Wireless Networks最新文献

In IoT paradigm, Sensor-Cloud Infrastructure provides sensor nodes that sense various environmental parameters, generates the data and sends the same to the desired destination, say a cloud server through a common gateway. Sensor nodes with different data streaming specifications, can generate huge amount of traffic, if streams data simultaneously towards the destination, according to a random schedule. This can lead to higher bandwidth requirements in the wireless medium and increase the amount of data to be received at the gateway in any time slot. This further increases the channel capacity required at the access link to transmit the received data from gateway to the server. An optimal schedule of the sensor nodes will lead to minimization of instantaneous aggregated traffic in both the wireless medium and the access link. Thus leading to minimization of required bandwidth at the wireless medium and channel capacity at the access link. This would further increase the resource utilization of minimize the service provisioning cost of the sensor-cloud infrastructure. A straight forward optimization of the problem of minimizing the instantaneous aggregated traffic load generated from n sensor nodes require an exponential time to find the optimal schedule. Thus, in this paper, an ILP formulation and a polynomial-time heuristic algorithm is presented.

The Industrial Internet of Things (IIoT) envisions enhanced surveillance and control for industrial applications through diverse IoT devices. However, the increasing heterogeneity of deployed end devices poses challenges to current practices, hampering overall performance as device numbers escalate. To tackle this issue, we introduce an innovative distributed power control algorithm leveraging the wireless channel's nature to approximate the centralized maximum-weight scheduling algorithm. Employing ubiquitous multi-protocol mobile devices as intermediaries, we propose a concurrent dual-hop/multi-hop backhauling strategy, improving interoperability and facilitating data relay, translation, and forwarding from end IoT devices. Our focus is directed towards addressing large-scale network stability and queue management challenges. We formulate a long-term time-averaged optimization problem, incorporating considerations of end-to-end rate control, routing, link scheduling, and resource allocation to guarantee essential network-wide throughput. Furthermore, we present a real-time decomposition-based approximation algorithm that ensures adaptive resource allocation, queue stability, and meeting Quality of Service (QoS) constraints with the highest energy efficiency. Comprehensive numerical results verify significant energy efficiency improvements across diverse traffic models, maintaining throughput requirements for both uniform and hotspot User Equipment (UE) distribution patterns. This work offers a comprehensive solution to enhance IIoT performance and address evolving challenges in industrial applications.

Internet of Things (IoT) devices are an essential part of several aspects of daily life for people. They are utilized in a variety of contexts, including industrial monitoring, environmental sensing, and so on. But, secure communication is the major challenge in the IoT environment. Therefore, a decentralized Blockchain-based Key Management protocol using Levy Flight-Equilibrium Optimization and Self-Attention-based Improved Faster Region-based Convolutional Neural Network (BlkKM) method is proposed to determine stable security in tamper-resistant hardware machine that can protect sensitive secret data in the healthcare field i.e., stored cryptographic keys. The keys are categorized as Key Encryption Keys (KEKs) and Data Encryption Keys (DEKs). The number of the keys is decreased by using Levy Flight- Equilibrium Optimization (LF-EO) as organizing nodes with logical sets. Also, Self-Attention-based Improved Faster Region-based Convolutional Neural Network (SA-based IFRCNN) is used for reordering a set of logical nodes to minimize the number of sets after a node exits the network. Additionally, the system makes use of smart contracts for access control as well as proxy encryption to data encryption. The proposed method is compared with existing techniques to validate the security enhancement performance. The evaluation is performed based on throughput, end-to-end delay, storage overheads, and energy consumption. The experimentation results revealed that the proposed method improved the throughput to 220.52bps and diminished the utilization of energy. A greater degree of memory usage is also decreased by using this technique.

The popularity of wireless communication technology and smart devices make emerging tasks tend to be computationally intensive. Unfortunately, mobile devices are often computationally resource-constrained. Mobile edge computing is proposed to offer computing power for these resource-limited devices to solve the computing requirement of their tasks. The unmanned aerial vehicle (UAV) enabled edge networks are flexible and low-cost, so they are considered to provide more flexible computing service for mobile devices. However, UAV-enabled edge networks are limited by the weak wireless propagation environment. To this end, we introduce intelligent reflecting surface (IRS) into the UAV-enabled edge networks in which IRS is used to construct a stronger link between the mobile devices and the UAV for task offloading. We formulate the IRS-aided offloading problem as an optimization problem to optimize the overall delay by jointly optimizing UAV movement, offloading decision, IRS configuration, and UAV’s computation resource. To solve the problem more efficiently, we use the deep reinforcement learning (DRL) model to explore the intelligent action that can minimize the task processing time. Our simulation demonstrates the DRL scheme is more effective compared with the benchmarks.

Software-defined vehicular networks (SDVN) is a promising technology for wireless data transmissions between vehicles. SDVN inherits software-defined networking principles and aims to improve the typical performance of safety and non-safety applications of vehicular adhoc networks. Consequently, enhancing the performance of Intelligent Transportation System (ITS). However, the performance of these ITS applications largely depends on the computational capability of the controller node, which involves creating or destroying a data path from the source vehicle to the destination vehicle and generating flow rules for the requests coming from the data plane elements. As a result, SDVN often suffers from the problems of overburdening the controller node with route requests under heavy traffic generation at vehicles and single-point controller failure. To counter these problems, solutions based on multiple controllers are proposed. In fact, the load-balancing problem remains an important issue. So, routing of data with multiple controllers and load-balancing, both topics in SDVN, go hand in hand. In this paper, we survey this state-of-the-art that discusses the above-mentioned challenges, starting with the SDVN preliminaries. We scrutinize the existing routing methodologies and also discuss load-balancing techniques. Furthermore, we provide real-time applications and services of SDVN, discuss trending research, potential future research directions, and the real-life applicability of SDVN that have not been addressed previously.

