UAV-to-Satellite geolocalization aims to estimate the location of an aerial-view query image taken by an Unmanned Aerial Vehicle (UAV) by matching it to satellite images annotated with known locations. However, it is difficult for existing methods to distinguish neighboring satellite images that exhibit a high degree of visual similarity. To address this issue, we introduce a plug-and-play adaptive confusion-aware contrastive loss (ACCL) to explicitly enhance the model’s discriminative ability, which gives more tolerance to high confusion query samples by means of elaborating a confusion metric function. As a plug-and-play loss module, ACCL can be easily incorporated into various UAV-to-Satellite geolocalization methods without additional modifications. To demonstrate the effectiveness of our proposed method, we conduct extensive experiments on one publicly available geolocalization dataset (i.e. NewYorkFly) and to further prove the effectiveness of our method in different scenarios, we collect two new geolocalization datasets (LasVegasFly and HollywoodFly), which contain drone-captured aerial images and dense sampled satellite images in various geomorphic regions. Experimental results indicate that our method can achieve an obvious performance improvement over the state-of-the-art methods on all three datasets. Our code and collected datasets are available at https://github.com/NWPU-CPS/ACCL.

Modular methods, which decompose complex joint policies into function-specific sub-policies, have been widely adopted to enhance asymptotic performance in single-task cooperative multi-agent reinforcement learning (MARL). However, modular policies trained on source tasks often struggle to generalize to unseen scenarios due to variations across tasks, such as mismatched action spaces and divergent state dynamics. To address this challenge, we propose Mutual Information-Guided Subtask Selection(MIGSS), a novel framework that enhances zero-shot generalization in MARL through two key innovations: a Discriminative Group Trajectory Encoder and Global Attention-Driven Coordination. Specifically, the Discriminative Group Trajectory Encoder remaps agent trajectories by maximizing mutual information between agent trajectories and dynamically assigned groups. This optimizes cross-task consistent group trajectory with broader embedding distributions. This encourages agents in distinct states to select specialized subtasks, effectively promoting functional modularity. Meanwhile, the Global Attention-Driven Coordination employs a global attention mechanism to integrate state information, coordinating group trajectories for expressive credit assignment. Extensive experiments in StarCraft II cooperative scenarios demonstrate that MIGSS significantly outperforms superior zero-shot generalization baselines in both single-task and multi-task settings.Visualization analyses confirm that the learned group trajectories successfully disperse agent trajectories into a consistent and broader embedding space, thereby enhancing subtask modularization.

Unmanned Aerial Vehicles (UAVs) are emerging as a pivotal component in the field of intelligent transportation systems. Leveraging virtual-physical interactions, digital twin technology significantly enhances the adaptability of UAVs in complex traffic environments. However, current approaches still pose three major challenges: contextual adaptability, timely responsiveness, and effective multi-UAV coordination. In this paper, we introduce EnFlexiTwin, a digital twin enhancement assistance platform seamlessly integrated with AdaSor, a lightweight adaptive data selector. EnFlexiTwin automates the construction of incremental learning datasets, enabling real-time enhancements that allow digital twins to adapt to new time series data while preserving historical knowledge. We test EnFlexiTwin on a real-world dataset from low-altitude small-parcel delivery. The results show improved performance and adaptability of digital twins. Furthermore, time-varying simulations on real-world dataset and experiments on a practical air-ground cooperative UAV swarm application highlight that EnFlexiTwin achieves superior enhancements under varying real-time requirements and swarm scale compared to baseline approaches.

The integration of deep neural network (DNN) intelligence into embedded mobile devices is expanding rapidly, supporting a wide range of applications. DNN compression techniques, which adapt models to resource-constrained mobile environments, often force a trade-off between efficiency and accuracy. Distributed DNN inference, leveraging multiple mobile devices, emerges as a promising alternative to enhance inference efficiency without compromising accuracy. However, effectively decoupling DNN models into fine-grained components for optimal parallel acceleration presents significant challenges. Current partitioning methods, including layer-level and operator or channel-level partitioning, provide only partial solutions and struggle with the heterogeneous nature of DNN compilation frameworks, complicating direct model offloading. In response, we introduce AdaKnife, an adaptive framework for accelerated inference across heterogeneous mobile devices. AdaKnife enables on-demand mixed-granularity DNN partitioning via computational graph analysis, facilitates efficient cross-framework model transitions with operator optimization for offloading, and improves the feasibility of parallel partitioning using a greedy operator parallelism algorithm. Our empirical studies show that AdaKnife achieves a 66.5% reduction in latency compared to baselines.

