Edge computing has low transmission delay and unites more agile interconnected devices spread across geographies, which enables cloud-edge storage more suitable for distributed data sharing. This paper proposes a trustworthy and reliable multi-keyword search (TRMS) in blockchain-assisted cloud-edge storage, where data users can choose a faster search based on edge servers or a wider search based on cloud servers. To acquire trustworthy search results and find reliable servers, the blockchain-based smart contract is introduced in our scheme, which will execute the search algorithm and update the score-based trust management model. In this way, search results and trust scores will be published and recorded on the blockchain. By checking search results, data users can judge whether the returned documents are top-k documents. Based on the trust management model, we can punish the malicious behavior of search servers, while data users can choose more reliable servers based on trust scores. To improve efficiency, we design a threshold-based depth-first search algorithm. Extensive experiments are simulated on Hyperledger Fabric v2.4.1, which demonstrate our scheme (with 16 threads) can reduce the time cost of index construction by 92% and the time cost of search by 82%, approximately. Security analysis and extensive experiments can prove the security and efficiency of the proposed scheme.

Due to the high mobility of Unmanned Aerial Vehicle (UAV), it can be an effective method for pollution detection of vessels on the sea. How to optimize the flight path of the UAV so that the visited energy consumption is minimized is a problem that remains to be solved. In this paper, the Lin-Kernighan-Helsgaun-based trajectory planning method (LKH-TPM) is used to solve the UAV scheduling problem to minimize the UAV visit path length and compare it with the ant colony (ACO) algorithm, simulated annealing (SA) algorithm and tabu search (TS) algorithm. The experiments are carried out under different ship numbers, different sea areas, and different base station numbers, and it is verified that LKH-TPM is a more effective solution for the problem under study.

“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). Firstly, 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 of the MCV. Simulation results show that BDRR outperforms the compared algorithms in terms of surviving rate of sensors as well as the energy efficiency of MCV with different network scales.

This article provides an overview of recent research on edge-cloud architectures in hybrid energy management systems (HEMSs). It delves into the typical structure of an IoT system, consisting of three key layers: the perception layer, the network layer, and the application layer. The edge-cloud architecture adds two more layers: the middleware layer and the business layer. This article also addresses challenges in the proposed architecture, including standardization, scalability, security, privacy, regulatory compliance, and infrastructure maintenance. Privacy concerns can hinder the adoption of HEMS. Therefore, we also provide an overview of these concerns and recent research on edge-cloud solutions for HEMS that addresses them. This article concludes by discussing the future trends of edge-cloud architectures for HEMS. These trends include increased use of artificial intelligence on an edge level to improve the performance and reliability of HEMS and the use of blockchain to improve the security and privacy of edge-cloud computing systems.

Odometry estimation plays a key role in facilitating autonomous navigation systems. While significant consideration has been devoted to research on monocular odometry estimation, sensor fusion techniques for Stereo Visual Odometry (SVO) have been relatively neglected due to their demanding computational requirements, posing practical challenges. However, recent advancements in hardware, particularly the integration of CPUs with dedicated artificial intelligence units, have alleviated these concerns. In this paper, we investigate the efficacy of attention mechanisms and the incorporation of stereo input in comparison to monocular odometry, aiming to enhance the performance of SVO. We tested two different types of attention mechanisms, i.e., Triplet Attention (TA) and Convolutional Block Attention Module (CBAM), and their fusion in two stages Early and Late. Our results show that the fusion of the second camera improves the performance of the model, as well as early fusion with TA provided the best results.

