Accurate landslide displacement prediction is a key component of IoT-enabled landslide monitoring and warning support frameworks, supporting risk-informed warning analysis and risk-informed decision-making. However, precise forecasting remains challenging due to the non-stationary characteristics of displacement data and the complex spatiotemporal correlations among monitoring points. To this end, this article proposes a novel attention-guided spatiotemporal fusion framework, named VMAG (Variational Mode Decomposition and Multi-head Attention-based GRU), for accurate multi-step landslide displacement prediction. Specifically, a differential fluctuation sequence is first constructed using Ground-Based Synthetic Aperture Radar (GB-SAR) displacement observations to enhance the perceptibility of displacement mutations. Then, based on the inherent characteristics of the time series, the Variational Mode Decomposition (VMD) algorithm is applied to decompose the sequence into multi-scale components to mitigate non-stationarity. Subsequently, a multi-head attention mechanism is employed to dynamically extract spatial dependencies between the target node and reference monitoring nodes, which are then fed into a Gated Recurrent Unit (GRU) network to capture temporal evolutions. Experimental results on datasets from the Moshi Gully (MSG) landslide demonstrate that the proposed VMAG significantly outperforms mainstream benchmarks. For instance, the Root Mean Square Error (RMSE) is reduced to 1.6960 mm, and the Mean Absolute Percentage Error (MAPE) achieves 0.63%, showing superior accuracy compared to ANN, LSTM, and standard GRU models.

People counting in smart buildings is crucial for the efficient management of building systems such as energy, space allocation, efficiency, and occupant comfort. This study investigates the use of two non-invasive binary Passive Infrared (PIR) sensors for estimating the number of people in seven office rooms with different people counting intervals. Previous studies often relied on sensor fusion or more complex signal-based PIR sensors, which increased hardware costs, raised privacy concerns, and added installation complexity. Our approach addresses these limitations by utilizing fewer sensors, reducing hardware costs, and simplifying installation, making it scalable and flexible for different room configurations, while also ensuring high consideration of privacy. Additionally, binary PIR sensors are typically part of smart building systems, eliminating the need for additional sensors. We employed several machine learning methods to analyze motion detected by binary PIR sensors, imp roving the accuracy of people counting estimates. We analyzed important features by extracting event count, duration, and density from sensor data, along with features from the room’s shape, to estimate the number of people. We used different machine learning models for estimating the number of people. Models like Gradient Boosting, XGBoost, MLP, and LGBM demonstrated superior performance for their strong ability to handle complex, non-linear relationships in sensor data, high-dimensional datasets, and imbalanced data, which are common challenges in people counting tasks using PIR sensors. These models were evaluated using performance metrics such as accuracy and F1-score. Additionally, the results show that features such as passage events and the number of detected events, combined with machine learning algorithms, can achieve good accuracy and reliability in people counting.

Currently, utilizing Mobile Vehicles (MVs) equipped with both wireless charging and data transmission capabilities to recharge nodes and collect their data in a “parallel” manner has become an effective approach to enhancing the efficiency of Wireless Rechargeable Sensor Networks (WRSNs). However, how to address the varying types and frequencies of service requests from nodes while minimizing the data loss ratio remains a critical issue that needs to be solved. To this end, this paper proposes an energy Replenishment and data Collection strategy based on Minimizing the data Loss ratio (RCML). First, the theoretical upper bound of service duration per round for MV was calculated, and reasonable service request thresholds were set for nodes accordingly. Then, an objective function was constructed with the primary and secondary goals of reducing data loss and minimizing travel duration of MV, respectively. Based on this, a Service Queue Generation algorithm (SQG) which utilizes the simulated annealing was proposed. To address the issue of a large number of requests, a Node Selection Strategy (NSS) was also proposed to prioritize the nodes at higher risk of data loss. Finally, an Idle-Time Service (ITS) strategy was adopted to further improve the overall service efficiency of MV. Simulation results show that RCML demonstrates significant advantages in terms of data loss ratio as well as energy consumption of MV compared to typical methods such as MPF and PMCDC.

