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Erschienen in:
Multiple Antenna Spectrum Sensing in Colored Noise Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

56. Dynamic Spectrum Access for Machine to Machine Communications: Opportunities, Standards, and Open Issues

verfasst von : Luca Bedogni, Marco Di Felice, Luciano Bononi

Erschienen in: Handbook of Cognitive Radio

Verlag: Springer Singapore

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Abstract

Cognitive radio can be applied to a multitude of domains, one of which is M2M communication. Specifically, M2M communication refers to communication between devices without human intervention. Hence, devices should be able to organize themselves and run the communication protocol autonomously. If cognitive radio is used, tasks such as dynamic spectrum access (DSA), spectrum sensing, and alike present additional challenges compared to traditional network, as all the decision framework should be implemented and automatized in the devices. In this chapter, we focus on DSA techniques for M2M. The main difference from other kinds of communication is relative both to the energy efficiency and to the low protocol overhead, as devices should run for long periods of time and run without human intervention. At first we present related work from literature, categorizing the different tasks devices which want to leverage DSA on M2M have to perform. At the end of the chapter, we present a proof of concept of a general framework, which can be applied to different scenario concerning M2M, encompassing all the spectrum management and measurement tasks M2M devices should generally perform. Finally, we derive open challenges and future research directions concerning this scenario.

