Abstract
As a common train of thought to save energy, sleep scheduling which turns sensor nodes on and off has become a significant method to prolong the lifetime of wireless sensor networks (WSNs). In recent years, many related sleep scheduling mechanisms with diverse emphases and application areas for WSNs have been proposed. This paper reviews those mechanisms and further classifies them in different taxonomies as well as provides an insight into them.
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1 Introduction
In wireless sensor networks (WSNs) [1,2,3,4], most sensor nodes generally have to rely on unrechargeable power sources, e.g., batteries, to provide the necessary power. For certain cases, e.g., outdoor monitoring, it is even difficult to replace the batteries that run out of power. Thus, the power management of sensor nodes is very crucial for WSNs. To address the energy shortage which is generally a bottleneck restricting the applications of WSNs [5], substantial attention have been devoted to sleep scheduling approaches, which have long been used in a wide variety of devices to save energy consumption and prolong the lifetime of equipments, such as air-conditioning compressors, pumps and electric motors [6].
Specifically, as for WSNs, most existing hardware, e.g., CC2420, can support several modes, i.e., transmission mode, idle mode and sleep mode [7]. The power consumption for idle listening is in the same order for transmitting. In other words, if the radio keeps listening for incoming messages, most of the battery energy will be consumed. For example, as much as tens to thousands times the current consumed energy can be drained if the sensor nodes are in the idle listening state for a certain period [8]. Thus, the major goal of sleep scheduling mechanism in WSNs is to reduce the energy consumption of idle listening state on the condition of guaranteeing network connectivity.
2 Sleep Scheduling
Sleep scheduling means that there is a ratio between the wake up time length in a predefined period and the total length of the period [9]. For example, for a period which is 1 s, if one node stays active for 0.1 s and sleeps for 0.9 s, then the ratio is 0.1.
Sleep scheduling mechanisms for WSNs can be classified into three categories generally, i.e., synchronous schemes, semi-synchronous schemes, and asynchronous schemes.
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Synchronous Schemes: For synchronous schemes, such as S-MAC [10], T-MAC [11], sleeping nodes wake up at the same time periodically to communicate with one another, which means the network has to keep a global synchronization.
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Semi-Synchronous Schemes: Regarding semi-synchronous schemes (e.g., [12,13,14]), sensor nodes are generally grouped into clusters. In the same cluster, sensor nodes wake up or go to sleep at the same time. But clusters act together with others asynchronously.
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Asynchronous Schemes: In terms of asynchronous schemes, such as [15,16,17], each sensor node has its own wake-up and sleep schedule.
3 Recent Research
There are a few surveys (e.g., [8]) about sleep scheduling in the last four years. This section summarizes the main conclusions after reviewing recent papers published from 2015 to 2017 about sleep scheduling for WSNs, as shown in Table 1. From the table, we can further observe that most papers focus on the asynchronous scheduling schemes. And machine learning is more widely applied into this field.
4 Future Directions
Sleep scheduling for WSNs aims at saving the energy consumption of WSNs. We list some potential directions in terms of this topic.
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1.
Mobile relays and sinks for WSNs: Sensor nodes around the sink node ran out of energy easily, which can lead to energy hole. Mobile relays and sinks can mitigate this kind of problem. The mobile relay or mobile sink, just like a mobile robot, can travel around to gather information, which offers a good trade-off between energy consumption, latency and delivery delay.
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2.
Clustering for WSNs: For semi-synchronous sleep scheduling schemes, network is generally divided into several clusters, and cluster heads are responsible for communicating with other clusters. In such a way, the cluster heads might need to consume more energy compared with normal sensor nodes. Therefore, how to save the energy consumption for cluster heads is very important.
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3.
Energy Harvesting for WSNs: As a common train of thought to prolong the network lifetime, sleep scheduling can only save energy consumption but cannot generate energy. Thus, energy harvesting becomes a promising area, which can further mitigate the problem of energy shortage.
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4.
Wireless charging for WSNs: Wireless charging provides a convenient way to power electrical devices, which can become a very helpful method to supply the energy.
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5.
Cloud computing for WSNs: Cloud computing can be utilized to process the data and share the data processing results with users. Therefore, cloud computing can be incorporated into WSNs to reduce the energy consumption in terms of delivering data to WSN users.
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6.
Cross-layer design for WSNs: Compared to the layered approaches, cross-layer design might achieve better energy efficiency, via utilizing the interaction among different layers.
