Location-Aided Energy-Aware Routing for UWB Sensor Networks

1. A Location-aided Energy-aware Routing Method for UWB Sensor Networks Xizhi An and Kyungsup Kwak Graduate School of Information Technology and Telecommunications, Inha University, Korea Mykonos, Greece, June 2006

2. Outline • Introduction • System Model • Routing Scheme Design • Simulation Results • Conclusions

3. Introduction • Sensor Network (SN) • Vast usages in people's life; • SN consists of, possibly a large number of, tiny devices with sensing, computing, and communicating capabilities. • Design issues • routing scheme, power management, data transfer protocols, etc. • Energy awareness is essential. • Ultra-WideBand (UWB) Technique • UWB is a promising candidate for sensor network applications. • low complexity and low cost; • noise-like signal; • robust to multipath fading and jamming; • high time-domain resolution; • UWB has the lowest consumed energy per bit among different low-power RF technologies.

4. Introduction • In this paper • emphasize the importance of utilizing location information in the route selection; • positioning capability of UWB physical layer • try to find the relationship between energy consumption and route properties; • derive a new routing metric concerned with energy.

5. System Model • TH-PPM UWB PHY [Win '00] • Max. achievable bit rate = 18 Mbps • The relationship between the BER and the SNR can be obtained through the link-level simulation. • Very low transmit power • Max. radio coverage radius of one node ~ 20 meters; • Nodes transmit at the max. power if they have data to send; • Positioning capability • Very short pulse wave ~ very high time resolution; • The location of node is estimated from the difference of the arrival time of pulse waves received; • Statistical UWB indoor path-loss model [Ghassemzadeh '03]

6. System Model • Dynamic Channel Coding MAC [Boudec '04] • rate-compatible punctured convolutional codes: adapt the data rate according to the interference and the channel condition; • private MAC ~ resolve contentions • Transport Layer • UDP, packet size = 512 bytes • QOS: transfer delay and packet delivery ratio • Energy considerations • Satisfy requirements of upper layers and consume as less as possible energy • 4 states of node (for the packet transmitting-receiving process) • Transmit : 60 mW • Receive : 30 mW • Idle : 0 mW • Sleep : 0 mW

7. Routing Scheme Design • Impact of Multi-hop Route • Energy consumption and QOS issues • Total Energy Consumption per Packet Delivered (Ep). • End-to-end Packet Transfer Delay (Td). • Packet Delivery Ratio (PDR). • A Simple Line Network Topology • Distance-related Parameters: • D: distance between the Sender and the Sink • L / Li : length of hop(s) • LR: length of the route connecting the Sender and the Sink • H: number of hops belonging to the route • C: max. radio-coverage radius of node • F : optimal forward distance, determined by energy efficiency

8. (a) Ep curve (c) PDR curve (b) Td curve Routing Scheme Design • Empirical Results of the Line Network

9. Routing Scheme Design • Some interesting findings • Energy Consumption • Processing Loss • a high-level combination of packet encoding, buffering, processor operating, competing and collision resolving, etc. • Path Loss • PL is proportional to the propagation distance raised to some exponent. • Larger the distance is, lower the SNR. • low-rate coding or retransmission? Efficiency is reduced. • Compromise – a kind of optimal forward distance (F)? • close nodes: better signal reception, but higher processing loss and larger number of hops • far nodes: lower processing loss and less number of hops, but worse signal reception • Relationship betweenEnergy and QOS

10. Routing Scheme Design • Energy Metric of Route • route lengthLR: • the lower bound of Ep : • the additional punishment on thehop longer than F: • the energy metric of route: • β accounts for energy loss of relay node.

11. Routing Scheme Design • Routing Algorithm -- eLAR • Based on the Dijkstra's Algorithm with modification; • The length (or cost) of a hop (or edge) is not directly used, but the overall Emetric of the route containing that hop is evaluated. • "Shortest" path problem ~ find the route with min. energy loss; • Min.Loss Tree rooted at a Source Node • V = {v1, v2, …, vN}; // set of all nodes, N: number of nodes • Y = {v1}; // v1 is the source node • F = Φ; // min. loss tree (initially empty) • while (V ≠ Y) { • select a node v from V–Y, that has a min. energy loss • from v1, using only nodes in Y as relay nodes; • add the new node v to Y; • add the hop (on the min-loss route) that touches v to F; • }

12. Routing Scheme Design • eLAR Implementation • Each node derives its routing table from its min. loss tree; • Complexity ~ O(N2) • Some simplification can be made. • Packets can be forwarded sequentially without an extra route header. • Routing Scheme: • Step 1. The source node (src) investigates whether the destination node (dst) is in its near vicinity. If the distance between src and dst, D, is not larger than F, src directly transmits packets to dst; otherwise, src searches its routing table to find the next-hop node (nxt) that is on the minimum loss route to dst and then forwards packets to nxt. • Step 2. The relay node (rly) checks the destination (dst) of each received packet. According to the distance between rly and dst, rly performs the same action as in Step 1.

13. Simulation Results • Network Configuration: • Platform: network simulator ns-2 v2.26; • Routing parameters: F = 10 m, C = 22 m, λ = 1,β = 0.01; • Scenario Area: 50 m × 50 m • Distribution of nodes’ location: random points; • Number of nodes: 48 (1 sink, 47 sensors); • Routing Scheme: LAR vs. AODV.

14. Simulation Results • Static Scenario -- Random network topology

15. Simulation Results • Performance Comparison -- Ep

16. Simulation Results • Performance Comparison -- Td

17. Simulation Results • Performance Comparison – PDR

18. Simulation Results • Static Scenario -- energy loss tree rooted at the sink

19. Conclusions • This is a practical work concerned with routing design. • Two main factors of energy consumption are taken into consideration and the relationship between energy and QOS is preliminarily discussed. • A new energy metric is developed based on a priori knowledge. • A corresponding routing scheme “eLAR” is proposed. • Simulation results demonstrate eLAR’s effectiveness and potential.

20. That’s all. Thanks!