Dr Amirhossein Mohajerzadeh

The smart electricity distribution network, known as Smart Grid (SG), represents an optimal and intelligent framework for electricity generation and distribution, facilitated through a bidirectional information and communication network. However, setting up such an expansive network presents considerable network security challenges. Consequently, numerous threats such as impersonation attacks, repeat attacks, and man-in-the-middle attacks can pose the network with substantial difficulties. To address security and privacy concerns in SG, researchers have proposed several security protocols intending to increase protection, but exchanging information in a public channel that does not have security conditions poses serious security risks. Despite numerous security protocols having been suggested to facilitate the authentication of network entities and the generation of session keys for ensuring secure communication, some of them have security flaws or high computational overhead that makes them unsuitable for resource-constrained components. This paper presents a lightweight protocol to address the security challenges in smart grid networks. The proposed scheme incorporates a multi-level authentication mechanism and the Elliptic Curve Diffie-Hellman (ECDH) technique to ensure the secure generation of session keys. The security of the proposed protocol has been formally verified using the AVISPA simulation tool and further validated through informal analysis against various attacks. Performance evaluation results demonstrate that the protocol is computationally efficient and well-suited to the resource-constrained environment of smart grid entities. Specifically, the session key generation process is more efficient compared to several existing approaches, thereby minimizing communication delays and enhancing the overall operational performance of the system.

The need for continuous coverage, as well as low-latency, and ultrareliable communication in 5G and beyond cellular networks encouraged the deployment of high-altitude platforms and low-altitude drones as flying base stations (FBSs) to provide last-mile communication where high cost or geographical restrictions hinder the installation of terrestrial base stations (BSs) or during the disasters where the BSs are damaged. The performance of unmanned aerial vehicle (UAV)-assisted cellular systems in terms of coverage and quality of service offered for terrestrial users depends on the number of deployed FBSs, their 3-D location as well as trajectory. While several recent works have studied the 3-D positioning in UAV-assisted 5G networks, the problem of jointly addressing coverage and user data rate has not been addressed yet. In this article, we propose a solution for joint 3-D positioning and trajectory planning of FBSs with the objectives of the total distance between users and FBSs and minimizing the sum of FBSs flight distance by developing a fuzzy candidate points selection method.

Integrating unmanned aerial vehicles (UAVs) into wireless communication as aerial platforms to mount small cell base stations has grown rapidly in recent years. One of the main objectives of UAV integration into wireless networks is to optimize UAV deployment while meeting user expectations with the fewest UAVs. To ensure that users receive the requested data rate, management of UAV placement and user association is necessary due to the limited capacity of aerial base stations. Besides the user-base station distance, environmental conditions and propagation mode affect the data rate received by the users. When accounting for uncertain conditions, network management decisions become more realistic and productive. This article considers a random propagation mode for each link depending on the environmental conditions of the desired area. We exploit the stochastic programming framework to reflect propagation mode uncertainty in the optimization problem, which impacts the received data rate and path loss. The suggested mathematical formulation determines the minimum number of required UAVs, their 3-D positions, and the best user association strategy. The proposed model also includes interference-aware constraints for optimal radio resource allocation to base stations. The nonlinear path loss and line-of-sight (LoS) probability distribution functions in terms of the base station positions lead to a nonlinear formulation. We obtain a mixed-binary linear formulation by replacing nonlinear functions with their piecewise linear approximations and solve the model accurately using the CPLEX solver. The implementation results show that stochastic approaches provide more accurate diagnoses of the environment, as well as superior performance to deterministic optimization.

Recently, narrowband Internet-of-Things (NB-IoT) networks have been on the rise in the IoT field due to their features like low power consumption and high penetration rate. However, NB-IoT's main drawbacks are its high delays and low data rates. To address these problems, in this paper, we present a two-tier cooperative solution to improve network throughput. Two-tier networks generally consist of cellular and device-to-device (D2D) communications. In this work, we use NB-IoT for cellular networks and Bluetooth low energy (BLE) for D2D communications. By leveraging these communications technologies, we enable idle nodes in a group to assist target nodes download and upload data. In doing this, we aim to maximize throughput, minimize consumed energy, and maximize the total remaining capacity of the node group batteries. To tackle the faced multi-objective optimization problem, we used the non-dominated sorting genetic algorithm (NSGA-II). Only one group gets selected from the candidate groups by adjusting the level of node participation. Simulation results show a 7.7-fold growth of throughput against only an 8 percent increase in energy consumption compared to the baseline download scenario and a 7.6-fold growth of throughput against just a 2 percent increase in energy consumption compared to the baseline upload scenario.

This paper provides a comprehensive overview of the evolution of Machine Learning (ML), from traditional to advanced, in its application and integration into unmanned aerial vehicle (UAV) communication frameworks and practical applications. The manuscript starts with an overview of the existing research on UAV communication and introduces the most traditional ML techniques. It then discusses UAVs as versatile actors in mobile networks, assuming different roles from airborne user equipment (UE) to base stations (BS). UAV have demonstrated considerable potential in addressing the evolving challenges of next-generation mobile networks, such as enhancing coverage and facilitating temporary hotspots but pose new hurdles including optimal positioning, trajectory optimization, and energy efficiency. We therefore conduct a comprehensive review of advanced ML strategies, ranging from federated learning, transfer and meta-learning to explainable AI, to address those challenges. Finally, the use of state-of-the-art ML algorithms in these capabilities is explored and their potential extension to cloud and/or edge computing based network architectures is highlighted.

