Long-range wide-area network (LoRaWAN) is one of the most promising IoT technologies that are widely adopted in low-power wide-area networks (LPWANs). LoRaWAN faces scalability issues due to a large number of nodes connected to the same gateway and sharing the same channel. Therefore, LoRa networks seek to achieve two main objectives: 1) successful delivery rate and 2) efficient energy consumption. This article proposes a novel game-theoretic framework for LoRaWAN named best equal LoRa (BE-LoRa), to jointly optimize the packet delivery ratio and the energy efficiency (bit/Joule). The utility function of the LoRa node is defined as the ratio of the throughput to the transmit power. LoRa nodes act as rational users (players) which seek to maximize their utility. The aim of the BE-LoRa algorithm is to maximize the utility of LoRa nodes while maintaining the same signal-to-interference-and-noise-ratio (SINR) for each spreading factor (SF). The power allocation algorithm is implemented at the network server, which leads to an optimum SINR, SFs, and transmission power settings of all nodes. Numerical and simulation results show that the proposed BE-LoRa power allocation algorithm has a significant improvement in the packet delivery ratio and energy efficiency as compared to the adaptive data rate (ADR) algorithm of legacy LoRaWAN. For instance, in very dense networks (624 nodes), BE-LoRa can improve the delivery ratio by 17.44% and reduce power consumed by 46% compared to LoRaWAN ADR.

The millimeter-wave (mmWave) is expected to deliver a huge bandwidth to address the future demands for higher data rate transmissions. However, one of the major challenges in the mmWave band is the increase in signal loss as the operating frequency increases. This has attracted several research interests both from academia and the industry for indoor and outdoor mmWave operations. This paper focuses on the works that have been carried out in the study of the mmWave channel measurement in indoor environments. A survey of the measurement techniques, prominent path loss models, analysis of path loss and delay spread for mmWave in different indoor environments is presented. This covers the mmWave frequencies from 28 GHz to 100 GHz that have been considered in the last two decades. In addition, the possible future trends for the mmWave indoor propagation studies and measurements have been discussed. These include the critical indoor environment, the roles of artificial intelligence, channel characterization for indoor devices, reconfigurable intelligent surfaces, and mmWave for 6G systems. This survey can help engineers and researchers to plan, design, and optimize reliable 5G wireless indoor networks. It will also motivate the researchers and engineering communities towards finding a better outcome in the future trends of the mmWave indoor wireless network for 6G systems and beyond.

The long-range wide-area network (LoRaWAN) uses the adaptive-data-rate (ADR) algorithm to control the data rate and transmission power. The LoRaWAN ADR algorithm adjusts the spreading factor (SF) to allocate the appropriate transmission rate and transmission power to reduce power consumption.The updating SF and transmission power of the standard ADR algorithm are based on the channel state, but it does not guarantee efficient energy consumption among all the nodes in complex environments with high-varying channel conditions. Therefore, this article proposes a new enhancement approach to the ADR+ algorithm at the network server, which only depends on the average signal-to-noise ratio (SNR). The enhancement ADR algorithm ADR++ introduces an energy-efficiency controller $\alpha$ related to the total energy consumption of all nodes, to use it for adjusting the average SNR of the last records. We implement our new enhanced ADR at the network server (NS) using the FLoRa module in OMNET++. The simulation results demonstrate that our proposed ADR++ algorithm leads to a significant improvement in terms of the network delivery ratio and energy efficiency that reduces the network energy consumption up to 17.5% and improves the packet success rate up to 31.55% over the existing ADR+ algorithm.

In future 5G systems, the millimeter wave (mmWave) band will be used to support a large capacity for current mobile broadband. Therefore, the radio access technology (RAT) should be made available for 5G devices to help in distinct situations, for example device-to-device communications (D2D) and multi-hops. This paper presents ultra-wideband channel measurements for millimeter wave bands at 19, 28, and 38 GHz. We used an ultra-wideband channel sounder (1 GHz bandwidth) in an indoor to outdoor (I2O) environment for non-line-of-sight (NLOS) scenarios. In an NLOS environment, there is no direct path (line of sight), and all of the contributed paths are received from different physical objects by refection propagation phenomena. Hence, in this work, a directional horn antenna (high gain) was used at the transmitter, while an omnidirectional antenna was used at the receiver to collect the radio signals from all directions. The path loss and temporal dispersion were examined based on the acquired measurement data—the 5G propagation characteristics. Two different path loss models were used, namely close-in (CI) free space reference distance and alpha-beta-gamma (ABG) models. The time dispersion parameters were provided based on a mean excess delay, a root mean square (RMS) delay spread, and a maximum excess delay. The path loss exponent for this NLOS specific environment was found to be low for all of the proposed frequencies, and the RMS delay spread values were less than 30 ns for all of the measured frequencies, and the average RMS delay spread values were 19.2, 19.3, and 20.3 ns for 19, 28, and 38 GHz frequencies, respectively. Moreover, the mean excess delay values were found also at 26.1, 25.8, and 27.3 ns for 19, 28, and 38 GHz frequencies, respectively. The propagation signal through the NLOS channel at 19, 28, and 38 GHz was strong with a low delay; it is concluded that these bands are reliable for 5G systems in short-range applications.

Despite all the acknowledged advantages and recent developments in terms of reducing the environmental impact, noise reduction and energy efficiency, the electric mobility market is still below the expectations. Among the most important challenges that limit the market penetration of Electric Vehicles (EVs) as well as achieving a sustainable mobility system in cities is the efficient distribution of adequate EV charging stations (CSs). In this paper, we propose a novel approach to find the best locations for EVCSs that considers a combination of factors including displacement between the EV and CS, elevation difference between their locations and finite capacities of CSs. The problem is formulated as a Mixed Integer Linear problem (MILP) to minimize the total energy consumption of EVs to reach CSs. A combination of the Genetic Algorithm (GA) technique and the Branch and Bound (B&B) algorithm are used to solve the problem. The proposed EVCSs placement technique is experimentally tested considering different case studies. With real world datasets, the results demonstrate the energy centric benefits of the proposed EVCSs placement technique.

Due to recent developments in electric mobility, public charging infrastructure will be essential for modern transportation systems. As the number of electric vehicles (EVs) increases, the public charging infrastructure needs to adopt efficient charging practices. A key challenge is the assignment of EVs to charging stations (CSs) in an energy efficient manner. In this paper, a Reinforcement Learning (RL)-based EV Assignment Scheme (RL-EVAS) is proposed to solve the problem of assigning EV to the optimal CS in urban environments, aiming at minimizing the total cost of charging EVs and reducing the overload on Electrical Grids (EGs). Travelling cost that is resulted from the movement of EV to CS, and the charging cost at CS are considered. Moreover, the EV’s Battery State of Charge (SoC) is taken into account in the proposed scheme. The proposed RL-EVAS approach will approximate the solution by finding an optimal policy function in the sense of maximizing the expected value of the total reward over all successive steps using Q-learning algorithm, based on the Temporal Difference (TD) learning and Bellman expectation equation. Finally, the numerous simulation results illustrate that the proposed scheme can significantly reduce the total energy cost of EVs compared to various case studies and greedy algorithm, and also demonstrate its behavioural adaptation to any environmental conditions.