Unmanned aerial vehicles (UAVs) are increasingly utilized in smart city applications, particularly for urban surveillance. UAVs can be provisioned as mobile surveillance to avoid various difficulties in ground operations and reduce extensive labor cost. However, their limited energy capacity restricts flight time and coverage, making it difficult to build a large-scale, long-term city-wide monitoring network. To address this problem, an ubiquitous public transportation network is introduced for UAVs to periodically recharge by landing on public transportation buses. We propose a novel public transportation-assisted UAV scheduling framework that leverages the existing bus network to enable recharging of UAVs. Two intertwined sub-problems are addressed: the UAV trajectory planning and the surveillance task offloading problems. The trajectory planning is modeled as a Traveling Salesman Problem (TSP) and it is solved via the Lin-Kernighan heuristic (LKH), decomposing the bus station network into sub-graphs for efficient routing. For task offloading, a time-slot-based scheduling method is proposed that dynamically assigns UAVs to monitor points of interest (PoIs) while ensuring energy constraints and full coverage. Experimental results demonstrate that the proposal outperforms baseline algorithms, achieving a 1.72%-3.46% average extension in system lifetime compared to state-of-the-art baselines, while maintaining computational efficiency (average runtime: 277.57 ms). The robustness of the proposal is further validated across diverse testing instances with various parameter settings.

“Request Triggered Recharging” has been a flexible type of scheduling schemes to allow the mobile charging vehicle (MCV) to supply energy for sensor nodes on demand. However, in most existing works, MCV always passively waits for the arrival of the unpredictable requests that may cause it missing the best departure time to serve nodes. To solve this problem, we propose a balanced distribution strategy for the number of recharging requests based on dynamic dual thresholds (BDRR). First, the adjustable double recharging request thresholds (DRRTs) are set for each node to ensure that all the requesting nodes can be successfully charged. Then, the method for setting the energy replenishment value (MSERV) is proposed to enable the distribution of the moments at which nodes send out their recharging requests being concentrated within each period. Furthermore, an efficient traversal path for the MCV is constructed by safe or dangerous scheduling strategy, and the charging capacity reduction scheme (CCRS) is also executed to help survive more nodes in need. Finally, a passer-by recharging scheme (PRS) is introduced to further improve the energy efficiency (EE) of the MCV. Simulation results show that BDRR outperforms the compared algorithms in terms of surviving rate of sensors as well as the EE of MCV with different network scales.

This paper proposes a novel agglomerated privacy-preservation model integrated with data mining and evolutionary Genetic Algorithm (GA). Privacy-pReservIng with Minimum Epsilon (PRIM$\epsilon $) delivers minimum privacy budget ($\epsilon $) value to protect personal or sensitive data during data mining and publication. In this work, the proposed Pattern identification in the Locale of Users with Mining (PLUM) algorithm, identifies frequent patterns from dataset containing users’ sensitive data.$\epsilon $-allocation by Differential Privacy (DP) is achieved in PRIM$\epsilon $with GA$_{\textbf {PRIM$\epsilon $}}$, yielding a quantitative measure of privacy loss ($\epsilon $) ranging from 0.0001 to 0.045. The proposed model maintains the trade-off between privacy and data utility with an average relative error of 0.109 on numerical data and an Earth Mover’s Distance (EMD) metric in the range between [0.2,1.3] on textual data. PRIM$\epsilon $model is verified with Probabilistic Computational Tree Logic (PCTL) and proved to accept DP data only when$\epsilon \le 0.5$. The work demonstrated resilience of model against background knowledge, membership inference, reconstruction, and privacy budget attack. PRIM$\epsilon $is compared with existing techniques on DP and is found to be linearly scalable with worst time complexity of$\mathcal {O}$(n log n).

