The emerge of Internet of Things (IoT) brings up revolutionary changes to wireless communications. Cognitive radio (CR) can be seen as one of the prominent solutions to spectrum scarcity in IoT, where multi-band cooperative spectrum sensing (CSS) is the key. However, lack of centralized control and increase in number of devices place a room for many challenges. One of the main challenges is secondary users’ (SUs’) scheduling to sense a subset of channels in heterogeneous distributed CR networks (CRNs). To overcome the aforementioned challenge, in this paper, we propose a novel heterogeneous multi-band multi-user CSS (HM2CSS) scheme. The proposed scheme allows heterogeneous SUs to sense multiple channels and consists of two stages. We formulate a mathematical model to optimize leader-selection for each channel in the first stage. We then formulate another optimization problem to determine corresponding cooperative SUs to sense these channels in the second stage. After that, diffusion learning is used to decide on the availability of channels. Simulations illustrate that the proposed scheme improves detection performance and CRN throughput, is scalable in terms of detection performance, and provides fair energy consumption for CSS on all channels compared to existing multi-band CSS schemes.

A. Gharib, W. Ejaz and M. Ibnkahla, “Scalable Learning-Based Heterogeneous Multi-Band Multi-User Cooperative Spectrum Sensing for Distributed IoT Systems,” IEEE Open Journal of the Comms Society, vol. 1, pp. 1066-1083, 2020.

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With adaptive transmission, the throughput and power can be adapted to the channel conditions, which leads to a variable amount of energy consumption for the Internet of Things (IoT) sensor nodes. Considering an IoT network with heterogeneous sensor nodes in terms of their battery levels, we propose three channel adaptive schemes: the energy efficient scheme (EES), the spectral efficient scheme (SES), and the hybrid scheme (HS). One of the main novelties of this work is the proposed hybrid scheme which takes into consideration the remaining battery level in the IoT sensor node when deciding on the transmit power and the modulation mode at every transmission. Analytical results are provided in terms of the average spectrum efficiency (ASE), power usage ratio (PUR), and delay performance. Selected numerical results are presented to illustrate and validate the analytical results.

C. S. Ferdowsy, Z. Bouida, and M. Ibnkahla, “Energy and Spectral Efficient Scheduling for Heterogeneous IoT Sensor Nodes,” in 2020 IEEE Eighth International Conference on Communications and Networking (ComNet), Oct. 2020, pp. 1–6. doi: 10.1109/ComNet47917.2020.9306072.

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The growing popularity of the Internet of Things’ (IoT) applications comes with new challenges for wireless communications. Indeed, wireless transmission systems should more efficiently support heterogeneous traffic from diverse types of information sources. In this paper, we propose a set of traffic-oriented transmission schemes for a massive MIMO system that adapts the number of antennas based on three types of IoT traffic (i) energy sensitive, (ii) throughput sensitive, and (iii) highly reliable traffic. We jointly consider the uplink and downlink transmission for every IoT traffic and our energy efficient model reveals the optimum number of antennas that ensure each traffic’s Quality of Service (QoS) when communicating with a certain number of IoT nodes. Numerical results are shown in terms of average transmit power, spectral efficiency, average area throughput and energy efficiency. The results demonstrate that the performance is improved with the number of nodes which ensures the scalability of the IoT network.

C. S. Ferdowsy, Z. Bouida, and M. Ibnkahla, “Traffic Adaptive Transmission Schemes for the Internet of Things,” in 2020 International Conference on Communications, Signal Processing, and their Applications (ICCSPA), Mar. 2021, pp. 1–6. doi: 10.1109/ICCSPA49915.2021.9385729.

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Multi-band cooperative spectrum sensing (CSS) can provide opportunistic spectrum access to secondary users (SUs) in cognitive radio networks (CRNs). In multi-band CSS, the sensing task is divided among SUs based on their capability, residual energy, channel conditions, etc. However, SUs’ scheduling to sense a subset of channels in distributed CRNs is challenging mainly because of the lack of a central entity and the changing conditions due to new SUs entering the network. To address these challenges, we propose a two-stage multi-band multi-user CSS (M2CSS) scheme to assign a subset of channels to SUs for spectrum sensing. We first formulate an optimization problem to choose a leader for each channel. We then formulate an optimization problem to select cooperative SUs for each channel such that SUs with similar sensing information for the same channel are not selected. Further, we propose an enhanced M2CSS (EM2CSS) scheme to allow new SUs to participate in the sensing process. We formulate an optimization problem to assign multiple channels to joining SUs. We then distribute the sensing assignments among existing and joining SUs to minimize sensing energy consumption. Extensive simulation results show the efficacy of the proposed schemes when compared to existing schemes.

A. Gharib, W. Ejaz and M. Ibnkahla, “Enhanced Multiband Multiuser Cooperative Spectrum Sensing for Distributed CRNs,” IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 1, pp. 256-270, 2020.

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