In the coming years, massive amounts of data are likely to be traded in vehicle-to-grid (V2G) networks to enhance traffic efficiency and safety through vehicular communications. However, several challenges need to be addressed before realizing the full potential of V2G networks. These challenges include the privacy-preservation of users, secure caching, scalability in deep environments, unreliability in high mobility events, and low efficiency in large networks. To overcome these challenges in V2G networks, named data networking (NDN) offers a good solution. It provides a new future Internet architecture: “named content-based” rather than “host addresses.” The main focus of NDN in V2G networks is to provide data availability, network performance, data retrieval, and data distribution. However, the presence of NDN in V2G networks introduces several issues like privacy and trust among vehicular nodes. Hence, this article proposes a system model based on blockchain technology in NDN-enabled V2G networks. This model provides secure and fast named content searching and enhances the trust among vehicular nodes. The simulation results show that the block propagation latency of NDN-based blockchain is less than the IP-based blockchain systems. In addition, the performance of the proposed scheme outperforms an existing scheme. The reason is to use the proof-of-authority consensus mechanism compared to proof of work, which uses high mining computation power. Furthermore, the proposed scheme increases the trust and transparency in NDN-enabled V2G networks.

The Tactile Internet of Things (TIoT) is transforming the landscape of real-time applications by enabling haptic interactions and immersive experiences . This paper explores the potential of TIoT applications in critical sectors such as healthcare and manufacturing, emphasizing the necessity of ultra-reliable, low-latency communication. Conventional network infrastructures fall short of meeting these demands, necessitating innovative solutions such as Network Slicing (NS) to customize the network according to user activities. One of the key challenges addressed in this research is the allocation of resources for tactile data, which requires specialized solutions to prevent performance degradation in shared environments. Additionally, the paper proposes a solution that includes in-content awareness, enabling precise resource allocation based on the user’s intent and requirements. Dynamic resource scaling, proactive resource allocation, and optimized bandwidth usage are essential components of the proposed architecture, guaranteeing responsive and efficient user experiences . Furthermore, the research introduces an end-to-end network slicing (NS) solution, emphasizing the importance of considering all components of the TIoT chain to prevent bottlenecks. Machine learning plays a crucial role in translating TIoT service profiles into specific requirements that are in line with the evolving needs of TIoT. To overcome the limitations of deep learning (DL), federated learning (FL) emerges as a groundbreaking approach, enabling collaborative model training without compromising data privacy. The paper explores the potential of FL and addresses its limitations within a centralized framework. It advocates for a novel architecture that integrates blockchain technology , Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Multi-Access Edge Computing (MEC) to enhance FL in TIoT applications. The study investigates the performance of lightweight deep learning methods used as local models in federated learning for TIoT applications. The research also analyzes various FL algorithms from different perspectives, considering various local models contributing to the global model. Additionally, the study evaluates how the selected FL algorithms and DL local models collaborate, providing valuable insights into the performance and efficiency of the proposed architecture. These advancements aim to revolutionize the applications of TIoT and usher in a new era of intelligent, context-aware, and efficient communication in 6G networks.

With the maturity of 5G and Intelligent Transportation Systems (ITS) technologies and the prospect of Beyond 5G (B5G) and 6G technologies, the limited lifetime and computing of mobile devices pose significant challenges to Quality of Service (QoS). In addition, the problem of inefficient use of computing, storage, communication, and other resources still exists in communication systems. In response to the above issues, Multi-tier Computing Networks (MTCNs) migrate computationally intensive tasks to the cloud, fog, or edge with sufficient resources, thereby realizing energy-efficient collaborative computing and multi-dimensional resource sharing. However, in the MTCN environment with complex heterogeneity, and high-intensity dynamics, how to provide sustainable solutions for resource scheduling strategies is a meaningful issue. Inspired by Virtual Network Embedding (VNE) to decouple physical network configuration, we propose a multi-target-aware dynamic resource scheduling algorithm for MTCN to improve resource flexibility, which is the first attempt in this direction. Specifically, we consider differentiated QoS requirements like computing, storage, bandwidth, delay, etc., and establish multi-target-aware embedded constraints. Additionally, we present a Deep Reinforcement Learning (DRL)-based scheduling network that can interact scientifically and efficiently with the MTCN environment. It extracts environmental information as state input to better focus on dynamic characteristics as well as calculates candidate nodes and links using a three-layer network architecture and related constraints. Furthermore, the learning process is optimized through the combination of the reward mechanism and the gradient descent mechanism. Finally, comparison experiments on three widely used evaluation indicators (long-term average revenue, long-term average revenue-cost ratio, and VNR acceptance rate) verify that the proposed algorithm has made an average improvement of$19.042\%$,$2.563\%$, and$3.932\%$respectively compared with all baselines.

