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  • Print publication year: 2012
  • Online publication date: August 2012

3 - PHY and MAC layer optimization for energy-harvesting wireless networks

from Part I - Communication architectures and models for green radio networks

Summary

Introduction

In typical wireless communication systems, nodes are deployed with pre-charged batteries. The energy stored in the battery is used by the node for communication, sensing, and signal processing tasks. However, when the battery drains out, the node “dies” and is no longer available in the network. When a sufficient number of nodes die, the network itself becomes dysfunctional. Therefore, periodic maintenance and battery replacement are necessary to ensure that the network continues to operate. Such maintenance is often operationally challenging or even impossible in several network deployments. The alternative option of running power cables to the nodes is also often infeasible.

An emerging green alternative that circumvents this problem is the use of energyharvesting (EH) functionality in the nodes [1]–[7]. An EH node harvests energy from the environment using solar, thermoelectric effects, vibration, and other phenomena. Unlike a conventional node, an EH node that drains out its battery can harvest energy later and, thus, again become “alive.” Energy harvesting eliminates the need for periodic battery replacements and significantly decreases the network maintenance overhead. This also makes it an attractive alternative to wireless networks that are powered by external power cables. Consequently, EH networks are finding applications in monitoring systems in aerospace, automobile, and civil applications, environmental/habitat monitoring, intrusion detection, inventory management, etc.

Unlike a conventional battery-operated node, a potentially infinite amount of energy is available to an EH node, albeit over an infinite duration of time. Hence, the focus of the physical layer and multiple access (MAC) layer protocols shifts to judiciously utilizing the harvested energy and ensuring that the energy is available when required – to the extent possible. Reducing the energy consumption and improving the spectral efficiency now become secondary goals of the design of these protocols. This motivates a redesign of the physical and multiple access layers of the network. For example, increasing the transmit energy improves the reliability of transmissions by a node as it counters noise. However, it also drains the node’s battery faster and lowers the odds that the node can transmit later. In a multi-node network, an additional multiple access problem that arises is the determination of which node(s) should transmit and when to transmit.

