Unmanned Aerial Vehicles (UAVs), as an aerial extension of the Internet of Things (IoT) sensing layer, have played an increasingly important role in applications such as environmental monitoring, disaster assessment, and precision agriculture. These tasks can be uniformly abstracted as Coverage Path Planning (CPP), which aims to achieve efficient scanning and surveying while ensuring complete coverage of single or multiple disconnected regions. While single-region CPP has been extensively studied, in multi-region settings existing methods often rely on predefined coverage patterns to guarantee completeness, which to some extent limits their flexibility. Meanwhile, constraints on UAV energy and onboard computation impose higher performance requirements on planning methods. To address these challenges, this paper targets an energy-constrained multi-UAV cooperative scenario and proposes a cross-layer, energy-constrained path optimization framework based on multi-agent reinforcement learning (CLMPO-EC). Specifically, the framework organizes the overall task into two layers—CPP and multi-agent path planning—and, on this basis, integrates Back-and-Forth Planning (BFP) with multi-agent reinforcement learning under a centralized training and distributed execution paradigm to construct a unified, interactive, and structured environmental model. CLMPO-EC further introduces a lightweight cross-layer connection network that propagates raw state information to higher layers to enhance learning efficiency. In addition, building on BFP, an entrance–exit exploration factor is proposed to dynamically adjust the exploration probability of regional entrances and exits in CPP according to the training phase and batch, thereby improving the efficiency of searching for optimal solutions. Theoretical analysis and experimental results demonstrate that the proposed method achieves superior performance in terms of optimality and efficiency.

With an increasing number of sensor devices connected to industrial systems, the efficient and reliable aggregation of sensor data has become a key topic in Industrial Internet of Things (IIoT). Computing First Networks (CFN) are emerging as a promising technology for aggregating vast quantities of IIoT data. However, existing CFN data collection frameworks are usually centralized, which overly rely on third-party trusted authorities and fail to fully schedule and utilize limited computing resources. More critically, that is prone to trust and security issues. In this paper, considering the heterogeneity and data security in complex industrial scenarios, we propose a blockchain-based and multi-edge CFN collaborative IIoT data hierarchical collection framework (ME-CIDC) to collect massive IIoT data securely and efficiently. In ME-CIDC, a blockchain-driven resource allocation algorithm is proposed for inter-domain CFN, which achieves distributed and efficient task scheduling and data collection by constructing multiple blockchains. A self-incentive mechanism is designed to encourage inter-domain nodes to contribute resources and support the operation of the inter-domain CFN. We also propose an efficient double-layered data aggregation algorithm, which distributes computational tasks across two layers to ensure the efficient collection and aggregation of IIoT data. Extensive simulation and numerical results demonstrate the effectiveness of our proposed scheme.

Generative Adversarial Networks(GANs) are powerful generative models on numerous tasks and datasets but are also known for their training instability and mode collapse. The latter is because the optimal transportation map is discontinuous, but DNNs can only approximate continuous ones. One way to solve the problem is to introduce multiple discriminators or generators. However, their impacts are limited because the cost function of each component is the same. That is, they are homogeneous. In contrast, multiple discriminators with different cost functions can yield various gradients for the generator, which indicates we can use them to search for more transportation maps in the latent space. Inspired by this, we have proposed a framework to combat the mode collapse problem, containing multiple discriminators with different cost functions, named CES-GAN. Unfortunately, it may also lead to the generator being hard to train because the performance between discriminators is unbalanced, according to the Cannikin Law. Thus, a gradient selecting mechanism is also proposed to pick up proper gradients. We provide mathematical statements to prove our assumptions and conduct extensive experiments to verify the performance. The results show that CES-GAN is lightweight and more effective for fighting against the mode collapse problem than similar works.

Mobile Edge Caching (MEC) can cache content at the edge of the network to reduce the delay and overhead of content transmission, which has become an effective method to solve the explosive growth of network traffic. To make good use of the limited resources in edge devices, many contents caching strategies use various methods to predict the popularity of content. However, caches get close to the edge of the network can lead to the rapid increase of caches' number and the user's requests are dispersed into a large number of caches, which leads to the popularity distribution of contents in edge caches is quite different and the number of first miss requests (the corresponding content is requested for the first time and is not in the cache) in edge caches becoming an essential factor affecting the cache hit rate. This paper first demonstrates the significant impact of the first miss requests through dataset analysis and establishes a mathematical model for the first miss problem in the edge cache. Then we analyze the similarity of requests received by caches and propose a proactive push algorithm based on similarity to improve the hit rate of edge caches. Through the trace-driven simulation experiment, we verify that the methods proposed in this paper can significantly improve the caches' hit rate.