Autonomous vehicles (AVs) are being enhanced by introducing wireless communication to improve their intelligence, reliability and efficiency. Despite all of these distinct advantages, the open wireless communication links and connectivity make the AVs’ vulnerability to cyber-attacks. This paper proposes an$L_{2}$-gain-based resilient path following control strategy for AVs under time-constrained denial-of-service (DoS) attacks and external interference. A switching-like path following control model of AVs is first built in the presence of DoS attacks, which is characterized by the lower and upper bounds of the sleeping period and active period of the DoS attacker. Then, the exponential stability and$L_{2}$-gain performance of the resulting switched system are analyzed by using a time-varying Lyapunov function method. On the basis of the obtained analysis results,$L_{2}$-gain-based resilient controllers are designed to achieve an acceptable path-following performance despite the presence of such DoS attacks. Finally, the effectiveness of the proposed$L_{2}$-gain-based resilient path following control method is confirmed by the simulation results obtained for the considered AVs model with different DoS attack parameters.

This paper investigates the control issue of marine surface vehicles (MSVs) subject to internal and external uncertainties without velocity information. Utilizing the specific advantages of adaptive neural network and disturbance observer, a classification reconstruction idea is developed. Based on this idea, a novel adaptive neural-based state observer with disturbance observer is proposed to recover the unmeasurable velocity. Under the vector-backstepping design framework, the classification reconstruction idea and adaptive neural-based state observer are used to resolve the control design issue for MSVs. To improve the control performance, the serial-parallel estimation model is introduced to obtain a prediction error, and then a composite learning law is designed by embedding the prediction error and estimate of lumped disturbance. To reduce the mechanical wear of actuator, a dynamic event triggering protocol is established between the control law and actuator. Finally, a new dynamic event-triggered composite learning adaptive neural output feedback control solution is developed. Employing the Lyapunov stability theory, it is strictly proved that all signals in the closed-loop control system of MSVs are bounded. Simulation and comparison results validate the effectiveness of control solution.

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This article investigates the event-triggered adaptive fuzzy output feedback setpoint regulation control for the surface vessels. The vessel velocities are noisy and small in the setpoint regulation operation and the thrusters have saturation constraints. A high-gain filter is constructed to obtain the vessel velocity estimations from noisy position and heading. An auxiliary dynamic filter with control deviation as the input is adopted to reduce thruster saturation effects. The adaptive fuzzy logic systems approximate vessel’s uncertain dynamics. The adaptive dynamic surface control is employed to derive the event-triggered adaptive fuzzy setpoint regulation control depending only on noisy position and heading measurements. By the virtue of the event-triggering, the vessel’s thruster acting frequencies are reduced such that the thruster excessive wear is avoided. The computational burden is reduced due to the differentiation avoidance for virtual stabilizing functions required in the traditional backstepping. It is analyzed that the event-triggered adaptive fuzzy setpoint regulation control maintains position and heading at desired points and ensures the closed-loop semi-global stability. Both theoretical analyses and simulations with comparisons validate the effectiveness and the superiority of the control scheme.

In this paper, we investigate the dynamic task scheduling problem with stochastic task arrival times and due dates in the UAV-based intelligent on-demand meal delivery system (UIOMDS) to improve the efficiency. The objective is to minimize the total tardiness. The new constraints and characteristics introduced by UAVs in the problem model are fully studied. An iterated heuristic framework SES (Stochastic Event Scheduling) is proposed to periodically schedule tasks, which consists of a task collection and a dynamic task scheduling phases. Two task collection strategies are introduced and three Roulette-based flight dispatching approaches are employed. A simulated annealing based local search method is integrated to optimize the solutions. The experimental results show that the proposed algorithm is robust and more effective compared with other two existing algorithms.

The main emphasis of this work is placed on the problem of collaborative coverage path planning for unmanned surface mapping vehicles (USMVs). As a result, the collaborative coverage improved$BA^{*}$algorithm ($C C I B A^{*}$) is proposed. In the algorithm, coverage path planning for a single vehicle is achieved by task decomposition and level map updating. Then a multiple USMV collaborative behavior strategy is designed, which is composed of area division, recall and transfer, area exchange and recognizing obstacles. Moverover, multiple USMV collaborative coverage path planning can be achieved. Consequently, a high-efficiency and high-quality coverage path for USMVs can be implemented. Water area simulation results indicate that our$CCIBA^{*}$brings about a substantial increase in the performances of path length, number of turning, number of units and coverage rate.

