Energy-Aware Proactive Routing in MANETs

1. Energy-Aware Proactive Routing in MANETs SANDEEP GUPTA Department of Computer Science and Engineering School of Computing and Informatics Ira A. Fulton School of Engineering Arizona State University Tempe, Arizona, USA sandeep.gupta@asu.edu Sponsor:

2. Tempe, Fulton School of Engg & CSE

3. IMPACT (Intelligent Mobile Pervasive Autonomic Computing & Technologies) LAB Research Goals • Enable Context-Aware Pervasive Applications • Dependable Distributed Sensor Networking Projects • Wireless Solution for Smart Sensors Biomedical Applications (NSF - ITR) • Context-Aware Middleware for Pervasive Computing (NSF – NMI) • Thermal Management Datacenters (SFAZ, NSF) • Location Based Access Control (CES) • Identity Assurance (NSF, CES) • Mobility-Tolerant Multicast (NSF) • Ayushman – Infrastructure Testbed for Sensor Based Health Monitoring (Mediserve Inc.) Group • Faculty: Dr. Sandeep K. S. Gupta • 1 PostDoc + 7 PhD + 2MS + 1 UG Department of Computer Science & Engineering, Tempe, Arizona http://impact.asu.edu Sponsors

4. Thermal Management for Data Centers Pervasive Health Monitoring Criticality Aware-Systems Mobile Ad-hoc Networks ID Assurance • Goal: • Protect people’s identity & consumer computing from viral threats • Features: • PKI based • Non-tamperable, non-programmable personal authenticator • Hardware and VM based trust management • Sponsor: • Goal: • Container Monitoring for Homeland Security • Dynamic Supply Chain Management • Features: • Integration of RFID and environmental sensors • Energy management • Communication security • Sponsor: • Goal: • Protocols for mobile ad-hoc networks • Features: • Energy efficiency • Increased lifetime • Data aggregation • Localization • Caching • Multicasting • Sponsor: Intelligent Container IMPACT: Research Use-inspired research in pervasive computing & wireless sensor networking • Goal: • Increasing computing capacity for datacenters • Energy efficiency • Features: • Online thermal evaluation • Thermal Aware Scheduling • Sponsor: • Goal: • Pervasive Health monitoring • Evaluation of medical applications • Features: • Secure, Dependable and Reliable data collection, storage and communication • Sponsor: • Goal: • Evaluation of crisis response management • Features: • Theoretical model • Performance evaluation • Access control for crisis management • Sponsor: Medical Devices, Mobile Pervasive Embedded Sensor Networks BOOK: Fundamentals of Mobile and Pervasive Computing, Publisher: McGraw-Hill  Dec. 2004

5. Mobile Ad hoc Networks (MANETs) Network Model • mobile nodes (PDAs, laptops etc.) • multi-hop routes between nodes • no fixed infrastructure Applications • Battlefield operations • Disaster Relief • Personal area networking Multi-hop routes generated among nodes Network Characteristics • Dynamic Topology • Constrained resources • battery power B C A C A B D D Links formed and broken with mobility

6. Reactive • Network divided in small zones. • Intra-region Proactive Routing. • Reactive Inter-region routing. • Balances Proactive & Reactive. • Scalable. • Latency higher than proactive. • Periodically maintainsroutes between every mobile node pair. • Predefined routes available • Low latency • Low scalability. Hybrid Proactive Routing in MANETs Routing • Routes NOT maintained. • Route established only if data to transmit. • High Scalability. • No pre-defined route. • High Latency. Real-time applications such as Disaster Relief and Battle-field operations require Proactive Maintenance of Routes.

7. Energy Consumption per bit transmitted Beacon Interval Number of Nodes Average size of beacon msg Bits transmitted due to beaconing per unit time Route Broadcast Interval Bit transmitted per unit time for periodic broadcast Proactive Route Maintenance E x N xlogN /β • Overhead • Periodic beacon messages for link state maintenance. • Periodic route update b’cast. • Triggered route update b’cast with each link change. E x N2xlogN /φ E x N2xlogN for each triggered update High Energy Overhead in Maintenance Operations Reduces Applicability Low Scalability Reduce maintenance operations and find optimumβ & φ to minimize energy overhead.

