Network slicing in the fifth generation (5G) cellular architecture provides fundamental support for the key performance required by users. To create the infrastructure of network slicing, enablers such as software-defined networking and network function virtualization facilitate a separation between the physical distributed infrastructure and the controls and functions that provide the services and

Vehicle Ad Hoc Network (VANET) is becoming an essential vehicle communication network paradigm. However, the safe and reliable operation of VANETs faces serious challenges, especially in how users authenticate to the network. Idealized access control rules for trusted network entities are highly susceptible to the leakage of critical private data from inbound users. In addition, the open network environment

The rapid proliferation of Intelligent Connected Vehicles (ICVs) presents escalating cybersecurity challenges, particularly within Controller Area Networks (CAN), where traditional Intrusion Detection Systems (IDS) often fail to meet the stringent requirements for real-time and fine-grained anomaly detection under limited computational resources. This study introduces a novel lightweight dual-tier

With the development of mobile computing and the Internet of Things (IoT), Sparse mobile crowd sensing (SMCS) has emerged as a new data collection paradigm, demonstrating significant application potential in fields. However, due to budget constraints and area inaccessibility, maximizing the quality of inferred data with limited sensing users and resources has become an important research challenge

In the mobile edge computing (MEC) scenario, numerous complex applications consist of dependent tasks. Efficient offloading of these applications is essential for reducing latency and minimizing terminal energy consumption. However, existing studies typically employ a decoupled decision-making paradigm, where the task scheduling sequence is predetermined before independently determining the offloading

Accurate node localization is critical for wireless network applications, yet existing algorithms struggle with low communication efficiency, inaccurate distance measurement, and unstable localization in irregular multi-hop networks. To address these challenges, this paper proposes an Efficient and Robust Network Localization algorithm using Maximum Hop Progress (NL-MHP). NL-MHP integrates routing

With the increasing complexity and connectivity of modern vehicles, the security of in-vehicle networks has become a critical concern. The Controller Area Network (CAN), as the primary communication protocol, lacks authentication and encryption, making it highly vulnerable to cyberattacks. Intrusion detection systems (IDSs) serve as a vital line of defense by monitoring CAN traffic and identifying

With the rapid proliferation of intelligent video analytics in real-time applications such as the Internet of Vehicles and urban surveillance, Mobile Edge Computing (MEC) systems face persistent challenges, including complex inter-frame dependencies, severe bandwidth fluctuations, and intensive resource contention. Conventional offloading strategies based on fixed rules or static weights are often

Unmanned Aerial Vehicle Base Stations (UAV-BSs) are effective to support mobile wireless systems in situations where an unusually high density of users require enhanced coverage and capacity such as in large sport events or music festivals. However, the joint deployment problem of UAV-BSs is NP-hard, and the optimization methods used to solve such a class of problems are often too slow for (quasi-)real-time

With the rapid evolution of interactive multimedia systems, ensuring strong security measures has become increasingly vital. Autonomous platforms, such as drones, are vulnerable to sophisticated cyber threats, including jamming and spoofing attacks. One common spoofing strategy involves manipulating Global Positioning System (GPS) signals. By broadcasting counterfeit signals, attackers can deceive

This work proposes a Collaborative Task Execution (Co-Task) scheme for the UAV-as-a-Service (UaaS) platform. The UaaS platform enables multiple Unmanned Aerial Vehicles (UAVs) to collaboratively serve diverse Internet of Things (IoT) applications, thereby enhancing resource utilization, reducing energy consumption, and improving overall task efficiency and system reliability. However, effective collaboration

Unmanned Aerial Vehicles (UAVs) have emerged as pivotal components in Air-Ground Mobile Edge Computing (AGMEC) systems, leveraging their line-of-sight connectivity and maneuverability to enhance service coverage. In order to mitigate the growing spectrum scarcity, AGMEC systems are increasingly adopting unlicensed bands, though this raises interference concerns for incumbent users such as WiFi systems

In emerging space–air–ground integrated networks, Low Earth Orbit (LEO) satellites offer edge computing capabilities. However, their limited onboard resources make efficient computation offloading a significant bottleneck. To address this challenge, we propose two novel algorithms that jointly optimize task offloading and resource allocation. The first is the Adaptive Large-Scale Task Priority Offloading

This paper addresses the problem of long-term service inequality of IoT devices in UAV-assisted wireless power communication networks. Considering factors such as the number of device historical services, the amount of device data, the UAV flight trajectory, and the binary service decision of the device, the scheduling and trajectory planning for UAV-assisted wireless power supply network data collection

