Derleme
BibTex RIS Kaynak Göster

Akıllı Tarım Uygulamalarında Enerji Hasatlayan Kablosuz Sensör Ağlarında Veri Toplama

Yıl 2023, Cilt: 13 Sayı: 3, 45 - 53, 18.10.2023

Öz

Bu literatür taraması, akıllı tarım uygulamalarında enerji hasatlayan kablosuz sensör ağlarında veri toplama için ortaya çıkan bir çizelgeleme probleminin farklı durumları ve bunlara önerilen çözümler incelemektedir. İlk olarak, bir füzyon merkezinin (FM) m EH sensörlerinden bilgi topladığı bir kablosuz ağı dikkate alıyoruz. FM, her bir zaman dilimi için k ortogonal kanal üzerinden iletim için k adet m düğüm planlar. FM, yalnızca iletim girişimlerinin sonuçları hakkında nedensel bilgiye sahiptir; düğümlerin pil durumları veya EH faaliyetleri hakkında doğrudan bilgisi yoktur. Toplanan enerji iletimi kısıtladığında FM'nin bu veri destekli sistemde mümkün olan en fazla verimi toplamasına izin verecek bir zamanlama mekanizması bulmak amaçtır. Düğüm pillerinde bir depolama kapasitesine kadar enerjinin kayıpsız olarak depolandığı varsayılır (sınırsız kapasite olasılığı da dikkate alınır). Sorunu ele almak için hem sonlu hem de belirsiz problem ufukları kullanılır. İkinci olarak, sonsuz bir veri birikimi önermesinin bozulduğu senaryoyu dikkate alıyoruz. Üçüncü olarak, birinci çizelgeleme probleminin ikili problemine bakıyoruz. Düşük karmaşıklığa sahip bir politika olan Düzgünleştiren Rastgele Sıralı Politikanın (DRSP) önerilir; genel enerji hasadı ve veri toplama süreçleri için optimuma yakın olduğu gösterilmiştir. Sayısal örneklere göre, DRSP, pil ve tampon kapasitesi kabul edilebilir bir boyutta olduğunda, gelen enerjiyi ve verileri neredeyse mükemmel bir verimlilikle kullanır. Buradaki problemler, başka birçok alanda görülebileceği için buradaki çözümler, haberleşme ağından daha geniş bir uygulama alanına sahip olabilir.

