Power Management for Throughput Enhancement in Wireless Ad-Hoc Networks

1. Power Management for Throughput Enhancement in Wireless Ad-Hoc Networks ElBatt, Krishnamurthy, Connors, and Dao IEEE International Conference on Communications (ICC) 2000

2. Outline • Introduction • System Model • Connectivity Range Optimization • Simulation Results • Possible Protocol Implementations • Conclusions

3. I. Introduction • The goal of power based connectivitydefinition is to improve • the average power consumption and • the end-to-end network throughput. • percentage of packet that are successfully delivered to their destinations • Tradeoff between power consumption and network throughput. (See next page) • This paper attempts to dynamically reach a near-optimal operating power level such that the network throughput is brought to the maximum achievable throughput.

4. ⊕ Power ↓ Power ↑ ⊕ Connectivity range ↓(Path length ↑) Connectivity range↑(Path length ↓) ⊕ Interference ↑(Collision ↑) # of TXs ↑ Interference ↓ (Collision ↑) ⊕ Throughput ↑ Throughput ↓

5. Typical networks that might benefit from the power-based routing is low mobility (typically pedestrians) wireless ad-hoc networks, such as battlefield, rescue operation, and sensor networks.

6. II. System Model • When the power management scheme is implemented, each node transmits at the minimum power level such that only a fixed number of neighboring nodes can hear the transmission. • For example, N=3 A D

7. Assumptions • A total of n nodes, each with unique Node ID; • Low mobility patterns • topology changes slowly; • shortest path routing is applicable • Each node has direct connectivity with its N closest neighbors only (i.e. a cluster of N nodes); and N is adapted dynamically • Connectionless (datagram) • Routing decision is made on a per packet basis • Maximum transmit power level, Pmax • Minimum transmit power level, Pmin

8. Assumptions (cont.) • Two MAC schemes: • A contention-free scheme for signaling • A slotted ALOHA scheme (or any contention scheme) for data transfer • A reliablereverse channel operating in a different frequency band for ACKs; ACKs are broadcasted at the maximum allowable power level, in order to reach the source directly. • Guard bands: the slot duration is larger than the packet duration by an interval equal to a guard band, in order to keep the nodes time-synchronized.

9. Assumptions (cont.) • Each mobile node has two buffers: • MAC buffer – to store the arrived packets • Retransmission buffer - to store the transmitted but not yet ACKed packets • Modifiedshortest path (Bellman-Ford) algorithm • Link costs are chosen to be the transmitted power • The received power has to be greater than a minimum power level, MinRecvPower, to guarantee reliable communication. • Consider a single cluster only.

10. Assumptions (cont.) • Signaling Packet format: • Data Packet format:

11. Assumptions (cont.) • The Connectivity Table:

12. Assumptions (cont.) • Node Throughput: • The percentage of successful transmission attempts • End-to-End Network Throughput: • The percentage of packets that reach their destinations successfully • Denoted by  • The Average Power Consumption • The average transmitted power/node/slot • Denoted by P • The channel model includes only path loss and shadowing effects.

13. III. Connectivity Range Optimization • Problem Formulation • All wireless nodes using the maximum power level (i.e. no power management) • A cluster (direct connection) of N nodes (2  N  n) No power adaptation within the cluster • A cluster (direct connection) of N nodes(2  N  n)Power adaptation within the cluster

14. III. Connectivity Range Optimization • Problem Formulation • All wireless nodes using the maximum power level (i.e. no power management) • Any mobile node can reach a large number of nodes in just one hop, or two at most. • A high power consumption • A higher level of interference • If the costs of all links are equal (= Pmax), the minimum power routing reduces to minimum hop routing.

15. III. Connectivity Range Optimization • Problem Formulation • A cluster (direct connection) of N nodes (2  N  n) No power adaptation within the cluster • The mobile node adjusts its power to reach at most the farthest node within its cluster, but does not adapts its power dynamically. • Low power consumption. • Low interference. • High number of hops per path. • High throughput • Incorporating the minimum power routing is crucial.

16. III. Connectivity Range Optimization • Problem Formulation • A cluster (direct connection) of N nodes(2  N  n)Power adaptation within the cluster • The mobile node would use the minimum power that guarantees reliable communication with its neighbor node. • Lower power consumption • Lower interference • Higher number of hops per path • Higher throughput • Higher complexity • Incorporating the minimum power routing is crucial.

17. III. Connectivity Range Optimization • Problem Formulation: consider the third case • The objective is to solve the following minimization problem: • Min (-+P) • Pmin Pti  Pmaxwhere  is the end-to-end network throughput,P is average transmitted power/node/slot, is the fixed weighting factor that reflects the relative importance of the two components,Pti is the transmitted power of node i, • There is no well-defined procedure for choosing . • An equivalent, but easier, formulation: • Max  • P   • Pmin Pti  Pmaxwhere  is equivalent to and one-to-one correspondent to .

18. III. Connectivity Range Optimization • System Operation • Each node is assigned a dedicated signaling (contention-free) time slot of a global signaling channel. The node is allowed to broadcast a beacon packet in this slot using Pmax. • In slot i, all other nodes obtain the beacon signal of node i, record the received power level (average) and NodeID. • Node ipicks the N closest nodes as its direct neighbors. • Each node adapts it transmit power level so that direct links are established only to its direct neighbors.