In the collaborative intelligent transportation system, providing precise positioning services is costly. Reducing resource consumption and improving revenue are crucial to the development of positioning services. Therefore, a practical algorithm that combines cloud and edge network environments is necessary to improve the positioning services. Integrating network function virtualization and edge computing can provide users with more flexible and efficient services. Based on the above issues, we use the service function chain (SFC) to improve the positioning services provided in cloud-edge-vehicle collaborative networks (CEVCN). We propose a deep reinforcement learning-assisted SFC embedding algorithm and improve its performance through training. We construct a five-layer policy network to sense the environment of CEVCN and derive the optimal node selection strategy. Finally, we use the breadth-first search algorithm to solve the embedding scheme for virtual links. The simulation results show that our proposed algorithm has excellent performance. The long-term average revenue is improved by 21%, the long-term average revenue-cost ratio is improved by 13%, and the embedding rate is improved by 8%.

The rapid development of Artificial Intelligence (AI) has had a significant impact on various industries, including healthcare. The Internet of Medical Things (IoMT) has played a vital role in this evolution. However, while AI has contributed to many benefits in healthcare, concerns about data privacy and security persist. To address these concerns, we propose a framework that combines Federated Learning (FL) and Differential Privacy (DP) to enhance data protection within IoMT. By integrating FL’s decentralized approach with DP’s mechanism to prevent data reconstruction from model outputs, we can improve data confidentiality. This integrated approach is used to develop and analyze high-performing Convolutional Neural Networks (CNNs) for detecting Tuberculosis using chest X-ray datasets. The framework undergo thorough performance evaluation, utilizing various metrics to establish its superiority over baseline models. The results demonstrate the effectiveness of our framework as a robust solution for secure and private AI applications in healthcare.

Vehicle-to-grid (V2G) technology is used in the modern eco-friendly environment for demand response management. It helps in reducing the carbon footprints in the environment. However, security and privacy of the information exchange between different entities are significant concerns keeping in view of the information exchange via an open channel, i.e., Internet among different entities such as plug-in hybrid electric vehicles (PHEVs), charging stations (CSs), and controllers in V2G environment. With an exponential rise in Electric vehicles (EVs) usage across the globe, there is a requirement of developing a seamless charging infrastructure for charging and billing. Moreover, secure information flow needs to be maintained at different levels in such an environment. Hence, this paper proposes a blockchain-based demand response management for efficient energy trading between EVs and CSs. In this proposal, miner nodes and block verifiers are selected using their power consumption and processing power. These nodes are responsible for the authentication of various transactions in the proposal. We also proposed a game theory-based solution to support energy management and peak load control off-peak and peak conditions. The proposed scheme has been evaluated using various performance evaluation metrics where its performance is found superior in comparison to the existing solutions in the literature.

To realize the Interconnection of Everything (IoE) in the 6G vision, the space-based, air-based, and ground-based networks have shown a trend of integration. Compared with the traditional communications system, Space-Air-Ground Integrated Networks (SAGINs) can provide a seamless global network connection, while making full use of different network characteristics for synergy and complementarity. However, the increasing global coverage of the Internet, the growing number and variety of smart terminals, and the emergence of various high-bandwidth services have led to an explosion in communication data transmission. Despite the continuous development of communication technologies such as airborne processing and forwarding and high-throughput satellites, the quality of service (QoS) and quality of experience (QoE) for different users still cannot be guaranteed due to the power limitations of satellites and the scarcity of spectrum resources. In this work, drawing on wireless edge caching, considering that the relay of SAGIN has edge caching capability, the hot task is cached in the network nodes in advance. More, this process is optimized using distributed Deep Reinforcement Learning (DRL), thereby reducing transmission delay and relieving the pressure of task offloading on space-based networks. Compared with advanced related works, the long-term node utilization, link utilization, long-term average revenue-to-cost ratio and acceptance ratio of the proposed algorithm are increased by about 4.22%, 31.36%, 11.75% and 7.14%, respectively.

The pandemic/epidemic of COVID-19 has affected people worldwide. A huge number of lives succumbed to death due to the sudden outbreak of this corona virus infection. The specified symptoms of COVID-19 detection are very common like normal flu; asymptomatic version of COVID-19 has become a critical issue. Therefore, as a precautionary measurement, the oxygen level needs to be monitored by every individual if no other critical condition is found. It is not the only parameter for COVID-19 detection but, as per the suggestions by different medical organizations such as the World Health Organization, it is better to use oximeter to monitor the oxygen level in probable patients as a precaution. People are using the oximeters personally; however, not having any clue or guidance regarding the measurements obtained. Therefore, in this article, we have shown a framework of oxygen level monitoring and severity calculation and probabilistic decision of being a COVID-19 patient. This framework is also able to maintain the privacy of patient information and uses probabilistic classification to measure the severity. Results are measured based on latency of blockchain creation and overall response, throughput, detection, and severity accuracy. The analysis finds the solution efficient and significant in the Internet of Things framework for the present health hazard in our world.