Kaynakça

  • [1] P. Samuel S., K. Malarvizhi, S. Karthik and M. Gowri S.G., "Machine Learning and Internet of Things based Smart Agriculture," 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2020, pp. 1101-1106.
  • [2] Chong, C.-Y.; Kumar, S.P. Sensor networks: Evolution, opportunities, and challenges. Proc. IEEE 2003, 91, 1247–1256.
  • [3] J. A. Paradiso and T. Starner. Energy scavenging for mobile and wireless electronics. IEEE Pervasive Computing, pp. 18-27, January 2005.
  • [4] A. Kansal, J. Hsu, S. Zahedi, and M. B. Srivastava. Power management in energy harvesting sensor networks. ACM Transactions on Embedded Computing Systems (TECS) , vol. 6, no. 4, p.32, September 2007.
  • [5] C. F. García-Hernández, P. H. Ibargüengoytia-González, J. García-Hernández, J. A. Pérez-Díaz. Wireless Sensor Networks and Applications: a Survey. IJCSNS Internacional Journal of Computer Science and Network Security, Vol. 7, No. 3, pp. 264-273, March 2007.
  • [6] S. Sudevalayam, P. Kulkarni. Energy Harvesting Sensor Nodes: Survey and Implications. IEEE Communications Surveys & Tutorials, vol.13, no.3, pp.443-461, Third Quarter 2011.
  • [7] S. Baghaee, H. Ulusan, S. Chamanian, O. Zorlu, E. Uysal-Biyikoglu, and H. Kulah. Demonstration of Energy-Neutral Operation on a WSN Testbed Using Vibration Energy Harvesting. European Wireless 2014 (EW2014), Barcelona, Spain, 14-16th May 2014.
  • [8] B. Prabhakar, E. Uysal-Biyikoglu, and A. E. Gamal. Energy-efficient transmission over a wireless link via lazy scheduling. Proc. IEEE/ACM INFOCOM, pp. 386-394, April 2001.
  • [9] E. Uysal-Biyikoglu, B. Prabhakar, and A. E. Gamal. Energy-efficient packet transmission over a wireless link. IEEE Transactions on Networking, vol. 10, pp. 487-499, August 2002.
  • [10] M. A. Zafer and E. Modiano. A calculus approach to minimum energy transmission policies with quality of service guarantees. Proceedings of IEEE INFOCOM, Miami, pp. 548-559, March 2005.
  • [11] W. Chen, M.J. Neely, and U. Mitra, "Energy Efficient Scheduling with Individual Packet Delay Constraints: Offline and Online Results," in Proceedings of IEEE INFOCOM, pp. 1136-1144, May 2007.
  • [12] M. A. Zafer and E. Modiano. A calculus approach to energy-efficient data transmission with quality of service constraints. IEEE Transactions on Networking, vol. 17, pp. 898-911, June 2009.
  • [13] R. Berry, E. Modiano, M. Zafer, Energy-Efficient Scheduling under Delay Constraints for Wireless Networks, Synthesis Lectures on Communication Networks, Morgan and Claypool Publishers, September 2009.
  • [14] M. Gatzianas, L. Georgiadis, and L. Tassiulas. Control of wireless networks with rechargeable batteries. IEEE Transactions on Communications, vol. 9, pp. 581-593, February 2010.
  • [15] V. Sharma, U. Mukherji, V. Joseph, and S. Gupta. Optimal energy management policies for energy harvesting sensor nodes. IEEE Transactions on Wireless Communications, vol. 9, no. 4, pp. 1326-1336, 2010.
  • [16] M. A. Antepli, E. Uysal-Biyikoglu, and H. Erkal. Optimal packet scheduling on an energy harvesting broadcast link. IEEE J. Selected Areas in Communications, vol. 29, pp. 1721-1731, September 2011.
  • [17] J. Yang and S. Ulukus. Optimal packet scheduling in an energy harvesting communication system. IEEE Transactions on Communications, vol. 60, pp. 220-230, January 2012.
  • [18] K. Tutuncuoglu and A. Yener. Optimum transmission policies for battery limited energy harvesting nodes. IEEE Transactions on Wireless Communications, vol. 11, pp. 1180-1189, March 2012.
  • [19] H. Erkal, F. M. Ozcelik, E. Uysal-Biyikoglu. Optimal offline broadcast scheduling with an energy harvesting transmitter. EURASIP Journal on Wireless Communications and Networking 2013:197.
  • [20] B. T. Bacinoglu and E. Uysal-Biyikoglu. Finite-horizon online transmission rate and power adaptation on a communication link with markovian energy harvesting. Journal Communications and Networks, June, vol. 16, no. 3, June 2014.
  • [21] B. T. Bacinoglu and E. Uysal-Biyikoglu. Finite Horizon Online Packet Scheduling with Energy and Delay Constraints. IEEE International Black Sea Conference on Communications and Networking, Batumi, Georgia, 3-5 July 2013.
  • [22] B. T. Bacinoglu and E. Uysal-Biyikoglu. Finite Horizon Online Lazy Scheduling with Energy Harvesting Transmitters over Fading Channels. IEEE International Symposium on Information Theory, Honolulu, HI, USA, pp. 1176-1180, June 29 - July 4, 2014. 80
  • [23] G. Uctu, O. M. Gul, B. T. Bacinoglu and E. Uysal-Biyikoglu. Implementation of Energy Efficient Transmission Scheduling Policies on Software Defined Radio. accepted to IEEE Globecom, Austin, TX, USA, December 8-12, 2014.
  • [24] G. Uctu. Optimal transmission scheduling for energy harvesting systems and implementation of energy efficient scheduling algorithms on software defined radio. Master’s thesis, METU, June 2014.
  • [25] B. Akgun. Duty cycle optimization in energy harvesting sensor networks with application to bluetooth low energy. Master’s thesis, METU, June 2014.
  • [26] M. Shakiba-Herfeh. Optimization of feedback in a multiuser MISO communication downlink with energy harvesting users. Master’s thesis, METU, June 2014.
  • [27] M. Shakiba-Herfeh and E. Uysal-Biyikoglu. Optimization of feedback in a MISO downlink with energy harvesting users. In 20th European Wireless 2014, Spain, May 2014.
  • [28] M. Shakiba-Herfeh, T. Girici, E. Uysal-Biyikoglu. Routing with Mutual Information Accumulation in Energy-Limited Wireless Networks. 24th Tyrrhenian Int. Workshop on Digital Comm.: Green ICT, Sept. 23-25, 2013.
  • [29] E. T. Ceran, T. Erkilic, E. Uysal-Biyikoglu, T. Girici, and K. Leblebicioglu. Wireless access point on the move: Dynamic knapsack with incremental capacity. In Globecom 2014 - Symposium on Selected Areas in Communications: GC14 SAC Green Communication Systems and Networks (GC14 SAC Green Communication Systems and Networks), Austin, USA, Dec. 2014. submitted.
  • [30] T. Erkilic. Optimizing the service policy of a mobile service provider through competitive online solutions to the 0/1 knapsack problem with dynamic capacity. Master’s thesis, METU, June 2014.
  • [31] E. T. Ceran. Dynamic Allocation of Renewable Energy through a Stochastic Knapsack Problem Formulation for an Access Point on the Move. MS thesis, METU, June 2014.