Energy efficiency is a very crucial requirement in battery-operated wireless sensor networks. The Cluster and Corona Ring-based Data Collection (CCRDC) scheme is proposed to prolong the network lifetime. The waiting-time-dependent Cluster Head (CH) awareness message is applied in the proposed cluster head selection and cluster formation. Also, the Corona ring based routing is formed depending on the

The growth of the Internet of Things (IoT), driven by Wireless Sensor Networks (WSNs) and Low-Power and Lossy Networks (LLNs), has raised significant security concerns, particularly regarding sinkhole attacks. These attacks compromise data integrity by redirecting traffic through malicious nodes. This paper reviews existing detection methods for sinkhole attacks in IoT and WSNs, analyzing their effectiveness

Modern Cyber–Physical Systems (CPSs) rely on sophisticated software applications, often modeled as Dependency-constrained Task Graphs (DTGs), to capture the intricate inter-dependencies among their functional components. Efficient scheduling of tasks and routing of inter-task messages over ad hoc switched network platforms remain significant challenges due to task precedence constraints, processing

Reliable communication systems are critical during emergency scenarios such as floods, earthquakes, fires, and hurricanes to ensure timely information exchange and minimize damage. However, traditional communication infrastructure is often severely compromised during such events. Unmanned Aerial Vehicles (UAVs) have emerged as a promising solution to deploy temporary communication networks in disaster-affected

The integration of Mobile Edge Computing (MEC) with Next-Generation Multiple-Input Multiple-Output (NG-MIMO) technologies has emerged as a cornerstone of next-generation wireless networks, enabling low-latency, high-bandwidth, and computationally efficient applications.This survey offers a comprehensive, structured analysis of recent advancements, key applications, and pressing challenges at the MEC–NG-MIMO

Ensuring the reliable delivery of alarm messages under emergency conditions is a critical challenge in wireless sensor networks (WSNs), particularly when employing flood transmission mode, as the simultaneous, unsynchronized broadcasting by multiple transmitters leads to mutual interference in channels. In the existing literature, the physical communication channel is typically evaluated using either

With the increasing deployment of Vehicular Ad Hoc Networks(VANETs) in intelligent transportation systems, the efficient dissemination of emergency event messages has become a critical issue for enhancing traffic safety and emergency response capabilities. This paper proposes an emergency message dissemination approach based on an approval voting mechanism, aiming to improve the efficiency and reliability

Recently, mobile edge computing (MEC) technology has been integrated with wireless blockchain networks to improve the computational capabilities of Internet of Things devices during the mining process. Jointly, optimizing miner selection (discrete) and resource allocation (continuous) in MEC-integrated blockchain networks is a challenging mixed-variable, NP-hard problem. Although several algorithms

The Random Access (RA) procedure of the current 3GPP cellular network has been adopted for small data packet transmissions by massive Machine Type Communication Devices (MTCDs). However, the initial steps of the RA procedure lack an authentication mechanism, making it susceptible to Distributed Denial of Service (DDoS) attacks, particularly in massive access scenarios. In these cases, attackers can

Named Data Networking (NDN) is an emerging content-centric communication paradigm envisioned for future IoT architectures. Its features, including in-network caching, multicast forwarding, and data-centric security, make it a promising network protocol for resource-constrained devices. However, the data-centric nature of NDN introduces confidentiality concerns, as the decoupling of data from its source

Vehicular Ad-Hoc Networks (VANETs) enable efficient traffic coordination through real-time broadcasting of Basic Safety Messages (BSMs), yet they also introduce significant location privacy concerns. Existing dynamic mix zone strategies trigger pseudonym changes based on vehicle density awareness. However, their reliance on single-time-slot verification often results in residual ”ghost vehicles” and

WiFi fingerprinting is a widely used approach for indoor localization, but its effectiveness is limited by signal instability and the challenges of real-time inference. To address these issues, we propose a dual-mode localization framework that combines a sequence-based long short-term memory (LSTM) model with a lightweight multilayer perceptron (MLP) trained through knowledge distillation. This framework

In the era of the Internet of Things (IoT) Ecosystem, multi-functional components have the potential to meet the needs of the exponential rise in the number of sensors/devices. Hence, in the future, the feasible complexity of the systems in IoT could be maintained using multi-functional components. This research aims to lower the hardware complexity at the edge devices in IoT architecture by incorporating

With the rapid development of Internet of Things (IoT) technology, Wireless Sensor Networks (WSNs) have been widely applied in various fields, such as environmental monitoring, health detection, and smart homes. Since wireless sensor nodes are typically deployed in open and dynamically changing environments, the data are susceptible to interference and outliers, directly affecting the network’s reliability