8. PP+BTP PP+BP PP+BT PP+B Proactive Proactive Protocol Classification • Research Goals: • Developing a PP+B type of protocol maintaining energy-efficient routes. • Uses Self-stabilization from Distributed Computing. • Improves Self-Stabilizing Shortest Path Spanning Tree (SS-SPST) for energy-efficiency. • Analytical Model for determining optimum β & φ for different proactive protocols. Employs Beacons, & Triggered Updates Employs only Beacons Employs Beacons, & Periodic Updates Employs Beacons, Periodic, & Triggered Update WRP, OLSR etc. BFST, SS-SPST etc. FSR, IARP etc. DSDV, TBRPF etc.

9. Energy-Aware Self-Stabilizing Protocol

10. Self-stabilization in Distributed Computing Topological Changes and Node Failures for MANETs. Self-stabilizing distributed systems • Guarantee convergence to valid state through local actions in distributed nodes. • Ensure closure to remain in valid state until any fault occurs. Can adaptto topological changes • Is it feasible for routing in MANETs? Fault Closure Invalid State Valid State Convergence Local actions in distributed nodes. Applied to Multicasting in MANETs

11. Self-stabilizing Multicast for MANETs Multicast source Topological Change • Maintains source-based multi-cast tree. • Actions based on local information in the nodes and neighbors. • Pro-active neighbor monitoring through periodic beacon messages. • Neighbor check at each round (with at least one beacon reception from all the neighbors) • Execute actions only in case of changes in the neighborhood. Convergence Based on Local actions Problem–energy-efficiency is not considered Self-Stabilizing Shortest Path Spanning Tree (SS-SPST)

12. Energy-Efficiency in Self-stabilization

13. j k i Ti l i non-intended neighbor Ti reaches all nodes in range Energy Consumption Model Ci = Ti+NixR Cost metric for node i Transmission energy of node i Reception cost at all the neighbors • Variable through Power Control • One transmission reaches all in range • Reception energy at intended neighbors. • Overhearing energy at non-intended neighbors. intended neighbor No communication schedule during broadcastin random access MAC (e.g. 802.11). Overhearing at j, k, and l Ci = Ti + 7R What is the additional cost if a node selects a parent?

14. Energy Aware Self-Stabilizing Protocol (SS-SPST-E) • Actions at each node • (parent selection) • Identify potential parents. • Estimate additional cost after joining potential parent. • Select parent with minimum additional cost. • Change distance to root. Loop Detected E Not in tree F A B D C X AdditionalCost (B → X) = TB + R AdditionalCost (A → X) = TA + 2R Potential Parents of X • Action Triggers • Parent disconnection. • Parent additional cost not minimum. • Change in distance of parent to root. Select Parentwith minimum Additional Cost Minimum overall cost when parent is locally selected Execute action when any action trigger is on • Tree validity– Tree will remainconnected • withno loops.

15. SS-SPST-E Execution Multicast source • No multicast tree • parent of each node NULL. • hop distance from root of each node infinity. • cost of each nodeis Emax. 2 2 A S B 1 2 2 G 3 1 No potential parents for any node. • First Round – source (root) stabilizes • hop distance of root from itself is 0. • no additionalcost. 1 D C H 2 2 • Second Round – neighbors of root stabilizes • hop distance of root’s neighbors is 1. • parent of root’s neighbors is root. Potential parent forA, B, C, D, F={S}. E F 2 AdditionalCost (F → E) = TF + 2R AdditionalCost (D → E) = TD + 3R AdditionalCost (S → {A, B, C, D}) = Ts + 4R AdditionalCost(D → E) = TD + 3R • And so on …… Potential parent forE={D, F}. AdditionalCost (S → F) = TS + 5R AdditionalCost (C → F) = TC + 3R AdditionalCost (S → F) = Ts + 5R Potential parent forF= {S,C}. • Tolerance to topological changes. • Convergence- From any invalid state the total energy cost of the graph reduces afterevery roundtill all the nodes in the system are stabilized. • Proof - through induction on round #. • Closure:Once all the nodes are stabilized it stays there untilfurther faultsoccur.