References
Zhu, C., Yang, L.T., Shu, L., Rodrigues, J.J.P.C., Hara, T.: A geographic routing oriented sleep scheduling algorithm in duty-cycled sensor networks. In: 2012 IEEE International Conference on Communications (ICC), pp. 5473–5477. IEEE (2012)
Zhu, C., Yang, L.T., Shu, L., Duong, T.Q., Nishio, S.: Secured energy-aware sleep scheduling algorithm in duty-cycled sensor networks. In: 2012 IEEE International Conference on Communications (ICC), pp. 1953–1957. IEEE (2012)
Zhu, C., Shu, L., Hara, T., Wang, L., Nishio, S., Yang, L.T.: A survey on communication and data management issues in mobile sensor networks. Wirel. Commun. Mobile Comput. 14(1), 19–36 (2014)
Zhu, C., Yang, L.T., Shu, L., Leung, V.C.M., Hara, T., Nishio, S.: Insights of top-k query in duty-cycled wireless sensor networks. IEEE Trans. Ind. Electron. 62(2), 1317–1328 (2015)
Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38(4), 393–422 (2002)
Ye, W., Heidemann, J., Estrin, D.: An energy-efficient MAC protocol for wireless sensor networks. In: The 21st Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM), vol. 3, pp. 1567–1576. IEEE (2002)
Texas Instruments: Cc2420: 2.4 GHz IEEE 802.15. 4/ZigBee-ready RF transceiver, vol. 53 (2006). http://wwww.ti.com/lit/gpn/cc2420
Carrano, R.C., Passos, D., Magalhaes, L.C., Albuquerque, C.V.: Survey and taxonomy of duty cycling mechanisms in wireless sensor networks. IEEE Commun. Surv. Tutorials 16(1), 181–194 (2014)
Ye, D., Zhang, M.: A self-adaptive sleep/wake-up scheduling approach for wireless sensor networks. IEEE Trans. Cybern. 48(3), 979–992 (2017)
Zhu, C., Chen, Y., Wang, L., Shu, L., Zhang, Y.: SMAC-based proportional fairness backoff scheme in wireless sensor networks. In: The International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 138–142 (2010)
Khalil, M.I., Hossain, M.A., Mamtaz, R., Ahmed, I., Akter, M.: Time efficient receiver oriented sleep scheduling for underwater sensor network. In: 2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pp. 1–6. IEEE (2017)
Wang, D., Mukherjee, M., Shu, L., Chen, Y., Hancke, G.: Sleep scheduling for critical nodes in group-based industrial wireless sensor networks. In: 2017 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 694–698. IEEE (2017)
Fang, W., Mukherjee, M., Shu, L., Zhou, Z., Hancke, G.P.: Energy utilization concerned sleep scheduling in wireless powered communication networks. In: 2017 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 558–563. IEEE (2017)
Wang, Y., Chen, H., Wu, X., Shu, L.: An energy-efficient SDN based sleep scheduling algorithm for WSNs. J. Netw. Comput. Appl. 59, 39–45 (2016)
Oller, J., Demirkol, I., Casademont, J., Paradells, J., Gamm, G.U., Reindl, L.: Has time come to switch from duty-cycled MAC protocols to wake-up radio for wireless sensor networks? IEEE/ACM Trans. Netw. 24(2), 674–687 (2016)
Baba, S.B., Rao, K.M.: Improving the network life time of a wireless sensor network using the integration of progressive sleep scheduling algorithm with opportunistic routing protocol. Indian J. Sci. Technol. 9(17), 1–6 (2016)
Gupta, H.P., Rao, S.V., Venkatesh, T.: Sleep scheduling protocol for \(k\)-coverage of three-dimensional heterogeneous WSNs. IEEE Trans. Veh. Technol. 65(10), 8423–8431 (2016)
Kordafshari, M., Movaghar, A., Meybodi, M.: A joint duty cycle scheduling and energy aware routing approach based on evolutionary game for wireless sensor networks. Iran. J. Fuzzy Syst. 14(2), 23–44 (2017)
Mostafaei, H., Montieri, A., Persico, V., Pescapé, A.: A sleep scheduling approach based on learning automata for WSN partial coverage. J. Netw. Comput. Appl. 80, 67–78 (2017)
Chen, Z., Liu, A., Li, Z., Choi, Y.-J., Li, J.: Distributed duty cycle control for delay improvement in wireless sensor networks. Peer-to-Peer Netw. Appl. 10(3), 559–578 (2017)
Kumar, S., Kim, H.: Low energy scheduling of minimal active time slots for multi-channel multi-hop convergence wireless sensor networks. In: 2017 International Conference on Computing, Networking and Communications (ICNC), pp. 1051–1057. IEEE (2017)
Chen, H., Li, X., Zhao, F.: A reinforcement learning-based sleep scheduling algorithm for desired area coverage in solar-powered wireless sensor networks. IEEE Sens. J. 16(8), 2763–2774 (2016)
Xie, R., Liu, A., Gao, J.: A residual energy aware schedule scheme for WSNs employing adjustable awake/sleep duty cycle. Wirel. Pers. Commun. 90(4), 1859–1887 (2016)
Xu, Z.-Y., Zhao, S.-G., Jing, Z.-J.: A clustering sleep scheduling mechanism based on sentinel nodes monitor for WSN. Int. J. Smart Home 9(1), 23–32 (2015)
Acknowledgments
This work is supported by China Maoming Engineering Research Center on Industrial Internet of Things (No. 517018) and major international cooperation projects of colleges in Guangdong Province (No. 2015KGJHZ026) and Science and Technology Planning Project of Guangdong Province (No. 2017A050506057).
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Zhang, Z., Shu, L., Zhu, C., Mukherjee, M. (2018). A Short Review on Sleep Scheduling Mechanism in Wireless Sensor Networks. In: Wang, L., Qiu, T., Zhao, W. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Systems. QShine 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 234. Springer, Cham. https://doi.org/10.1007/978-3-319-78078-8_7
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