Cognitive Spectrum Sharing: An Enabling Wireless Communication Technology for a Wide Use of Smart Systems
Abstract
:1. Introduction
2. The Communication Infrastructure of a Smart City
2.1. Communication Challenges
- Heterogeneity: heterogeneous communication technologies have to be integrated to provide reliable and functional access to the system elements in different environments. Indeed, in order to have a wide spread of smart city applications, it is required that people can use their own devices and not dedicated devices and software. This will allow low cost, flexible, and scalable solutions. The resulting communication infrastructure must integrate any technology that may be considered relevant by a smart city actor.
- Quality of Service: the diversity of smart city solutions determines the wide variability of supported services and applications. This leads to multiple traffic types within the network and, hence, the need to manage all of these respecting their different Quality of Service (QoS) requirements in terms of priority, delay, data rate, reliability, and security. Moreover, the amount of data varies tremendously during a day, so the traffic conditions change quickly, and the system must be able to adapt itself to the scenario variability.
- Security: smart city networks have to carry reliable and real-time information toward monitoring and control centres. This exposes the system to outside attacks, unauthorized accesses, and data modifications. Data can be captured and carried over the system. This makes it necessary to foresee suitable mechanisms to prevent cyber attacks that might block the city functionalities and carry unwanted alarms or data theft.
- Energy consumption: smart cities are based on large diffusion of smart devices and sensors whose operations are strongly affected by their battery life. Hence, energy efficient communication protocols are needed, especially for local connections. Moreover, the use of energy harvesting solutions should be considered.
- Communication resource availability: smart cities and smart devices will have an explosive growth in the next years. Hence, the traffic generated by the applications running on smart systems will require a huge amount of bandwidth and network resources, thus challenging the communication infrastructure. Especially in wireless communications, having sufficient and dedicated spectrum resources for smart city applications will be infeasible. Therefore, the availability of sufficient spectrum to accommodate current and future needs of smart environments is expected to be a critical requirement.
2.2. Related Works
3. Cognitive Radio for Smart City
3.1. Cognitive Radio Concepts
3.2. Benefits of Cognitive Radio in Smart City
- Communication resource availability. CR improves spectrum utilization and communication capacity to support large-scale data transmissions. Indeed, the unlicensed spectrum (i.e., Industrial, Scientific, and Medical, ISM) mainly used in local area connections is becoming dramatically crowded and interfered, while other licensed frequency bands are fixedly assigned and utilized in an inefficient way. In addition, the application of CR can also alleviate the burden of purchasing licensed spectrum for utility providers. CR uses the existing spectrum through opportunistic access to the licensed bands without interfering with the licensed users. CR determines the spectrum portions unoccupied by the licensed users—known as spectrum holes or white spaces—and allocates the best available channels for communicating.
- Heterogeneity. Heterogeneous communication technologies have to be integrated to provide reliable and efficient access to the system elements in different environments. As a consequence, devices should be able to acquire context awareness and to reconfigure themselves. Hardware reconfigurability can help to manage communications in areas where different technologies are present.
- Quality of Service. Communications over white spaces can provide dedicated low-latency communications for critical data.
- Energy consumption. CR can be used to reduce power consumption, and hence to have energy efficient systems, by sensing the environment and then adaptively adjusting the transmission power, avoiding energy waste.
3.3. Related Literature Review
3.4. Cognitive M2M
3.5. Cognitive HetNets
3.6. Cognitive Communication Architecture for Smart City
4. Cognitive Solutions for HetNets and M2M Communications
- long-term (i.e., seconds) cognitive systems;
- short-term (i.e., milliseconds) cognitive systems.
4.1. Long-Term Cognitive Approaches
4.2. Short-Term Cognitive Approaches
5. Conclusions
Conflicts of Interest
References
- Ma, R.; Chen, H.H.; Huang, Y.R.; Meng, W. Smart Grid Communication: Its Challenges and Opportunities. IEEE Trans. Smart Grid 2013, 4, 36–46. [Google Scholar] [CrossRef]
- Fan, Z.; Kulkarni, P.; Gormus, S.; Efthymiou, C.; Kalogridis, G.; Sooriyabandara, M.; Zhu, Z.; Lambotharan, S.; Chin, W.H. Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities. IEEE Commun. Surv. Tutor. 2013, 15, 21–38. [Google Scholar] [CrossRef]
- Kuzlu, M.; Pipattanasomporn, M. Assessment of communication technologies and network requirements for different smart grid applications. In Proceedings of the IEEE PES Innovative Smart Grid Technologies (ISGT), Washington, DC, USA, 24–27 February 2013; pp. 1–6.