Heat pumps, being complex systems, are susceptible to various malfunctions. By harnessing contemporary IoT technologies, these devices continuously transmit data which enables monitoring, maintenance, and efficiency. This study focuses on identifying compressor short duration cycles as faults through supervised machine learning algorithms such as XGBoost, Random Forest, SVM, and k-NN. Data preprocessing and labeling were conducted using extensive logged data from heat pump systems, addressing issues like high dimensionality, data sparsity, and temporal dependencies. The methodology included feature engineering, interpolation of missing data, and downsampling for compressor short duration cycles. Supervised machine learning models were applied to classify these short duration cycles. Among the models, XGBoost achieved the highest accuracy and F1-scores, effectively distinguishing between normal and fault conditions. The findings highlight the potential of machine learning to enhance predictive maintenance and operational efficiency in heat pumps.

The advent of intertwined technology, conjoined with powerful centralized machine algorithms, spawns the need for privacy. The efficiency and accuracy of any Machine Learning (ML) algorithm are proportional to the quantity and quality of data collected for training, which could often compromise the data subject’s privacy. Federated Learning (FL) or collaborative learning is a branch of Artificial Intelligence (AI) that decentralizes ML algorithms across edge devices or local servers. This chapter discusses privacy threat models in ML and expounds on FL as a Privacy-preserving Machine Learning (PPML) system by distinguishing FL from other decentralized ML algorithms. We elucidate the comprehensive secure FL framework with Horizontal FL, Vertical FL, and Federated Transfer Learning that mitigates privacy issues. For privacy preservation, FL extends its capacity to incorporate Differential Privacy (DP) techniques to provide quantifiable measures on data anonymization. We have also discussed the concepts in FL that comprehend Local Differential Privacy (LDP) and Global Differential Privacy (GDP). The chapter concludes with 184open research problems and challenges of FL as PPML with implications, limitations, and future scope. 

The research delves into intelligent hybrid heat management Systems, exploring the challenges faced and solutions for enhancing efficiency. Hybrid heating systems are complex cyber-technical systems that combine city heating networks with renewable energy sources, such as heat pumps and solar panels. Traditional heating systems often lack adaptability to internal and external conditions, leading to suboptimal performance and user expectations. This paper proposes a new approach by integrating smart technologies, the Internet of Things, Artificial Intelligence, Machine Learning, optimization techniques, and trade-offs into the management of hybrid heat systems. The emphasis is also placed on the fact that the introduction of smart technologies makes it possible to make hybrid heating systems human-oriented and meet individual needs. Energy efficiency improvement is achievable by combining solutions, such as actual forecasting, with intelligent management that adapts to changing climates and user behaviors. The challenges addressed include inadequate responsiveness to load changes, inaccurate heat consumption forecasting, and inefficient data management. The paper emphasizes the need for intelligent systems that comply with the current standards, providing cost optimization, socializing and ensuring resilience, customer orientation, reliability, safety, and trustworthiness. This exploration of intelligent hybrid heat management systems seeks to overcome existing challenges and pave the way for a sustainable, digitally optimized future in district heating systems.

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.

Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings' status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

This paper proposes a novel agglomerated privacy-preservation model integrated with data mining and evolutionary Genetic Algorithm (GA). Privacy-pReservIng with Minimum Epsilon (PRIMϵ) delivers minimum privacy budget (ϵ) 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. ϵ-allocation by Differential Privacy (DP) is achieved in PRIMϵ with GA PRIMϵ , yielding a quantitative measure of privacy loss (ϵ) 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. PRIMϵ model is verified with Probabilistic Computational Tree Logic (PCTL) and proved to accept DP data only when ϵ ≤ 0.5. The work demonstrated resilience of model against background knowledge, membership inference, reconstruction, and privacy budget attack. PRIMϵ is compared with existing techniques on DP and is found to be linearly scalable with worst time complexity of O(n log n) .

This paper aims to analyze and identify the most promising opportunities for Artificial Intelligence (AI) applications in the Power Systems (PS) domain. It identifies major challenges faced in PS and explores the corresponding technical tasks: forecasting and optimal control. Then, the paper investigates the key AI techniques commonly employed in PS for these tasks, e.g. reinforcement learning (RL) and time series forecasting. It also highlights promising methods with great potential in advancing PS solutions: attention-based models (Transformers, LLMs) and explainable AI (XAI) approaches. This study’s primary contribution lies in identifying critical research gaps in AI for PS, highlighting areas where research and development may have the biggest impact. Additionally, the paper provides a structured literature overview, serving as a valuable resource for researchers and practitioners in the field.

