Abstract
Due to the random and dynamic energy-harvesting process, it is challenging to conduct optimal rate control in Energy-Harvesting Communication Systems (EHCSs). Existing works mainly focus on two cases: (1) the traffic load is infinite (as long as there is energy, there is data to transmit), in which the objective is to optimize the rate control policy subject to the dynamic energy arrivals, thus maximizing the average system throughput; and (2) the traffic load is finite, in which the objective is to optimize the rate control policy, thus minimizing the time by which all packets are delivered. In this work, we focus on the optimal rate control of EHCSs from another important and practical perspective, where the data and energy arrivals are both random. Given any deadline of T, our goal is to maximize the total throughput in [0,T]. Specifically, two scenarios are considered: (1) energy is ready before the transmission; and (2) energy arrives randomly during the transmission. In both scenarios, we assume that the data arrive randomly during the transmission. For the first scenario, we develop a novel Stepwise Searching Algorithm (SSA) based on the cumulative curve methodology, which is shown to achieve the optimal solution and the complexity grows only linearly with the problem size. In addition, the SSA can provide a simple and appealing graphical visualization of approximating the optimal solution. For the second scenario, we provide a simplified case study that can be solved by the SSA with low computation overhead and demonstrate the difficulties in solving the general setting, which initiates a first step toward the full understanding of the scenario when energy arrives randomly during the transmission.
- M. Andrews, S. Antonakopoulos, and L. Zhang. 2015. Rate-adaptive scheduling policies for network stability and energy efficiency. IEEE/ACM Trans. on Networking 23, 6 (2015), 1755--1764. Google ScholarDigital Library
- A. Arafa, T. Tong, M. Fu, S. Ulukus, and W. Chen. 2018. Delay minimal policies in energy harvesting communication systems. IEEE Transactions on Communications 66, 7 (2018), 2918--2930.Google ScholarCross Ref
- E. U. Biyikoglu, B. Prabhakar, and A. E. Gamal. 2002. Energy-efficient packet transmission over a wireless link. IEEE/ACM Trans. on Networking 10, 4 (2002), 487--499. Google ScholarDigital Library
- J. L. Boudec and P. Thiran. 2001. Network Calculus. Lecture Notes in Computer Science, Vol. 2050. New York: Springer.Google Scholar
- A. Colin et al. 2016. An energy-interference-free hardware-software debugger for intermittent energy-harvesting systems. In Proc. of ACM ASPLOS. Atlanta, GA, USA, ACM, 577--589. Google ScholarDigital Library
- F. Cottone, H. Vocca, and L. Gammaitoni. 2009. Nonlinear Energy Harvesting. Phys. Rev. Lett. 102, 8 (2009), 080601.Google ScholarCross Ref
- R. Cruz. 1991. A calculus for network delay (Parts I 8 II). IEEE Transactions on Information Theory 37, 1 (1991), 114--131, 132--141. Google ScholarDigital Library
- Y. Dong, F. Farnia, and A. Ozgur. 2015. Near optimal energy control and approximate capacity of energy harvesting communication. IEEE Journal on Selected Areas in Communications 33, 3 (2015), 540--557.Google ScholarDigital Library
- L. Gao, Y. Xu, and X. Wang. 2011. Map: Multiauctioneer progressive auction for dynamic spectrum access. IEEE Transactions on Mobile Computing 10, 8 (2011), 1144--1161. Google ScholarDigital Library
- B. Gaudette, V. Hanumaiah, M. Krunz, and S. Vrudhula. 2014. Maximizing quality of coverage under connectivity constraints in solar-powered active wireless sensor networks. ACM Transactions on Sensor Networks 10, 4 (2014), Article 59. Google ScholarDigital Library
- H. Han, J. Yu, H. Zhu, Y. Chen, J. Yang, G. Xue, Y. Zhu, and M. Li. 2013. Energy-efficient engine for frame rate adaptation on smartphones. In Proc. of ACM SenSys. Rome, Italy. ACM, 1--14. Google ScholarDigital Library