- Zaballos, A.; Vallejo, A.; Selga, J.M. Heterogeneous communication architecture for the smart grid. IEEE Netw. 2011, 25, 30–37. [Google Scholar] [CrossRef]
- ITU. Ubiquitous Sensor Networks (USN); ITU-T Technology Watch Briefing Report Series, No.4; ITU-T: Geneva, Switzerland, February 2008. [Google Scholar]
- Avelara, E.; Marquesa, L.; dos Passosa, D.; Macedob, R.; Diasa, K.; Nogueirab, M. Interoperability issues on heterogeneous wireless communication for smart cities. Comput. Commun. 2015, 58, 4–15. [Google Scholar] [CrossRef]
- Komninos, N.; Philippou, E.; Pitsillides, A. Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures. IEEE Commun. Surv. Tutor. 2014, 16, 1933–1954. [Google Scholar] [CrossRef]
- Finster, S.; Baumgart, I. Privacy-Aware Smart Metering: A Survey. IEEE Commun. Surv. Tutor. 2015, 17, 1088–1101. [Google Scholar] [CrossRef]
- Wu, J.; Ota, K.; Dong, M.; Li, C. A Hierarchical Security Framework for Defending Against Sophisticated Attacks on Wireless Sensor Networks in Smart Cities. IEEE Access 2016, 4, 416–424. [Google Scholar] [CrossRef]
- Chakrabarty, S.; Engels, D.W. A secure IoT architecture for Smart Cities. In Proceedings of the 13th IEEE Annual Consumer Communications Networking Conference (CCNC), Las Vegas, NV, USA, 9–12 January 2016; pp. 812–813.
- Al-Anbagi, I.; Erol-Kantarci, M.; Mouftah, H.T. A Survey on Cross-Layer Quality-of-Service Approaches in WSNs for Delay and Reliability-Aware Applications. IEEE Commun. Surv. Tutor. 2016, 18, 525–552. [Google Scholar] [CrossRef]
- Alvi, A.N.; Bouk, S.H.; Ahmed, S.H.; Yaqub, M.A.; Sarkar, M.; Song, H. BEST-MAC: Bitmap-Assisted Efficient and Scalable TDMA-Based WSN MAC Protocol for Smart Cities. IEEE Access 2016, 4, 312–322. [Google Scholar] [CrossRef]
- Jin, J.; Gubbi, J.; Luo, T.; Palaniswami, M. Network architecture and QoS issues in the internet of things for a smart city. In Proceedings of the International Symposium on Communications and Information Technologies (ISCIT), Gold Coast, Australia, 2–5 October 2012; pp. 956–961.
- Erol-Kantarci, M.; Mouftah, H.T. Energy-Efficient Information and Communication Infrastructures in the Smart Grid: A Survey on Interactions and Open Issues. IEEE Commun. Surv. Tutor. 2015, 17, 179–197. [Google Scholar] [CrossRef]
- Imen, B.; Mahmoud, P.A. Hierarchical organization with a cross layers using smart sensors for intelligent cities. In Proceedings of the SAI Intelligent Systems Conference (IntelliSys), London, UK, 10–11 November 2015; pp. 446–451.
- Haykin, S. Cognitive radio: Brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 2005, 23, 201–220. [Google Scholar] [CrossRef]
- Khan, A.A.; Rehmani, M.H.; Reisslein, M. Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols. IEEE Commun. Surv. Tutor. 2016, 18, 860–898. [Google Scholar] [CrossRef]
- Niyato, D.; Hossain, E. Cognitive radio for next-generation wireless networks: An approach to opportunistic channel selection in ieee 802.11-based wireless mesh. IEEE Wirel. Commun. 2009, 16. [Google Scholar] [CrossRef]
- Felice, M.D.; Doost-Mohammady, R.; Chowdhury, K.R.; Bononi, L. Smart Radios for Smart Vehicles: Cognitive Vehicular Networks. IEEE Veh. Technol. Mag. 2012, 7, 26–33. [Google Scholar] [CrossRef]
- Gungor, V.C.; Sahin, D. Cognitive Radio Networks for Smart Grid Applications: A Promising Technology to Overcome Spectrum Inefficiency. IEEE Veh. Technol. Mag. 2012, 7, 41–46. [Google Scholar] [CrossRef]
- Kouhdaragh, V.; Tarchi, D.; Coralli, A.V.; Corazza, G.E. Cognitive Radio based Smart Grid Networks. In Proceedings of the 24th Tyrrhenian International Workshop on Digital Communications—Green ICT (TIWDC), Genoa, Italy, 23–25 September 2013; pp. 1–6.