  • [32] O. M. Gul, E. Uysal-Biyikoglu. A Randomized Scheduling Algorithm for Energy Harvesting Wireless Sensor Networks Achieving Nearly 100% Throughput. IEEE Wireless Communication and Networking Conference, Istanbul, Turkey, pp. 2492-2497, April 2014.
  • [33] Ö. M. Gül, "A low-complexity near-optimal scheduling policy for solving a restless multi-armed bandit problem occurring in a single-hop wireless network", MSc. Thesis, June 2014.
  • [34] R. E. Bellman, Dynamic Programming. Princeton, N.J.: Princeton University Press, 1957. 81
  • [35] C. J. Watkins. Learning from delayed rewards. Ph.D. dissertation, University of Cambridge, Psychology Dep., 1989.
  • [36] L. P. Kaelbling, Michael L. Littman, Andrew W. Moore. Reinforcement learning: a survey. Journal of Artificial Intelligence Research, v.4 n.1, pp.237-285, January 1996.
  • [37] C. J. Watkins, P. Dayan. Q-learning. Machine Learning, 8 (3), pp. 279-292, 1992.
  • [38] S. Mahadevan. Average reward reinforcement learning: Foundations, algorithms, and empirical results. Machine Learning, Special Issue on Reinforcement Learning, vol. 22, pp. 159-196, 1996.
  • [39] R. S. Sutton, A. G. Barto, Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, 1998.
  • [40] J. C. Gittins. Bandit processes and dynamic allocation indices (with discussion). J. Roy. Statist. Soc. Series B, Vol. 41, No.2, pp. 148-177, 1979.
  • [41] P. Whittle. Restless bandits: Activity allocation in a changing world. In A Celebration of Applied Probability J. Gani (Ed.), J. Appl. Prob. 25A pp. 287-298, 1988.
  • [42] C. H. Papadimitriou and J. N. Tsitsiklis. The complexity of optimal queueing network control. Math. Oper. Res., vol 24, pp. 293-305, May 1999.
  • [43] S. H. A. Ahmad, M. Liu, T. Javidi, Q. Zhao and B. Krishnamachari. Optimality of myopic sensing in multi-channel opportunistic Access. IEEE Trans. Inf. Theory, vol 55, No. 9, pp. 4040-4050, September 2009.
  • [44] S. H. A. Ahmad, L. Mingyan. Multi-channel opportunistic access: A case of restless bandits with multiple plays. in Proc. 47th Ann. Allerton Conf. Commun., Contr., Comput., Monticello, IL, pp. 1361-1368, September 2009.
  • [45] K. Liu and Q. Zhao. Indexability of restless bandit problems and optimality of Whittle index for dynamic multichannel access. IEEE Trans. Inf. Theory, voL 56, no. 11, pp. 5547-5567, November 2010.
  • [46] A. Hero, D. Castanon, D. Cochran, K. Kastella. Foundations and Applications of Sensor Management, Chapter 6, Springer, US, 2007.
  • [47] P. Blasco, D.Gunduz, and M. Dohler. Low-Complexity Scheduling Policies for Energy Harvesting Communication Networks. IEEE International Symposium on Information Theory (ISIT),Istanbul, Turkey, pp. 1601-1605, July 2013.
  • [48] M. Johnston, E. Modiano, I. Keslassy. Channel Probing in Communication Systems: Myopic Policies Are not Always Optimal. IEEE International Symposium on Information Theory (ISIT),Istanbul, Turkey, pp. 1934-1938, July 2013.
  • [49] F. Iannello, O. Simeone, and U. Spagnolini. Optimality of myopic scheduling and whittle indexability for energy harvesting sensors. in 46th Annual Conference on Information Sciences and Systems(CISS), Princeton, NJ, USA, pp. 1-6, March 2012.
  • [50] J. Gittins, K. Glazerbrook, R. Weber, Multi-armed bandit allocation indices. West Sussex, UK, Wiley, 2011.
  • [51] A. Arapostathis, V. S. Borkar, E. Fernandez-gaucherand, M. K. Ghosh, and S. I. Marcus. Discrete-time controlled Markov processes with average cost criterion: A survey. SIAM J. Control Optim., vol. 31, no. 2, pp. 282-344, 1993.
  • [52] O. M. Gul and E. Uysal-Biyikoglu. Achieving Nearly 100% Throughput without Feedback in Energy Harvesting Wireless Networks. IEEE International Symposium on Information Theory (ISIT’2014), Honolulu, HI, USA, pp. 1171-1175, June 29 - July 4, 2014.
  • [53] F. Iannello, O. Simeone. On the Optimal Scheduling of Independent, Symmetric and Time-Sensitive Tasks. IEEE Transactions on Automatic Control, vol. 58, no. 9, pp. 2421-2425, September 2013.
  • [54] P. Blasco and D. Gündüz, "Multi-Access Communications With Energy Harvesting: A Multi-Armed Bandit Model and the Optimality of the Myopic Policy," in IEEE Journal on Selected Areas in Communications, vol. 33, no. 3, pp. 585-597, March 2015, doi: 10.1109/JSAC.2015.2391852.
  • [55] O. M. Gul, "Asymptotically Optimal Scheduling for Energy Harvesting Wireless Sensor Networks", 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, 08-13 October 2017, Montreal, QC, Canada, pp.1-7.
  • [56] O.M.Gul,M.Demirekler,"Average throughput performance of myopic policy in energy harvesting wireless sensor networks", Sensors, vol.17,no.10, Sept.2017,pp. 1-20.
  • [57] O.M.Gul,"Average Throughput of Myopic Policy for Opportunistic Access over Block Fading Channels",IEEE Networking Letters,vol.1,no.1, March 2019, pp. 38-41.
  • [58] Ö. M. Gül, "Average Throughput Performance of Greedy Policy in Cognitive Radio Enabled Vehicular Networks," 2021 29th Signal Processing and Communications Applications Conference (SIU), 2021, pp. 1-4.
  • [59] O. M. Gul, M. Demirekler, "Asymptotically Throughput Optimal Scheduling for Energy Harvesting Wireless Sensor Networks", IEEE Access, vol. 6, pp. 45004-45020, September 2018.
  • [60] O. M. Gul, "Achieving Near-Optimal Fairness in Energy Harvesting Wireless Sensor Networks", IEEE International Symposium on Computers and Communications (ISCC 2019), 30 June-3 July 2019,Barcelona, Spain, pp. 673-678.
  • [61] O. M. Gul, "Near-Optimal Data Communication Between Unmanned Aerial and Ground Vehicles", 20th International Conference on Intelligent Systems Design and Applications (ISDA 2020), December 12-15, 2020, pp. 1-12.
  • [62] Ö. M. Gül, "Fair Data Collection in Wireless Networks," 2021 29th Signal Processing and Communications Applications Conference (SIU), 2021, pp. 1-4.
  • [63] Ö. M. Gül, "Fair Data Collection in Wireless Communications Networks," 2022 30th Signal Processing and Communications Applications Conference (SIU), Safranbolu, Turkey, 2022, pp. 1-4.
  • [64] O. Taşkın ve A. Vardar , "Tarımsal Üretimde Bazı Yenilenebilir Enerji Kaynakları Kullanımı", Uludağ Üniversitesi Ziraat Fakültesi Dergisi, c. 30, sayı. 1, ss. 179-184, Tem. 2016.
  • [65] Mutlu, N. (2020). Tarımsal Üretim İçin Entegre Kaynak Verimliliği . Avrupa Bilim ve Teknoloji Dergisi, (20) , 293-298.
  • [66] Yüksel, E. & Yüksel, A. (2023). Means of Using Renewable Energy Resource: Wind Energy for Controlling Climate in Greenhouses . Türk Tarım ve Doğa Bilimleri Dergisi , 10 (2) , 431-437