Multiagent routing problems, arising from practical applications, such as logistics, transportation, and emergency response, face challenges due to the exponential growth of the search space with increasing problem scales. This article proposes RouteMaker to address the often-overlooked multiagent routing problems involving dedicated multiple depots. RouteMaker leverages role-interaction-based graph neural network (RIGNN) to realize effective locations assignments and integrates an advanced planner to plan travel path for each agent. RouteMaker is trained on small-scale problems and can produce comparable or superior approximate optimal solutions compared with the best heuristic baselines. Notably, the learned RouteMaker generalizes seamlessly to large-scale problems and real-world problems without the need for fine-tuning, delivering significantly higher quality solutions in relatively less time. For scenarios involving 40 agents and 1000 locations, RouteMaker achieves over $600\times $ speed improvement and more than 88% cost reduction, compared with the representative classical heuristic solver (ORTools).

A neural processing unit (NPU) is a microprocessor which is specially designed for various types of neural network applications. Because of its high acceleration efficiency and lower power consumption, the airborne embedded system has widely deployed NPU to replace GPU as the new accelerator. Unfortunately, the inherent scheduler of NPU does not consider real-time scheduling. Therefore, it cannot meet real-time requirements of airborne embedded systems. At present, there is less research on the multi-task real-time scheduling of the NPU device. In this article, we first design an NPU resource management framework based on Kubernetes. Then, we propose WAMSPRES, a workload-aware NPU performance model based soft preemptive real-time scheduling method. The proposed workload-aware NPU performance model can accurately predict the remaining execution time of the task when it runs with other tasks concurrently. The soft preemptive real-time scheduling algorithm can provide approximate preemption capability by dynamically adjusting the NPU computing resources of tasks. Finally, we implement a prototype NPU scheduler of the airborne embedded system for the fixed-wing UAV. The proposed models and algorithms are validated on both the simulated and realistic task sets. Experimental results illustrate that WAMSPRES can achieve low prediction error and high scheduling success rate.

In autonomous robotic systems, the parallel processing of multiple tasks often competes for limited resources, affecting system performance and the robot’s responsiveness to environmental changes. Traditional computational task scheduling methods often overlook the dynamic nature of task priorities in autonomous robotic systems, where task importance can shift based on interactions with the external environment. Therefore, there’s a crucial need for a mechanism capable of adaptively adjusting task scheduling in response to environmental changes, ensuring timely access to resources for critical tasks. To address this challenge, this study presents Priorest, a neural network model that incorporates multimodal data processing and multitask learning. Priorest integrates sensor data with logs monitoring computational device performance to predict events influencing task priority, enabling task adjustments while preserving essential resource allocations. When deployed in autonomous robotic systems, Priorest’s event-prediction-based adjustment strategy reduced critical task completion times by 18.7%, which demonstrates the effectiveness of Priorest in enhancing parallel task scheduling.

Multiedge cooperative computing that combines constrained resources of multiple edges into a powerful resource pool has the potential to deliver great benefits, such as a tremendous computing power, improved response time, and more diversified services. However, the mass heterogeneous resources composition and lack of scheduling strategies make the modeling and cooperating of multiedge computing system particularly complicated. This article first proposes a system-level state evaluation model to shield the complex hardware configurations and redefine the different service capabilities at heterogeneous edges. Second, an integer linear programming model is designed to cater for optimally dispatching the distributed arriving requests. Finally, a learning-based lightweight real-time scheduler, CoRaiS is proposed. CoRaiS embeds the real-time states of the multiedge system and requests information, and combines the embeddings with a policy network to schedule the requests, so that the response time of all requests can be minimized. Evaluation results verify that the CoRaiS can make a high-quality scheduling decision in real-time, and can be generalized to other multiedge computing system, regardless of the system scales. Characteristic validation also demonstrates that the CoRaiS successfully learns to balance loads, perceive real-time state and recognize heterogeneity while scheduling.

