k cliques) • k nodes are temporary cluster-heads • Start with some minimum radius of discovery -  • Goal is to discover a minimum of 3 nodes for each clique • Increments of , if cannot find any node in the periphery • After the first node is discovered it tries to look for another node, incrementing by  each time

  31. Our Approach (cont…) • All three nodes then adjust their transmission power to reach other • This results in a Hamiltonian Cycle • If more than 3 nodes are possible without increasing power then OK to have > 3 nodes in clique • After forming cliques, use TDMA to allocate time-slots for nodes to be cluster-head. • The nodes also use TDMA to schedule updates to cluster-head (intra-clique communication).

  32. Our Approach (cont…) • The other nodes are put to sleep (turn-off radios) when not communicating, similar to PAMAS • A cluster head is responsible for discovering other cliques and sharing information within the clique. • Possibility of adding multiple hierarchies depending upon the trade-off between complexity and advantages

  33. Our Approach (cont…) Considerations • GPS is available but might not be viable • Next generation design of Low power ICs can make adjusting duty cycle easy • Exploring node density as a measure of reducing computation and communication overhead • CDMA codes allow efficient use of the channel bandwidth

  34. Outline • Introduction • Problem Definition • Existing Approaches • Our Approach • Future Work • Conclusion • Q&A

  35. Future Work • Evaluating model through simulations • Tuning density to trade operational quality against lifetime • Using multiple sensor modalities to obtain robust measurements • Exploiting fixed environmental characteristics • Using a more comprehensive radio model that takes into account time to wake up from sleep cycles • Exploring of various benchmarks for “lifetime” of a network

  36. Outline • Introduction • Problem Definition • Existing Approaches • Our Approach • Future Work • Conclusion • Q&A

  37. Conclusion • Our model is based on work that has already been done • We exploit characteristics of proven approaches • Simulations would provide a measure of advantages incurred by using our approach

  38. Outline • Introduction • Problem Definition • Existing Approaches • Our Approach • Future Work • Conclusion • Q&A

  39. Q&A

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