- Sum, C.S.; Harada, H.; Kojima, F.; Lan, Z.; Funada, R. Smart utility networks in TV white space. IEEE Commun. Mag. 2011, 49, 132–139. [Google Scholar] [CrossRef]
- Gao, J.; Wang, J.; Wang, B.; Song, X. Cognitive radio based communication network architecture for smart grid. In Proceedings of the International Conference on Information Science and Technology (ICIST), Changsha, Hubei, China, 23–25 March 2012; pp. 886–888.
- Yu, R.; Zhang, Y.; Gjessing, S.; Yuen, C.; Xie, S.; Guizani, M. Cognitive radio based hierarchical communications infrastructure for smart grid. IEEE Netw. 2011, 25, 6–14. [Google Scholar] [CrossRef]
- Vineeta, N.; Thathagar, J.K. Cognitive radio communication architecture in smart grid reconfigurability. In Proceedings of the 1st International Conference on Emerging Technology Trends in Electronics, Communication and Networking (ET2ECN), Surat, Gujarat, India, 19–21 December 2012; pp. 1–6.
- Chang, S.; Nagothu, K.; Kelley, B.; Jamshidi, M.M. A Beamforming Approach to Smart Grid Systems Based on Cloud Cognitive Radio. IEEE Syst. J. 2014, 8, 461–470. [Google Scholar] [CrossRef]
- Basharat, M.; Ejaz, W.; Ahmed, S.H. Securing cognitive radio enabled smart grid systems against cyber attacks. In Proceedings of the First International Conference on Anti-Cybercrime (ICACC), Riyadh, Kingdom of Saudi Arabia, 10–12 November 2015; pp. 1–6.
- Bicen, A.O.; Akan, O.B.; Gungor, V.C. Spectrum-aware and cognitive sensor networks for smart grid applications. IEEE Commun. Mag. 2012, 50, 158–165. [Google Scholar] [CrossRef]
- Yang, H.C.; Zhang, D.; Kong, X.; Jia, H. Performance Analysis of Cognitive Transmission in Dual-Cell Environment and its Application to Smart Meter Communications. In Proceedings of the Seventh International Conference on Broadband, Wireless Computing, Communication and Applications (BWCCA), Victoria, BC, Canada, 12–14 November 2012; pp. 40–45.
- Huang, J.; Wang, H.; Qian, Y.; Wang, C. Priority-Based Traffic Scheduling and Utility Optimization for Cognitive Radio Communication Infrastructure-Based Smart Grid. IEEE Trans. Smart Grid 2013, 4, 78–86. [Google Scholar] [CrossRef]
- Siya, X.; Lei, W.; Zhu, L.; Shaoyong, G.; Xuesong, Q.; Luoming, M. A QoS-aware packet scheduling mechanism in cognitive radio networks for smart grid applications. China Commun. 2016, 13, 68–78. [Google Scholar] [CrossRef]
- Yu, R.; Zhong, W.; Xie, S.; Zhang, Y.; Zhang, Y. QoS Differential Scheduling in Cognitive-Radio-Based Smart Grid Networks: An Adaptive Dynamic Programming Approach. IEEE Trans. Neural Netw. Learn. Syst. 2016, 27, 435–443. [Google Scholar] [CrossRef] [PubMed]
- Wan, J.; Li, D.; Zou, C.; Zhou, K. M2M Communications for Smart City: An Event-Based Architecture. In Proceedings of the IEEE 12th International Conference on Computer and Information Technology (CIT), Chengdu, China, 27–29 October 2012; pp. 895–900.
