Optimization of Cognitive Radio Secondary Information Gathering Station Positioning and Operating Channel Selection for IoT Sensor Networks

The Internet of Things (IoT) is the interconnection of different objects through the internet using different communication technologies. The objects are equipped with sensors and communications modules. The cognitive radio network is a key technique for the IoT and can effectively address spectrum-related issues for IoT applications. In our paper, a novel method for IoT sensor networks is proposed to obtain the optimal positions of secondary information gathering stations (SIGSs) and to select the optimal operating channel. Our objective is to maximize secondary system capacity while protecting the primary system. In addition, we propose an appearance probability matrix for secondary IoT devices (SIDs) to maximize the supportable number of SIDs that can be installed in a car, in wearable devices, or for other monitoring devices, based on optimal deployment and probability. We derive fitness functions based on the above objectives and also consider signal to interference-plus-noise ratio (SINR) and position constraints. The particle swarm optimization (PSO) technique is used to find the best position and operating channel for the SIGSs. In a simulation study, the performance of the proposed method is evaluated and compared with a random resources allocation algorithm (parts of this paper were presented at the ICTC2017 conference (Wen et al., 2017)).

[1]  Ibrahim Abe M. Elfadel,et al.  A versatile hardware platform for the development and characterization of IoT sensor networks , 2016, 2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS).

[2]  Sheikh Tahir Bakhsh,et al.  Energy-efficient distributed relay selection in wireless sensor network for Internet of Things , 2017, 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC).

[3]  Peter Shepherd,et al.  Optimising the location and power of wireless base stations within a dynamic indoor environment , 2014, 2014 Loughborough Antennas and Propagation Conference (LAPC).

[4]  Qihui Wu,et al.  Cognitive Internet of Things: A New Paradigm Beyond Connection , 2014, IEEE Internet of Things Journal.

[5]  Sang-Jo Yoo,et al.  Dynamic Interference Control in OFDM-Based Cognitive Radio Network Using Genetic Algorithm , 2015, Int. J. Distributed Sens. Networks.

[6]  Victor C. M. Leung,et al.  Performance Comparison of Cognitive Radio Sensor Networks for Industrial IoT With Different Deployment Patterns , 2017, IEEE Systems Journal.

[7]  Didier Le Ruyet,et al.  Energy-Efficiency-Based Resource Allocation Framework for Cognitive Radio Networks With FBMC/OFDM , 2017, IEEE Transactions on Vehicular Technology.

[8]  Yang Qin,et al.  Optimization of cognitive radio secondary base station positioning and operating channel selection for IoT sensor networks , 2017, 2017 International Conference on Information and Communication Technology Convergence (ICTC).

[9]  Mustafa Cenk Gursoy,et al.  Performance analysis of primary and secondary users in a cognitive multiple-access channel , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[10]  Yonghui Song,et al.  Multi-Armed Bandit Channel Access Scheme With Cognitive Radio Technology in Wireless Sensor Networks for the Internet of Things , 2016, IEEE Access.

[11]  Mubashir Husain Rehmani,et al.  When Cognitive Radio meets the Internet of Things? , 2016, 2016 International Wireless Communications and Mobile Computing Conference (IWCMC).

[12]  Mauro Fonseca,et al.  A strategy for opportunistic cognitive channel allocation in wireless Internet of Things , 2014, 2014 IFIP Wireless Days (WD).

[13]  Chen Liu,et al.  Fairness-aware resource allocation in OFDM-based cognitive radio networks for energy efficiency , 2016, 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD).

[14]  Xiaohui Zhao,et al.  Robust resource allocation for orthogonal frequency division multiplexing-based cooperative cognitive radio networks with imperfect channel state information , 2017, IET Commun..

[15]  Mohsen Guizani,et al.  Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[16]  Fambirai Takawira,et al.  Optimal Channel Selection and Power Allocation for Channel Assembling in Cognitive Radio Networks , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[17]  Xiaodai Dong,et al.  Resource-Allocation Strategy for Multiuser Cognitive Radio Systems: Location-Aware Spectrum Access , 2017, IEEE Transactions on Vehicular Technology.

[18]  Pramod K. Varshney,et al.  Enhanced Dynamic Spectrum Access in Multiband Cognitive Radio Networks via Optimized Resource Allocation , 2016, IEEE Transactions on Wireless Communications.

[19]  Youxi Tang,et al.  Optimal location of the base station based on measured interference power , 2015, 2015 IEEE International Wireless Symposium (IWS 2015).

[20]  Haythem Bany Salameh,et al.  Security-aware channel assignment in IoT-based cognitive radio networks for time-critical applications , 2017, 2017 Fourth International Conference on Software Defined Systems (SDS).

[21]  Sang-Min Han,et al.  RF spectrum sensing receiver system with improved frequency channel selectivity for cognitive iot sensor network applications , 2016, 2016 IEEE MTT-S International Microwave Symposium (IMS).

[22]  Deepak Choudhary,et al.  Internet of things: A survey on enabling technologies, application and standardization , 2018 .

[23]  Seemanti Saha,et al.  Co-channel interference constrained spectrum allocation with simultaneous power and network capacity optimization using PSO in Cognitive Radio Network , 2015, 2015 IEEE International Conference on Advanced Networks and Telecommuncations Systems (ANTS).

[24]  Lin Ma,et al.  Positional proportional fairness scheduling based on spectrum aggregation in cognitive radio , 2014, 2014 21st International Conference on Telecommunications (ICT).

[25]  Mubashir Husain Rehmani,et al.  Cognitive-Radio-Based Internet of Things: Applications, Architectures, Spectrum Related Functionalities, and Future Research Directions , 2017, IEEE Wireless Communications.