Segmenting images is an intricate and exceptionally demanding field within computer vision. Instance Segmentation is one of the subfields of image segmentation that segments objects on a given image or video. It categorizes the class labels according to individual instances, ensuring that distinct instance markers are assigned to each occurrence of the same object class, even if multiple instances exist. With the development of computer systems, segmentation studies have increased very rapidly. One of the state-of-the-art algorithms recently published by Meta AI, which segments everything on a given image, is called the Segment Anything Model (SAM). Its impressive zero-shot performance encourages us to use it for diverse tasks. Therefore, we would like to leverage the SAM for an effective instance segmentation model. Accordingly, in this paper, we propose a hybrid instance segmentation method in which Object Detection algorithms extract bounding boxes of detected objects and load SAM to produce segmentation, called Box Prompted SAM (BP-SAM). Experimental evaluation of the COCO2017 Validation dataset provided us with promising performance.

Navigating robots with precision and efficiency is a fundamental challenge in the field of robotics. Central to this challenge is the critical aspect of odometry, the ability to estimate a robot's motion relative to its environment. In this context, this paper presents an evaluation of the generalizability and effectiveness of a monocular visual odometry method in the context of navigation on a wheeled robot. The study aims to assess TartanVO's performance in real-time motion estimation and its ability to handle various challenges encountered in indoor and outdoor environments. For this purpose, we designed our methodology framework to evaluate the real-time effectiveness of the TartanVO method by utilizing data streams from a robot's on-board sensors. To validate the performance of TartanVO, we compared its pose estimations against ZED pose estimations, analyzing the mean absolute error of the trajectories produced by each method. We collected time-synchronized data from both TartanVO and ZED positional estimate methods, enabling simultaneous position estimation from both methods. Experimental results reveal that the TartanVO method demonstrates impressive real-time efficiency and generalizability, positioning it as a promising solution for odometry in robots operating in various environments. However, challenges were identified, including scale drift and suboptimal pose estimation in lowlight conditions and open outdoor areas when tested with the Jackal robot. These findings underscore the need for further refinement in addressing specific environmental nuances, while acknowledging the overall potential of the method in real-time motion estimation.

This paper explores the potential of Tiny Machine Learning (TinyML) for privacy-preserving building energy management systems on mobile devices. While TinyML offers reduced latency and improved privacy, its effectiveness in predicting building energy consumption on mobile devices is not well studied. The proposed approach prioritizes user privacy by processing and storing energy data locally on users' mobile devices, leveraging smartphone, tablets, edge nodes, and secure cloud storage. This empowers users with control over their data and adheres to privacy regulations. Predicting building energy usage on mobile devices is crucial because it offers portability, accessibility, and privacy, as well as fosters user engagement. Mobile predictions allow users to conveniently monitor and regulate energy consumption, improving accessibility. Additionally, processing data locally ensures privacy by keeping sensitive information under user control. The paper also investigates the feasibility of converting a TensorFlow-based long short-term memory (LSTM) neural network model for energy prediction to a CoreML or TensorFlow Lite model for deployment on mobile devices. The results indicate a significant degradation in model accuracy after conversion to a CoreML and almost no degradation after conversion to a TensorFlow Lite model. Further research is recommended to explore optimization techniques for the conversion process and to compare models with other criteria.