- Elmangoush, A.; Coskun, H.; Wahle, S.; Magedanz, T. Design aspects for a reference M2M communication platform for Smart Cities. In Proceedings of the 9th International Conference on Innovations in Information Technology (IIT), Abu Dhabi, United Arab Emirates, 17–19 March 2013; pp. 204–209.
- Skouby, K.E.; Lynggaard, P. Smart home and smart city solutions enabled by 5G, IoT, AAI and CoT services. In Proceedings of the International Conference on Contemporary Computing and Informatics (IC3I), Mysore, India, 27–29 November 2014; pp. 874–878.
- Datta, S.K.; Bonnet, C. Internet of Things and M2M Communications as Enablers of Smart City Initiatives. In Proceedings of the 9th International Conference on Next Generation Mobile Applications, Services and Technologies, Cambridge, UK, 9–11 September 2015; pp. 393–398.
- Zhang, Y.; Yu, R.; Nekovee, M.; Liu, Y.; Xie, S.; Gjessing, S. Cognitive machine-to-machine communications: Visions and potentials for the smart grid. IEEE Netw. 2012, 26, 6–13. [Google Scholar] [CrossRef]
- Aijaz, A.; Aghvami, A.H. PRMA-Based Cognitive Machine-to-Machine Communications in Smart Grid Networks. IEEE Trans. Veh. Technol. 2015, 64, 3608–3623. [Google Scholar] [CrossRef]
- Bartoli, G.; Fantacci, R.; Letaief, K.; Marabissi, D.; Privitera, N.; Pucci, M.; Zhang, J. Beamforming for Small Cells Deployment in LTE-Advanced and Beyond. IEEE Commun. Mag. 2014, 21, 50–56. [Google Scholar] [CrossRef]
- Bhushan, N.; Li, J.; Malladi, D.; Gilmore, R.; Brenner, D.; Damnjanovic, A.; Sukhavasi, R.; Patel, C.; Geirhofer, S. Network densification: The dominant theme for wireless evolution into 5G. IEEE Commun. Mag. 2014, 52, 82–89. [Google Scholar] [CrossRef]
- Mazza, D.; Tarchi, D.; Corazza, G.E. A partial offloading technique for wireless mobile cloud computing in smart cities. In Proceedings of the European Conference on Networks and Communications (EuCNC), Bologna, Italy, 23–26 June 2014; pp. 1–5.
- Zhou, L.; Hu, X.; Zhu, C.; Ngai, E.C.H.; Wang, S.; Wei, J.; Leung, V.C.M. Green small cell planning in smart cities under dynamic traffic demand. In Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Hong Kong, China, 26 April–1 May 2015; pp. 618–623.
- Huang, L.; Zhu, G.; Du, X. Cognitive femtocell networks: An opportunistic spectrum access for future indoor wireless coverage. IEEE Wirel. Commun. 2013, 20, 44–51. [Google Scholar] [CrossRef]
- Bu, S.; Yu, F.R. Green Cognitive Mobile Networks With Small Cells for Multimedia Communications in the Smart Grid Environment. IEEE Trans. Veh. Technol. 2014, 63, 2115–2126. [Google Scholar] [CrossRef]
- Augé-Blum, I.; Boussetta, K.; Rivano, H.; Stanica, R.; Valois, F. Capillary Networks: A Novel Networking Paradigm for Urban Environments. In Proceedings of the First Workshop on Urban Networking, Nice, France, 10–13 December 2012; pp. 25–30.
- Shen, Z.; Papasakellariou, A.; Montojo, J.; Gerstenberger, D.; Xu, F. Overview of 3GPP LTE-advanced carrier aggregation for 4G wireless communications. IEEE Commun. Mag. 2012, 50, 122–130. [Google Scholar] [CrossRef]
- Al-Dulaimi, A.; Al-Rubaye, S.; Ni, Q.; Sousa, E. 5G Communications Race: Pursuit of More Capacity Triggers LTE in Unlicensed Band. IEEE Veh. Technol. Mag. 2015, 10, 43–51. [Google Scholar] [CrossRef]
- Yucek, T.; Arslan, H. A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun. Surv. Tutor. 2009, 11, 116–130. [Google Scholar] [CrossRef]
- Leung, H.; Chandana, S.; Wei, S. Distributed sensing based on intelligent sensor networks. IEEE Circuits Syst. Mag. 2008, 8, 38–52. [Google Scholar] [CrossRef]
- Bansal, G.; Hossain, M.; Bhargava, V.; Le-Ngoc, T. Subcarrier and Power Allocation for OFDMA-Based Cognitive Radio Systems With Joint Overlay and Underlay Spectrum Access Mechanism. IEEE Trans Veh. Technol. 2013, 62, 1111–1122. [Google Scholar] [CrossRef]
- Lee, H.K.; Kim, D.M.; Hwang, Y.; Yu, S.M.; Kim, S.L. Feasibility of cognitive machine-to-machine communication using cellular bands. IEEE Wirel. Commun. 2013, 20, 97–103. [Google Scholar] [CrossRef]
- Ng, P. Optimization of Spectrum Sensing for Cognitive Sensor Network using Differential Evolution Approach in Smart Environment. In Proceedings of the IEEE 12th International Conference on Networking, Sensing and Control, Taipei, Taiwan, 9–11 April 2015; pp. 592–596.