The fiercely competitive business environment and increasingly personalized customization needs are driving the digital transformation and upgrading of the manufacturing industry. IIoT intelligence, which can provide innovative and efficient solutions for various aspects of the manufacturing value chain, illuminates the path of transformation for the manufacturing industry. It’s time to provide a systematic vision of IIoT intelligence. However, existing surveys often focus on specific areas of IIoT intelligence, leading researchers and readers to have biases in their understanding of IIoT intelligence, that is, believing that research in one direction is the most important for the development of IIoT intelligence, while ignoring contributions from other directions. Therefore, this paper provides a comprehensive overview of IIoT intelligence. We first conduct an in-depth analysis of the inevitability of manufacturing transformation and study the successful experiences from the practices of Chinese enterprises. Then we give our definition of IIoT intelligence and demonstrate the value of IIoT intelligence for industries in fucntions, operations, deployments, and application. Afterwards, we propose a hierarchical development architecture for IIoT intelligence, which consists of five layers. The practical values of technical upgrades at each layer are illustrated by a close look on lighthouse factories. Following that, we identify seven kinds of technologies that accelerate the transformation of manufacturing, and clarify their contributions. The ethical implications and environmental impacts of adopting IIoT intelligence in manufacturing are analyzed as well. Finally, we explore the open challenges and development trends from four aspects to inspire future researches.

Communication plays a vital role in multi-agent systems, fostering collaboration and coordination. However, in real-world scenarios where communication is bandwidth-limited, existing multi-agent reinforcement learning (MARL) algorithms often provide agents with a binary choice: either transmitting a fixed amount of data or no information at all. This rigid communication strategy hinders the ability to effectively utilize bandwidth. To overcome this challenge, we present the Dynamic Size Message Scheduling (DSMS) method, which introduces finer-grained communication scheduling by considering the actual size of the information being exchanged. Our approach lies in adapting message sizes using Fourier transform-based compression techniques with clipping, enabling agents to tailor their messages to match the allocated bandwidth according to importance weights. This method realizes a balance between information loss and bandwidth utilization. Receiving agents reliably decompress the messages using the inverse Fourier transform. We evaluate DSMS in cooperative tasks where the agent has partial observability. Experimental results demonstrate that DSMS significantly improves performance by optimizing the utilization of bandwidth and effectively balancing information importance.

Visual object tracking is a very important task for unmanned aerial vehicle (UAV). Limited resources of UAV lead to strong demand for efficient and robust trackers. In recent years, deep learning-based trackers, especially, siamese trackers achieve very impressive results. Though siamese trackers can run a relatively fast speed on the high-end GPU, they are becoming heavier and heavier which restricts them to be deployed on UAV platform. In this work, we propose a lightweight aerial tracker based on the siamese network. We use EfficientNet as the backbone, which has less parameters and stronger feature extract ability compared with ResNet-50. After a pixel-wise correlation, a classification branch and a regression branch are applied to predict the front/back score and offset of the target without the predefined anchor. The results show that our tracker works efficiently and achieves impressive performance on UAV tracking datasets. In addition, the real-world test shows that it runs effectively on the Nvidia Jetson NX deployed on DJI UAV.

The Ubiquitous Operating System(UOS) is a new type of operating system in response to the new patterns and scenarios of future human-cyber-physical ternary ubiquitous computing. Compared with traditional operating systems, one of the fundamental requirements of UOS is to adaptively manage numerous heterogeneous sensors according to dynamic environments and diverse tasks. However, traditional management focuses on the sensors’ parameters and interfaces without highlighting the perception effect that is users’ concern and dynamic changing. It also lacks a unified management approach for heterogeneous sensors. To overcome the limitations, we propose a novel heterogeneous sensors dynamic management model UbiCap, i.e., Ubiquitous Capability, which is based on the capability abstraction and adaptive run-time capability management mechanism. The capability provides a unified abstract for heterogeneous sensors. The adaptive run-time capability management mechanism transfers the management object from low-level hardware sensors to high-level sensing capability. The capability required and the available capability are matched to support run-time adaptive sensors selection. We implement a software prototype iS2ROS(intelligent Sensor Selection Robot Operating System) based on the UbiCap model. We then simulate a forest fire spot monitoring scenario where iS2ROS selects the optimal image sensor during the identification task execution while light or weather condition changes. Experiment results show that the iS2ROS achieves comparative sensing effectiveness through UbiCap with 50% power consumption lower compared to the traditional both-sensors approach.