Data Collection in Energy Harvesting Wireless Sensor Networks in Smart Agricultural Applications

Yıl 2023, Cilt: 13 Sayı: 3, 45 - 53, 18.10.2023

Öz

This literature survey investigates various versions of a scheduling problem occurring in data collection in energy-harvesting (EH) wireless sensors networks (WSN) in smart agricultural applications and their solutions. First, we take into account a wireless network where a fusion center (FC) gathers information from m EH sensors. FC plans k of m nodes for transmission over k orthogonal channels for each time slot. FC only has causal knowledge about the results of transmission attempts; it has no direct knowledge of battery conditions of nodes or EH activities. Finding a scheduling mechanism that will allow FC to collect the most throughput possible in this data-backed up system, when harvested energy restricts transmission, is the goal. It is assumed that energy is stored losslessly in node batteries up to a storage capacity (the possibility of unlimited capacity is also taken into consideration). Both finite and indefinite problem horizons are used to treat the issue. Second, we take into account the scenario in which the premise of an infinite data backlog is broken. Thirdly, we look at the first scheduling problem's dual problem. Uniformizing Random Ordered Policy (UROP), a low-complexity policy, is suggested; its near-optimality is demonstrated for general energy harvesting and data arriving processes. According to numerical examples, UROP utilises the incoming energy and data with practically perfect efficiency when the battery and buffer capacity are of an acceptable size. As these problems may be faced in many other areas, their solutions may have a wider application area than communication network.