References
[1] V., Sharma, et al., “Optimal energy management policies for energy harvesting sensor nodes, IEEE Trans. Wireless Commun., vol. 9, pp. 1326–1336, Apr. 2008.
[2] A., Kansal, et al., “Power management in energy harvesting sensor networks, ACM Trans. Embedded Comput. Syst., vol. 7, pp. 1–38, Sept. 2007.
[3] P. S., Khairnar and N. B., Mehta, “Power and discrete rate adaptation for energy harvesting wireless nodes, in Proc. of IEEE ICC, Jun. 2011.
[4] D., Niyato, E., Hossain, and A., Fallahi, “Sleep and wakeup strategies in solar-powered wireless sensor/mesh networks: performance analysis and optimization, IEEE Trans. Mobile Comput., vol. 6, pp. 221–236, Feb. 2007.
[5] A., Seyedi and B., Sikdar, “Energy efficient transmission strategies for body sensor networks with energy harvesting, IEEE Trans. on Commun., vol. 58, pp. 2116–2126, Jul. 2010.
[6] O., Ozel, et al., “Transmission with energy harvesting nodes in fading wireless channels: optimal policies, IEEE J. Sel. Areas Commun., vol. 29, pp. 1732–1743, Sept. 2011.
[7] M., Gatzianas, L., Georgiadis, and L., TassiulasControl of wireless networks with rechargeable batteries, IEEE Trans. on Wireless Commun., vol. 9, pp. 581–593, Feb. 2010.
[8] J., Lei, R., Yates, and L., Greenstein, “A generic model for optimizing single-hop transmission policy of replenishable sensors, IEEE Trans. on Wireless Commun., vol. 8, pp. 547–551, Feb. 2009.
[9] B., Medepally, N. B., Mehta, and C. R., Murthy, “Implications of energy profile and storage on energy harvesting sensor link performance, in Proc. of IEEE Globecom, Nov. 2009.
[10] J., Ammer and J., Rabaey, “Low power synchronization for wireless sensor network modems, in Proc. of IEEE WCNC, pp. 670–675, Mar. 2005.
[11] S., Cui and A. J., Goldsmith, “Cross-layer design in energy-constrained networks using cooperative MIMO techniques, EURASIP Signal Process. J., Special Issue on Advances in Sig. Proc.-based Cross-layer Designs, vol. 86, pp. 1804–1814, Aug. 2006.
[12] J. A., Paradiso and M., Feldmeier, “A compact, wireless, self powered pushbutton controller, in Proc. of Int. Conf. Ubiquitous Comput., pp. 299–304, 2001.
[13] B., Medepally and N. B., Mehta, “Voluntary energy harvesting relays and selection in cooperative wireless networks, IEEE Trans. on Wireless Commun., vol. 9, pp. 3543–3553, Nov. 2010.
[14] IEEE standard 802, part 15.4: wireless medium access control (MAC) and physical layer (PHY) specifications for low rate wireless personal area networks (WPANs), 2003.
[15] V., Shah, N. B., Mehta, and R., Yim, “Splitting algorithms for fast relay selection: generalizations, analysis, and a unified view, IEEE Trans. Wireless Commun., vol. 9, pp. 1525–1535, Apr. 2010.
[16] X., Qin and R., Berry, “Opportunistic splitting algorithms for wireless networks, in Proc. INFOCOM, pp. 1662–1672, Mar. 2004.
[17] V., Shah, N. B., Mehta, and R., Yim, “Optimal timer-based selection schemes, IEEE Trans. Commun., vol. 58, pp. 1814–1823, Jun. 2010.
[18] A., Bletsas, et al., “A simple cooperative diversity method based on network path selection, IEEE J. on Sel. Areas Commun., vol. 24, pp. 659–672, Mar. 2006.
[19] V., Shah, N. B., Mehta, and R., Yim, “The relay selection and transmission trade-off in cooperative communication systems, IEEE Trans. Wireless Commun., vol. 9, pp. 2505–2515, Aug. 2010.
[20] A., Ribeiro, X., Cai, and G. B., Giannakis, “Symbol error probabilities for general cooperative links, IEEE Trans. Wireless Commun., vol. 4, pp. 1264–1273, May 2005.
[21] P. A., Anghel and M., Kaveh, “Exact symbol error probability of a cooperative network in a Rayleigh-fading environment, IEEE Trans. on Wireless Commun., vol. 3, pp. 1416–1421, Sept. 2004.
[22] M., Abramowitz and I., Stegun, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Dover, 9th ed., 1972.
[23] M. K., Simon and D., Divsalar, “Some new twists to problems involving the Gaussian probability integral, IEEE Trans. on Commun., vol. 46, pp. 200–210, Feb. 1998.
[24] A. J., Goldsmith and P. P., Varaiya, “Capacity of fading channels with channel side information, IEEE Trans. Inf. Theory, vol. 43, no. 6, pp. 1986–1992, Nov. 1997.
[25] S., Cui, A. J., Goldsmith, and A., Bahai, “Energy-constrained modulation optimization, IEEE Trans. on Wireless Commun., vol. 4, no. 5, pp. 2349–2360, Sept. 2005.
[26] T., Starr, J. M., Cioffi, and P. J., Silverman, Understanding Digital Subscriber Line Technology. 1st ed., Prentice-Hall, 1999.
[27] C., Murthy, “Power management and data rate maximization in wireless energy harvesting sensors, Intl. J. Wireless Inf. Netw., vol. 16, no. 3, pp. 102–117, Jul. 2009.
[28] C. K., Ho and R., Zhang, “Optimal energy allocation for wireless communications powered by energy harvesters, in Proc. of IEEE ISIT, Austin, TX, USA, Jun. 2010.
[29] V., Shenoy and C., Murthy, “Throughput maximization of delay-constrained traffic in wireless energy harvesting sensors, in Proc. of IEEE ICC, Cape Town, South Africa, May 2010.
[30] S., Reddy and C., Murthy, “Profile-based load scheduling in wireless energy harvesting sensors for data rate maximization, in Proc. of IEEE ICC, Cape Town, South Africa, May 2010.