This paper delves into the challenges encountered in decision-making processes within Hybrid Energy Systems (HES), placing a particular emphasis on the critical aspect of data integration. Decision-making processes in HES are inherently complex due to the diverse range of tasks involved in their management. We argue that to overcome these challenges, it is imperative to possess a comprehensive understanding of the HES architecture and how different processes and interaction layers synergistically operate to achieve the desired outcomes. These decision-making processes encompass a wealth of information and insights pertaining to the operation and performance of HES. Furthermore, these processes encompass systems for planning and management that facilitate decisions by providing a centralized platform for data collection, storage, and analysis. The success of HES largely hinges upon its capacity to receive and integrate various types of information. This includes real-time data on energy demand and supply, weather data, performance data derived from different system components, and historical data, all of which contribute to informed decision-making. The ability to accurately integrate and fuse this diverse range of data sources empowers HES to make intelligent decisions and accurate predictions. Consequently, this data integration capability allows HES to provide a multitude of services to customers. These services include valuable recommendations on demand response strategies, energy usage optimization, energy storage utilization, and much more. By leveraging the integrated data effectively, HES can deliver customized and tailored services to meet the specific needs and preferences of its customers. 

This paper compares machine learning models for short-term heat demand forecasting in residential and multi-family buildings, evaluating model suitability, data impact on accuracy, computation time, and accuracy improvement methods. The findings are relevant for energy suppliers, researchers, and decision-makers in optimizing energy management and improving heat demand forecasting. The included models in the study are k-NN, Polynomial Regression, and LSTM with weather data, building type, and time index as input variables. Single-dimensional models (Autoregression, SARIMA, and Prophet) based on historical consumption are also studied. LSTM consistently outperforms other models in accuracy across different input variable combinations, measured using mean absolute percentage error (MAPE). The incorporation of historical consumption data improved the performance of k-NN and Polynomial Regression models. The paper also explores dataset volume impact on accuracy and compares training and prediction times. k-NN has the least prediction times, Polynomial Regression takes longer, and LSTM requires more time. All models exhibit acceptable prediction times for heat consumption. LSTM outperforms single-dimensional models in accuracy and has lower prediction times compared to AR, SARIMA, and Prophet models.

Data security has remained a challenging problem in cloud storage, especially in multiowner data sharing scenarios. As one of the most effective solutions for secure data sharing, multikeyword ranked searchable encryption (MRSE) has been widely used. However, most of the existing MRSE schemes have some shortcomings in multiowner data sharing, such as index trees generated by data owners, relevance scores in plaintext form, and lack of verification function. In this article, we propose a verifiable and efficient secure sharing scheme in multiowner multiuser settings, where the index tree is generated by the trusted authority. To achieve verifiable functionality, the blockchain-based smart contract is adopted to execute the search algorithm. Based on a distributed two-trapdoor public-key cryptosystem, the data uploaded and used are in ciphertext form, and the proposed algorithms are secure in our scheme. For improving efficiency, the encrypted data are aggregated according to the category and the Category ID-based index tree is generated. Extensive experiments are conducted to demonstrate that it can reduce the time cost of index construction by 75% and the time cost of search by 53%, approximately. Moreover, multithreaded optimization is introduced in our scheme, which can reduce the time cost of index construction by 76% and the time cost of search by 67%, approximately (with 16 threads).

The world is gearing towards renewable energy sources, due to the numerous negative repercussions of fossil fuels. There is a need to increase the efficiency of power generation, transmission, distribution, and use. The proposed work intends to decrease household electricity use and provide an intelligent home automation solution with ensembled machine learning algorithms. It also delivers organized information about the usage of each item while automating the use of electrical appliances in a home. Experimental results show that with XGBoost and Random Forest classifiers, electricity usage can be fully automated at an accuracy of 79%, thereby improving energy utilization efficiency and improving quality of life of the user.

