Internet of Things (IoT) devices and edge-platform robots require privacy-aware path planning under strict compute and bandwidth constraints, where continual on-device learning from real-world data is desirable but costly. We propose and evaluateSplit Federated Learning (SFL)–enabledDeep Q-Networks (DQN), termedSFL-DQN, which partitions the Q-network into a lightweight on-device encoder and a shared server-side action–value head with online/target stabilization. Devices keep raw observations local and exchange only intermediate feature representations and their gradients; periodicFederated Averaging (FedAvg)realigns client encoders to mitigate representation drift under heterogeneous, non-independent and identically distributed (non-IID) experience. In grid-world navigation withonemoving obstacle, SFL-DQN matches a non-federated, non-split DQN baseline in episodic return and task success and attainsBreadth-First Search (BFS)–equivalent shortest-path optimality on held-out maps. WithKclients, each device uses only 1/Kof the single-device interaction budget (e.g., 1,000 vs. 4,000 episodes whenK=4), reducing theclient-sidetraining burden by offloading the value-learning head to the server. Split–federated training introduces feature-level communication overhead and does not necessarily reduce system-wide energy under all network conditions; instead, SFL-DQN enables a practical edge–cloud learning loop without raw-data aggregation.

Extending Intelligent Transportation Systems (ITS) toward suburban and peripheral regions is challenging because dense roadside infrastructure is expensive to deploy and underutilized outside peak hours. This paper proposes a V2X-enabledresting droneframework as a dynamic traffic flow management solution for ITS, in which drones equipped with Vehicle-to-Everything (V2X) connectivity are dispatched on demand to congested suburban corridors, provide temporary ITS services, and then land on attachment points to rest in a low-power state when not needed. The framework combines a synthetic multi-city road network, a time-slot–based traffic model, and a load-dependent V2X Quality-of-Service abstraction that maps latency and packet loss into an effective drone availability metric and explicitly captures the impact of non-ideal V2X conditions on control reliability. Within this framework, we develop and evaluate GOLD, a Greedy Overlap-Limited Drone deployment algorithm that prioritizes high-gain, low-overlap locations to maximize effective (overlap-removed) ITS expansion with a limited drone fleet. GOLD is compared against a conventional local threshold-based drone deployment rule that independently scales each road point’s coverage radius with traffic intensity, modeling existing overlap-unaware UAV/ITS extensions. Simulation results over multiple random map and traffic realizations show that GOLD achieves a large fraction of the baseline’s effective coverage with substantially fewer active drones under ideal V2X conditions and maintains its relative advantage when V2X latency and packet loss degrade drone availability, demonstrating that resting drones coordinated by GOLD provide a scalable and robust complement to fixed roadside ITS infrastructure.

ABSTRACT No‐reference image quality assessment (NR‐IQA) has garnered significant attention due to its critical role in various image processing applications. This survey provides a comprehensive and systematic review of NR‐IQA methods, datasets, and challenges, offering new perspectives and insights for the field. Specifically, we propose a novel taxonomy for NR‐IQA methods based on distortion scenarios and design principles, which distinguishes this work from previous surveys. Representative methods within each category are thoroughly examined, with a focus on their strengths, limitations, and performance characteristics. Additionally, we review 20 widely used NR‐IQA datasets that serve as benchmarks for evaluating these methods, providing detailed information on the number of images, distortion types, and distortion levels for each dataset. Furthermore, we identify and discuss key challenges currently faced by NR‐IQA methods, such as handling diverse and complex distortions, ensuring generalisation across datasets and devices, and achieving real‐time performance. We also suggest potential future research directions to address these issues. In summary, this survey offers a comprehensive and systematic examination of NR‐IQA methods, datasets, and challenges, offering valuable insights and guidance for researchers and practitioners working in the NR‐IQA domain.

Addressing the limitations of traditional sensor-based systems vulnerable to environmental constraints and trust issues, this article introduces a blockchain-assisted maximum evacuation framework in zero trust hiking trail and mountainous terrain using Internet of Things (IoT) devices that leverages blockchain technology for enhanced security and reliability. We devise three innovative algorithms that are designed to maximize the activation of evacuation nodes, ensuring rapid and efficient disaster response. By updating safe areas and evacuation routes dynamically in real-time IoT environment, the developed algorithms aim to significantly improve emergency response capabilities in challenging terrains. Also, the extensive simulations are achieved to demonstrate the performances of the proposed schemes with discussions for obtained outcomes.