The Internet of Things (IoT) enables pervasive connectivity and large-scale data exchange among heterogeneous devices, driving advancements in intelligent automation and decision-making. However, the resource-constrained nature of IoT nodes and the increasing data traffic in dense deployments demand energy-efficient and adaptive communication strategies. This paper presents a hybrid framework that

The growing complexity of cyberattacks in the Internet of Things (IoT) highlights the need for intrusion detection systems that are both accurate and efficient. We present CNN-MHBiGRU, a two-stage framework that integrates enhanced convolutional feature extraction with multi-head recurrent temporal modeling. The CNN stage employs residual blocks, SE attention, SoftPool, and attention pooling to obtain

As a transformative technology in next-generation digital innovation, digital twin is instrumental in enhancing the efficiency and safety of traffic management while driving advancements in intelligent transportation systems. To this end, we systematically examine the primary challenges associated with the implementation of digital twin technology in transportation and introduce a novel 5 + 2 digital

While Large AI Models (LAMs) offer transformative intelligence, their deployment in real-time Internet-of-Things (IoT) applications is constrained by the heterogeneity of edge networks and high cloud latency. To enable cooperative LAM deployment across diverse edge hardware, we propose a modular, hardware-aware framework. Our approach introduces two key innovations. First, a heterogeneity-aware training

Beyond 5G (B5G) networks are designed to significantly enhance network capacity and reduce latency by utilizing higher frequency spectrum bands. These networks rely on effective network slicing—the partitioning of physical infrastructure into independent logical networks, each optimized for specific service requirements to support a wide range of emerging application. However, B5G networks face substantial

Traditional opportunistic crowdsensing is usually managed by a central platform that assigns tasks, which imposes significant demands on the platform’s performance and increases the risk of privacy breaches for participants. To address these issues, this paper proposes a decentralized opportunistic sensing-based solution to achieve task allocation under a global budget constraint and maximize task

Enabled by Mobile Edge Computing (MEC) equipped on Base Station (BS), Collaborative Vehicle–Infrastructure Systems (CVIS) can provide efficient and reliable computing services for mobile vehicles. Vehicles can achieve intelligent applications such as autonomous driving by forward offloading tasks to base stations. However, most existing studies focus on the BS merely as a task receiver and integrator

Decentralized authentication in dynamic mobile networks faces significant challenges due to high node mobility, resource constraints, and vulnerabilities to side-channel attacks. In this work, we present MobiAuth, a blockchain-driven framework based on Hyperledger Iroha and OMNET++ that enables secure, peer-to-peer authentication using compact Ed25519 signatures and ephemeral session keys. Our protocol

Task offloading in Mobile Edge Computing (MEC) enables resource-constrained IoT devices to reduce latency and energy consumption while enhancing computational performance. However, designing effective offloading strategies presents a multi-objective optimization challenge, particularly in ensuring task reliability while optimizing energy efficiency and latency under dynamic conditions with unpredictable

The communication quality in the engine room of the ship is not ideal because of the complicated internal environment of the ship during the voyage at sea. The communication quality of the existing communication methods is not high, which is affected by the environment of the engine room, and the signal is unstable and the transmission fails. In view of this situation, a communication optimization

The Internet of Drones (IoD) has become increasingly important in applications such as forest inventory, leveraging advanced sensors and internet connectivity to enable efficient data collection. Compared to traditional methods, IoD offers superior cost-effectiveness. However, its reliance on public channels, unreliable connectivity, and dynamic environments poses significant security and privacy challenges

Cross-Technology Interference (CTI) significantly degrades the performance of heterogeneous wireless communication systems operating within a shared spectrum. Traditional mitigation techniques, such as Clear Channel Assessment (CCA), often fail due to, amongst others, varying bandwidth and detection threshold. Orthogonal Frequency Division Multiple Access (OFDMA), introduced to Wi-Fi in IEEE 802.11ax

Message Queuing Telemetry Transport (MQTT) is essential for resource-constrained Internet of Things (IoT) environments; however, its widespread adoption has introduced significant security vulnerabilities. Although machine learning (ML) offers a promising solution for anomaly detection, existing models are often hindered by unrealistic data, severe class imbalances, and high computational costs. To

The Mobile Internet of Things (MIoT) represents a significant evolution of traditional IoT by enabling seamless connectivity for mobile devices and sensors in dynamic environments. Given the resource constraints and mobility challenges in MIoT networks, developing adaptive and energy-efficient routing strategies is important. This paper proposes a novel routing protocol that integrates Grey Wolf Optimization

The advent of next generation networks designated as 5G and beyond signifies a revolution in wireless communications technology. They are intended to overcome the shortcomings of earlier generations with extraordinary degrees of speed, connectivity, and dependability. The integration of Unmanned Aerial Vehicles (UAVs) into new-generation communication networks results in numerous advantages, advancing