This paper investigates the dynamic event-triggered adaptive neural coordinated disturbance rejection for marine vehicles with external disturbances as the sinusoidal superpositions with unknown frequencies, amplitudes and phases. The vehicle movement mathematical models are transformed into parameterized expressions with the neural networks approximating nonlinear dynamics. The parametric exogenous systems are exploited to express external disturbances, which are converted into the linear canonical models with coordinated changes. The adaptive technique together with disturbance filters realize the disturbance estimation and rejection. By using the vectorial backstepping, the dynamic event-triggered adaptive neural coordinated disturbance rejection controller is derived with the dynamic event-triggering conditions being incorporated to reduce execution frequencies of vehicle's propulsion systems. The coordinated formation control can be achieved with the closed-loop semi-global stability. The dynamic event-triggered adaptive disturbance rejection scheme achieves the disturbance estimation and cancellation without requiring the a priori marine vehicle's model dynamics. Illustrative simulations and comparisons validate the proposed scheme.

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.

This article reflects on effective supervision and possible guidance for enhancing quality of doctoral research in the computer science and engineering field. The aims of this study are (1) to understand supervision and the role of supervisors in the quality of doctoral research, (2) to elaborate on effective supervision in the computer science and engineering field and challenges in effective supervision, and (3) to identify key indicators for evaluating effective supervision with a view to improving the quality of doctoral research. After studying various pieces of literature and conducting interviews with experienced supervisors and doctoral students, the article concludes by describing important characteristics in effective supervision. Some of the features for effective supervision are common to other areas of research; however, in computer science and engineering and similar fields, it is important that a supervisor takes the role of a team member by giving proper advice on the reports, algorithm and mathematical modeling developed in the research, and demonstrating the ability to provide advice on complex problems with practical approaches.

A promising approach to improve sustainability within the energy sector is to incorporate renewable energy sources into existing energy systems. However, such hybrid energy systems have several characteristics that make developing and coordinating the challenging, e.g. due to the need to manage large amounts of heterogeneous data in a distributed and dynamic manner. This paper analyses important characteristics of hybrid heating systems, such as interoperability, scalability, and availability of energy sources. The purpose is to determine how the availability of different energy sources within a hybrid heating system affects sustainability and environmental impact, as well as how interoperability and scalability can affect the overall performance of the hybrid heating system. All these quality characteristic parameters were considered in the aspect of heterogeneous data management.

The development of autonomous vehicles has prompted an interest in exploring various techniques in navigation. One such technique is simultaneous localization and mapping (SLAM), which enables a vehicle to comprehend its surroundings, build a map of the environment in real time, and locate itself within that map. Although traditional techniques have been used to perform SLAM for a long time, recent advancements have seen the incorporation of neural network techniques into various stages of the SLAM pipeline. This review article provides a focused analysis of the recent developments in neural network techniques for SLAM-based localization of autonomous ground vehicles. In contrast to the previous review studies that covered general navigation and SLAM techniques, this paper specifically addresses the unique challenges and opportunities presented by the integration of neural networks in this context. Existing review studies have highlighted the limitations of conventional visual SLAM, and this article aims to explore the potential of deep learning methods. This article discusses the functions required for localization, and several neural network-based techniques proposed by researchers to carry out such functions. First, it presents a general background of the issue, the relevant review studies that have already been done, and the adopted methodology in this review. Then, it provides a thorough review of the findings regarding localization and odometry. Finally, it presents our analysis of the findings, open research questions in the field, and a conclusion. A semisystematic approach is used to carry out the review.

Water pollution is a global issue that has an impact on the entire ecosystems’ life cycles. Traditional sampling and laboratory testing techniques are labor-intensive and error-prone, making them ineffective for quickly detecting changes in water quality. This paper presents the development of a low-cost, portable and efficient prototype sensor-based system for monitoring water quality in real-time. The system consists of a microcontroller, temperature, turbidity, pH, and distance sensors, and an application for a visual representation of the data. Extensive testing was carried out to ensure uninterrupted operation. The prototype is a user-friendly sensor system that can be positioned close to the target area in order to assist in preventing environmental and biological harm. This can ensure safe, healthy, and sustainable water supplies for the communities, environment, and the economy. Continuous monitoring of water parameters can also help avoid critical situations. The experimental results demonstrate a successful development of a smart water quality monitoring system with potential applications in various scenarios.