This paper investigates the tracking control problem of marine surface vessels (MSVs) in the presence of uncertain dynamics and external disturbances. The facts that actuators are subject to undesirable faults and input saturation are taken into account. Benefiting from the smoothness of the Gaussian error function, a novel saturation function is introduced to replace each nonsmooth actuator saturation nonlinearity. Applying the hand position approach, the original motion dynamics of underactuated MSVs are transformed into a standard integral cascade form so that the vector design method can be used to solve the control problem for underactuated MSVs. By combining the neural network technique and virtual parameter learning algorithm with the vector design method, and introducing an event triggering mechanism, a novel event-triggered indirect neuroadaptive fault-tolerant control scheme is proposed, which has several notable characteristics compared with most existing strategies: 1) it is not only robust and adaptive to uncertain dynamics and external disturbances but is also tolerant to undesirable actuator faults and saturation; 2) it reduces the acting frequency of actuators, thereby decreasing the mechanical wear of the MSV actuators, via the event-triggered control (ETC) technique; 3) it guarantees stable tracking without the aprioriknowledge of the dynamics of the MSVs, external disturbances or actuator faults; and 4) it only involves two parameter adaptations—a virtual parameter and a lower bound on the uncertain gains of the actuators—and is thus more affordable to implement. On the basis of the Lyapunov theorem, it is verified that all signals in the tracking control system of the underactuated MSVs are bounded. Finally, the effectiveness of the proposed control scheme is demonstrated by simulations and comparative results.

A multimode hybrid automaton is proposed for setting vehicle platoon modes with velocity, distance, length, lane position, and other state information. Based on a vehicle platoon shift movement under different modes, decisions are made based on key conditional actions, such as sudden acceleration changes because of vehicle distance changes, emergency braking to avoid collisions and free-lane changing choices adapted to various traffic conditions, so as to ensure effortless movement and safety in the multimode shift. With a 3-degree (longitudinal, lateral, and yaw directions) of the freedom coupled model, a hybrid vehicle platoon controller is proposed using nonsingular terminal sliding-mode control to ensure fast and steady tracking on the hybrid automaton outputs during the multimode shift process. The convergence of the hybrid controller in finite time is also analyzed with the Lyapunov exponential stability. The analysis result proves that the proposed controller not only ensures the stability of the individual vehicle and the vehicle platoon but also ensures the stability of the multimode shift movement system. The proposed cooperative driving strategy for vehicle platoon is evaluated using simulations, where varying traffic conditions and the influence of cutting off are considered in conjunction with demonstration simulations of a vehicle platoon's cruising, following, lane changing, overtaking, and moving in/out of garage functions.

The issue of using vehicles to wirelessly recharge nodes for energy supplement in wireless sensor networks has become a research hotspot in recent works. Unfortunately, most of the researches did not consider the rationality of the recharging request threshold (RRT) and also overlooked the difference of node's power consumption, which may lead to the premature death of nodes as well as low efficiency of wireless charging vehicles (WCVs). In order to solve the above problems, a periodic and distributed energy supplement method based on maximum recharging benefit (PDESM) is proposed in this article. First, to avoid frequent recharging requests from nodes, we put forward an annuluses-based cost-balanced data uploading strategy under deterministic deployment. Then, one WCV in each annulus periodically selects and recharges nodes located in this region which sends the energy supplement requests. In addition, the predicted values of power consumption of nodes are calculated out according to the real-time energy consumption rate, and thus the most appropriate RRT is obtained. Finally, a moving path optimization scheme based on the minimum spanning tree is constructed for distributed recharging. Simulation results show that PDESM performs well on enhancing the proportion of the alive nodes as well as the wireless recharging efficiency compared with node failure avoidance online charging and first come first served. Moreover, it also has an advantage in balancing the energy consumption of WCVs.