Kaynakça

  • [1] P. Samuel S., K. Malarvizhi, S. Karthik and M. Gowri S.G., "Machine Learning and Internet of Things based Smart Agriculture," 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2020, pp. 1101-1106.
  • [2] Chong, C.-Y.; Kumar, S.P. Sensor networks: Evolution, opportunities, and challenges. Proc. IEEE 2003, 91, 1247–1256.
  • [3] J. A. Paradiso and T. Starner. Energy scavenging for mobile and wireless electronics. IEEE Pervasive Computing, pp. 18-27, January 2005.
  • [4] A. Kansal, J. Hsu, S. Zahedi, and M. B. Srivastava. Power management in energy harvesting sensor networks. ACM Transactions on Embedded Computing Systems (TECS) , vol. 6, no. 4, p.32, September 2007.
  • [5] C. F. García-Hernández, P. H. Ibargüengoytia-González, J. García-Hernández, J. A. Pérez-Díaz. Wireless Sensor Networks and Applications: a Survey. IJCSNS Internacional Journal of Computer Science and Network Security, Vol. 7, No. 3, pp. 264-273, March 2007.
  • [6] S. Sudevalayam, P. Kulkarni. Energy Harvesting Sensor Nodes: Survey and Implications. IEEE Communications Surveys & Tutorials, vol.13, no.3, pp.443-461, Third Quarter 2011.
  • [7] S. Baghaee, H. Ulusan, S. Chamanian, O. Zorlu, E. Uysal-Biyikoglu, and H. Kulah. Demonstration of Energy-Neutral Operation on a WSN Testbed Using Vibration Energy Harvesting. European Wireless 2014 (EW2014), Barcelona, Spain, 14-16th May 2014.
  • [8] B. Prabhakar, E. Uysal-Biyikoglu, and A. E. Gamal. Energy-efficient transmission over a wireless link via lazy scheduling. Proc. IEEE/ACM INFOCOM, pp. 386-394, April 2001.
  • [9] E. Uysal-Biyikoglu, B. Prabhakar, and A. E. Gamal. Energy-efficient packet transmission over a wireless link. IEEE Transactions on Networking, vol. 10, pp. 487-499, August 2002.
  • [10] M. A. Zafer and E. Modiano. A calculus approach to minimum energy transmission policies with quality of service guarantees. Proceedings of IEEE INFOCOM, Miami, pp. 548-559, March 2005.
  • [11] W. Chen, M.J. Neely, and U. Mitra, "Energy Efficient Scheduling with Individual Packet Delay Constraints: Offline and Online Results," in Proceedings of IEEE INFOCOM, pp. 1136-1144, May 2007.
  • [12] M. A. Zafer and E. Modiano. A calculus approach to energy-efficient data transmission with quality of service constraints. IEEE Transactions on Networking, vol. 17, pp. 898-911, June 2009.
  • [13] R. Berry, E. Modiano, M. Zafer, Energy-Efficient Scheduling under Delay Constraints for Wireless Networks, Synthesis Lectures on Communication Networks, Morgan and Claypool Publishers, September 2009.
  • [14] M. Gatzianas, L. Georgiadis, and L. Tassiulas. Control of wireless networks with rechargeable batteries. IEEE Transactions on Communications, vol. 9, pp. 581-593, February 2010.
  • [15] V. Sharma, U. Mukherji, V. Joseph, and S. Gupta. Optimal energy management policies for energy harvesting sensor nodes. IEEE Transactions on Wireless Communications, vol. 9, no. 4, pp. 1326-1336, 2010.
  • [16] M. A. Antepli, E. Uysal-Biyikoglu, and H. Erkal. Optimal packet scheduling on an energy harvesting broadcast link. IEEE J. Selected Areas in Communications, vol. 29, pp. 1721-1731, September 2011.
  • [17] J. Yang and S. Ulukus. Optimal packet scheduling in an energy harvesting communication system. IEEE Transactions on Communications, vol. 60, pp. 220-230, January 2012.
  • [18] K. Tutuncuoglu and A. Yener. Optimum transmission policies for battery limited energy harvesting nodes. IEEE Transactions on Wireless Communications, vol. 11, pp. 1180-1189, March 2012.
  • [19] H. Erkal, F. M. Ozcelik, E. Uysal-Biyikoglu. Optimal offline broadcast scheduling with an energy harvesting transmitter. EURASIP Journal on Wireless Communications and Networking 2013:197.
  • [20] B. T. Bacinoglu and E. Uysal-Biyikoglu. Finite-horizon online transmission rate and power adaptation on a communication link with markovian energy harvesting. Journal Communications and Networks, June, vol. 16, no. 3, June 2014.
  • [21] B. T. Bacinoglu and E. Uysal-Biyikoglu. Finite Horizon Online Packet Scheduling with Energy and Delay Constraints. IEEE International Black Sea Conference on Communications and Networking, Batumi, Georgia, 3-5 July 2013.
  • [22] B. T. Bacinoglu and E. Uysal-Biyikoglu. Finite Horizon Online Lazy Scheduling with Energy Harvesting Transmitters over Fading Channels. IEEE International Symposium on Information Theory, Honolulu, HI, USA, pp. 1176-1180, June 29 - July 4, 2014. 80