The use of artificial intelligence (AI) is increasing in our everyday applications. One emerging field within AI is image recognition. Research that has been devoted to predicting fires involves predicting its behaviour. That is, how the fire will spread based on environmental key factors such as moisture, weather condition, and human presence. The result of correctly predicting fire spread can help firefighters to minimise the damage, deciding on possible actions, as well as allocating personnel effectively in potentially fire prone areas to extinguish fires quickly. Using neural networks (NN) for active fire detection has proven to be exceptional in classifying smoke and being able to separate it from similar patterns such as clouds, ground, dust, and ocean. Recent advances in fire detection using NN has proved that aerial imagery including drones as well as satellites has provided great results in detecting and classifying fires. These systems are computationally heavy and require a tremendous amount of data. A NN model is inextricably linked to the dataset on which it is trained. The cornerstone of this study is based on the data dependencieds of these models. The model herein is trained on two separate datasets and tested on three dataset in total in order to investigate the data dependency. When validating the model on their own datasets the model reached an accuracy of 92% respectively 99%. In comparison to previous work where an accuracy of 94% was reached. During evaluation of separate datasets, the model performed around the 60% range in 5 out of 6 cases, with the outlier of 29% in one of the cases. 

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 (CCIBA*) 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.

In this paper, we developed a power line communication (PLC) system design, power measurement sensor design, light sensor design, temperature sensor design, and the integration of these components into an advanced sensor network to allow for energy metering and environment monitoring. A power measurement sensor was implemented through a current and voltage sensing circuitry was interfaced multi-plug power adapter to allow for non-invasive measurement of power usage of appliances. The sensors produce signals corresponding to the drawn voltage and current, which are then sampled and processed to estimate power usage. The PLC communications operated at an average accuracy of 95%. The power measurement sensor had an accuracy of 92%, which made it appropriate for home user estimations. The light sensor had an accuracy of between 91-97%, which was suitable for home lighting measurement.

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. 

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 a priori knowledge 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. 

Accent invariance in speech recognition is a chal- lenging problem especially in the are of aviation. In this paper a speech recognition system is developed to transcribe accented speech between pilots and air traffic controllers. The system allows handling of accents in continuous speech by modelling phonemes using Hidden Markov Models (HMMs) with Gaussian mixture model (GMM) probability density functions for each state. These phonemes are used to build word models of the NATO phonetic alphabet as well as the numerals 0 to 9 with transcriptions obtained from the Carnegie Mellon University (CMU) pronouncing dictionary. Mel-Frequency Cepstral Co-efficients (MFCC) with delta and delta-delta coefficients are used for the feature extraction process. Amplitude normalisation and covariance scaling is implemented to improve recognition accuracy. A word error rate (WER) of 2% for seen speakers and 22% for unseen speakers is obtained.

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.

With the increasing applications of magnetic robots in medical instruments, the research on different structures and locomotion approaches of magnetic robots has become a hotspot in recent years. A ferrofluid rolling robot (FRR) with magnetic actuation is proposed and enabled to realize a novel locomotion approach in this article. The drive performance of ferrofluid is elaborated, which is characterized by the magnetic torque of a rectangle-shaped object filled with ferrofluid under magnetic field. First, the proposed structure and locomotion mechanism of the FRR are detailed. Moreover, based on the established mathematical models of the FRR, the simulations with straight and turning locomotion are carried out, respectively. Finally, the FRR prototype is manufactured by 3-D printing, and experimental results demonstrate that the feasibility of straight and turning locomotion is verified. The locomotion performance of the FRR is in good agreement with the theoretical models where the root mean square (rms) value of displacement for experiments and simulations is 1.2 mm. In this work, the proposed FRR can automatically switch from straight to turning locomotion with a fast response in an external magnetic field, and does not has magnetism when without a magnetic field. 