Drones or UAVs (Unmanned Aerial Vehicles) are increasingly utilized with IoT (Internet of Things) to enhance security surveillance and environmental monitoring within smart cities. Although path planning algorithms, deployment strategies, and charging station optimization techniques have been explored to improve operational efficiency, the physical limitations of battery capacity remain a significant challenge. This study introduces the concept of a "resting drone," a specialized drone capable of attaching to structures or walls through structural modifications. By doing so, it can perform environmental monitoring and surveillance tasks without operating its motors, thus conserving energy. Furthermore, we conducted simulations based on real hardware data of resting drones to analyze their operations, including resting, movement, target area monitoring, and resource charging, within a smart city environment. A new movement algorithm was proposed to enhance the resource efficiency of the resting drones. The study compares the resource efficiency of resting drones with that of conventional drones. The results show that environmental monitoring coverage increases significantly, from 60% to 315%, depending on the movement algorithm used.

The recent study on security surveillance is expanding into numerous research branches and applicable industrial areas for smart devices, mobile robots, drones, autonomous vehicles, internet of things components with a consideration of energy-efficient requirements and environmental features. Also, it is highly necessary to proceed the study of how to build and manage disaster prevention system in complex regions according to eco-friendly services and reliable constructions of relevant infrastructures with communications. In this paper, we introduce a harmonized all-ways security surveillance and disaster prevention system in eco-cities that supports mixed groups of high performance devices with high capability and low performance components with limited resource. The proposed framework provides all-ways surveillance by vertical, horizontal, reinforced crossing shaped formations. With a formal representation, the main research problem of maximizing the extended line for all-ways mixture surveillance and disaster prevention detection is defined with integer linear programming formulation. To resolve the research problem, we devise novel algorithms with initialization as well as demonstrate their performances based on numerical outcomes through various simulations.

Recent vehicle cooperation is utilized for advanced missions, including next-generation communication and dynamic real-time information. active safety control for interconnected vehicles, collection of a heavy traffic information. In particular, the urban traffic surveillance should be one promising topic toward advanced vehicle road cooperation service supported by smart camera and Internet of Things (IoT). In this article, we introduce an augmented fluid surveillance system to create an adaptive fluid surveillance using a group of single lens cameras for intelligent transportation service. The research problem for augmented fluid surveillance is formally defined. Its objective is to optimize the surveillance efficiency for various traffic circumstances of traffic volumes so that the optimal deployment of smart camera is achieved. To settle the problem, two different methods are proposed with clear execution procedures and specifications. Then, their performances are evaluated with obtained results by extensive simulations for diverse settings and applicable scenarios. Furthermore, future research issues and works are described briefly.

A Maritime Transportation System (MTS) accomplishes diverse tasks including efficient route, information management, transportation stations. In particular, security and surveillance should be one of critical topics in IoT-supported MTS. In this paper, we introduce a secure framework that creates differential security barriers for virtual emotion detection in IoT-assisted maritime transportation systems. We formally define a problem whose goal is to maximize the number of differential security barriers by mobile robots and UAVs. To solve the problem, we propose two different schemes to provide an improved security detection in priority area so that the proposed methods create the maximum number of security barriers to fit with discriminative security levels in MTS subareas. Then, we analyze their performances through various settings and scenarios with detailed discussions.

Electrocardiogram (ECG) based user identification has received considerable attention with the advent of wearable devices. It provides emerging applications including personal healthcare a convenient way to authenticate users as the process can be performed at the moment the user makes contact with the device. However, a recent study discovered that injecting noise into the signal transmitted from the user to an application can effectively hinder the classification process. Many efforts have been made to deal with this noise injection attack, but most approaches have focused on noise removal. In contrast, this paper proposes Defensive Adversarial Training (DAT), which involves training a model with various noisy data to enhance the robustness of deep learning-based identification algorithms. We used two types of noise, Gaussian and Laplacian, to create noisy data. In addition, a sliding-window technique was used to effectively extract useful features and to achieve better accuracy. Our simulation results demonstrate that the proposed approach is highly robust to noise injection attacks and even against random noise. A comparative analysis with noise removal schemes also shows that the proposed DAT significantly enhances the robustness of ECG-based user identification.

