Energy utilities are constantly under pressure to meet the growing and complicated energy demands. The traditional energy grid allows for one-way communication of energy usage between customers and the utilities. This does not allow the utilities to have control or to suggest any changes in consumption based on the energy data they obtain. In this paper, we propose and implement an innovative two-way communication system between the transformer agent (TA), attached to a neighborhood’s electric transformer, and its customer agents (CAs), that are attached to each house using inexpensive and common-use devices and modules. In this context, different houses communicate their energy usage, while an electric transformer relays action requests from the energy utility’s headquarters. This enables the real-time tracking of energy usage by both the consumers and the utility. Therefore, the efficiency of energy generation and distribution is enhanced, and consumers are empowered to make smarter decisions about their consumption. In our system, Raspberry Pi3 modules are used to represent CAs, while an Intel Edison is used to represent the TAs. CAs form a self- healing mesh network using the high data rate Wi-Fi in mesh mode while TAs communicate with the utility headquarters using LTE. The proposed system is compliant with the IEEE 2030.5 smart energy profile 2.0 requirements and several tests were performed in real neighborhoods and across the �Ӱ�ԭ�� University campus to prove the system’s operation and reliability. This paper is a part of a bigger project to achieve a complete and IoT-compatible platform for future smart grids that includes the whole cycle starting from the Home Energy Management System (HEMS) and ending with data analytics and power consumption prediction in the utility headquarter.

M. Ghalib, A. Ahmed, I. Al-Shiab, Z. Bouida and M. Ibnkahla, “Implementation of a Smart Grid Communication System Compliant with IEEE 2030.5,” 2018 IEEE International Conference on Communications Workshops (ICC Workshops), Kansas City, MO, 2018, pp. 1-6.

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In this paper, we consider an overlay cognitive radio (CR) scenario where the primary transmitter (PT) and the primary receiver (PR) communicate via the help of a secondary users’ (SUs) system. Under a worst-case scenario, we assume that the link between the primary users (PUs) is broken and the help of a selected secondary transmitter (ST) is required. Taking advantage of this opportunity, this ST will be able to transmit its own data. The communications of the PUs and the SUs take place over two phases. In the first phase, receive space shift keying (R-SSK) is employed at the PT in order to activate one ST for reception. This ST is scheduled to transmit its own data during the second phase using conventional SSK, which also allows the PR to decode the PT’s message. The proposed scheduling scheme is initiated by the PT based on its incoming bits which provides fairness among STs. The proposed system comes with other advantages including the low receivers’ complexity and the improved energy efficiency (EE) all gained by the use of SSK. We analyze the performance of the proposed scheme in terms of the average bit error probability (ABEP). We finally provide comparisons to existing schemes and we generate numerical results through which we confirm the derived analysis and we demonstrate the effectiveness of the proposed overlay cognitive scheduling scheme.

Z. Bouida, A. Gharib and M. Ibnkahla, “Secondary system’s scheduling using precoding-aided space shift keying for overlay cognitive radio,” 2018 IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, 2018, pp. 1-5.

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Multi-band cooperative spectrum sensing can provide access to a wide range of spectrum in cognitive radio networks (CRNs). The design of multi-band spectrum sensing is very challenging mainly due to scheduling of secondary users (SUs) to sense a subset of channels. In this paper, we propose a distributed learning-based multi-band multi-user cooperative spectrum sensing (M2CSS) scheme to select most appropriate SUs to sense channels. The proposed scheme allows SUs to sense multiple channels, and consists of two stages: 1) leader selection for each channel, and 2) selection of corresponding cooperative SUs to sense these channels. We formulate an optimization problem to select leaders that can effectively communicate with other SUs subject to the constraint that each SU can act as a leader for only one channel, and there will be only one leader for each channel. We then formulate another optimization problem to select corresponding cooperative SUs for each channel. After this stage, selected cooperative SUs sense channels, and use consensus learning to determine the availability of channels in a distributed manner. Simulation results show that the proposed M2CSS scheme can enhance detection performance, avoid the choice of redundant cooperative SUs, owning similar sensed information, and provide fair energy consumption for all channels compared to the existing schemes.

A. Gharib, W. Ejaz and M. Ibnkahla, “Distributed Learning-Based Multi-Band Multi-User Cooperative Sensing in Cognitive Radio Networks,” 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, 2018, pp. 1-6.