This work aims to explore the effect of Resource Allocation and Control (RAC) of the Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) communication equipment. The communication congestion problem is caused by insufficient computing resources of macro base stations when many user terminals access the IoT system. To this end, this work innovatively constructs the IoT system combined with UAVs to satisfy the user’s communication experience and allocate channel resources simultaneously. Finally, a UAV-assisted IoT resource allocation and control model is constructed and tested through experiments on the simulation software to analyze its performance. The results indicate that the transmission delay of the model reported here is significantly lower than that of other algorithms with the increased number of end users and the proportion of tasks. When the number of users is 500, the transmission delay of data resources is 47.5s. Besides, it requires lower energy consumption and shorter task completion time while maintaining lower throughput. At the same time, the uplink transmission rate of the model is generally stable at 3.10bps; the uplink transmission rate of the entire wireless communication system shows an increasing trend with the increase of the transmit power. Therefore, it is found that the model constructed here has strong robustness and achieves an excellent data resource allocation effect while ensuring lower data delay. This model provides an experimental reference and contribution to the improvement of the performance of IoT systems and the accurate allocation of network resources.

To further improve the capacity of airborne embedded system for dealing with deep learning (DL) applications and reduce overall power consumption, it is necessary to equip Neural Processing Units (NPUs). Comparing with the cloud system, the airborne embedded system usually has a fixed application set, but strict real-time constraints. Unfortunately, the inherent NPU scheduler does not consider the application priority, which cannot provide the sufficient real-time capability for the airborne embedded system. At present, there are few researches on multi-task real-time scheduling for NPUs. Therefore, we propose WMDRS, a workload-aware performance model multi-task dynamic-quota real-time scheduling for Neural Processing Units. The NPU performance model based on workload-awareness can accurately predict the remaining execution time of a task, which is running concurrently with other tasks on NPU. The multi-task dynamic-quota real-time scheduling algorithm can provide the approximate preemption by dynamically adjusting NPU computing resources for active applications. In addition, we implement a prototype NPU scheduler without any hardware extension. Furthermore, the proposed NPU performance model and real-time scheduling algorithm are evaluated in realistic application sets. Experimental results demonstrate that WMDRS can achieve low prediction error and high scheduling success ratio.

With the proliferation of increasingly powerful mobile devices and wireless networks, mobile crowdsourcing has emerged as a novel service paradigm. It enables crowd workers to take over outsourced location-dependent tasks, and has attracted much attention from both research communities and industries. In this paper, we consider a mobile crowdsourcing scenario, where a mobile crowdsourcing task is too complex (e.g., post-earthquake recovery, citywide package delivery) but can be divided into a number of easier subtasks, which have interdependency between them. Under this scenario, we investigate an important problem, namelytask graph scheduling in mobile crowdsourcing(TGS-MC), which seeks to optimize a compact scheduling, such that the task completion time (i.e., makespan) and overall idle time are simultaneously minimized with the consideration of worker reliability. We analyze the complexity and NP-complete of the TGS-MC problem, and propose two heuristic approaches, including BFS-based dynamic priority schedulingBFSPriDalgorithm, and an evolutionary multitasking-basedEMTTSchalgorithm, to solve our problem from local and global optimization perspective, respectively. We conduct extensive evaluation using two real-world data sets, and demonstrate superiority of our proposed approaches.