Named data networking (NDN) is an emerging information-centric networking paradigm, in which the Internet of Things (IoT) achieves excellent scalability. Recent literature proposes the concept of NDN-IoT, which maximizes the expansion of IoT applications by deploying NDN in the IoT. In the NDN, the security is built into the network by embedding a public signature in each data package to verify the authenticity and integrity of the content. However, signature schemes in the NDN-IoT environment are facing several challenges, such as signing security challenge for resource-constrained IoT end devices (EDs) and verification efficiency challenge for NDN routers. This article mainly studies the data package authentication scheme in the package-level security mechanism. Based on mobile edge computing (MEC), an efficient certificateless group signature scheme featured with anonymity, unforgeability, traceability, and key escrow resilience is proposed. The regional and edge architecture is utilized to solve the device management problem of IoT, reducing the risks of content pollution attacks from the data source. By offloading signature pressure to MEC servers, the contradiction between heavy overhead and shortage of ED resources is avoided. Moreover, the verification efficiency in NDN router is much improved via batch verification in the proposed scheme. Both security analysis and experimental simulations show that the proposed MEC-based certificateless group signature scheme is provably secure and practical.

Accurate positioning is an essential requirement of autonomous vehicular navigation system (AVNS) for safe driving. Although the vehicle position can be obtained in global position system friendly environments, in GPS denied environments (such as suburb, tunnel, forest, or underground scenarios) the positioning accuracy of AVNS is easily reduced by the trajectory error of the vehicle. In order to solve this problem, the plane, sphere, cylinder and cone are often selected as the ground control targets to eliminate the trajectory error for AVNS. However, these targets usually suffer from the limitations of incidence angle, measuring range, scanning resolution, and point cloud density, etc. To bridge this research gap, an adaptive continuum shape constraint analysis (ACSCA) method is presented in this article to design a new target with optimized identifiable specific shape to eliminate the trajectory error for AVNS. First of all, according to the proposed ACSCA method, we conduct extensive numerical simulations to explore the optimal ranges of the vertexes and the faces for target shape design, and based on these trials, the optimal target shape is found as icosahedron, which composes of ten vertexes, 20 faces and combines the properties of plane and volume target. Moreover, the algorithm of automatic detection and coordinate calculation is developed to recognize the icosahedron target and calculate its coordinates information for AVNS. Finally, a series of experimental investigation were performed to evaluate the effectiveness of the designed icosahedron target in GPS denied environments. The experimental results demonstrate that compared with the plane, sphere, cylinder and cone targets, the developed icosahedron target can produce better performances than the above targets in terms of the clustered minimum registration error, ambiguity and range of field-of-view; also can significantly improve the positioning accuracy of AVNS in GPS denied environments.

This paper addresses the co-design problem of a fault detection filter and controller for a networked-based unmanned surface vehicle (USV) system subject to communication delays, external disturbance, faults, and aperiodic denial-of-service (DoS) jamming attacks. First, an event-triggering communication scheme is proposed to enhance the efficiency of network resource utilization while counteracting the impact of aperiodic DoS attacks on the USV control system performance. Second, an event-based switched USV control system is presented to account for the simultaneous presence of communication delays, disturbance, faults, and DoS jamming attacks. Third, by using the piecewise Lyapunov functional (PLF) approach, criteria for exponential stability analysis and co-design of a desired observer-based fault detection filter and an event-triggered controller are derived and expressed in terms of linear matrix inequalities (LMIs). Finally, the simulation results verify the effectiveness of the proposed co-design method. The results show that this method not only ensures the safe and stable operation of the USV but also reduces the amount of data transmissions.

This paper aims to solve the path following problem for an underactuated unmanned-surface-vessel (USV) based on deep reinforcement learning (DRL). A smoothly-convergent DRL (SCDRL) method is proposed based on the deep Q network (DQN) and reinforcement learning. In this new method, an improved DQN structure was developed as a decision-making network to reduce the complexity of the control law for the path following of a three-degree of freedom USV model. An exploring function was proposed based on the adaptive gradient descent to extract the training knowledge for the DQN from the empirical data. In addition, a new reward function was designed to evaluate the output decisions of the DQN, and hence, to reinforce the decision-making network in controlling the USV path following. Numerical simulations were conducted to evaluate the performance of the proposed method. The analysis results demonstrate that the proposed SCDRL converges more smoothly than the traditional deep Q learning while the path following error of the SCDRL is comparable to existing methods. Thanks to good usability and generality of the proposed method for USV path following, it can be applied to practical applications.