16. Simulation Model • Goals • performance analysis with beacon reduction. • study reliability energy-efficiency trade-off. • scalability study with number of receivers. • comparative study to verify feasibility of self-stabilization • SS-SPST – non-energy efficient self-stabilizing multicast • MAODV – tree-based multicast (non self-stabilizing) • ODMRP – mesh-based multicast (non self-stabilizing) • NS-2 used for simulating 50 nodes placed at random positions • Random way-point mobility model. • Omni-directional antennawithpower control. • CBR packets @ 64Kbps. • Performance Measures: • Packet Delivery Ratio (PDR) - for reliability • Energy Consumed / Packet Delivered - for energy efficiency

17. Simulation Results – Varying Beacon Interval PDR decreases with less beaconing

18. Simulation Results – Varying Beacon Interval Energy consumption per packet delivered increases due to decrease in number of packets delivered.

19. Simulation Results – Varying Node Mobility 10m/s 15m/s 20m/s 5m/s 1m/s Low packet delivery with high dynamicity ODMRP has high PDR due to redundant routes

20. Simulation Results – Varying Node Mobility 1m/s 5m/s 10m/s 15m/s 20m/s SS-SPST-Eleads to energy-efficiency ODMRP has high overhead to generate redundant routes

21. Simulation Results - Varying Multicast Group Size 40 10 20 30 50 Self-stabilizing protocols scale better. MAODV has highest delay due to reactive tree construction

22. Simulation Results - Varying Multicast Group Size 20 10 40 30 50 ODMRP leads to high control overhead and less PDR.

23. Analytical Model

24. Periodic Broadcast based on Link Dynamics (LD) • Determines optimum φ[Samar ‘06]. • periodic b’cast of route update only when link changes. • Optimumβ and scalability needs to be considered. • Requirement for Application Parameters considerations • Traffic – route maintenance can be reduced for low traffic. • Reliability Requirements • Measured in terms of Packet Delivery Ratio (PDR). • PDR = Total Number of Delivered Packets / Total Packets Transmitted • Route maintenance can be reduced for low PDR.

25. Goals • Balance Proactivity based on Application Parameters. • Minimize Energy Overhead. • Maintain Reliability. • Improve Scalability.

26. Contributions • Analytical Model determining optimum beacon and route update intervals. • Analysis applied to all classes of protocols.

27. Delay in route reconstruction Delay in route reconstruction Disconnection 1 Disconnection 2 Disconnection 3 Disconnection 4 1/  1/ k k Triggered Update Triggered Update Assumptions & System Model • Network Parameters • Link changes Poisson distributed (avg. rate = ) [Samar ‘06]. • Avg. rate of triggered update depends on  and β. • determines overhead for triggered b’cast. • Packet loss due to delay in route reconstruction. • Link reliability assumed. • No packet re-transmission. Average interval between consecutive triggered update • Application Parameters • Bulk Poisson Traffic Model (avg. rate = ). • Voice/Audio/Video/Media Traffic. • PDR requirement () known. Packet loss dependent on route reconstruction delay PROBLEM: Determine optimum  = f(, N, , ) &  = g(, N, , ).

28. Analytical Model • Objective Function • Constraints • Optimization

29. 2 2 2 2 N N N N N N D D N N d d d d e e e e d d e e l l l l E E E E ¹ ¹ d d N N N N E E E E e e l l N N E E l l N N E E o o o o g g g g o o g g = = = = o o g g O O O O v v v v ¯ ¯ ¯ ¯ k k ¯ ¯ 1 1 + + ' ' ¹ ¹ Objective Function: Overhead Energy Cost of Triggered B’cast Cost of Periodic B’cast Cost of Beacons PP+BTP + + PP+BP + + PP+BT PP+B

30. Constraints • PDR Constraint • P = Probability of packet loss due to each link failure. • PDR = (1 - P)D. • (1 - P)D >= . • Find P = function of , , β,φ. • Capacity Constraint • Control Traffic. • Data Traffic. P