- J. Hester et al. 2014. Ekho: Realistic and repeatable experimentation for tiny energy-harvesting sensors. In Proc. of ACM SenSys. Memphis, TN. ACM, 330--331. Google ScholarDigital Library
- J. Hester et al. 2015. Tragedy of the coulombs: Federating energy storage for tiny, intermittently-powered sensors. In Proc. of ACM SenSys. Seoul, South Korea. ACM, 5--16. Google ScholarDigital Library
- J. Hester et al. 2017. Timely execution on intermittently powered batteryless sensors. In Proc. of ACM SenSys. Delft, The Netherlands. ACM, 1--13. Google ScholarDigital Library
- K. Huang and B. Bensaou. 2010. Distributed rate control and contention resolution in multi-cell IEEE 802.11 WLANs with hidden terminals. In Proc. of ACM MobiCom. Chicago, IL. ACM, 51--60. Google ScholarDigital Library
- A. Kansal, J. Hsu, S. Zahedi, and M. B. Srivastava. 2007. Power management in energy harvesting sensor networks. ACM Transactions on Embedded Computing Systems 6, 4 (2007), Article 32. Google ScholarDigital Library
- L. Lin, X. Lin, and N. B. Shroff. 2010. Low-complexity and distributed energy minimization in multihop wireless networks. IEEE/ACM Trans. on Networking 18, 2 (2010), 501--514. Google ScholarDigital Library
- X. Liu, S. Aeron, V. Aggarwal, X. Wang, and M. Wu. 2016. Adaptive sampling of RF fingerprints for fine-grained indoor localization. IEEE Transactions on Mobile Computing 15, 10 (2016), 2411--2423.Google ScholarDigital Library
- X. Liu, Y. Zhu, L. Kong, C. Liu, Y. Gu, A. V. Vasilakos, and M. Wu. 2015. CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems 26, 8 (2015), 2188--2197.Google ScholarDigital Library
- K. Manu, N. Adam, C. Tapparello, H. Ayatollahi, and W. Heinzelman. 2018. Energy-harvesting wireless sensor networks (EH-WSNs): A review. ACM Transactions on Sensor Networks 14, 2 (2018), Article 10. Google ScholarDigital Library
- A. Ortiz, H. Shatri, X. Li, T. Weber, and A. Klein. 2016. Reinforcement learning for energy harvesting point-to-point communications. In Proc. of IEEE ICC. Kuala Lumpur, Malaysia. IEEE, 1--6.Google Scholar
- O. Ozel, S. Ulukus, and P. Grover. 2015. Optimal scheduling for energy harvesting transmitters under temperature constraints. In Proc. of IEEE ISIT. Hong Kong, China. IEEE, 1129--1133.Google Scholar
- J. Paek, J. Kim, and R. Govindan. 2010. Energy-efficient rate-adaptive GPS-based positioning for smartphones. In Proc. of ACM MobiSys. San Francisco, CA. ACM, 299--314. Google ScholarDigital Library
- I. Pefkianakis, Y. Hu, S. Wong, H. Yang, and S. Lu. 2010. MIMO rate adaptation in 802.11n wireless networks. In Proc. of ACM MobiCom. Chicago, IL. ACM, 257--268. Google ScholarDigital Library
- T. Porta, C. Petrioli, C. Phillips, and D. Spenza. 2014. Sensor mission assignment in rechargeable wireless sensor networks. ACM Transactions on Sensor Networks 10, 4 (2014), Article 60. Google ScholarDigital Library
- D. Rakhmatov and S. Vrudhula. 2003. Energy management for battery-powered embedded systems. ACM Transactions on Embedded Computing Systems 2, 3 (2003), 277--324. Google ScholarDigital Library
- C. Renner, S. Unterschutz, V. Turau, and K. Romer. 2014. Perpetual data collection with energy-harvesting sensor networks. ACM Transactions on Sensor Networks 11, 1 (2014), Article 12. Google ScholarDigital Library
- J. Ryu, V. Bhargava, N. Paine, and S. Shakkottai. 2010. Design, implementation and evaluation of back-pressure routing/rate control for intermittently connected networks. In Proc. of ACM MobiCom. Chicago, IL. ACM, 365--376. Google ScholarDigital Library
- F. Shan, J. Luo, W. Wu, M. Li, and X. Shen. 2015. Discrete rate scheduling for packets with individual deadlines in energy harvesting systems. IEEE Journal on Selected Areas in Communications 33, 3 (2015), 438--451.Google ScholarDigital Library
- D. Shaviv and A. Ozgur. 2016. Universally near optimal online power control for energy harvesting nodes. IEEE Journal on Selected Areas in Communications 34, 12 (2016), 3620--3631. Google ScholarDigital Library