Vehicular edge computing has been a promising paradigm to offer low-latency and high reliability vehicular services for users. Nevertheless, for compute-intensive vehicle applications, most previous researches cannot perform them efficiently due to both the inadequate of infrastructure construction and the computing resource bottleneck of the edge server. Motivated by the fact that there is a large number of outside parked vehicles with rich and underutilized resources in the urban area, we propose the idea of parking edge computing, which makes use of the parked vehicles to assist edge servers in offloaded task handling. Specifically, on-street and off-street parked vehicles are first organized into parking clusters to act as virtual edge servers, participating in offloaded tasks execution in our framework. Second, a novel task scheduling algorithm is designed to jointly decide edge server selection and resource assignment. Furthermore, a local task scheduling policy is proposed as well, which reasonably allocates parked vehicles to perform the tasks with the aim of further improving task offloading performance. Finally, a time-related trajectory prediction model based on the random forest model is built, which helps to send back output result accurately. Our framework not only requires no additional infrastructure investment but also provides adequate computing resources. Simulation results based on a real city map and realistic traffic situations demonstrate that our framework provides more efficient and stable offloading services, especially in a large number of task requests condition.

The energy problem is one of the most important challenges in the application of sensor networks. With the development of wireless charging technology and intelligent mobile charger (MC), the energy problem can be solved by the wireless charging strategy. In the practical application of wireless rechargeable sensor networks (WRSNs), the energy consumption rate of nodes is dynamically changed due to many uncertainties, such as the death and different transmission tasks of sensor nodes. However, existing works focus on on-demand schemes, which not fully consider real-time global charging scheduling. In this article, a novel dynamic charging scheme (DCS) in WRSN based on the actor-critic reinforcement learning (ACRL) algorithm is proposed. In the ACRL, we introduce gated recurrent units (GRUs) to capture the relationships of charging actions in time sequence. Using the actor network with one GRU layer, we can pick up an optimal or near-optimal sensor node from candidates as the next charging target more quickly and speed up the training of the model. Meanwhile, we take the tour length and the number of dead nodes as the reward signal. Actor and critic networks are updated by the error criterion function of R and V. Compared with current on-demand charging scheduling algorithms, extensive simulations show that the proposed ACRL algorithm surpasses heuristic algorithms, such as the Greedy, DP, nearest job next with preemption, and TSCA in the average lifetime and tour length, especially against the size and complexity increasing of WRSNs.

With the breakthrough of deep learning, lip reading technologies are under extraordinarily rapid progress. It is well-known that Chinese is the most widely spoken language in the world. Unlike alphabetic languages, it involves more than 1,000 pronunciations as Pinyin, and nearly 90,000 pictographic characters as Hanzi, which makes lip reading of Chinese very challenging. In this paper, we implement visual-only Chinese lip reading of unconstrained sentences in a two-step end-to-end architecture (LipCH-Net), in which two deep neural network models are employed to perform the recognition of Pictureto-Pinyin (mouth motion pictures to pronunciations) and the recognition of Pinyin-to-Hanzi (pronunciations to texts) respectively, before having a jointly optimization to improve the overall performance. In addition, two modules in the Pinyin-to-Hanzi model are pre-trained separately with large auxiliary data in advance of sequence-to-sequence training to make the best of long sequence matches for avoiding ambiguity. We collect 6-month daily news broadcasts from China Central Television (CCTV) website, and semi-automatically label them into a 20.95 GB dataset with 20,495 natural Chinese sentences. When trained on the CCTV dataset, the LipCH-Net model outperforms the performance of all stateof-the-art lip reading frameworks. According to the results, our scheme not only accelerates training and reduces overfitting, but also overcomes syntactic ambiguity of Chinese which provides a baseline for future relevant work.

Wireless sensor and actuator networks (WSAN) have such superiorities of real-time sense, response, and action on the environment, but WSAN's two kinds of key member sensor and actuator both suffer the serious energy constrained problem similar to that of wireless sensor networks (WSN). Currently, the breakthrough of wireless charging technology provides a new significant opportunity to solve the energy limited problem for WSN, and many fruitful works are emerging. However, the wireless energy supplement problem of WSAN has not been addressed yet. In this paper, we explore the wireless charging issue in WSAN, and propose a mobile energy replenishment framework which can well adapt to actuators' characters such as automatous mobility, long charging duration, and high dynamic energy consumption resulted by responding the abrupt events. Through extensive simulation, we validate the effectiveness of our proposed framework, and the results show that our solution can achieve efficient mobile energy replenishment for WSAN.