New generation airborne embedded system has deployed Graphical Processing Units (GPUs) to raise processing capability to meet growing computational demands. Comparing with the cloud system, the airborne embedded system usually has a fixed application set, but strict real-time constraints. Unfortunately, the inherent GPU scheduler does not consider the application priority, which cannot provide the sufficient real-time capability to the airborne embedded system. To meet timeliness requirements, it is necessary to predict timing behaviors of those applications and design a real-time scheduling policy based on priority and deadline. We therefore propose WAMP2S, a workload-aware GPU performance model based pseudo-preemptive real-time scheduling algorithm for the airborne embedded system. The workload-aware GPU performance model can accurately predict the execution time of an application, which is running concurrently with other applications on GPU. The pseudo-preemptive real-time scheduling algorithm can provide the approximate preemption by dynamically adjusting GPU computing resources for active applications. Unlike previous work on GPU performance model and GPU real-time scheduling, WAMP2S considers the impact of co-executing workload on the execution time estimation and provides a software-only approach for preemption support. In addition, WAMP2S implements a prototype GPU scheduler without any source code analysis. We evaluate the proposed GPU performance model and real-time scheduling algorithm in both simulated and realistic application sets. Experimental results illustrate that WAMP2S can achieve low prediction error and high scheduling success ratio.

Intelligent unmanned systems (IUSs) are distributed systems composed of multiple agents that share information or cooperate to accomplish specific complex tasks. Agents of the IUS are capable of perception, cognition, control, decision-making, and action. In some cases, the environmental situation and task objectives faced by the IUSs are constantly changing with time. Thus, IUSs are time-sensitive systems. To accelerate the task execution time and response speed, IUSs use artificial intelligence technology to increase the speed and quality of the `observation-orientation-decision-action' (OODA) cycle of task execution. IUSs will tend to decompose the system into different functional units in the future, and individuals take different task roles from the functional perspective of OODA. The system is evolving from a linear OODA cycle of individuals to a cooperative OODA (Co-OODA) with different node roles. At present, the reinforcement learning (RL) algorithm is the mainstream method to solve IUSs cooperation problems. However, it does not adapt to the Co-OODA with different roles; and cannot maximize the Co-OODA system's potential. This paper introduces the role-based Co-OODA system. Furthermore, we propose and design a role-based deep reinforcement learning framework and its corresponding information sharing mechanism.

New generation airborne embedded system has deployed Graphical Processing Units (GPUs) to raise processing capability to meet growing computational demands. Applications in the airborne embedded system have strict real-time constraints. Therefore, it is necessary to accurately predict timing behaviors of those applications. Many previous work propose GPU performance models to estimate the execution time of applications. However, most of those models do not consider the impact of co-execution on the GPU performance. In this paper, we propose a workload-aware GPU performance model to predict the execution time of applications executed concurrently on a single GPU. Experimental results illustrate that the proposed model can achieve a 5.1%-11.6% prediction error in a real airborne embedded hardware platform.

Vehicularad hocnetworks (VANETs) have far-reaching application potentials in the intelligent transportation system (ITS) such as traffic management, accident avoidance and in-car infotainment. However, security has always been a challenge to VANETs, which may cause severe harm to the ITS. Sybil attack is considered as a serious security threat to VANETs since the adversary can disseminate false messages with multiple forged identities to attack various applications in the ITS. RSSI-based Sybil nodes detection is an efficient scheme against Sybil attacks, which adopts position estimation, distribution verification or similarity comparison to identify Sybil nodes. However, when Sybil nodes conduct power control to deliberately change transmission powers, the received RSSI values would change correspondingly, which leads to inaccurate localization or different RSSI time series of these Sybil nodes. Thus, it is very difficult to differentiate Sybil nodes from normal nodes via conventional RSSI-based methods. This paper first discusses potential power control models (PCMs) for launching Sybil attacks in VANETs, then presents two simple Sybil attack models and three sophisticated Sybil attack ones with or without power control in detail, finally proposes a power control identification Sybil attack detection (PCISAD) scheme to find anomalous variations in RSSI time series, which are then used to identify Sybil nodes via a linear SVM classifier. Extensive simulations and real-world experiments prove that the proposed scheme can effectively deal with Sybil attacks with power control.

Information about urban safety, e.g., the safety index of a position, is of great importance to protect humans and support safe walking route planning. Despite some research on urban safety analysis, the accuracy and granularity of safety index inference are both very limited. The problem of analyzing urban safety to predict safety index throughout a city has not been sufficiently studied and remains open. In this paper, we propose U-Safety, an urban safety analysis system to infer safety index by leveraging multiple cross-domain urban location-based data. We first extract spatially-related and temporally-related features from various urban location-based data, including urban map, housing rent and density, population, positions of police stations, point of interests (POIs), crime event records, and taxi GPS trajectories. Then, these features are fed into a novel sparse auto-encoder (SAE) framework with feature correlation constraint to obtain the final discriminative feature representation. Finally, we design a new co-training-based learning method, which consists of two separated classifiers, to calculate safety index accurately. We implement U-Safety and conduct extensive experiments by utilizing various real data sources obtained in New York City. The evaluation results demonstrate the advantages of U-Safety over other methods.