  • [23] G. Uctu, O. M. Gul, B. T. Bacinoglu and E. Uysal-Biyikoglu. Implementation of Energy Efficient Transmission Scheduling Policies on Software Defined Radio. accepted to IEEE Globecom, Austin, TX, USA, December 8-12, 2014.
  • [24] G. Uctu. Optimal transmission scheduling for energy harvesting systems and implementation of energy efficient scheduling algorithms on software defined radio. Master’s thesis, METU, June 2014.
  • [25] B. Akgun. Duty cycle optimization in energy harvesting sensor networks with application to bluetooth low energy. Master’s thesis, METU, June 2014.
  • [26] M. Shakiba-Herfeh. Optimization of feedback in a multiuser MISO communication downlink with energy harvesting users. Master’s thesis, METU, June 2014.
  • [27] M. Shakiba-Herfeh and E. Uysal-Biyikoglu. Optimization of feedback in a MISO downlink with energy harvesting users. In 20th European Wireless 2014, Spain, May 2014.
  • [28] M. Shakiba-Herfeh, T. Girici, E. Uysal-Biyikoglu. Routing with Mutual Information Accumulation in Energy-Limited Wireless Networks. 24th Tyrrhenian Int. Workshop on Digital Comm.: Green ICT, Sept. 23-25, 2013.
  • [29] E. T. Ceran, T. Erkilic, E. Uysal-Biyikoglu, T. Girici, and K. Leblebicioglu. Wireless access point on the move: Dynamic knapsack with incremental capacity. In Globecom 2014 - Symposium on Selected Areas in Communications: GC14 SAC Green Communication Systems and Networks (GC14 SAC Green Communication Systems and Networks), Austin, USA, Dec. 2014. submitted.
  • [30] T. Erkilic. Optimizing the service policy of a mobile service provider through competitive online solutions to the 0/1 knapsack problem with dynamic capacity. Master’s thesis, METU, June 2014.
  • [31] E. T. Ceran. Dynamic Allocation of Renewable Energy through a Stochastic Knapsack Problem Formulation for an Access Point on the Move. MS thesis, METU, June 2014.
  • [32] O. M. Gul, E. Uysal-Biyikoglu. A Randomized Scheduling Algorithm for Energy Harvesting Wireless Sensor Networks Achieving Nearly 100% Throughput. IEEE Wireless Communication and Networking Conference, Istanbul, Turkey, pp. 2492-2497, April 2014.
  • [33] Ö. M. Gül, "A low-complexity near-optimal scheduling policy for solving a restless multi-armed bandit problem occurring in a single-hop wireless network", MSc. Thesis, June 2014.
  • [34] R. E. Bellman, Dynamic Programming. Princeton, N.J.: Princeton University Press, 1957. 81
  • [35] C. J. Watkins. Learning from delayed rewards. Ph.D. dissertation, University of Cambridge, Psychology Dep., 1989.
  • [36] L. P. Kaelbling, Michael L. Littman, Andrew W. Moore. Reinforcement learning: a survey. Journal of Artificial Intelligence Research, v.4 n.1, pp.237-285, January 1996.
  • [37] C. J. Watkins, P. Dayan. Q-learning. Machine Learning, 8 (3), pp. 279-292, 1992.
  • [38] S. Mahadevan. Average reward reinforcement learning: Foundations, algorithms, and empirical results. Machine Learning, Special Issue on Reinforcement Learning, vol. 22, pp. 159-196, 1996.
  • [39] R. S. Sutton, A. G. Barto, Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, 1998.
  • [40] J. C. Gittins. Bandit processes and dynamic allocation indices (with discussion). J. Roy. Statist. Soc. Series B, Vol. 41, No.2, pp. 148-177, 1979.
  • [41] P. Whittle. Restless bandits: Activity allocation in a changing world. In A Celebration of Applied Probability J. Gani (Ed.), J. Appl. Prob. 25A pp. 287-298, 1988.
  • [42] C. H. Papadimitriou and J. N. Tsitsiklis. The complexity of optimal queueing network control. Math. Oper. Res., vol 24, pp. 293-305, May 1999.
  • [43] S. H. A. Ahmad, M. Liu, T. Javidi, Q. Zhao and B. Krishnamachari. Optimality of myopic sensing in multi-channel opportunistic Access. IEEE Trans. Inf. Theory, vol 55, No. 9, pp. 4040-4050, September 2009.
  • [44] S. H. A. Ahmad, L. Mingyan. Multi-channel opportunistic access: A case of restless bandits with multiple plays. in Proc. 47th Ann. Allerton Conf. Commun., Contr., Comput., Monticello, IL, pp. 1361-1368, September 2009.
  • [45] K. Liu and Q. Zhao. Indexability of restless bandit problems and optimality of Whittle index for dynamic multichannel access. IEEE Trans. Inf. Theory, voL 56, no. 11, pp. 5547-5567, November 2010.