The papers in this special section focus on hybrid human-artificial intelligene (AI) for multimedia computing. Multimedia computing has experienced a tremendous growth in the last decades, with applications ranging from multimedia information retrieval and analysis to multimedia compression and communication. However, the increasing volume and complexity of multimedia data driven by the large-scale spread of various new devices and sensors is posing a serious challenge to traditional multimedia computing algorithms. Artificial intelligence (AI), in particular deep learning techniques, has improved the performance of multimedia computing algorithms for many tasks, including computer vision and natural language processing. But unlike humans, AI is poor at solving tasks across multiple domains or in dealing with an uncontrolled dynamic environment. Hybrid Human-Artificial Intelligence (HH-AI) is an emerging field that aims at combining the benefits of human intelligence, such as semantic association, inference, and generalization with the computing power of AI.

Water-filled mining goaves are extremely prone to water inrush accidents in coal mines, and the transient electromagnetic method (TEM) is a good geophysical method for detecting water-rich areas. Considering that conventional TEM was mainly carried out on the ground, to increase the detection resolution, the underground TEM was used to detect the water-filled goaves in this study. Based on the whole-space model, the data-processing method of the underground TEM was studied. The whole-space geoelectric model was established based on actual coal-measure strata data, and the whole-space TEM response of the water-filled goaves was modeled using the finite-difference time-domain method. The results showed that the low-resistance areas of the apparent resistivity contours can accurately reflect the water abundance of the mining goaves. The underground TEM was used to detect the water abundance of the mining goaf in a mine environment and its detection results were consistent with the actual results.

Industrial Internet is applicable across a broad industrial spectrum including manufacturing, aviation, road and rail transport, power, oil and gas, healthcare, smart cities and buildings. Some of the major impacts of the Industrial Internet include the development of new and innovative services and products, which in turn also has economic benefits. The purpose of this special issue is to bring together research studies proposing novel techniques, algorithms, models, and solutions to address challenges such as interoperability, security, and privacy associated with Industrial Internet, blockchain and Cyber-physical systems. We accepted seven articles after two review rounds consisting of three reviews from experts in the areas. The special issue contains seven articles organized in the following categories. 1) Secure searching for edge-cloud assisted industrial Internet of Things (IoT) devices. 2) Privacy protection framework for mobile crowdsensing in Industrial Internet of Things (IIoT). 3) Content privacy for autonomous vehicles in cyberphysical system (CPS). 4) Delegated Proof of Stake (DPoS) consensus mechanism in blockchain. 5) Balancing privacy and accountability for industrial mortgage management. 6) Performance and security in wireless blockchain networks. 7) False data injection attacks in networked control systems.

With the rapid development of Internet services, mobile communications, and IoT applications, Location-Based Service (LBS) has become an indispensable part in our daily life in recent years. However, when users benefit from LBSs, the collection and analysis of users’ location data and trajectory information may jeopardize their privacy. To address this problem, a new privacy-preserving method based on historical proximity locations is proposed. The main idea of this approach is to substitute one existing historical adjacent location around the user for his/her current location and then submit the selected location to the LBS server. This method ensures that the user can obtain location-based services without submitting the real location information to the untrusted LBS server, which can improve the privacy-preserving level while reducing the calculation and communication overhead on the server side. Furthermore, our scheme can not only provide privacy preservation in snapshot queries but also protect trajectory privacy in continuous LBSs. Compared with other location privacy-preserving methods such as k -anonymity and dummy location, our scheme improves the quality of LBS and query efficiency while keeping a satisfactory privacy level.