Vehicular Ad Hoc Networks (VANETs) bring many benefits and conveniences to road safety and drive comfort in future transportation systems. However, VANETs suffer from almost all security issues as same as wireless networks. Sybil attack is one of the most risky threats since it violates the fundamental assumption of VANETs-based applications that all received information are correct and trusted. Sybil attacker can generate multiple fake identities to disseminate false messages. In this paper, we propose a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs. Unlike most of previous RSSI-based methods that compute the absolute position or relative distance according to RSSI values, or make statistic testing based on RSSI distributions, Voiceprint adopts RSSI time series as the vehicular speech and compares the similarity among all received series. Voiceprint does not rely on any predefined radio propagation model, and conducts independent detection without support of the centralized node. Moreover, we improve Voiceprint by allowing it to conduct detection on Service Channel (SCH) to shorten observation time. Furthermore, we extend Voiceprint with change-points detection to identify those illegitimate nodes performing power control. Extensive simulations and real-world experiments demonstrate that Voiceprint is an effective method considering the cost, complexity, and performance.

Network-based malware has posed serious threats to the security of host machines. When malware adopts a private TCP/IP stack for communications, personal and network firewalls may fail to identify the malicious traffic. Current firewall policies do not have a convenient update mechanism, which makes the malicious traffic detection difficult.

Vehicular Ad Hoc Networks (VANETs) enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications that bring many benefits and conveniences to improve the road safety and drive comfort in future transportation systems. Sybil attack is considered one of the most risky threats in VANETs since a Sybil attacker can generate multiple fake identities with false messages to severely impair the normal functions of safety-related applications. In this paper, we propose a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs. To avoid the inaccurate position estimation according to predefined radio propagation models in previous RSSI-based detection methods, Voiceprint adopts the RSSI time series as the vehicular speech and compares the similarity among all received time series. Voiceprint does not rely on any predefined radio propagation model, and conducts independent detection without the support of the centralized infrastructure. It has more accurate detection rate in different dynamic environments. Extensive simulations and real-world experiments demonstrate that the proposed Voiceprint is an effective method considering the cost, complexity and performance.

Information about urban safety, e.g., the safety index of a position, is of great importance to protect humans and support safe walking route planning. Despite some research on urban safety analysis, the accuracy and granularity of safety index inference are both very limited. The problem of analyzing urban safety to predict safety index throughout a city has not been sufficiently studied and remains open. In this paper, we propose U-Safety, an urban safety analysis system to infer safety index by leveraging multiple cross-domain urban data. We first extract spatially-related and temporally-related features from various urban data, including urban map, housing rent and density, population, positions of police stations, point of interests (POIs), crime event records, and taxi GPS trajectories. Then, these features are feeded into a sparse auto-encoder (SAE) model to obtain the final discriminative feature representation. Finally, we design a new co-training-based learning method, which consists of two separated classifiers, to calculate safety index accurately. We implement U-Safety and conduct extensive experiments based on real data sources obtained in New York City. The evaluation results demonstrate the advantages of U-Safety over other methods.

Route planning is an important service in the map navigation. However, most of commercial map applications provide an optimal path that only minimize a single metric such as distance, time or other costs, while ignoring a critical criterion: safety. When citizens or travellers walk in a city, they may prefer to find a safe walking route to avoid the potential crime risk and to have a short distance, which can be formulated as a multi-objective optimization problem. Many previous methods are proposed to solve the multi-objective route planning, however, most of them are not efficient or optimized in a large-scale road network. In this paper, we propose a reinforcement learning based Multi-Objective Hyper-Heuristic (MOHH) approach to route planning in a smart city. We conduct experiments on the safety index map constructed based on the historical urban data of the New York city. Comprehensive experimental results show that the proposed approach is almost 34 and 1.4 times faster than the exact multi-objective optimization algorithm and the NSGA-II algorithm respectively. Moreover, it can obtain more than 80% Pareto optimal solutions in a large-scale road network.