As an original property of human beings, human emotion recognition has gained a lot of interests of many researchers. The recent emotion recognition scheme using wireless signal is considered as one of emerging techniques toward intelligent smart cities. It is highly reasonable to form virtual emotion barrier that can detect human emotion when people move from one side to another side through at least one device in the barrier using wireless signal. Also, it is critical that the emotion recognition delay by virtual emotion barrier with possible two-way detection must be minimized to provide emotion-based services to citizen in timely manner. In this article, we introduce a virtual emotion detection model with two-way enabled delay bound, which pursues to provide timely emotion-based IoT services in advanced smart cities. Also, we formally define a problem whose objective is to generate two-way enabled virtual emotion barriers such that the virtual emotion detection maximum delay of those barriers should be minimized. Then, we propose a novel Two-Way-Enabled-Border-Slab scheme and evaluate its performance through extensive simulations with various settings and scenarios. Furthermore, relevant to the proposed system, we discuss possible research issues, challenges and future works.

Since a long time, the integrity of physical borders is seen as a challenging concern. Indeed, governments have to facilitate travelling and trade so that economies continue to grow while preventing the entry of dangerous entities. To this aim, several conventional techniques were enforced to secure borders in the recent past. However, due to the immensity of the area to be monitored, such solutions require an intensive human involvement and high maintenance costs. This advocated to use new technologies, such as Wireless Sensor Networks (WSN), to reduce the costs and improve the efficiency of the border surveillance system. Although using and combining these technologies has been already addressed in different existing solutions, it still some key points to be considered like energy efficiency, load balancing, and redundancy elimination. In this paper, we first propose a multilayer hybrid architecture based on cameras, scalar sensors, radars, and UAVs to design a border surveillance system. Then, a detailed deployment strategy is discussed. Finally, an activation scheduling strategy based on load balancing and energy saving is addressed. The simulation results show that our solution could not only detect the intrusion in border areas, but also outperforms the other solutions by managing efficiently the network and extending its lifetime.

Vehicular Ad Hoc Network (VANET) is a promising technology for autonomous driving as it provides many benefits and user conveniences to improve road safety and driving comfort. Sybil attack is one of the most serious threats in vehicular communications because attackers can generate multiple forged identities to disseminate false messages to disrupt safety-related services or misuse the systems. To address this issue, we propose a Sybil attack detection scheme using ADAS (Advanced Driving Assistant System) sensors installed on modern passenger vehicles, without the assistance of trusted third party authorities or infrastructure. Also, a deep learning based object detection technique is used to accurately identify nearby objects for Sybil attack detection and the multi-step verification process minimizes the false positive of the detection.

Vehicle to everything (V2X) in heterogeneous networks concurrently retains multiple communication links within a channel: such as vehicle to vehicle (V2V), Vehicle to macro base station (V2C), and cellular user equipment to femtocell base station (U2F). To provide high spectral efficiency, there were many efforts such as non-orthogonal multiple access (NOMA) and channel aggregation. However, combining these schemes on the top of NOMA-V2X-femtocell is extremely challenging as it increases the number of dimensions to be considered. To address this issue, this paper proposes a new genetic deep learning algorithm. It employs a genetic algorithm (GA) to find a pair of communication links per channel in a way to maximize the throughput and a neural network to reduce the dimension gradually. The neural network is trained to predicts which pair can be part of the final result. The suitable pairs are marked by deep learning, then they are not shuffled in the subsequent generations. The simulation results show that the proposed scheme achieved higher throughput greater than 20%, compared to the existing GA.

Location information plays a vital role in vehicular ad-hoc networks (VANETs) as numerous applications are location dependent. Thus, it is essential to preserve the integrity of the location information of vehicles. With the advancement of the vehicular technologies, it is observed that advanced driver-assistance system (ADAS) sensors are being installed on vehicles. In this paper, we propose a detection mechanism of location spoofing by leveraging the on-board ADAS sensors. Under our proposed scheme, attacks based on location spoofing such as sybil attack could be efficiently detected using unique ADAS sensor data like fingerprint, without the involvement of the infrastructure and third party trusted authority.