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Long Term Evolution (LTE) is a promising technology to be used for Mission-Critical Networks (MCNs); emulating such technology is important to test different scenarios before real deployment. However, using the Network Simulator (NS-3) to simulate large-scale LTE networks has proven to be very time consuming. Hence, there is a need to speed up such simulations in order to facilitate real-time emulation and interaction of large-scale LTE networks with external systems. In this paper, we propose a new approach to enable the emulation of large-scale LTE networks by employing distributed topologies along with the Message Passing Interface (MPI) protocol. The proposed approach is integrated with the Common Open Research Emulator (CORE) to enable exchange of real-time traffic between the simulated LTE network and Hardware-In-the Loop (HIL). Performance studies were carried out to evaluate the scaling performance of emulated LTE networks in real time. The results show that distributed implementation succeeds in running scenarios within the wall-clock time.

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A. Sabbah, A. Jarwan, I. Al-Shiab, M. Ibnkahla, and M. Wang, “Emulation of Large-Scale LTE Networks in NS-3 and CORE: A Distributed Approach,” in MILCOM 2018 – 2018 IEEE Military Communications Conference (MILCOM), Oct. 2018, pp. 1–6. doi: 10.1109/MILCOM.2018.8599762.

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In usual ways of programming, a program is built by building instructions in order to reach desired outputs from inputs. However, in machine learning (ML), that process is flipped. The inputs and desired set of outputs are given, and the program should learn what instructions or policy should be followed. ML is concerned with algorithms that observe data, learn from it, grow up, and make more intelligent decisions. Based on the way that machines accumulate knowledge and become able to function as needed, ML can be classified into supervised, semi-supervised, unsupervised, and reinforcement learning.

A. Jarwan, A. Sabbah, and M. Ibnkahla, “Machine Learning in Wireless Sensor Networks for the Internet of Things,” in Encyclopedia of Wireless Networks, X. (Sherman) Shen, X. Lin, and K. Zhang, Eds. Cham: Springer International Publishing, 2018, pp. 1–7. doi: 10.1007/978-3-319-32903-1_274-1.

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Cognitive radio (CR) technology offers a promising solution to the spectrum scarcity problem via dynamic spectrum access (DSA). Due to the nature of cooperative cognitive radio networks (CRNs), where two distinct networks are active simultaneously, a significant amount of control messaging is required in order to coordinate channel access, schedule sensing, and establish and release connections. Efficient control plane messaging can be achieved by the selection of an appropriate control channel (CC). Major selection strategies of CCs are categorized as either dedicated or dynamic strategies. This paper studies the major potential techniques for selecting reliable CCs for coordination and information distribution in license-exempt (LE) bands. This involves determining the potential and limitations of each technique in terms of availability, complexity, and robustness. We consider real-life scenarios including an outdoor stadium and an indoor environment. Recommendations are given for different situations.

Sabbah, M. Ibnkahla, O. Issa, and B. Doray, “Control channel selection techniques in cognitive radio networks: A comparative performance analysis,” Journal of Communications and Networks, vol. 20, no. 1, pp. 57–68, Feb. 2018, doi: 10.1109/JCN.2018.000006.

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The proliferation of the Internet of Things (IoT) demands a diverse and wide range of requirements in terms of latency, reliability, energy efficiency, etc. Future IoT systems must have the ability to deal with the challenging requirements of both users and applications. Cognitive fifth generation (5G) network is envisioned to play a key role in leveraging the performance of IoT systems. IoT systems in cognitive 5G network are expected to provide flexible delivery of broad services and robust operations under highly dynamic conditions. In this paper, we present multiband cooperative spectrum sensing and resource allocation framework for IoT in cognitive 5G networks. Multiband approach can significantly reduce energy consumption for spectrum sensing compared to the traditional single-band scheme. We formulate an optimization problem to determine a minimum number of channels to be sensed by each IoT node in multiband approach to minimize the energy consumption for spectrum sensing while satisfying probabilities of detection and false alarm requirements. We then propose a cross-layer reconfiguration scheme (CLRS) for dynamic resource allocation in IoT applications with different quality-of-service (QoS) requirements including data rate, latency, reliability, economic price, and environment cost. The potential game is employed for crosslayer reconfiguration, in which IoT nodes are considered as the players. The proposed CLRS efficiently allocate resources to satisfy QoS requirements through opportunistic spectrum access. Finally, extensive simulation results are presented to demonstrate the benefits offered by the proposed framework for IoT systems.

Ejaz and M. Ibnkahla, “Multiband Spectrum Sensing and Resource Allocation for IoT in Cognitive 5G Networks,” IEEE Internet of Things Journal, vol. 5, no. 1, pp. 150–163, Feb. 2018, doi: 10.1109/JIOT.2017.2775959.

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