Related papers: Congestion-Aware Routing in Dynamic IoT Networks: …
This study focuses on the development of a simulation-driven reinforcement learning (RL) framework for optimizing routing decisions in complex queueing network systems, with a particular emphasis on manufacturing and communication…
In response to the advent of the 5G era, enhancing throughput and increasing transmission efficiency within limited spectrum resources is an important research topic. In the LTE system, utilizing unlicensed spectrum to assist traditional…
Transportation is the backbone of the economy and urban development. Improving the efficiency, sustainability, resilience, and intelligence of transportation systems is critical and also challenging. The constantly changing traffic…
Fog computing emerged as a promising paradigm to address the challenges of processing and managing data generated by the Internet of Things (IoT). Load balancing (LB) plays a crucial role in Fog computing environments to optimize the…
Traffic congestion is a serious problem in urban areas. Dynamic congestion pricing is one of the useful schemes to eliminate traffic congestion in strategic scale. However, in the reality, an optimal dynamic congestion pricing is very…
This study proposes a novel approach for dynamic load balancing in Software-Defined Networks (SDNs) using a Transformer-based Deep Q-Network (DQN). Traditional load balancing mechanisms, such as Round Robin (RR) and Weighted Round Robin…
In many application areas such as large-scale disaster detection, IoT networks connote the characteristics of LLN (Low power and Lossy Network). With few exceptions, prior work on RPL(Routing Protocol for LLN), a standard routing protocol…
The pursuit of rate maximization in wireless communication frequently encounters substantial challenges associated with user fairness. This paper addresses these challenges by exploring a novel power allocation approach for delay…
Energy efficiency has emerged as a defining constraint in the evolution of sustainable Internet of Things (IoT) networks. This work moves beyond simulation-based or device-centric studies to deliver measurement-driven, network-level smart…
Federated Learning (FL) has emerged as a promising paradigm for enabling collaborative machine learning while preserving data privacy, making it particularly suitable for Internet of Things (IoT) environments. However, resource-constrained…
Effective data routing is vital in the Internet of Things (IoT) paradigm, especially in underwater mobile sensor networks where inefficiency can lead to significant resource consumption. This article presents an innovative method designed…
Offline reinforcement learning (RL) aims to learn a policy from a static dataset without further interactions with the environment. Collecting sufficiently large datasets for offline RL is exhausting since this data collection requires…
This paper describes the principles and implementation results of reinforcement learning algorithms on IoT devices for radio collision mitigation in ISM unlicensed bands. Learning is here used to improve both the IoT network capability to…
The Internet of Things (IoT) technology has rapidly gained popularity with applications widespread across a variety of industries. However, IoT devices have been recently serving as a porous layer for many malicious attacks to both personal…
To efficiently support the real-time control applications, networked control systems operating with ultra-reliable and low-latency communications (URLLCs) become fundamental technology for future Internet of things (IoT). However, the…
Although AI-based models have achieved high accuracy in IoT threat detection, their deployment in enterprise environments is constrained by reliance on stationary datasets that fail to reflect the dynamic nature of real-world IoT NetFlow…
The Newton-Raphson (NR) method is widely used for solving power flow (PF) equations due to its quadratic convergence. However, its performance deteriorates under poor initialization or extreme operating scenarios, e.g., high levels of…
Reinforcement Learning (RL) has gained significant momentum in the development of network protocols. However, RL-based protocols are still in their infancy, and substantial research is required to build deployable solutions. Developing a…
As the Routing Protocol for Low Power and Lossy Networks (RPL) became the standard for routing in the Internet of Things (IoT) networks, many researchers had investigated the security aspects of this protocol. However, no work (to the best…
Co-existence of 5G New Radio (5G-NR) with IoT devices is considered as a promising technique to enhance the spectral usage and efficiency of future cellular networks. In this paper, a unified framework has been proposed for allocating…