Information reception in artificially synthesized molecular communication (MC) systems ideally follows the mechanisms employed by natural nanosystems to communicate. One of such reception mechanism is the so called ligand-receptor binding. Contemporary research in MC has considerably discussed this mechanism; however, the impact of a crucial parameter associated with the ligand-receptor binding action has not been given appropriate attention in the MC literature. This parameter is termed the residence time, and has played very crucial role in defining for instance, the efficacy of drugs in therapeutic processes; hence, it is critical in the performance of MC. In this paper, we employ biophysical approach to model and discuss the influence of the ligand residence time on the performance of MC systems. The performance metrics considered here are the receiver sensitivity and the intersymbol interference. Numerical results that expose the impact of the residence time on these metrics, and the interrelationships between these metrics in MC system, are discussed.

Summary Artificial intelligence has been widely used in reliability analysis for industrial equipment. The gear transmission systems are the most common components in mining machines. A simple fault in the gearbox may break down the mining machine for couple of days, resulting in enormous economic loss. Condition monitoring techniques can prevent unscheduled failures in the gear transmission systems. Although many techniques have been developed for gearbox fault diagnosis, one challenging task that the condition monitoring still faces is how to extract quantitative fault indicators. To this end, this paper proposes an improved quantitative recurrence analysis (IQRA) based on artificial intelligence theory. This new method takes advantages of chaos and fractal properties of the gear transmission system to obtain the recurrence of the system. The characteristics of different gear faults can be observed through the visualization of recurrence. Quantitative parameters can be then calculated from the recurrence plots. Experimental data acquired from a gearbox under variable working conditions was used to evaluate the proposed method. The analysis results demonstrate that the proposed IQRA method is able to effectively quantify different the gear faults.

This paper presents a novel cooperative unmanned surface vehicle-unmanned aerial vehicle (USV-UAV) platform to form a powerful combination, which offers foundations for collaborative task executed by the coupled USV-UAV systems. Adjustable buoys and unique carrier deck for the USV are designed to guarantee landing safety and transportation of UAV. The deck of USV is equipped with a series of sensors, and a multiultrasonic joint dynamic positioning algorithm is introduced for resolving the positioning problem of the coupled USV-UAV systems. To fulfill effective guidance for the landing operation of UAV, we design a hierarchical landing guide point generation algorithm to obtain a sequence of guide points. By employing the above sequential guide points, high-quality paths are planned for the UAV. Cooperative dynamic positioning process of the USV-UAV systems is elucidated, and then UAV can achieve landing on the deck of USV steadily. Our cooperative USV-UAV platform is validated by simulation and water experiments.

This paper proposes a variable structure control approach for vehicles platooning based on a hierarchical fuzzy logic. The leader-follower vehicle dynamics with model uncertainties is discussed from the viewpoint of a consensus problem. A practical two-layer fuzzy control for the platooning is designed by employing two common spacing policies to ensure system robustness in different scenarios. The two policies, i.e., constant distance and constant time headway, utilize the predecessor-successor information flow from the immediate predecessor and follower other than controlled vehicles. The first layer of the fuzzy system combines spacing control with velocity-acceleration control to achieve a rapid tracking for the desired control commands, and the second layer combines the sliding mode control to adaptively compensate for reducing the state errors caused by parameter uncertainties and disturbances. Shift between different controller parameters is based on performance boundaries to guarantee the stability of individual vehicle and platooning for arbitrary initial spacing and velocity errors. These performance boundaries can be determined by using a Lyapunov method with exponential stability. Simulation of a ten-vehicle large platooning with two spacing policies shows that the control performance of the newly proposed method is effective and promising.

Connected vehicles (CVs) are one of the critical components of intelligent transportation systems. CVs enable any vehicle to act as a smart node that collects and shares information on vehicles, roads, and their surroundings. This information can then be distributed to other vehicles via vehicle-to-vehicle (V2V) communication, and also to road users via vehicle-to-human (V2H) communication, for an improved driving experience. The information can also be forwarded toward traffic control systems via vehicle-to-infrastructure (V2I) communication, for improved traffic management and road safety. Making use of of connected vehicles in intelligent transportation systems will revolutionize the way we drive. Many issues, however, need to be resolved to achieve better performance of connected vehicles. Improvements relate to data processing and storage, the development of standards and regulations across all platforms, design and deployment of new communication protocols and system architectures, and the creation and introduction of new services and applications.