Forest wildfires often lead to significant casualties and economic losses, making early detection crucial for prevention and control. Internet of Things connected cameras mounted on drone provide wide monitoring coverage and flexibility, while computer vision technology enhances the accuracy and response time of forest wildfire monitoring. However, the small-scale nature of early wildfire targets and the complexity of the forest environment pose significant challenges to accurately and promptly identify fires. To address challenges such as high false-positive rates and inefficiency in existing methods, we propose a Forest Wildfire and Smoke Recognition Network termed FWSRNet. Firstly, we adopt Vision Transformer, which has shown superior performance in recent traditional classification tasks, as the backbone network. Secondly, to enhance the extraction of subtle differential features, we introduce a self-attention mechanism to guide the network in selecting discriminative image patches and calculating their relationships. Next, we employ a contrastive feature learning strategy to eliminate redundant information, making the model more discriminative. Finally, we construct a target loss function for model prediction. Under various proportions of training and testing dataset allocations, the model exhibits recognition accuracies of 94.82, 95.05, 94.90, and 94.80% for forest fires. The average accuracy of 94.89% surpasses five comparative models, demonstrating the potential of this method in IoT-enhanced aerial forest fire recognition.

The Internet of Things (IoT) consists of non-standardized computer devices that can create wireless network connections to send data. These devices have limited storage, bandwidth, and computing capacities, which may cause network congestion when nodes move or leave their allotted area. IoT networks need congestion control to enhance the efficiency of data transfer. The study examines IoT congestion and proposes using alternate nodes to maintain dataflow and quality of service (QoS). The study presents RAoNC, a novel algorithm designed to improve routing algorithms in network clusters for the purposes of congestion monitoring, avoidance, and mitigation. Congestion management techniques efficiently process network information update query packets and reduce large-header handshaking packets. Improve network performance by reducing congestion, packet loss, and throughput. The proposed method speeds up packet transfer to reduce network node packet transmission delays. The optimization approach minimizes power usage across all network nodes. We assessed the efficacy of our approach by comparative analysis utilizing NS2 simulations and contrasted the suggested algorithm with prior studies. The simulation shows that RAoNC significantly improves congestion performance. We will assess the novel RAoNC algorithm in relation to DCCC6, LEACH, and QU-RPL. The throughput increased by 28.36%, weighted fairness index by 28.2%, end-to-end delay by 48.7%, energy consumption by 31.97%, and the number of missed packets in the buffer decreased by 90.35%.

Wireless Sensor Networks are essential for monitoring physical objects in smart systems powered by the Internet of Things. It gathers information by detecting the surroundings and transmits it to a central repository. In this study, an unknown domain was explored using multi-objective optimization. This proposed work employs Multi-objective Grey Wolf Optimization to form effective clustering among nodes and also for choosing the cluster head. Based on the multi-objective fitness function, the cluster heads are selected. For every iteration, the cluster heads are changed thereby saving the consumption of energy and also resulting in an increase in network lifespan. The suggested method divides the network into various optimal-sized clusters and chooses the best cluster heads. The performance of the multi-objective exploration is presented. The proposed method`s key contributions are by utilizing MOGWO for efficient clustering and CH selection, ultimately enhancing network performance. It dynamically adjusts CHs, resulting in energy savings and an extended network lifespan. MOGWO takes into account multiple objectives simultaneously. Through network configuration optimization, MOGWO enhances resource utilization, resulting in lower energy consumption, extended network lifetime, and improved overall efficiency.

The Internet of Vehicles (IoV) is currently experiencing significant development, which has involved the introduction of an efficient Access Control Mechanism (ACM). Reliable access control is evolving into mandatory in order to provide security and efficient transmission within the IoV environment as the volume of vehicles equipped with connectivity continues to expand and they become more incorporated into any number of applications. The primary objective of this research is to develop an ACM for the IoV system based on the use of a Genetically Optimized Twin-Delayed Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The TD3 model modifies access policies to be in line with the current scenario using deep reinforcement learning (Deep RL) techniques. This allows vehicles to make access decisions that are intelligent about the environment in which they are performing. To prevent energy loss while the vehicle is in transit into the client system, the model also emphasizes access based on the vehicle's energy consumption (EC). Finally, with the support of the genetic algorithm (GA), the accuracy of the access control model can be improved by optimizing the high-level parameters in a manner in which they improves efficiency. In order to further enhance the model's environmental sustainability and reliability, the recommended model provides an approach that is both profound and efficient for access control in the constantly changing setting of the IoV.

Deep learning models are vulnerable to backdoor attacks, where an adversary aims to fool the model via data poisoning, such that the victim models perform well on clean samples but behave wrongly on poisoned samples. While researchers have studied backdoor attacks in depth, they have focused on specific attack and defense methods, neglecting the impacts of basic training tricks on the effect of backdoor attacks. Analyzing these influencing factors helps facilitate secure deep learning systems and explore novel defense perspectives. To this end, we provide comprehensive evaluations using a weak clean-label backdoor attack on CIFAR10, focusing on the impacts of a wide range of neglected training tricks on backdoor attacks. Specifically, we concentrate on ten perspectives, e.g., batch size, data augmentation, warmup, and mixup, etc. The results demonstrate that backdoor attacks are sensitive to some training tricks, and optimizing the basic training tricks can significantly improve the effect of backdoor attacks. For example, appropriate warmup settings can enhance the effect of backdoor attacks by 22% and 6% for the two different trigger patterns, respectively. These facts further reveal the vulnerability of deep learning models to backdoor attacks.