19. III. Connectivity Range Optimization • System Operation • Node i updates its local connectivity table with the link cost being the transmit power. • Each node broadcasts a Signaling Packet containing its local connectivity table in the signaling channel. • Each node obtains and stores the global topology information, forms a local routing table. Note: Not feasible for large networks because of the heavy communication overhead.

20. i j III. Connectivity Range Optimization • (1) Power Measurement • Relying on average power measurements so as to eliminate the effect of fast multipath fading

21. III. Connectivity Range Optimization • (2) Power Management • Two approaches: • No power adjustment with a cluster • Use the power to communicate with the farthest node as well as any closer nodes • Power adjustment with a cluster • Use the minimum power needed for a reliable communication with each node • Less interference, thus improving end-to-end throughput

22. i j III. Connectivity Range Optimization • (2) Power Management • The minimum required level of received power, MinRecvPower

23. III. Connectivity Range Optimization • (3) Minimum Power Routing (MPR) • A hop-by-hop shortest path routing mechanism where the link costs are the transmitted power levels • Based on the routing table constructed, create the set of all possible routes from the source to the destination. • Search, within the created route set, for the minimum cost route from source to destination. • Determine the next relay node on the minimum power route. • Modify the Next Node ID field in the data packet being routed. • Copy the packet to the retransmission buffer until its successful reception at the next node is indicated via an ACK message. • Send the packet to the MAC module for transmission to the next relay node.

24. III. Connectivity Range Optimization • Mobility Model • Low mobility patterns, namely pedestrians • The position of each node is updated periodically, with a predetermined period. • The speed is drawn from a random variable uniformly distributed minimum and maximum values. • The direction of motion is assumed to be uniformly distributed over [0,2].

25. IV.Simulation Results

26. IV.Simulation Results • The main objective is to investigate the impact of manipulating the Connectivity Range N on the average end-to-end network throughput and on the average power consumption. • First consider the “no power adjustment within a cluster” approach.

27. First, consider the “no power adjustment within a cluster” approach. • The average node throughput decreases as N increases. • interference increases(dominating factor) • fewer hops traversed

28. Max.

29. The average power consumption decreases as N increases. • interference increases(dominating factor) • fewer hops traversed

30. For network load  = 0.1 packets/sec, the trade-off between the end-to-end network throughputand the average power consumption: • Network throughput has the max. of 0.36 at N = 9, but consuming power of 75mW (0.36% / 75mW = 4.8 % / W) • Thus, for 2  N  9, we restrict power to be Pav  50mW (same as N = 6).Thus, the maximum network throughput is limited at 0.32 (same as N = 6).(0.32% / 50mW = 6.4 % / W)

31. Then, consider the “power adjustment within a cluster” approach. • Power Adjustment(Better) • No Power Adjustment

32. Power Adjustment(Better) • No Power Adjustment

33. No Power Adjustment • Power Adjustment(Better)

34. V. Possible Protocol Implementations • Two protocols that enable each node to dynamically adapts the connectivity range parameter N in order to achieve a near-optimal operating point. • Periodical Update Protocol (PUP) • Quasi-Periodical Update Protocol (QPUP)

35. Periodical Update Protocol (PUP) • Initially, each node independently chooses its connectivity range to be the minimum, N=2. • The node operates for a pre-specified number of frames (k) with this chosen value of N. • By the end of this period (called the checkpoint), the performance measure, namely the end-to-end throughput of this node is computed. • At this checkpoint, each node broadcasts its end-to-end throughput on the signaling channel. This value is stored in a data structure N. • N is then increased by one.

36. Periodical Update Protocol (PUP) • At the next checkpoint, the new value of the average end-to-end network throughput is computed and stored in N. • If (N N-1){increase the connectivity to N+1, go to step 6}else {reduce the connectivity to N-1, , go to step 6} • As long as the network throughput increases with N, keep increasing N. Until N=i+1, the network throughput starts decreasing. Then the maximum network throughput is achieved at N=i.

37. Periodical Update Protocol (PUP) • At each checkpoint, as long as i  I+1 andi  i-1, N needs not be changed. • If the topology changes and N=i dose not achieve the maximum network throughput, pick any of the two neighboring node, I+1 ori-1. • If N = i+1 achieves a higher throughput, increase N and go to step 6. • If N = i-1 achieves a higher throughput, decrease N and go to step 6. . .

38. Quasi-Periodical Update Protocol (QPUP) • Identical to PUP, except that • When the network achieves the maximum network throughput, the algorithm less frequently attempts to test if the current connectivity range is the optimal. • Trade simplicity with performance.

39. VI.Conclusions • The main objective of this paper is to investigate the impact of manipulating the Connectivity Range Non the average end-to-end network throughput and on the average power consumption. • A cluster concept is introduced. And two power management approaches are used within a cluster: no power adjustment and power adjustment. • Simulations show that • The Power management scheme performs better than the scheme without power management. • With Power management, the power adjustment scheme outperforms the no power adjustment scheme.

40. VI.Conclusions • Possible extensions: • Inter-cluster power management routing • Different connectivity range for each node