A multi-mode 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 multi-mode shift. With a 3-degree (longitudinal, lateral, and yaw directions) of freedom coupled model, a hybrid vehicle platoon controller is proposed using non-singular terminal sliding mode control to ensure fast and steady tracking on the hybrid automaton outputs during the multi-mode shift process. 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 stability of the multi-mode 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 and also overlooked the difference of node’s power consumption, which may lead to 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 paper. Firstly, 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 send the energy supplement requests. In addition, the predicted value of power consumption of nodes are calculated out according to the real-time energy consumption rate, and thus the most appropriate recharging request threshold is obtained. Finally, a moving path optimization scheme based on 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 NFAOC and FCFS. Moreover, it also has advantage in balancing the energy consumption of WCVs.

In this paper, we investigate the problem of trajectory tracking control for marine surface vehicles (MSVs), which are subject to dynamic uncertainties, external disturbances and unmeasurable velocities. To recover the unmeasurable velocities, a novel adaptive neural network-based (NN-based) state observer is constructed. To guarantee the transient and steady-state tracking performance of the system, a novel nonlinear transformation method is proposed by employing a tracking error transformation together with a newly constructed performance function, which is characterized by a user-defined settling time and tracking control accuracy. With the aid of the state observer and the nonlinear transformation method in combination with the adaptive NN technique and vector-backstepping design tool, an adaptive neural output-feedback trajectory tracking control scheme with predefined performance is developed. With regard to the developed control scheme, uncertainties can be reconstructed only by utilizing the position and heading of the MSVs. Independent designs of the state observer and the controller can be achieved, and the position tracking error can be guaranteed to fall into a predefined residual set in the user-defined time frame and remain in the above set. A rigorous stability analysis validates that all signals in the closed-loop trajectory tracking control system for MSVs are uniformly ultimately bounded. Simulation results verify the effectiveness of the developed adaptive neural output-feedback trajectory tracking control scheme.

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.

Intelligent telehealth system (ITS) provides patients and medical institutions with a lot of convenience, medical institutions can achieve medical services for patients in time through monitored health data. However, as the scope of people?s daily activities extends, the traditional single-server architecture is no longer applicable. To deal with this problem, a multi-server architecture has been proposed recently while there remains security and privacy challenges, including handover authentication. In this paper, we investigate a blockchain-assisted handover authentication and key agreement scheme for ITS in a multi-server edge computing environment. Specifically, we first propose a novel handover authentication model of ITS with multi-server edge computing architecture. Second, the proposed handover authentication scheme allows the authenticated server to assist users subsequently authenticate with other server, thereby achieving interactions with the server anytime and anywhere with low overhead. Finally, blockchain technology and strong anonymity mechanism are introduced to protect users? privacy strictly. To our best knowledge, the proposed scheme is the first in the literature to provide efficient authentication, strict anonymity and computational load transfer simultaneously. The security analysis and performance evaluation show that our scheme can not only satisfy the security requirements but also achieve higher efficiency in computation and communication cost.

In order to pursue high-accuracy localization for intelligent vehicles (IVs) in semi-open scenarios, this study proposes a new map creation method based on multi-sensor fusion technique. In this new method, the road scenario fingerprint (RSF) was employed to fuse the visual features, three-dimensional (3D) data and trajectories in the multi-view and multi-sensor information fusion process. The visual features were collected in the front and downward views of the IVs; the 3D data were collected by the laser scanner and the downward camera and a homography method was proposed to reconstruct the monocular 3D data; the trajectories were computed from the 3D data in the downward view. Moreover, a new plane-corresponding calibration strategy was developed to ensure the fusion quality of sensory measurements of the camera and laser. In order to evaluate the proposed method, experimental tests were carried out in a 5 km semi-open ring route. A series of nodes were found to construct the RSF map. The experimental results demonstrate that the mean error of the nodes between the created and actual maps was 2.7 cm, the standard deviation of the nodes was 2.1 cm and the max error was 11.8 cm. The localization error of the IV was 10.8 cm. Hence, the proposed RSF map can be applied to semi-open scenarios in practice to provide a reliable basic for IV localization.

