Green Communications

Kaya Tutuncuoglu4/26/2010

Outline The “Green” Concept

Green Communications Alternative Energy Sources

Energy-Aware Routing Simulation Results

Conclusion

The “Green” ConceptUGP’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

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

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

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

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

Alternative Energy Sources

Both large and small scale

Different energy models and performances.

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?

Energy-Aware Routing

Battery Model

BatteryReplenishment Transmission

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.

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.

Energy-Aware Routing

Energy Efficient Clustering

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.

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.

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

Simulation Results

Direct Transmission100/500 packets lost

Energy-Aware Clustering58/500 packets lost

• 500 packets to base station, Emax=2000 / Erep=20

Simulation Results

DSAP56/500 packets lost

Energy-Aware Clustering58/500 packets lost

• 500 packets to base station, Emax=2000 / Erep=20

Simulation Results

DSAP56/500 packets lost

Energy-Aware DSAP3/500 packets lost

• 500 packets to base station, Emax=2000 / Erep=20

Simulation Results

DSAP17/500 packets lost

Energy-Aware DSAP0/500 packets lost

• 500 packets to another node, Emax=2000 / Erep=20

Simulation Results

DSAP112/500 packets lost

Energy-Aware DSAP16/500 packets lost

• 500 packets to another node, Emax=1000 / Erep=10

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.

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

[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.

[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.

[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

[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.

[6] M. Adamou and S. Sarkar, "A framework for optimal battery management for wireless nodes," in Proc. IEEE INFOCOM, 2002.

[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 2002

[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.

[9] S. Bandyopadhyay and E. Coyle, "An Energy-Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks," in Proceedings of IEEE INFOCOM, April 2003.

[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 communicationsSociety Conference on Sensor, Mesh and Ad Hoc Communications and Networks,(SECON '07), pages 284-293. 18-21 June 2007, San Diego, CA.