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Vorheriges Kapitel Spectrum Database and Smart Spectrum
Nächstes Kapitel Reinforcement Learning-Based Spectrum Management for Cognitive Radio Networks: A Literature Review and Case Study
Literatur
1.
3GPP. Study on provision of low-cost machine-type communications (MTC) user equipments (UEs) based on LTE. TSG GERAN R1
2.
Altintas O et al (2011) Demonstration of vehicle to vehicle communications over TV white space. In: 2011 IEEE vehicular technology conference (VTC Fall), San Francisco, pp 1–3
3.
Bedogni L, Trotta A, Di Felice M, Bononi L (2013) Machine-to-Machine communication over TV white spaces for smart metering applications. In: 2013 22nd international conference on computer communications and networks (ICCCN), Nassau, pp 1–7
4.
Bedogni L, Achtzehn A, Petrova M, Mähönen P (2014) Smart meters with TV gray spaces connectivity: a feasibility study for two reference network topologies. In: 2014 eleventh annual IEEE international conference on sensing, communication, and networking (SECON), Singapore, pp 537–545
5.
Bedogni L, Trotta A, Di Felice M (2015) On 3-dimensional spectrum sharing for TV white and gray space networks. In: 2015 IEEE 16th international symposium on a world of wireless, mobile and multimedia networks (WoWMoM), Boston, pp 1–8
6.
Bedogni L, Franzoso F, Bononi L (2016) A self-adapting algorithm based on atmospheric pressure to localize indoor devices. In: 2016 IEEE global communications conference (GLOBECOM), Washington, DC, pp 1–6
7.
Bedogni L, Trotta A, Di Felice M, Gao Y, Zhang X, Zhang Q, Malabocchia F, Bononi L (2017) Dynamic adaptive video streaming on heterogeneous TVWS and Wi-Fi networks. IEEE/ACM Trans Netw (IEEE TON) 25:3253–3266CrossRef
8.
Bedogni L, Achtzehn A, Petrova M, Mähönen P, Bononi L (2017) Performance Assessment and Feasibility Analysis of IEEE 802.15.4m Wireless Sensor Networks in TV Grayspaces. ACM Trans Sensor Netw 13(1), Article 8 (January 2017), 27 pagesCrossRef
9.
Bedogni L, Malabocchia F, Di Felice M, Bononi L (2017) Indoor use of gray and white spaces: another look at wireless indoor communication. IEEE Veh Technol Mag 12(1):63–71CrossRef
10.
Brew M, Darbari F, Crockett LH, Waddell MB, Fitch M, Weiss S, Stewart RW (2011) UHF white space network for rural smart grid communications. In: 2011 IEEE international conference on smart grid communications (SmartGridComm), Brussels, pp 138–142
11.
Chang HB, Chen KC (2011) Cooperative spectrum sharing economy for heterogeneous wireless networks. In: 2011 IEEE GLOBECOM workshops (GC Wkshps), Houston, pp 458–463
12.
Chatziantoniou E, Allen B, Velisavljevic V (2015) Threshold optimization for energy detection-based spectrum sensing over Hyper-Rayleigh fading channels. IEEE Commun Lett 19(6):1077–1080CrossRef
13.
Condoluci M, Militano L, Orsino A, Alonso-Zarate J, Araniti G (2015) LTE-direct vs. WiFi-direct for machine-type communications over LTE-A systems. In: 2015 IEEE 26th annual international symposium on personal, indoor, and mobile radio communications (PIMRC), Hong Kong, pp 2298–2302
14.
Cui P, Liu H, Rajan D, Camp J (2014) A measurement study of white spaces across diverse population densities. In: 2014 12th international symposium on modeling and optimization in mobile, ad hoc, and wireless networks (WiOpt), number WiNMeE, May 2014. IEEE, pp 30–36
15.
Datta SK, Bonnet C (2014) Smart M2M gateway based architecture for M2M device and endpoint management. In: 2014 IEEE international conference on internet of things (iThings), and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom), Taipei, pp 61–68
16.
Di Felice M, Chowdhury KR, Bononi L (2010) Analyzing the potential of cooperative cognitive radio technology on inter-vehicle communication. In: Wireless days (WD), 2010 IFIP, Venice, pp 1–6
17.
Di Felice M, Bedogni L, Bononi L (2012) DySCO: a dynamic spectrum and contention controlframework for enhanced broadcast communication invehicular networks. In: Proceedings of the 10th ACM international symposium on mobility management and wireless access (MobiWac’12)
18.
Di Felice M, Bedogni L, Bononi L (2013) Group communication on highways: an evaluation study of Geocast protocols and applications. Elsevier’s Ad Hoc Netw J 11: 818–832CrossRef
19.
Ebrahimzadeh A, Najimi M, Andargoli SMH, Fallahi A (2015) Sensor selection and optimal energy detection threshold for efficient cooperative spectrum sensing. IEEE Trans Veh Technol 64(4):1565–1577CrossRef
20.
Fang YX, Xue G (2012) HERA: an optimal relay assignment scheme for cooperative networks, selected areas in communications. IEEE J 30(2):245–253
21.
22.
Gao Y, Qin Z, Feng Z, Zhang Q, Holland O, Dohler M (2016) Scalable and reliable IoT enabled by dynamic spectrum management for M2M in LTE-A. IEEE Internet Things J 3(6): 1135–1145CrossRef
23.
Gao L, Duan L, Huang J (2017) Two-sided matching based cooperative spectrum sharing. IEEE Trans Mob Comput 16(2):538–551CrossRef
24.
Gu Y, Saad W, Bennis M, Debbah M, Han Z (2015) Matching theory for future wireless networks: fundamentals and applications. IEEE Commun Mag 53(5):52–59CrossRef
25.
Harris J, Beach M, Nix A, Thomas P (2016) Spectrum sharing for M2M applications through Whitetime exploitation in WiFi networks. In: 2016 IEEE wireless communications and networking conference, Doha, pp 1–6
26.
Hsu LK, Chou CT (2013) A dynamic spectrum access (DSA)-based multichannel protocol for large scale machine-to-machine (M2M) networks. In: 2013 9th international wireless communications and mobile computing conference (IWCMC), Sardinia, pp 1217–1222. https://​doi.​org/​10.​1109/​IWCMC.​2013.​6583730
27.
28.
29.
Ihara Y et al (2013) Distributed autonomous multi-hop vehicle-to-vehicle communications over TV white space. In: 2013 IEEE consumer communications and networking conference (CCNC), Las Vegas, pp 336–344
30.