Human emotion recognition has attracted much interest of researchers since it can be utilized for various applications and communication software services. For emotion recognition, there are several approaches including facial expression, human motion. In particular, the emotion derivation through wireless signal and its reflection has been developed recently. Also, a concept of virtual emotion barrier has been introduced, which the emotion can be detected by the built virtual emotion barrier. Because human emotion can be changed frequently, the emotion detection delay through virtual emotion barrier should be minimized for possible accurate emotion recognition. In this paper, we introduce delay bounded virtual emotion barriers in IoT-enabled smart cities, which deliberate on the minimum virtual emotion detection latency among the constructed virtual emotion barriers for next generation software services. Then, we formally define a problem whose goal is to create delay-bounded virtual emotion barriers in IoT-enabled area such that the virtual emotion detection maximum delay among virtual emotion barriers is minimized. To solve the problem, we devise a novel scheme which admits virtual emotion recognition with minimum delay. Furthermore, the performance of the proposed approach is evaluated through extensive experiments.

Securing physical borders is a critical task and a real concern for all the countries over the world. Conventional techniques which have been largely investigated to secure borders over the past, are progressively abandoned due to the intensive human involvement and high maintenance costs. Recently, new technologies, such as Wireless Sensor Networks (WSN), have been implemented to reduce the costs and improve the efficiency of the border surveillance system. One of the critical issue in WSN it to define an efficient activation strategy of the different sensors to reduce the resource consumption and enhance the lifetime of the network. We propose in this paper to study the effectiveness of adapting the activation period of scalar sensors and cameras on the lifetime of the network. Simulation results show that this period should be reduced in crisis time to enhance the fault tolerance of the network while it should be augmented in peacetime to extend the network lifetime.

In this paper, a seamless virtual emotion system is introduced toward appropriate emotion-based services in advanced IoT-enabled smart cities. The proposed system is supported by virtual emotion barrier with replaceable detection and hole-free property, which recognizes human emotion using wireless signal. Also, we formally define a problem with ILP formulation whose goal is to maximize the lowest detection accuracy with weak hole in replaceable barrier in order to construct seamless replaceable hole-free virtual emotion barrier ultimately. Then, a novel scheme is proposed to solve the problem. Moreover, we perform the proposed approach through extensive simulations with various scenarios and its performance is evaluated with detailed discussion.

Performance evaluation is still a topic that attracts a lot of attention in both distributed and mobile systems as well as web-based services architectures. Due to the recent advances in Internet based applications as well as distributed and mobile communication systems, we are witnessing a variety of new technologies. However, these systems are becoming very large and complex at the same time. Several challenges remain to be resolved before these systems become a commodity. Guaranteeing Quality of Service (QoS) and effective provisioning of web-based systems as well as distributed and mobile systems as well as evaluating their communication performance still represent open issues in the design of these systems. Quantitative analysis can be very difficult and may be intractable because of the state space explosion.

A barrier-coverage has attracted much interests of researchers because it can guarantee to detect any movement of mobile objects. Also, thanks to recent advancement of technology, it is possible to recognize human emotion by facial expression and human motion or activity. Then, the emotion recognition can be applied to various services and applications appropriately. Recently, it has been developed to sense emotion by wireless signal. One of issues for emotion recognition is to increase the recognition accuracy. In this paper, we introduce a new type of barrier, virtual emotion barrier, which is able to detect emotion by devices with wireless signal in Internet of Things (IoT) environment. Then, we formally define a problem whose objective is to construct virtual emotion barrier in the given area including IoT devices such that the detection accuracy of emotion by virtual emotion barrier is maximized. To solve the problem, we propose a greedy-emotion-accuracy approach. Moreover, we discuss future issues and possible research directions for virtual emotion barrier.

A recent proliferation of UAVs accompanies several critical issues to be considered for efficient, secure use of UAVs. One of those issues should be a privacy of people. Also, when the UAVs fly to perform specific objectives, minimizing movements of UAVs is a big issue to provide a minimum mission completion time and a maximum network lifetime. One intuitive solution is that UAVs may pass through private area of citizens whereas people do not want any penetration into own area without a permission. Then, we may take into account a compromise between those situations by giving a differential permission for each UAV to access citizens' areas depending on specific benefits by use of UAVs or emergent situation for public safety. In this paper, we introduce a framework for privacy-preserving movements of UAVs with differential UAVs' permissions given by citizens. Then, we formally define a problem whose objective is to minimize total movements of UAVs with preserving privacy of people. To solve the problem, we create a new graph type called as UDiPP graph and propose a novel priority-based approach. Furthermore, we discuss future issues and directions for differential privacy-preserving movements by UAVs.