31. Delay in route reconstruction Delay in route reconstruction Delay in route reconstruction Delay in route reconstruction Probability of Packet Loss (P) • CASE I: Link disconnection rate greater than traffic generation rate • Route-reconstruction delay MUST be less than consecutive link disconnections in the route. • P1 =  x route-reconstruction delay • CASE II: Link disconnection rate less than traffic generation rate • Route-reconstruction delay MUST be less than average interval between consecutive packets. • P2 =  x route-reconstruction delay P=P1xprob of CASE I+P2xprob of CASE II =xroute-reconstruction delay PDR Constraint: route-reconstruction delay <= [1 – 1/D] / 

32. Valid Route Establishment Link Disconnection Route-reconstruction delay   Beacons Received k Beacons Not Received Optimization • Step 1: route-reconstruction delay in terms of βand φ. • Step 2: take the equality of the PDR constraint • optimum value at the boundary. • one variable represented in terms of other. • objective re-written as a convex function of one variable. Worst case route-reconstruction delay = kβ + φ + end-to-end broadcast delay. • Step3: non-linear optimization of the objective • equate first order derivative to 0. • the resulting equation solved • second order derivative checked for +ve slope.

33. 1 1 µ 1 1 1 ¶ 1 ¡ 1 ³ ´ ¡ ¡ ¡ ¡ 1 1 1 1 ¡ ¡ ¡ ¡ 1 D D D D D p ¡ ¡ ¡ 1 3 ¯ ¯ ¯ k N N ¡ 1 + ¡ ³ ´ D d ¡ d 1 ¸ ' ¡ + = = = = N > t t t t 1 ' r e c ¸ ¤ + ( ) ¯ o o o o p p p p p p p k ­ 1 ¸ µ ¶ D t ® + > k ­ N k k ­ N + o p r e c + ¸ ¤ N 1 + ® t o p p i k P k ­ 3 + k N + c i 0 = PP+B PP+BP PP+BTP Proactive PP+BT Employs Beacons, & Triggered Updates Employs Beacons, & Periodic Updates Employs Beacons, Periodic, & Triggered Updates Employs Beacons WRP, OLSR etc. BFST, SS-SPST etc. FSR, IARP etc. DSDV, TBRPF etc. Single variable Equating PDR constraint gives the result Single variable Equating PDR constraint gives the result 1st Derivative Quadraticequation 1st Derivative Quarticequation Optimizations for different Proactive Protocols

34. Optimum Periods w.r.t. link change PP+BP PP+B PP+BT LD PP+BTP PP+BP PP+BTP

35. Optimum Periods w.r.t. traffic intensity PP+B PP+BT LD PP+BP PP+BTP PP+BP PP+BTP

36. Optimum Periods w.r.t. Network Size • Decrease Periodic Update Frequency • Decreases broadcast. • Increases Scalability. • Increase beacon frequency to meet PDR Constraint.

37. Conclusions & Future Work • SS-SPST-E provides energy-efficiency and self-stabilization. • High adaptability to topological changes. • Self-stabilization leads to scalability. • Novel analytical model presented for optimization of maintenance operations in Proactive Routing Protocols for MANETs. • Minimizes overhead • Maintains Reliability • Improves Scalability. • Reduces wastage for low traffic & mobility. • Future Work • Application of β & φ optimization for other proactive protocols • Local stabilization • Comparison with other energy-efficient protocols

38. List of Publications • T. Mukherjee, S. K. S. Gupta, and G. Varsamopoulos, Analytical Model for Optimizing Periodic Route Maintenance in Proactive Routing for MANETs, To appear in Proc of ACM MSWiM, Crete Island, Greece, Oct 2007. To appear. • T. Mukherjee, G. Sridharan, S. K. S. Gupta, Energy-Aware Self-Stabilization in Mobile Ad Hoc Networks: A Multicasting Case Study, 21st IEEE Int'l Parallel and Distributed Processing Symposium (IPDPS), Long Beach, California, 26-30th March 2007. • S. K. S. Gupta and P. K. Srimani, Self-Stabilizing Multicast Protocols for Mobile Ad Hoc Networks, Journal of Parallel and Distributed Computing, 63(1), pp. 87-96, 2003.

39. Questions ??