Using unmanned aerial vehicles (UAVs) to cover users in wireless networks has increased in recent years. Deploying UAVs in appropriate positions is important to cover users and nodes properly. In this article, we propose an efficient approach to determine the minimum number of required UAVs and their optimal positions. To this end, we use an iterative algorithm that updates the number of required UAVs at each iteration. To determine the optimal position for the UAVs, we present a mathematical model and solve it accurately after linearizing. One of the inputs of the mathematical model is a set of candidate points for UAV deployments in 2-D space. The mathematical model selects a set of points among candidate points and determines the altitude of each UAV. To provide a suitable set of candidate points, we also propose a candidate point selection method: the MergeCells method. The simulation results show that the proposed approach performs better than the 3-D P-median approach introduced in the literature. We also compare different candidate point selection approaches, and we show that the MergeCells method outperforms other methods in terms of the number of UAVs, user data rates, and simulation time.

Nowadays, Intrusion Detection System (IDS) is an active research topic with machine learning nature. A single-hidden layer feedforward neural network (SLFN) trained on the approach of extreme learning machine (ELM) is used for (IDS). The encouraging factors for its usage are its fast learning and supportability of sequential learning in its online sequential extreme learning machine (OSELM) variant. An issue with OSELM that has been addressed by researchers is its random weights nature of the input-hidden layer. Most approaches use the concept of metaheuristic optimisation for determining the optimal weights of OSELM and resolve the random weight. However, metaheuristic approaches require many trials to determine the optimal one. Hence, there is concern about the convergence aspect and speed. This article proposes a novel approach for finding the optimal weights of the input-hidden layer. This article presents an approach for an integration between OSELM and back-propagation designated as (OSELM-BP). After integration, BP changes the random weights iteratively and uses an iterated evaluation of the generated error for feedback correction of the weights. The approach is evaluated based on various scenarios of activation functions for OSELM on the one hand and the number of iterations for BP on the other. An extensive evaluation of the approach and comparison with the original OSELM reveal a superiority of OSELM-BP in reaching optimal accuracy with a small number of iterations.

Unmanned aerial vehicles (UAVs) in cellular networks have garnered considerable interest. One of their applications is as flying base stations (FBSs), which can increase coverage and quality of service (QoS). Because FBSs are battery-powered, regulating their energy usage is a vital aspect of their use; therefore, the appropriate placement and trajectories of FBSs throughout their operation are critical to overcoming this challenge. In this article, we propose a method of solving a multi-FBS 3D trajectory problem that considers FBS energy consumption, operation time, flight distance limits, and intercell interference constraints. Our method is divided into two phases: FBS placement and FBS trajectory. In taking this approach, we break the problem into several snapshots. First, we find the minimum number of FBSs required and their proper 3D positions in each snapshot. Then, between every two snapshots, the trajectory phase is executed. The optimal path between the origin and destination of each FEB is determined during the trajectory phase by utilizing a proposed binary linear problem (BLP) model that considers FBS energy consumption and flight distance constraints. Then, the shortest path for each FBS is determined while taking obstacles and collision avoidance into consideration. The number of FBSs needed may vary between snapshots, so we present an FBS set management (FSM) technique to manage the set of FBSs and their power. The results demonstrate that the proposed approach is applicable to real-world situations and that the outcomes are consistent with expectations.

In this paper we have considered an efficient adapted Unscented Kalman Filter based target tracking in directional wireless sensor networks while observations are noise-corrupted. In directional sensor networks, sensors are able to observe the target only in specified (and certainly changeable) directions. Also, sensor nodes are capable of measuring the bearings (relative angle to the target). To make target tracking efficient, first, we use scheduling algorithm which determines the sensor nodes activity. Also coverage is a challenge that we will discuss in this paper as well. Sensor nodes activation algorithm directly affects the target areas coverage. Second, we use time series to predict the motion of the target. Using ARIMA, in each step of target position estimation, an area will be predicted where the target would be there with high probability. Third, we use a version of UKF, which is adjusted to the requirements of the target tracking application, to determine the position of the target with desired precision. Fourth, a routing algorithm called as C-RPL is used to perform the communications between sensor nodes in each step. Simulation results approve that the proposed efficient target tracking algorithm achieves its goals.

Wireless sensor networks (WSNs) are employed in a variety of applications. One of the key applications of WSNs, which gained much attention, is the target tracking. Directional sensor networks (DSNs) are a subset of WSNs with some unique characteristics. Since optimizing the tracking system under the energy and coverage constraints in DSNs is of paramount importance, in this paper, we introduce a reliable algorithm for tracking mobile targets using directional WSNs. First, by selecting a minimum set of boundary and borderline sensor nodes, we achieve the desired coverage for an incoming detection. Second, for both deterministic ordered and random node deployments, we propose an efficient mechanism for determining the minimal interior sensor nodes that should be activated. Doing so, the network lifetime can be maximized by the employment of much fewer sensor nodes. Third, we use a geometric method for collecting data using two active sensors at a time. Accordingly, target position is estimated using the extended Kalman filter (EKF). Finally, we compare the proposed algorithm with a genetic algorithm and present the comparative simulation results of the EKF and the random walk. The results demonstrate the effectiveness of our proposed scheme in terms of the energy efficiency, coverage, and tracking accuracy.

In the Internet of Things networks deploying fixed infrastructure is not always the best and most economical solution. Advances in efficiency and durability of Unmanned Aerial Vehicles (UAV) made flying small cell base stations (BS) a promising approach by providing coverage and capacity in environments where using fixed infrastructure is not economically justified. A key challenge in covering an area with UAV-based small cell BSs is optimal positioning the UAVs to maximize the coverage and minimize the number of required UAVs. In this paper, we propose an optimization problem that helps to determine the number and position of the UAVs. Moreover, to have efficient results in a reasonable time, we propose complementary heuristic methods that effectively reduce the search space. The simulation results show that our proposed method performs better than genetic algorithms.