- Liu, F.; Wang, J.; Han, Y.; Han, P. Smart Grid Communication using Next Generation Heterogeneous Wireless Networks. In Proceedings of the IEEE Third International Conference on Smart Grid Communications, Tainan, Taiwan, 5–8 November 2012; pp. 229–234.
- Tani, A.; Fantacci, R.; Marabissi, D. A low-complexity cyclostationary spectrum sensing for Interference Avoidance in femto-cells LTE-A based Networks. IEEE Trans. Veh. Technol. 2015, 65, 2747–2753. [Google Scholar] [CrossRef]
- Oh, D.; Lee, H.; Lee, Y. Cognitive radio based femtocell resource allocation. In Proceedings of the International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea, 17–19 November 2010; pp. 274–279.
- Sun, D.; Zhu, X.; Zeng, Z.; Wan, S. Downlink power control in cognitive femtocell networks. In Proceedings of the International Conference on Wireless Wireless Communications and Signal Processing (WCSP), Nanjing, China, 9–11 November 2011; pp. 1–5.
- Yiu, S.; Chae, C.B.; Yang, K.; Calin, D. Uncoordinated Beamforming for Cognitive Networks. IEEE Trans. Commun. 2012, 60, 1390–1397. [Google Scholar] [CrossRef]
- Bartoli, G.; Fantacci, R.; Marabissi, D.; Pucci, M. LTE-A femto-cell interference mitigation with MuSiC DOA estimation and null steering in an actual indoor environment. In Proceedings of the IEEE International Conference on Communications (ICC), Budapest, Hungary, 9–13 June 2013; pp. 2707–2711.
- Tarchi, D.; Fantacci, R.; Marabissi, D. Proposal of a cognitive based MAC protocol for M2M environments. In Proceedings of the IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), London, UK, 8–11 September 2013; pp. 1609–1613.
Cognitive approaches features | Long-Term | Short-Term |
---|---|---|
Smart city communication layer | local and access layer (CM2M and CHetNets) | access layer (CHetNets) |
Sensing period | several frames | scheduling period |
Transmission opportunity | sub-bands | Resource Units |
Technical challenges | suitable trade off: cost-accuracy of sensing | fast sensing feedback information joint resource allocation |
Spectrum efficiency | Low | High |
Secondary Network Requirements | no | synchronization and legacy terminal |
Distributed sensing | yes | no |
Cognitive approaches features | Sensing | Signaling (Scheduling maps) |
---|---|---|
Computational Complexity | high | low |
Challenges | real-time sensing | scheduling map |
availability | ||
Applications | HetNets | HetNets and M2M |
Spectrum Efficiency | high | medium |
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Fantacci, R.; Marabissi, D. Cognitive Spectrum Sharing: An Enabling Wireless Communication Technology for a Wide Use of Smart Systems. Future Internet 2016, 8, 23. https://doi.org/10.3390/fi8020023
Fantacci R, Marabissi D. Cognitive Spectrum Sharing: An Enabling Wireless Communication Technology for a Wide Use of Smart Systems. Future Internet. 2016; 8(2):23. https://doi.org/10.3390/fi8020023
Chicago/Turabian StyleFantacci, Romano, and Dania Marabissi. 2016. "Cognitive Spectrum Sharing: An Enabling Wireless Communication Technology for a Wide Use of Smart Systems" Future Internet 8, no. 2: 23. https://doi.org/10.3390/fi8020023