- D. Shaviv, P. M. Nguyen, and A. Ozgur. 2015. Capacity of the energy harvesting channel with a finite battery. In Proc. of IEEE ISIT, Hong Kong, China. IEEE, 131--135.Google Scholar
- W. Shen, Y. Tung, K. Lee, K. Lin, S. Gollakota, D. Katabi, and M. Chen. 2012. Rate adaptation for 802.11 multiuser MIMO networks. In Proc. of ACM MobiCom. Istanbul, Turkey. ACM, 29--40. Google ScholarDigital Library
- S. Shresthamali, M. Kondo, and H. Nakamura. 2017. Adaptive power management in solar energy harvesting sensor node using reinforcement learning. ACM Transactions on Embedded Computing Systems 16, 5s (2017), Article 181. Google ScholarDigital Library
- S. Sudevalayam and P. Kulkarni. 2011. Energy harvesting sensor nodes: Survey and implications. IEEE Communications Surveys and Tutorials 13, 3 (2011), 443--461.Google ScholarCross Ref
- K. Tutuncuoglu and A. Yener. 2012. Optimum transmission policies for battery limited energy harvesting nodes. IEEE Transactions on Wireless Communications 11, 3 (2012), 1180--1189.Google ScholarCross Ref
- K. Tutuncuoglu, A. Yener, and S. Ulukus. 2015. Optimum policies for an energy harvesting transmitter under energy storage losses. IEEE Journal on Selected Areas in Communications 33, 3 (2015), 467--481.Google ScholarDigital Library
- C. Wang, J. Li, Y. Yang, and F. Ye. 2016. A hybrid framework combining solar energy harvesting and wireless charging for wireless sensor networks. In Proc. of IEEE INFOCOM, San Francisco, CA. IEEE, 1--9.Google Scholar
- X. Wang, W. Huang, S. Wang, J. Zhang, and C. Hu. 2011. Delay and capacity tradeoff for MotionCast. IEEE/ACM Transactions on Networking 19, 5 (2011), 1354--1367. Google ScholarDigital Library
- Y. Wei, F. Yu, M. Song, and Z. Han. 2018. User scheduling and resource allocation in HetNets with hybrid energy supply: An actor-critic reinforcement learning approach. IEEE Transactions on Wireless Communications 17, 1 (2018), 680--692. Google ScholarDigital Library
- Z. Yan and C. Chen. 2016. RnB: Rate and brightness adaptation for rate-distortion-energy tradeoff in HTTP adaptive streaming over mobile devices. In Proc. of ACM MobiCom. New York. ACM, 308--319. Google ScholarDigital Library
- J. Yang and S. Ulukus. 2012. Optimal packet scheduling in an energy harvesting communication system. IEEE Transactions on Communications 60, 1 (2012), 220--230.Google ScholarCross Ref
- M. A. Zafer and E. Modiano. 2009. A calculus approach to energy-efficient data transmission with quality-of-service constraints. IEEE/ACM Transactions on Networking 17, 3 (2009), 898--911. Google ScholarDigital Library
- G. Zhu et al. 2013. Toward large-scale energy harvesting by a nanoparticle-enhanced triboelectric nanogenerator. Nano Lett. 13, 2 (2013), 847--853.Google ScholarCross Ref
Index Terms
- Optimal Rate Control for Energy-Harvesting Systems with Random Data and Energy Arrivals
Recommendations
Optimal transmission schemes for parallel and fading Gaussian broadcast channels with an energy harvesting rechargeable transmitter
We consider an energy harvesting transmitter sending messages to two users over parallel and fading Gaussian broadcast channels. Energy required for communication arrives (is harvested) at the transmitter and a finite-capacity battery stores it before ...
Optimal energy management policies for energy harvesting sensor nodes
We study a sensor node with an energy harvesting source. The generated energy can be stored in a buffer. The sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy ...
Energy Harvesting Two-Hop Communication Networks
Energy harvesting multihop networks allow for perpetual operation of low cost limited range wireless devices. Compared with their battery-operated counterparts, the coupling of energy and data causality constraints with half-duplex relay operation makes ...
Comments