To address the growing demand for latency-sensitive Internet of Things (IoT) applications in remote regions, satellite–integrated mobile edge computing (SMEC) deploys computational resources on Low Earth Orbit (LEO) satellites to provide seamless network access and edge computing services for IoT devices lacking terrestrial network coverage. However, ensuring quality of service (QoS) for latency-sensitive

Prior work on Internet-of-Things (IoT) security often splits between hardware roots of trust and decentralized key management: Physically Unclonable Function (PUF) schemes frequently depend on centralized helper infrastructures, whereas blockchain systems typically lack a hardware seed. We present a unified, protocol-enforced framework that integrates PUFs, blockchain-backed Shamir’s Secret Sharing

Aiming at the “energy hole” problem caused by high energy consumption of linear wireless sensor networks in the environmental detection of natural disasters along high-speed railways, a routing algorithm based on weighted least squares estimation method and particle swarm optimization theory is proposed. Firstly, a node residual energy estimation model based on adaptive unscented Kalman filter estimation

The rapid growth of IoT in healthcare demands reliable, secure, and energy-efficient communication solutions. We propose CLEHTO, a novel cross-layer optimization framework that dynamically adapts to network conditions by integrating real-time energy monitoring, joint mobility–security assessment, and adaptive congestion control. Unlike conventional approaches, CLEHTO introduces a unified reliability

In the Internet of Medical Things (IoMT), securing patient data access is critical, but must be achieved without overwhelming the limited computational resources of edge devices. While cryptographic methods and access policies are widely applied to secure medical data, existing solutions often assume high computational capacity, centralized infrastructure, or predefined key structures, which are not

In-network caching and name-based forwarding features of Named Data Networks (NDN) enable it to counteract the unstable connectivity and resource constraints problems of Mobile Ad Hoc Networks (MANET). The topology-aware and hybrid NDN caching schemes often struggle to adapt to dynamic topology changes in NDN-based MANET. Moreover, the decentralized nature of MANET leads the existing schemes, especially

False Data Injection Attacks (FDIAs) present a significant threat to smart grids by manipulating measurement data, which may lead control centers to make incorrect operational decisions. Accurate and efficient detection of FDIAs is critical for ensuring reliable grid operation. Existing deep learning approaches often fail to capture both short-term local features and long-term dependencies in power

Trajectory privacy is a critical issue in mobile crowd sensing (MCS) network, but existing methods ignore location sensitivity differences, making it difficult to achieve personalized protection. To this end, this paper proposes a personalized trajectory synthesis method (PTS-TCGAN) that fuses Transformer and conditional generative adversarial networks (CGAN). Firstly, the trajectory point sensitivity

Wireless-based human sensing (WHS) is a powerful technique for device-free activity recognition. It analyzes channel state information (CSI) extracted from wireless signals. However, domain shift remains a major challenge, as performance degrades significantly when models are applied across users, environments, or locations. Few-shot learning (FSL) offers a practical solution by enabling rapid adaptation

In this paper, we investigate the multi-hop transmission scheme for quality of experience (QoE)-centric air-ground collaborative emergency communication system (ECS). In unmanned aerial vehicle (UAV) assisted communication, existing efforts focus on the quality of service (QoS) metric, such as fairness and throughput. The optimization mechanism centered on the QoS metric ignores the matching relationship

Mobile Edge Computing (MEC) improves system response speed and efficiency by offloading computing tasks to the edge of the network. However, with the rapid development of the Internet of Things (IoT), the number of IoT devices and traffic surge, and the pressure on the computing and communication resources of MEC increases dramatically. Traditional computation offloading methods have been difficult

Ensuring secure and efficient message authentication in vehicular ad hoc networks (VANETs) is a significant challenge, particularly in the face of insider threats and high mobility. This paper proposes a trust-driven batch authentication scheme, dynamically optimized through real-time trust updates. We introduce a lightweight certificateless elliptic curve cryptography (CL-ECC) protocol for efficient

Mobile vehicular ad hoc networks with fog computing in 6 G present a novel distributed architecture to enhance network computing capabilities in dynamic environments. However, these self-organizing networks face significant quality-of-service challenges including large average latency, high total energy consumption, and poor load balancing among fog nodes during multi-task offloading in mobile scenarios

Satellite remote sensing technology has promoted the emergence of a variety of earth observation (EO) tasks with Ultra HD resolution and/or high real-time requirements. Limited by the bandwidth of space-ground links and the computing power of a single satellite, on-orbit collaborative edge computing improves task processing efficiency. The task is split into subtasks and offloaded to different computing