Cloud computing opens a new door for designing the next-generation video distribution platform. As video services move to the cloud, some related data-intensive applications, such as recommender system, automatic scoring mechanism, and prediction algorithm, should also be cloud-based. Since traditional cloud file systems like MapReduce/Hadoop exhibit cost disadvantages in data accessing, a Cache A Replica On Modification (CAROM) cloud file system is designed to achieve high data availability and low storage cost, which provides resiliency in cloud file systems with high efficiency. In this paper, we focus on moving data-intensive applications to the CAROM cloud file system, for optimizing access latencies while maintaining the benefit of low storage cost. To achieve this, we propose a novel scheduling mechanism as a lubricant between CAROM and data-intensive applications. Our scheme consists of three parts. First, tripartite graph is employed to describe the relationships among tasks, computation nodes, and data nodes. Second, we give a 1:1:1 framework based on the situation that the data of task have been stored in the cache, and introduce two variant frameworks 1:1:M and 1:N:M with the consideration of limitations of cache size and performance of task. Finally, a k-list algorithm is proposed as an approximation algorithm, and its mathematical definitions and proofs are given in detail. We conduct simulations to evaluate our scheme and the results prove that the performance of our algorithm is significantly better than that of the general two-layer algorithm.

The breakthrough progress of wireless charging technology provides a significant opportunity to solve the energy constrained problem in wireless sensor networks. However, most of existing mobile energy replenishment schemes either cannot well adapt to the high diversity of energy consumption or leave out of consideration about the fairness of charging response, and thus may still suffer from non-negligible performance degradation resulted from energy starvation. Particularly when there is a large number of charging requirements, the energy starvation may bring about quite a number of sensor nodes invalid due to energy depletion. In this paper, we explore the energy starvation issue while provisioning energy for wireless sensor networks and propose a Starvation Avoidance Mobile Energy Replenishment scheme (SAMER) which can avoid energy starvation through calculating and considering the maximum tolerable latency of each charging requirement. The simulation results show that SAMER scheme can effectively solve the energy starvation problem and achieve efficient mobile energy supplement for wireless sensor networks.

The development of the smartphones with various sensors, and powerful capabilities (computing, storage, and communication), motivates a popular computing and sensing paradigm, crowdsensing. In general, in crowdsensing, the smart-phones sense and collect the sensory data from a large number of smartphone users, for distinguishing the uncertain objects. However, some existing solutions for crowdsensing usually prefer to utilize only one or few features to distinguish the uncertain objects. In this paper, due to the limitation of less features, we propose to utilize multiple features to distinguish the uncertain objects for crowdsensing. For distinguishing uncertain objects with multiple features, we propose to utilize KL divergence based clustering. Moreover, we introduce two other mutated forms, the symmetry KL divergence and Jensen-Shannon KL divergence, to improve our algorithm. We evaluate our proposed schemes with real data of multiple features, which are collected by the smartphones with the sensors.

Uncertain data are inherent in mobile crowd sensing applications, and the objects that they correspond to are usually vaguely specified. In order to improve performance, we often increase the number of features. However, the more features are used, the more redundancy and cost are involved correspondingly. Therefore, the number of features we selected for a specified application is a tradeoffs between the accuracy and the cost. In this paper, we model such tradeoffs between accuracy and cost as an optimization problem. Moreover, for investigating this problem, we propose to model the sensing with multiple features under a hypercube structure. In our scheme, each feature of uncertain objects is represented as a component of the vertex's coordinate in hypercube. At the same time, we prefer to define the edges between vertices with relative entropy rather than Euclidean distance. Because the former one could accurately measures the difference between two probability distributions of data. We evaluate our proposed schemes with real data of a crowd sensing recognition case, which are collected by smartphones with sensors.

To facilitate the safe and comfortable driving, vehicular ad hoc networks (VANETs) will be flooded with plenty of multimedia files, such as images, music and video clips. However, due to the dynamic and transient contacts between moving vehicles, these multimedia files distribution over VANETs often involves transmission failure and terrible user experience. In this paper, we propose an efficient infrastructure-less multimedia publish/subscribe scheme for an urban area. In cities, there are lots of parked vehicles, presenting as parking clusters, owning the ability of calculation, storage and communication. Our scheme relies on these parking clusters to cache and distribute the multimedia files for moving users. For each subscription, the parking cluster distributes the file chunks to the subscriber during their contact time. For the remained content chunks, the parking cluster will distribute them to slave vehicles that have no downloading request. Then, the slave vehicles transfer the received file chunks to a parking cluster, where the subscriber can continue the unfinished downloading when it drives through. Theoretical results illustrate the effectiveness of our approach and extensive simulations results demonstrate that the proposed scheme achieves a higher downloading ratio with different file sizes, especially in sparse traffic and multiple subscribers conditions.