  • [46] A. Hero, D. Castanon, D. Cochran, K. Kastella. Foundations and Applications of Sensor Management, Chapter 6, Springer, US, 2007.
  • [47] P. Blasco, D.Gunduz, and M. Dohler. Low-Complexity Scheduling Policies for Energy Harvesting Communication Networks. IEEE International Symposium on Information Theory (ISIT),Istanbul, Turkey, pp. 1601-1605, July 2013.
  • [48] M. Johnston, E. Modiano, I. Keslassy. Channel Probing in Communication Systems: Myopic Policies Are not Always Optimal. IEEE International Symposium on Information Theory (ISIT),Istanbul, Turkey, pp. 1934-1938, July 2013.
  • [49] F. Iannello, O. Simeone, and U. Spagnolini. Optimality of myopic scheduling and whittle indexability for energy harvesting sensors. in 46th Annual Conference on Information Sciences and Systems(CISS), Princeton, NJ, USA, pp. 1-6, March 2012.
  • [50] J. Gittins, K. Glazerbrook, R. Weber, Multi-armed bandit allocation indices. West Sussex, UK, Wiley, 2011.
  • [51] A. Arapostathis, V. S. Borkar, E. Fernandez-gaucherand, M. K. Ghosh, and S. I. Marcus. Discrete-time controlled Markov processes with average cost criterion: A survey. SIAM J. Control Optim., vol. 31, no. 2, pp. 282-344, 1993.
  • [52] O. M. Gul and E. Uysal-Biyikoglu. Achieving Nearly 100% Throughput without Feedback in Energy Harvesting Wireless Networks. IEEE International Symposium on Information Theory (ISIT’2014), Honolulu, HI, USA, pp. 1171-1175, June 29 - July 4, 2014.
  • [53] F. Iannello, O. Simeone. On the Optimal Scheduling of Independent, Symmetric and Time-Sensitive Tasks. IEEE Transactions on Automatic Control, vol. 58, no. 9, pp. 2421-2425, September 2013.
  • [54] P. Blasco and D. Gündüz, "Multi-Access Communications With Energy Harvesting: A Multi-Armed Bandit Model and the Optimality of the Myopic Policy," in IEEE Journal on Selected Areas in Communications, vol. 33, no. 3, pp. 585-597, March 2015, doi: 10.1109/JSAC.2015.2391852.
  • [55] O. M. Gul, "Asymptotically Optimal Scheduling for Energy Harvesting Wireless Sensor Networks", 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, 08-13 October 2017, Montreal, QC, Canada, pp.1-7.
  • [56] O.M.Gul,M.Demirekler,"Average throughput performance of myopic policy in energy harvesting wireless sensor networks", Sensors, vol.17,no.10, Sept.2017,pp. 1-20.
  • [57] O.M.Gul,"Average Throughput of Myopic Policy for Opportunistic Access over Block Fading Channels",IEEE Networking Letters,vol.1,no.1, March 2019, pp. 38-41.
  • [58] Ö. M. Gül, "Average Throughput Performance of Greedy Policy in Cognitive Radio Enabled Vehicular Networks," 2021 29th Signal Processing and Communications Applications Conference (SIU), 2021, pp. 1-4.
  • [59] O. M. Gul, M. Demirekler, "Asymptotically Throughput Optimal Scheduling for Energy Harvesting Wireless Sensor Networks", IEEE Access, vol. 6, pp. 45004-45020, September 2018.
  • [60] O. M. Gul, "Achieving Near-Optimal Fairness in Energy Harvesting Wireless Sensor Networks", IEEE International Symposium on Computers and Communications (ISCC 2019), 30 June-3 July 2019,Barcelona, Spain, pp. 673-678.
  • [61] O. M. Gul, "Near-Optimal Data Communication Between Unmanned Aerial and Ground Vehicles", 20th International Conference on Intelligent Systems Design and Applications (ISDA 2020), December 12-15, 2020, pp. 1-12.
  • [62] Ö. M. Gül, "Fair Data Collection in Wireless Networks," 2021 29th Signal Processing and Communications Applications Conference (SIU), 2021, pp. 1-4.
  • [63] Ö. M. Gül, "Fair Data Collection in Wireless Communications Networks," 2022 30th Signal Processing and Communications Applications Conference (SIU), Safranbolu, Turkey, 2022, pp. 1-4.
  • [64] O. Taşkın ve A. Vardar , "Tarımsal Üretimde Bazı Yenilenebilir Enerji Kaynakları Kullanımı", Uludağ Üniversitesi Ziraat Fakültesi Dergisi, c. 30, sayı. 1, ss. 179-184, Tem. 2016.
  • [65] Mutlu, N. (2020). Tarımsal Üretim İçin Entegre Kaynak Verimliliği . Avrupa Bilim ve Teknoloji Dergisi, (20) , 293-298.
  • [66] Yüksel, E. & Yüksel, A. (2023). Means of Using Renewable Energy Resource: Wind Energy for Controlling Climate in Greenhouses . Türk Tarım ve Doğa Bilimleri Dergisi , 10 (2) , 431-437
Toplam 66 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Elektrik Mühendisliği (Diğer)
Bölüm Akademik ve/veya teknolojik bilimsel makale
Yazarlar