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 (GPS) 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 paper 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 10 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. Lastly, a series of experimental investigation were performed to evaluate the effectiveness of our 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.

Emotion recognition, as a challenging and active research area, has received considerable awareness in recent years. In this study, an attempt was made to extract complex network features from electroencephalogram (EEG) signals for emotion recognition. We proposed a novel method of constructing forward weighted horizontal visibility graphs (FWHVG) and backward weighted horizontal visibility graphs (BWHVG) based on angle measurement. The two types of complex networks were used to extract network features. Then, the two feature matrices were fused into a single feature matrix to classify EEG signals. The average emotion recognition accuracies based on complex network features of proposed method in the valence and arousal dimension were 97.53% and 97.75%. The proposed method achieved classification accuracies of 98.12% and 98.06% for valence and arousal when combined with time-domain features.

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.

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.

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.

Calibration is an essential prerequisite for the combined application of light detection and ranging (LiDAR) and inertial measurement unit (IMU). However, current LiDAR-IMU calibration usually relies on particular artificial targets or facilities and the intensive labor greatly limits the calibration flexibility. For these reasons, this article presents a novel multifeature based on-site calibration method for LiDAR-IMU system without any artificial targets or specific facilities. This new on-site calibration combines the point/sphere, line/cylinder, and plane features from LiDAR scanned data to reduce the labor intensity. The main contribution is that a new method is developed for LiDAR extrinsic parameters on-site calibration and this method could incorporate two or more calibration models to generate more accurate calibration results. First of all, the calibration of LiDAR extrinsic parameters is performed through estimating the geometric features and solving the multifeature geometric constrained optimization problem. Then, the relationships between LiDAR and IMU intrinsic calibration parameters are determined by the coordinate transformation. Lastly, the full information maximum likelihood estimation (FIMLE) method is applied to solve the optimization of the IMU intrinsic parameters calibration. A series of experiments are conducted to evaluate the proposed method. The analysis results demonstrate that the proposed on-site calibration method can improve the performance of the LiDAR-IMU.

In the era of Big Data and Mobile Internet, how to ensure the terminal devices (e.g., sensor nodes) work steadily for a long time is one of the key issues to improve the efficiency of the whole network. However, a lot of facts have shown that the unattended equipments are prone to failure due to energy exhaustion, physical damage and other reasons. This may result in the emergence of energy-hole, seriously affecting network performance and shortening its lifetime. To reduce data redundancy and avoid the generation of sensing blind areas, a type of Virtual Force based Energy-hole Mitigation strategy (VFEM) is proposed in this paper. Firstly, the virtual force (gravitation and repulsion) between nodes is introduced that makes nodes distribute as uniformly as possible. Secondly, in order to alleviate the "energy-hole problem", the network is divided into several annuluses with the same width. Then, another type of virtual force, named "virtual gravity generated by annulus", is proposed to further optimize the positions of nodes in each annulus. Finally, with the help of the "data forwarding area", the optimal paths for data uploading can be selected out, which effectively balances energy consumption of nodes. Experiment results show that, VFEM has a relatively good performance on postponing the generation time of energy-holes as well as prolonging the network lifetime compared with other typical energy-hole mitigation methods.

Fog computing is an architecture that is used to distribute resources such as computing, storage, and memory closer to end-user to improve applications and service deployment. The idea behind fog computing is to improve cloud computing and IoT infrastructures by reducing compute power, network bandwidth, and latency as well as storage requirements. This paper presents an overview of what fog computing is, related concepts, algorithms that are present to improve fog computing infrastructure as well as challenges that exist. This paper shows that there is a great advantage of using fog computing to support cloud and IoT systems.