Summary Recently, barrier‐coverage in wireless sensor networks is a critical issue because it can be used for various applications (e.g., intrusion detection and border protection). Many existing works for barrier‐coverage assume that an intruder penetrates through two opposite sides such as from top to bottom or from bottom to top and focus on constructing barriers to detect those penetrations. However, in many practical scenarios, it should be desirable to detect an intruder that enters the area of interest through any of its sides and passes through any other of its sides. In this paper, we introduce a new barrier‐coverage problem whose goal is maximizing the network lifetime such that any penetration variation of the attacker is guaranteed to be detected. In order to solve the problem, we create a new type of sensor barriers, which is referred asreinforced barriers, that can sense any movement variation of the intruder. Also, we propose four different approaches to construct reinforced barriers from a given layout of sensors and we compare their relative performances for maximum number of reinforced barriers through extensive simulations by various scenarios. Copyright © 2016 John Wiley & Sons, Ltd.

Recently, Unmanned Aerial Vehicle (UAV) networks attracts a lot of interest as one of promising research areas since it can be used for a large portfolio of relevant applications. Among several issues in UAV networks, a collision avoidance among multiple UAVs should be addressed due to its significance. Furthermore, a barrier-coverage is considered as an important coverage concept because it is also appropriate for various applications such as intrusion detection and border surveillance. In this paper, we introduce a barrier-coverage system in UAV networks to construct collision-free UAV reinforced barrier. Then, we formally define a problem whose objective is to minimize total moving distance of UAVs such that collision-free is guaranteed among multiple UAVs when they move from initial locations to positions of constructing a reinforced barrier. To solve the problem, we introduce a novel strategy based on dividing a region into zones and describe our proposed approach. Moreover, we discuss future issues for barrier-coverage of UAV networks.

A barrier-coverage in wireless sensor networks (WSN), which is able to detect a mobile object from one side to another opposite side, is a critical problem for lots of applications. Because it is highly appropriate to consider a barrier-coverage in event-driven environment, Kim et al. recently introduced k-event-driven partial barriers (k-EP barriers) with static sensor nodes, which at least k sensors can detect or monitor every movement of mobile objects on paths among multiple hubs in event-driven environment. But, in case of failures of sensors, k-EP barriers should be recovered from those failures. In this paper, we introduce resilient event-driven partial barriers with mobile sensors, which can maintain k-EP barriers continuously as well as can recover from a failed status when there exist failed sensors on initially constructed k-EP barriers due to energy depletion of those sensors. Also, we formally define a problem whose objective is to minimize total moving distance of mobile sensors such that k-EP are maintained continuously by considering movements of mobile sensors. To solve the problem, we propose a novel approach to minimize total movement distance of mobile sensors to form k-EP barriers continuously. Then, we address future issues and directions for resilient event-driven partial barriers.

Wireless sensor and actor networks (WSANs) are composed of static sensor nodes and mobile actor nodes. We assume actors have a random initial location in the two-dimensional sensing area. The objective is to move each actor to a location such that every sensor node is within a bounded number of hops from some actor. Because sensor nodes have limited energy, the new actor locations are chosen as to minimize the transmission range required from the sensor nodes. However, actors also have a limited (although larger) power supply, and their movement depletes their resources. It follows that by carefully choosing the new actor locations, the total actor movement can be minimized. In this paper, we study the trade-off between minimizing sensor transmission radius and minimizing actor movement. Due to the complexity of the problem, we introduce an optimal ILP formulation, and compare its results against a proposed heuristic. For the ILP solution to be feasible, we introduce a finite set of potential actor positions such that an optimal solution is guaranteed to be found within this set.

Wireless sensor and actor networks (WSANs) consist of fixed sensor nodes and mobile actor nodes. Data is generated at the sensor nodes, and collected at the more powerful actor nodes. We consider the problem of finding the location for K actor nodes such that every sensor node is within d-hop from an actor node, where K and d are parameters of the problem. Our approach distinguishes itself in obtaining the minimum transmission radius necessary for such coverage to be possible. We provide an exact solution by via an integer-linear- programming formulation (ILP), and evaluate two heuristic approaches.

We consider the design of wireless sensor and actor networks. Sensors are network nodes with a fixed location and limited transmission power. Actors are mobile nodes that may relocate their position to provide coverage to the sensor nodes. We consider the problem of relocating K actors from initial random locations to locations where every sensor node is able to communicate directly with at least one actor node. The new actor locations are chosen to minimize the transmission power that is necessary for the sensor nodes. Also, the pairing of actors to their new locations is such that the cost of relocating the actors is minimized.