Ömer Melih Gül

Yayımlanma Tarihi 18 Ekim 2023
Gönderilme Tarihi 16 Temmuz 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 13 Sayı: 3

Kaynak Göster

APA Gül, Ö. M. (2023). Akıllı Tarım Uygulamalarında Enerji Hasatlayan Kablosuz Sensör Ağlarında Veri Toplama. EMO Bilimsel Dergi, 13(3), 45-53.
AMA Gül ÖM. Akıllı Tarım Uygulamalarında Enerji Hasatlayan Kablosuz Sensör Ağlarında Veri Toplama. EMO Bilimsel Dergi. Ekim 2023;13(3):45-53.
Chicago Gül, Ömer Melih. “Akıllı Tarım Uygulamalarında Enerji Hasatlayan Kablosuz Sensör Ağlarında Veri Toplama”. EMO Bilimsel Dergi 13, sy. 3 (Ekim 2023): 45-53.
EndNote Gül ÖM (01 Ekim 2023) Akıllı Tarım Uygulamalarında Enerji Hasatlayan Kablosuz Sensör Ağlarında Veri Toplama. EMO Bilimsel Dergi 13 3 45–53.
IEEE Ö. M. Gül, “Akıllı Tarım Uygulamalarında Enerji Hasatlayan Kablosuz Sensör Ağlarında Veri Toplama”, EMO Bilimsel Dergi, c. 13, sy. 3, ss. 45–53, 2023.
ISNAD Gül, Ömer Melih. “Akıllı Tarım Uygulamalarında Enerji Hasatlayan Kablosuz Sensör Ağlarında Veri Toplama”. EMO Bilimsel Dergi 13/3 (Ekim 2023), 45-53.
JAMA Gül ÖM. Akıllı Tarım Uygulamalarında Enerji Hasatlayan Kablosuz Sensör Ağlarında Veri Toplama. EMO Bilimsel Dergi. 2023;13:45–53.
MLA Gül, Ömer Melih. “Akıllı Tarım Uygulamalarında Enerji Hasatlayan Kablosuz Sensör Ağlarında Veri Toplama”. EMO Bilimsel Dergi, c. 13, sy. 3, 2023, ss. 45-53.
Vancouver Gül ÖM. Akıllı Tarım Uygulamalarında Enerji Hasatlayan Kablosuz Sensör Ağlarında Veri Toplama. EMO Bilimsel Dergi. 2023;13(3):45-53.

EMO BİLİMSEL DERGİ
Elektrik, Elektronik, Bilgisayar, Biyomedikal, Kontrol Mühendisliği Bilimsel Hakemli Dergisi
TMMOB ELEKTRİK MÜHENDİSLERİ ODASI 
IHLAMUR SOKAK NO:10 KIZILAY/ANKARA
TEL: +90 (312) 425 32 72 (PBX) - FAKS: +90 (312) 417 38 18
bilimseldergi@emo.org.tr