As driverless vehicles are increasingly becoming possible, so does the use of such vehicles as intelligent carriers in different domains. Intelligent transportation systems (ITSs) show increasingly heterogeneous, cyber-physical, cooperative, and service-oriented features and are beginning to be merged with the emerging cyber-physical-social systems. Given this new trend, how to make these intelligent vehicles cooperate more safely and efficiently with one another according to novel constraints, such as mission type and quality-of-service (QoS), has become a vital aspect of cooperative ITS (C-ITS). With these emerging characteristics, the classical passing-through-intersection problem has gained new connotations, worth further exploring. After analyzing the essences of this new problem, service-oriented cooperation models and mechanisms for whole autonomous vehicles approaching intersections are investigated in this paper. First, related traffic objects and possible vehicular behaviors are abstracted and modeled with the cyber-physical cooperative features and QoS constraints. A new reservation-based scheduling procedure is then conducted by employing the concepts of vehicle-to-infrastructure communication, and typical vehicular passing-through behaviors and several spatial-temporal constraints are designed to coordinate vehicles passing through an intersection divided as a series of continuous static critical sections. Given these considerations, a priority-based centralized scheduling algorithm, named csPriorFIFO, which adopts a novel priority inheritance mechanism to promote the traffic QoS of emergent vehicles, is proposed. Finally, all these designs are implemented in a traffic simulator named QoS-CITS, and the functions and the performance of these studied methods are verified and compared.

Dedicated Short Range Communication (DSRC) protocol is a key enabling technology for enhancing road safety and transportation efficiency. Wireless Access in Vehicular Environments (WAVE) 1609.4 is a new amendment that enables multi-channel operations in DSRC. Operating intervals are divided into alternating Control Channel (CCH) Intervals and Service Channel (SCH) Intervals with an identical length. This alternating feature causes high packet losses in CCH and low throughput in SCH, and thus hinders the deployment of this protocol. The goal of our work is to provision sufficient reliability for safety messages in CCH while optimising non-safety service delivery in SCH. We develop analytical models to explore the relationship among traffic density, CCH packet loss ratio, SCH throughput, and the duration of each kind of intervals. We also design a multi-channel coordination algorithm which adaptively adjusts the duration of intervals to achieve better performance and reliability based on these models. Theoretical analysis and extensive simulation results demonstrate the accuracy of our model and the efficacy of the proposed algorithm.

As a key enabling technology for the next generation inter-vehicle safety communications, The IEEE 802.11p protocol is currently attracting much attention. Many inter-vehicle safety communications have stringent real-time requirements on broadcast messages to ensure drivers have enough reaction time toward emergencies. Most existing studies only focus on the average delay performance of IEEE 802.11p, which only contains very limited information of the real capacity for inter-vehicle communication. In this paper, we propose an analytical model, showing the performance of broadcast under IEEE 802.11p in terms of the mean, deviation and probability distribution of the MAC access delay. Comparison with the NS-2 simulations validates the accuracy of the proposed analytical model. In addition, we show that the exponential distribution is a good approximation to the MAC access delay distribution. Numerical analysis indicates that the QoS support in IEEE 802.11p can provide relatively good performance guarantee for higher priority messages while fails to meet the real-time requirements of the lower priority messages.

Now, autonomous tasks planning and allocating (TPA) in Multi Agent System (MAS) has been one key and fundamental problem to promote the intelligent level of such system. Autonomous TPA means that, all tasks should be (re)planned and (re)allocated automatically according to the synthesis constraints and the dynamic environment aspects, such as the changing mission, status of each member, and topology, etc. In this article, the formal descriptions of hierarchical tasks and models of logic constraints are studied firstly. And then, some new methods are proposed to evaluate the efficiency of synthesis constraints. Moreover, the key elements, e.g. task allocation vector (TAV), are designed with the theory of genetic algorithm (GA), and a TPA problem can be mapped to the solving model of GA. Based on above, the crossover and mutation operators of GA are optimized with the domain knowledge to perfect the solving efficiency and quality while ensuring the randomicity of evolution. The simulation results show that the solving quality and velocity are improved with studied methods.