An Energy-Efficient Flooding Algorithm in ad hoc network(APE)

1. An Energy-Efficient Flooding Algorithm in ad hoc network(APE) Concrete Mathematic mid-term presentation of term project Professor:Kwangjo Kim Group 16: Tran Minh Trung, Nguyen Duc Long

2. An Energy-efficient Flooding Algorithm in ad hoc network (EFA) • Introduction • Related works • Proposed solution • Simulation (Ongoing)

3. I. Introduction(1) – Ad hoc Network • Ad hoc Network • lack of fixed infrastructure • peer-to-peer (all nodes act as routers) • multi-hop routing • frequent connection / topology changes • Challenges: • Security, Scalability • QOS, load balancing • Effect on device’s battery life – Network’s life time

4. I. Introduction(2) – Paper objective • Objective • Prolong Network life • Reduce traffic load at Routing discovery phase • Related works: MBCR, MMBCR • Make power consumptions eventually distributed on every node. • Limitations: • Redundancy routing discovery processes • All nodes take part in a routing process passively that makes a nodes run out of energy fast, especially, when it has to serve many routing process at the same time • Proposed solution: • enable each node actively in saving its residual energy capacity while the network connectivity is still be guarantied

5. f(i)=40 f(i)=10 Chosen route Route 1 Source Destination Route 2 f(i)=30 f(i)=30 II. Related work(1) - MBCR • MBCR: (Minimum battery cost routing) • This protocol use remaining battery capacity of each host as a metric to describe the lifetime of each mobile host. Over Used Node

6. Eng=2 Eng=4 Eng=8W Eng=0W Eng=8W Eng=4 II. Related work(2) - MMBCR • MMBCR: Min-Max battery cost routing • Eliminate routing containing week node: f(i)=40 f(i)=10 Route 1 Source Destination Waste energy in case of short time connection Route 2 f(i)=30 f(i)=30 f(i)=20

7. III. Proposed solution:EFA(1) • Overview • Enable each node actively in saving its residual energy capacity while the network connectivity is still be guarantied • Algorithm: Flooding filter • New RREQ header: • Source Addr, current seq#, Dest Addr, Dest seq# Broadcast ID • Require energy level: • Eth = (packets)*Pcs • Eth = (packets)*Prc

8. III. Proposed solution:EFA(2) • Immediate node: • Calculate available energy • In case of serving j node at the same time • Eav = Nre - Erq(j) • Otherwise • Eav = Nre • Comparing available energy with require energy level • Case 1: Eav >= Eth : take part in routing process • Case 2: Eav < Eth : reject routing process

9. III. Proposed solution:EFA(3) Flooding filter • Advantages of flooding filter: • Reduce traffic load at discovery routing phase • Reduce interference between nodes • Reduce power consumption at discovery routing phase • Reduce the deviation between require energy level and the energy available of each node

10. III. Proposed solution:EFA(4) • Case 1:Eav >= Eth • Check current routing process in routing table (Check fresh route, hope count …) • Update/add routing table if necessary (set reserve path for new routing process: • source node’s IP address, seq.# • the number of hops to the source • IP address of the neighbor from which the RREQ was received • Energy requirement for this routing process • Send IACK back to the node which the RREQ was received from

11. III. Proposed solution:EFA(5) • Case 2:Eav < Eth • Discard RREQ packet • If : P = {Ni | Eav ≥ Eth, Ni Є Immediate nodes} = Ø • After Tfck, Reduce Eth at source node automatically • Eth= Eth - Dst; Dst = Sre/λ (λ=10) • This step will repeat until P ≠ ØOr Tfck ≥ TTL • Re broadcast RREQ with new Eth

12. IV. Simulation • Simulation model: • 50 mobile nodes • are generated randomly in an area of 500M*500M. • The moving speed of each node is 10m/s. • 20 connections is established during 900 seconds simulation times. • The energy model: • initial energy of each node is 20mW. • The energy usage for receiving and sending each packet are txPower = 0.6mW and rxPower = 0.3mW respectively.