A Novel Approach to Improve the Adaptive-Data-Rate Scheme for IoT LoRaWAN

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 α 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

A Reinforcement Learning-based Assignment Scheme for EVs to Charging Stations

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 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 charging station 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

An Efficient Game Theory-Based Power Control Algorithm for D2D Communication in 5G Networks

Device-to-Device (D2D) communication is one of the enabling technologies for 5G networks that support proximity-based service (ProSe) for wireless network communications. This paper proposes a power control algorithm based on the Nash equilibrium and game theory to eliminate the interference between the cellular user device and D2D links. This leads to reliable connectivity with minimal power consumption in wireless communication. The power control in D2D is modeled as a non-cooperative game. Each device is allowed to independently select and transmit its power to maximize (or minimize) user utility. The aim is to guide user devices to converge with the Nash equilibrium by establishing connectivity with network resources. The proposed algorithm with pricing factors is used for power consumption and reduces overall interference of D2Ds communication. The proposed algorithm is evaluated in terms of the energy efficiency of the average power consumption, the number of D2D communication, and the number of iterations. Besides, the algorithm has a relatively fast convergence with the Nash Equilibrium rate. It guarantees that the user devices can achieve their required Quality of Service (QoS) by adjusting the residual cost coefficient and residual energy factor. Simulation results show that the power control shows a significant reduction in power consumption that has been achieved by approximately 20% compared with algorithms in [11]

Utility-based non-cooperative power control game in wireless environment / Yousef Ali Mohammed Al-Gumaei

The spectrum resources, interference, and energy of battery-based devices are predominant problems and challenges in modern wireless networks. This thesis therefore addresses these issues by studying a theoretical framework for the design and analysis of distributed power control algorithms for modern cognitive radio and femtocell networks. It is shown that game theory tools are appropriate and efficient to develop scalable, balanced and energy-efficient, distributed power control schemes to be practically used in battery-based devices in wireless networks. Practically, the problem of power control is modelled as a non-cooperative game in which each user chooses its transmit power to maximize (or minimize) its own utility (or cost). The utility is defined as the ratio of throughput to transmit power, which is used to represent the energy efficiency scheme, whereas the cost is defined as the sum of the sigmoid weighting of transmit power and the square of the signal-to-interference ratio (SIR) error which is used to represent the SIR balancing scheme. Novel utility and cost functions proposed in this work are the method to derive efficient distributed power control algorithms. Also, the proposed pricing techniques in this thesis guide users to the efficient Nash equilibrium point by encouraging them to use network resources efficiently. These frameworks are more general and they are applied on cognitive and femtocell networks due to the critical and important issue of interference. Numerical simulations are used to prove the effectiveness of these algorithms compared with other existing power control algorithms. The simulated analytical and numerical results of this thesis indicate that the proposed algorithms can achieve a significant reduction of the user’s transmit power and thus a mitigation of the overall interference. Moreover, these algorithms have a relatively fast convergence rate and guarantee that all users can achieve their required QoS

A novel utility function for energy-efficient power control game in cognitive radio networks.

Spectrum scarcity is a major challenge in wireless communications systems requiring efficient usage and utilization. Cognitive radio network (CRN) is found as a promising technique to solve this problem of spectrum scarcity. It allows licensed and unlicensed users to share the same licensed spectrum band. Interference resulting from cognitive radios (CRs) has undesirable effects on quality of service (QoS) of both licensed and unlicensed systems where it causes degradation in received signal-to-noise ratio (SIR) of users. Power control is one of the most important techniques that can be used to mitigate interference and guarantee QoS in both systems. In this paper, we develop a new approach of a distributed power control for CRN based on utility and pricing. QoS of CR user is presented as a utility function via pricing and a distributed power control as a non-cooperative game in which users maximize their net utility (utility-price). We define the price as a real function of transmit power to increase pricing charge of the farthest CR users. We prove that the power control game proposed in this study has Nash Equilibrium as well as it is unique. The obtained results show that the proposed power control algorithm based on a new utility function has a significant reduction in transmit power consumption and high improvement in speed of convergence