Software-Defined Wireless Sensor Networking (SDWSN) is an emerging paradigm that seeks to alleviate the inherent resource constraint issues present in Wireless Sensor Networks (WSN) by adopting a Software-Defined Networking (SDN) approach to the management of WSN. This SDWSN paradigm is said to play a crucial role in both the developing Internet of Things (IoT) paradigm and the development of smart city grids. The northbound and southbound SDWSN interfaces are important for realizing efficient network understanding and programmability, however there has been a lack of attention towards the northbound interface as most work done has been surrounding the southbound interface. Therefore some work is needed to improve the northbound interface so that it may allow for a better degree of network programmability. In order to achieve network programmability and automation, there is a need for a metadata based Application Programing Interface (API). The work done in this paper seeks to improve the northbound interface communications by addressing the issue of a metadata in REST as well as identifying potential platforms for the development of a metadata framework.

Hidden, water-abundant areas in coal mines pose a serious threat to mine safety and production. Underground transient electromagnetic method (TEM) is one of the most effective means of detecting water-abundant areas in front of the roadway head. Traditional TEM theories and applications are interpreted mainly on the vertical component. In this study, multicomponent responses of TEM in underground roadways were modeled using the finite-difference time-domain method. Physical simulation was also used for advanced detection of TEM in the roadway. Both the numerical and physical simulation results show that the horizontal component is more sensitive to the location of water-abundant areas. The results of the joint interpretation with both horizontal and vertical components were verified in a practical coal mine application, indicating that it is feasible to use the horizontal component in interpreting TEM data. Thus, the horizontal component could serve as a new approach for coal mine TEM data processing and interpretation.

The degree of modernization of transportation is currently an important criterion for urban development. Progress in communication techniques and networking, together with vehicle localization methods, have become the key enablers of innovative transportation systems.

Physical activity recognition of everyday activities such as sitting, standing, laying, walking, and jogging was performed, through the use of smartphone accelerometer data. Activity classification was done on a remote server through the use of machine learning algorithms, data was received from the smartphone wirelessly. The smartphone was placed in the subject's trouser pocket while data was gathered. A large sample set was used to train the classifiers and then a test set was used to verify the algorithm accuracies. Ten different classifier algorithm configurations were evaluated to determine which performed best overall, as well as, which algorithms performed best for specific activity classes. Based on the results obtained, very accurate predictions could be made for offline activity recognition. The kNN and kStar algorithms both obtained an overall accuracy of 99.01%.

With the advent of nanotechnology, concepts related to the Internet of Things, such as the Internet of NanoThings and Internet of Bio-NanoThings (IoBNT) have also emerged in the classical literature. The main concern of this paper is the IoBNT, which projects the prospective application domain where the activities of very tiny, biocompatible, and non-intrusive devices operating in an in-body nanonetwork can be monitored and controlled through the Internet. In this paper, we present an illustrative scenario and system model of an IoBNT for application in an advanced healthcare delivery system. To address one of the major challenges of the IoBNT, we present an exemplary architecture and model of a bio-cyber interface for connecting the conventional electromagnetic-based Internet to the biochemical signaling-based bionanonetwork. The bio-cyber interface is designed and modeled by employing biological concepts, such as the responsiveness of certain biomolecules to thermal and light stimuli, and the bioluminescence phenomenon of some biochemical reactions. The analysis in this paper focuses on the system that comprises the bio-cyber interface and the information propagation network of the blood vessel that leads to the in-body nanonetwork location. The effects of the system and design parameters associated with the IoBNT models presented are numerically evaluated.

The aim of molecular communication (MC) is to develop communication systems for biochemical information interchange among nano devices. While there have been research efforts to model, design and analyze various MC network and devices, a lot still have to be done before we can develop and deploy such systems. Following the traditional physical layered architecture of communication networks, this paper presents design initiatives, open research issues and proffer suggestion towards modeling and designing diffusion-based MC devices. Specifically, we present biologically inspired modular theoretical design approach to modeling and designing MC devices. We also discussed various issues that should be considered in developing an accurate system-theoretic model of an MC system.