In Vehicular Ad Hoc Networks (VANETs), content distribution directly relies on the fleeting and dynamic contacts between moving vehicles, which often leads to prolonged downloading delay and terrible user experience. Deploying Wifi-based Access Points (APs) could relieve this problem, but it often requires a large amount of investment, especially at the city scale. In this paper, we propose the idea of ParkCast, which doesn't need investment, but leverages roadside parking to distribute contents in urban VANETs. With wireless device and rechargable battery, parked vehicles can communicate with any vehicles driving through them. Owing to the extensive parking in cities, available resources and contact opportunities for sharing are largely increased. To each road, parked vehicles at roadside are grouped into a line cluster as far as possible, which is locally coordinated for node selection and data transmission. Such a collaborative design paradigm exploits the sequential contacts between moving vehicles and parked ones, implements sequential file transfer, reduces unnecessary messages and collisions, and then expedites content distribution greatly. We investigate ParkCast through theoretic analysis and realistic survey and simulation. The results prove that our scheme achieve high performance in distribution of contents with different sizes, especially in sparse traffic conditions.

If an automobile can recognize whether a place is refuel station, parking lot or maintenance shop automatically, and it can tell other automobiles where the place is, the automobile will be able to fulfill its basic needs like refueling, parking and maintaining without any manual intervention. People will be freed from asking passengers or searching in the map of Geographic Information System (GIS) about whereto refuel, park or maintain. In this paper, we propose a novel vehicle self-gratification system using vehicular sensors and mobile phones to achieve the aforementioned assumption. Also, through statistics analysis, some new and useful information like popularity of a maintenance shop and probability of an available parking space at a certain time can be generated and guide people to make better choices. Experiments in real driving environment show that our system can accurately recognize the locations of fuel stations, parking lots and maintenance shops, and the information of popularity and probability make people's choices not random but good.

In Vehicular Ad Hoc Networks (VANETs), the major communication challenge lies in very poor connectivity, which can be caused by sparse or unbalanced traffic. Deploying supporting infrastructure could relieve this problem, but it often requires a large amount of investment and elaborate design, especially at the city scale. In this paper, we propose the idea of Parked Vehicle Assistance (PVA), which allows parked vehicles to join VANETs as static nodes. With wireless device and rechargable battery, parked vehicles can easily communicate with one another and their moving counterparts. Owing to the extensive parking in cities, parked vehicles are natural roadside nodes characterized by large number, long-time staying, wide distribution, and specific location. So parked vehicles can serve as static backbone and service infrastructure to improve connectivity. We investigate network connectivity in PVA through theoretic analysis and realistic survey and simulations. The results prove that even a small proportion of PVA vehicles could overcome sparse or unbalanced traffic, and promote network connectivity greatly. Thus, PVA enhances VANETs from down to top, and paves the way for new hybrid networks with static and mobile nodes.

Information interaction is a crucial part of modern transportation activities. In this paper, we propose the idea of Vehicle-to-Passenger communication (V2P), which allows direct, instant, and flexible communication between moving vehicles and roadside passengers. With pocket wireless devices, passengers can easily join VANETs as roadside nodes, and express their travel demands, e.g., taking a free ride or calling a taxi via radio queries over VANETs. Once a matched vehicle is found through the disseminated queries, the driver can decide whether to provide corresponding services, especially the carrying of passengers and goods. We investigate the main challenges in vehicle calling, establish a trip history model to predict vehicle movement, and develop typical query dissemination schemes to match the target vehicle in vehicular networks. With V2P over VANETs, vehicle transportation is capable of open and efficient P2P information interaction, and thus benefits from relevant efficiency improvement. Based on a realistic travel survey and simulation, we prove that vehicle calling is effective and efficient in casual carpooling and taxi calling.

Marking in DiffServ at the edge of the network often follows a demand side policy. It meters a traffic stream and marks its packets according to some predefined traffic parameters as Committed Information Rate, Committed Burst Size, Excess Burst Size and so on. Such marking based on traffic characteristics is irrespective to network dynamics, which causes collision and QoS degradation in DiffServ. This paper proposes flow-based Reservation Marking as a supply side marking at the network edge, which marks stream packets reserved or unreserved according to flow-specific reservation in a distributed resource reservation environment. When congestion occurs, anticipant per flow QoS is secured by protecting reserved packets on core routers without any per flow or per trunk management. It provides a simple, scalable and adaptive mechanism of implementing quantitative end-to-end QoS by mapping per flow in IntServ into per class in DiffServ. A "once reserve, no more manage" framework is constructed to eliminate flow state, avoiding unexpected collision and flow management simultaneously on core routers. Performance evaluation reveals that it affords controllable and quantitative QoS, keeps networks core-stateless and achieves high link utilization at the same time.