1. Green Communications Kaya Tutuncuoglu 4/26/2010

2. Outline  The “Green” Concept  Green Communications  Alternative Energy Sources  Energy-Aware Routing  Simulation Results  Conclusion

3. The “Green” Concept UGP’s Definition*: Green is the design, commercialization, and use of processes & products that are feasible & economical while: Reducing the generation of pollution at the source. Minimizing the risk to human health & the environment. The field of "green technology" encompasses a continuously evolving group of methods and materials, from techniques for generating energy to non-toxic cleaning products. * Urban Green Partnership (UGP), http://urbangreenpartnership.org

4. The “Green” Concept Images from: “Is ICT Green?” by Mario Pickavet, ICC 2009 Energy! Perhaps the most urgent issue for green technology, this includes the development of alternative fuels, new means of generating energy and energy efficiency. green-technology.org

5. Green Communications  Total energy consumption for a network of 20,000 3G base stations is 58MW* (equivalent to a large wind farm) resulting in annual electricity costs of about $62 million.  A carbon footprint of about 11 tons of carbon dioxide for each cell site, each year.  Annual mobile network energy consumption of an estimated 61 billion kWh worldwide (circa 2007) * “Green issues challenge basestation power”, EETimes Europe, September 19, 2007

6. Green Communications  The ICT industry is responsible for about 2% to 2.5% of global greenhouse gas emissions, according to the International Telecommunications Union (ITU).  This value is expected to double in the next decade.  Mobile telecommunications contribute by 9% * “Green issues challenge basestation power”, EETimes Europe, September 19, 2007

7. Green Communications  Keywords:  Energy Efficiency  How to spend less energy per information?  How to minimize network energy consumption?  How to do this without sacrificing performance?  Alternative Energy Sources  Solar, Wind, Bio Fuels  Vibrations, Microbial fuel cells

8. Alternative Energy Sources  Both large and small scale  Different energy models and performances.

9. Alternative Energy Sources  How to intelligently schedule transmissions?  How to route packets for minimum energy as well as high performance?  How to utilize the limited energy?

10. Energy-Aware Routing  Battery Model Battery ReplenishmentTransmission

11. Energy-Aware Routing  Energy Efficient Clustering  [9] S. Bandyopadhyay and E. Coyle, "An Energy-Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks," in Proceedings of IEEE INFOCOM, April 2003.An Energy-Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks  Nodes randomly elect themselves as cluster heads based on their energy.  A packet moves up the hierarchy until a common clusterhead is found, which routes the packet down to its destination.

12. Energy-Aware Routing  Energy Efficient Clustering

13. Energy-Aware Routing  Power Aware DSAP  [3] A. Salhieh, J. Weinmann, M. Kochha, and L. Schwiebert. "Power Efficient Topologies for Wireless Sensor Networks," Proceedings of the IEEE International Conference on Parallel Processing 2001, pages 156-163, 2001. "Power Efficient Topologies for Wireless Sensor Networks,"  Multi-hop routing where each node decides on the next hop using neighboring nodes’ positions and energy levels.  Requires battery and position information from all network nodes.

14. Simulation Results  100 nodes in a 100x100 space  A path loss exponent of 2  500 packets of random sources and sizes to a base or random destination  Performance criteria: number of packets lost

15. Simulation Results Direct Transmission 100/500 packets lost Energy-Aware Clustering 58/500 packets lost 500 packets to base station, Emax=2000 / Erep=20

16. Simulation Results DSAP 56/500 packets lost Energy-Aware Clustering 58/500 packets lost 500 packets to base station, Emax=2000 / Erep=20

17. Simulation Results DSAP 56/500 packets lost Energy-Aware DSAP 3/500 packets lost 500 packets to base station, Emax=2000 / Erep=20

18. Simulation Results DSAP 17/500 packets lost Energy-Aware DSAP 0/500 packets lost 500 packets to another node, Emax=2000 / Erep=20

19. Simulation Results DSAP 112/500 packets lost Energy-Aware DSAP 16/500 packets lost 500 packets to another node, Emax=1000 / Erep=10

20. Conclusion  Green Communications is a new and broad research area where energy efficiency and alternative sources in communications are investigated.  Various questions arise when efficiency and alternative sources are considered.  For the energy aware routing problem, intelligent algorithms provide significant performance improvements.

21. References [1] Longbi Lin, Ness B. Shroff, R. Srikant, "Asymptotically Optimal Power-Aware Routing for Multihop Wireless Networks with Renewable Energy Sources," IEEE/ACM Transactions on Networking (TON), Vol 15, Issue 5, p1021-1034, 2007"Asymptotically Optimal Power-Aware Routing for Multihop Wireless Networks with Renewable Energy Sources," [2] S. D. Servetto and G. Barrenechea. "Constrained Random Walks on Random Graphs: Routing Algorithms for Large Scale Wireless Sensor Networks," Proceedings of the first ACM Int. workshop on Wireless sensor networks and applications, pages 12-21. ACM Press, 2002. "Constrained Random Walks on Random Graphs: Routing Algorithms for Large Scale Wireless Sensor Networks," [3] A. Salhieh, J. Weinmann, M. Kochha, and L. Schwiebert. "Power Efficient Topologies for Wireless Sensor Networks," Proceedings of the IEEE International Conference on Parallel Processing 2001, pages 156-163, 2001. "Power Efficient Topologies for Wireless Sensor Networks," [4] Gatzianas, M.; Georgiadis, L.; Tassiulas, L. "Control of Wireless Networks with Rechargeable Batteries," IEEE Transactions on Wireless Communications, Vol 9 Issue 2 p581-593. 2010"Control of Wireless Networks with Rechargeable Batteries," [5] A. Fu, E. Modiano, and J. Tsitsiklis, "Optimal energy allocation and admission control for communication satellites," IEEE/ACM Trans. Networking, vol. 11, no. 3, p. 488, June 2003.Optimal energy allocation and admission control for communication satellites [6] M. Adamou and S. Sarkar, "A framework for optimal battery management for wireless nodes," in Proc. IEEE INFOCOM, 2002."A framework for optimal battery management for wireless nodes," [7] Elif Uysal-Biyikoglu, Balaji Prabhakar, Abbas El Gamal, "Energy-Efficient Packet Transmission over a Wireless Link," IEEE/ACM Transactions on Networking, Vol. 10, No 4, August 2002Energy-Efficient Packet Transmission over a Wireless Link, [8] S. Gandham, M. Dawande, R. Prakash, and S. Venkatesan, "Energy-efficient schemes for wireless sensor networks with multiple mobile base stations," in Proceedings of IEEE GLOBECOM, Dec 2003.Energy-efficient schemes for wireless sensor networks with multiple mobile base stations [9] S. Bandyopadhyay and E. Coyle, "An Energy-Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks," in Proceedings of IEEE INFOCOM, April 2003.An Energy-Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks [10] S. Sharma and D. Teneketzis, "An externality-based decentralized optimal power allocation scheme for wireless mesh networks." In Proceedings of the 4th Annual IEEE communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, (SECON '07), pages 284-293. 18-21 June 2007, San Diego, CA.An externality-based decentralized optimal power allocation scheme for wireless mesh networks.