Islam MH, Koh CL, Oh SW, Qing X, Lai YY, Wang C, Liang YC, Toh BE, Chin F, Tan L, Toh W (2008) Spectrum survey in Singapore: occupancy measurements and analyses. In: Proceedings of the 3rd international conference on cognitive radio oriented wireless networks and communications (CrownCom), May 2008. IEEE
31.
Ji Z, Liu KJR (2007) Cognitive radios for dynamic spectrum access – dynamic spectrum sharing: a game theoretical overview. IEEE Commun Mag 45(5):88–94CrossRef
32.
Kerttula J, Jäntti R (2011) DVB-T receiver performance measurements under secondary system interference. In: Proceedings of COCORA 2011
33.
Laya A, Wang K, Widaa AA, Alonso-Zarate J, Markendahl J, Alonso L (2014) Device-to-device communications and small cells: enabling spectrum reuse for dense networks. IEEE Wirel Commun 21(4):98–105CrossRef
34.
Lee J, Bagheri B, Kao H-A (2015) A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett 3:18–23. ISSN:2213–8463CrossRef
35.
Li TL, Chou CT, Hsu LK (2015) Proportional sharing in distributed dynamic spectrum access-based networks. IEEE Trans Mob Comput 14(1):155–168CrossRef
36.
37.
Lim JH, Kim W, Naito K, Yun JH, Cabric D, Gerla M (2014) Interplay between TVWS and DSRC: optimal strategy for safety message dissemination in VANET. IEEE J Sel Areas Commun 32(11):2117–2133CrossRef
38.
39.
Marcu I, Marghescu I (2010) Evaluation of spectrum occupancy in an urban environment in a cognitive radio context. Adv Telecommun 3(3):172–181
40.
Masonta MMT, Johnson DL, Mzyece M (2011) The white space opportunity in Southern Africa: measurements with Meraka cognitive radio platform. In: Proceedings of AFRICOMM. Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering, vol 92. Springer, pp 64–73
41.
McHenry MA, Tenhula PA, McCloskey D, Roberson DA, Hood CS (2006) Chicago spectrum occupancy measurements & analysis and a long-term studies proposal. In: Proceedings of the first international workshop on technology and policy for accessing spectrum (TAPAS)
42.
Montori F, Contigiani R, Bedogni L (2017) Is WiFi suitable for energy efficient IoT deployments? In: A performance studyon proceedings of the 3rd IEEE internal forum on research and technologies for society and industry, technologies for smarter societies (IEEE RTSI), 11–13 Sept, Modena
43.
Niyato D, Hossain E (2008) Competitive spectrum sharing in cognitive radio networks: a dynamic game approach. IEEE Trans Wirel Commun 7(7):2651–2660CrossRef
44.
Pérez-Romero J, Noguet D, López-Benítez M, Casadevall F (2011) Towards more-efficient spectrum usage: spectrum-sensing and cognitive-radio techniques. in URSI Radio Science Bulletin, 2011(336):59–74, March 2011
45.
Qin Z, Gao Y, Plumbley MD, Parini CG, Cuthbert LG (2014) Efficient compressive spectrum sensing algorithm for M2M devices. In: 2014 IEEE global conference on signal and information processing (GlobalSIP), Atlanta, pp 1170–1174
46.
Reed JH (2010) Enabling rural Virginia’s smart grid through white space communications, Virginia Tech, Technical report
47.
Selen Y, Tullberg H, Kronander J (2008) Sensor Selection for Cooperative Spectrum Sensing. 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, Chicago, pp. 1–11
48.
Southwell R, Chen X, Huang J (2014) Quality of service games for spectrum sharing. IEEE J Sel Areas Commun 32(3):589–600CrossRef
49.
Sum CS, Harada H, Kojima F, Lan Z, Funada R (2011) Smart utility networks in tv white space. IEEE Commun Mag 49(7):132–139CrossRef
50.
Taher TM, Bacchus RB, Zdunek KJ, Roberson DA (2011) Long-term spectral occupancy findings in Chicago. In: Proceedings of IEEE international symposium on dynamic spectrum access networks (DySPAN), pp 100–107, May 2011
51.
Tracking and monitoring of animals with combined wireless technology and geofencing, US Patent Application US20130340305
52.
Umar R, Sheikh AUH, Deriche M (2014) Unveiling the hidden assumptions of energy detector based spectrum sensing for cognitive radios. IEEE Commun Surv Tutor 16(2):713–728. Second Quarter 2014CrossRef
53.
Varghese A, Tandur D (2014) Wireless requirements and challenges in industry 4.0. In: 2014 international conference on contemporary computing and informatics (IC3I), Mysore, pp 634–638
54.
Wang J, Liu A, Yan T et al. (2018) Peer-to-Peer Netw Appl. 11:(679)
55.
Wellens M, Wu J, Mähönen P (2007) Evaluation of spectrum occupancy in indoor and outdoor scenario in the context of cognitive radio. In: 2007 2nd international conference on cognitive radio oriented wireless networks and communications (CROWNCOM), Aug 2007. IEEE, pp 420–427
56.
Yin S, Chen D, Zhang Q, Liu M, Li S (2012) Mining spectrum usage data: a large-scale spectrum measurement study. IEEE Trans Mob Comput 11(6):1033–1046CrossRef
57.
Ying X, Zhang J, Yan L, Zhang G, Chen M, Chandra R (2013) Exploring indoor white spaces in metropolises. In: Proceedings of the 19th annual international conference on Mobile computing & networking – MobiCom’13, New York. ACM Press, p 255
58.
Yuan Ma, Gao Y, Parini CG (2015) Sub-Nyquist rate wideband spectrum sensing over TV white space for M2M communications. In: 2015 IEEE 16th international symposium on a world of wireless, mobile and multimedia networks (WoWMoM), Boston, pp 1–6
59.
Zhang Y, Yu R, Nekovee M, Liu Y, Xie S, Gjessing S (2012) Cognitive machine-to-machine communications: visions and potentials for the smart grid. IEEE Netw 26(3):6–13CrossRef
60.
Zhou H et al (2015) Enabling efficient and wide-coverage vehicular content distribution over TV white spaces. In: 2015 international conference on wireless communications & signal processing (WCSP), Nanjing, pp 1–6
Metadaten
Titel
Dynamic Spectrum Access for Machine to Machine Communications: Opportunities, Standards, and Open Issues
verfasst von
Luca Bedogni
Marco Di Felice
Luciano Bononi
Copyright-Jahr
2019
Verlag
Springer Singapore
Buch
Handbook of Cognitive Radio

Print ISBN: 978-981-10-1393-5

Electronic ISBN: 978-981-10-1394-2

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-981-10-1394-2_57

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