On the Construction of Energy-Efficient Broadcast Tree with Hitch-hiking in Wireless Networks

1. On the Construction of Energy-Efficient Broadcast Tree withHitch-hiking in Wireless Networks Source: 2004 International Performance Computing and Communications Conference (IEEE InfoCom ) Author : My T. Thai / Yingshu Li / Ding-Zhu Du Repoter : Yen-Lin Chen Date : 2005/03/23

2. Outline • Introduction • Preliminaries • Communication Model • Hitch-hiking Model • Network Model • Related Work • The Broadcast with Hitch-hiking Algorithm • Simulation Results • Conclusion

3. Introduction • In this paper, we study the problem of minimizing the total broadcast energy in wireless networks. • The broadcast problem in wireless networks is to decide a transmission power levelfor each node so that the source node can broadcast to all the other nodes.

4. Introduction (Cont.) • Our objective is to construct a minimum-power broadcast tree, rooted at the source node, including all the nodes. • Our key idea is to reduce the total energy consumption of the broadcast tree by taking the Hitch-hiking model and Wireless Multicast Advantage (WMA) into consideration.

5. Introduction (Cont.) • By successfully combining partial signals to obtain complete information, we can efficiently reduce the total energy consumed in broadcasting data.

6. Communication Model • Assume that any node in the network can beused as a relay node to forward data to other nodes. • All nodes are equipped with omnidirectional antennas. • Assume that all nodescan adjust their power levels. • Each node can chooseits transmission power from 0 to some maximum valuePmax.

7. Communication Model (Cont.) • Attenuation model: • d : the signal travelling distance • α : an environmentally dependent real constant between 2 and 4 • Pi : power level , a node j can properly receive a signal from a node i . • γ : represents the receiver's power threshold for Signal to Noise Ratio (SNR), often normalized to 1. • For node i in the network, the power required to successfully transmit data to node j is given by:

8. Hitch-hiking Model • Introducing two thresholds on the SNR: • Threshold energy required for the successful decoding of the message • Threshold energy required for the successful capture of a packet • Usually • A packet received with a SNR γis: • Full reception if • Partial reception if • Failed reception if

9. Hitch-hiking Model (Cont.) • If a node receives the packet containing the same information n times from different neighbors with such as • Assume that ° for simplicity. • Node can successfully receive the packet.

10. Network Model • Let V denote the collection of wireless nodes • Let G = (V,E) denote the directed graph on V that contains all edges. • Every node has an associated transmission power level . • : node i transmits a packet, the amount of reception by node j is quantified by the coverage of node j .

11. Network Model (Cont.) • The coverage function : • The coverage provided by node i on node j is:

12. Related Work • Broadcast Incremental Power (BIP) • Similar to Prim’s algorithm for forming minimum spanning tree • Weights are dynamically updated at each step • The BWHH algorithm can be obtained from BHH with a change on the coverage function. The coverage provided by node i on node j is defined as:

13. pik i pij k j Related Work (Cont.) • Wireless Multicast Advantage (WMA) • That is a single transmission can be received by all the nodes that are within the transmission range, reduces the total energy of the broadcast tree. • Nodes have omnidirectional antenna • ‘i’ transmits at and reaches both j and k • Energy expenditure

14. Y T(5) U(5) X V(1) S(8) W(2) Z Q (0.48) (0.55) U(2.4) (1) (0.38) (1) (0.24) (1) (0.62) (1) (1) (0.76) The Broadcast with Hitch-hiking Algorithm • Start with Minimum Spanning Tree • Improve upon the initial solution starting with source node • At each step, pick a fully covered node (Ex: node ‘S’) and decide its power level at which the gain is maximum • Calculate the coverage of all other uncovered nodes based on the final power level of node ‘S’ (Ex: node X ,Y and Z) • Calculate the power level of forwarding nodes based on new coverage value of their child nodes (Ex : node U, T and V) Y T(1.9) X S(10) V W Z Q

15. The Broadcast with Hitch-hiking Algorithm (Cont.) • Let us first introduce the following notations: • c(ji): the coverage of node i provided by node j. c(ji) is defined in formula (1) • c(i): the total coverage of node i provided by all the other nodes. c(i) = • Ph(i): the power level of node i at the iteration h • N(i): the neighbors of node i • Ph :be the set of nodes that are not fully covered in the network at the iteration h. • Fh= V – Ph :are fully covered in the network at the iteration h.

16. The Broadcast with Hitch-hiking Algorithm (Cont.) • Let us define a ratio r(i): • ph(i) – ph-1(i) is the incremental power of node i • is the sum of the updated coverage of all nodes j after increasing the power level of node i where j is in the partially covered node set of the previous iteration, ph-1.

17. The Broadcast with Hitch-hiking Algorithm (Cont.) Figure 1(a) represents the network where the maximum power level of node S and A are 10 and 5 respectively.

18. The Broadcast with Hitch-hiking Algorithm (Cont.) Figure 1(b) shows the broadcast tree T at the h iteration. At this step, Ph= {B,C} and Fh= {S,A}.The ratio r of node S when increasing the power level of node S to fully cover node B is:

19. The Broadcast with Hitch-hiking Algorithm (Cont.) • In order to minimize the total power consumption ,we not only want the incremental power of a node at each step to be small, but also want the sum of the coverage of all nodes to be large.

20. Simulation Results • We evaluate the performance of BHH by comparing it to another two algorithms, BIP and WMH. • In this simulation, we considered the following parameters: • n: the number of nodes in a network. Thereby increasing the network density when the number of nodes increases. n is from 10 to 100. • Pmax: The maximum power level of each node israndomly assigned on each simulation setup.

21. Simulation Results (Cont.)

22. Simulation Results (Cont.) • The total power of the broadcast tree constructed using BHH is almost 77% less than that of BIP, and 15% less than that of WMH. • The power of the broadcast tree constructed using BWHH is 49% less than that of BIP. • The total power of a broadcast tree decreases when the number of nodes increase. • Because as the network density increases, more nodes are available to work as relay nodes and the nodes are becoming closer.

23. Simulation Results (Cont.)

24. Simulation Results (Cont.) • We present the comparison of the improvement on saving energy over another three algorithms in percentage: BIP vs. BHH, BIP vs. BWHH, WMH vs. BHH. • As can be seen in this Figure, BHH is the one that can save the most energy, comparing to both BIP and WMH. • This analysis indicates that combining WMA and Hitch-hiking model does achieve a better result.

25. Conclusion • We proposed the BHH algorithm based on the Hitch-hiking model. • This algorithm takes advantages of WMA and of the Hitch-hiking concept. • It is our interest to further develop the distributed version of BHH in mobile environment.