Unmanned aerial vehicles (UAVs) with communication, computing, and storage capabilities have high mobility. Based on this advantage, it can push the service closer to the user. Our research group is concerned with implementing the Internet of Things (IoT) enabled massive crowd management platform that employs 5G to facilitate network connectivity among the UAV and sensory networks. In such a highly dynamic environment, IoT devices, users, and UAVs are the key factors to determine the caching strategies. Due to the limitations of drone batteries and changes in UAV cluster density, the environment is characterized as highly dynamic. However, the existing UAV caching strategy does not consider both the changes of the users and UAVs. Therefore, this paper proposes a three-layer UAV cache architecture in 5G network to achieve hierarchical adaptation to the dynamic changes of users and UAVs. Based on this architecture, we propose a dual dynamic adaptive caching(DDAC) algorithm. The DDAC algorithm is divided into two parts: user adaptation and UAV adaptation. For user adaptation, we designed a user-adaptive UAV trajectory model, which ensures the transmission efficiency of the UAV. For UAV adaptation, we designed and deployed a UAV-adaptive cache model based on a greedy algorithm in the cognitive center layer. The UAV can dynamically adjust the caching strategy according to the cluster density. Finally, the results of the experiment prove that our proposed UAV adaptive cache model has better performance in the cache hit ratio compared with the existing UAV cache model.

With the advantage of being close to the network, edge cloud-enabled computing mode brings flexibility to task scheduling. However, with the heterogeneity of computing resources between cloud and edge cloud, and the complexity of computing and communication processes between multi-edge cloud, challenges have been brought to the deployment and computing of tasks in cloud-edge collaborative environments. In order to solve this challenge, firstly a deep reinforcement learning controller based cloud-edge collaborative computing framework has been proposed. Then a system QoS model has been estab-lished considering both the user benefits and the service provider benefits. By using deep Q-network, a deep reinforcement learning based collaborative task placement algorithm has been proposed for dynamically optimizing the target system utility. Finally, the experimental results show that the proposed method has a good learning ability for the computing cost of cloud and edge cloud as well as the communication cost between multi-edge cloud. In addition, compared with Q-table learning, random computing and cloud computing, a 10% improvement of system utility has been achieved with the proposed method.

This article proposes a novel ultralarge-scale crowd monitoring system, namely, the ULCM system. The ULCM system enables advanced sensing and networking technologies aimed at collecting and processing multimodal, multiperspective, and real-time crowding data relevant to crowd management. This data will be further analyzed to provide a global realization of evolving events over a large geographical area as they occur in real time. The ULCM is the infrastructure component of an intelligent platform that is being developed by our research group to provide crowd intelligence to decision makers through an interactive digitized visual environment. In order to achieve a full comprehensive scene overview, the ULCM deployment utilizes a multiplicity of unmanned aerial vehicle (UAV) agents in different operational scenarios. The aerial deployment and control are realized by custom multiple UAV networks and airborne LiDAR sensors. The deployment and control on the ground sensory agents are based on multiple subnetworks, including closed-circuit television (CCTV) and infrared gas and ultrasonic sensors networks. Eventually, ULCM employs the software-defined network (SDN) and edge cloud technologies to optimize the networking and data analytics performance from the perspective of infrastructure.

Effective screening of COVID-19 cases has been becoming extremely important to mitigate and stop the quick spread of the disease during the current period of COVID-19 pandemic worldwide. In this article, we consider radiology examination of using chest X-ray images, which is among the effective screening approaches for COVID-19 case detection. Given deep learning is an effective tool and framework for image analysis, there have been lots of studies for COVID-19 case detection by training deep learning models with X-ray images. Although some of them report good prediction results, their proposed deep learning models might suffer from overfitting, high variance, and generalization errors caused by noise and a limited number of datasets. Considering ensemble learning can overcome the shortcomings of deep learning by making predictions with multiple models instead of a single model, we proposeEDL-COVID, an ensemble deep learning model employing deep learning and ensemble learning. The EDL-COVID model is generated by combining multiple snapshot models of COVID-Net, which has pioneered in an open-sourced COVID-19 case detection method with deep neural network processed chest X-ray images, by employing a proposed weighted averaging ensembling method that is aware of different sensitivities of deep learning models on different classes types. Experimental results show that EDL-COVID offers promising results for COVID-19 case detection with an accuracy of 95%, better than COVID-Net of 93.3%.

With the rapid development of wireless communication and flight control technologies, the unmanned aerial vehicles (UAVs) have been widely used in multiple application scenarios. A typical scenario is massive crowd management of the multi-millions annual Hajj Pilgrimage to Mecca where UAVs are widely utilized to conduct crowd monitoring by carrying sensory devices. The safe flight of a UAV is crucial for ensuring the successful execution of missions. With the aim to overcome the disadvantage caused by the ground station intrusion detection, the combination of UAV and airborne LiDAR has been widely studied in the field of UAV obstacle recognition. This article studies the UAV network architecture under a common scenario and proposes an obstacle recognition and intrusion detection algorithm for UAV based on an airborne LiDAR (ALORID). First, the preprocessing of the data obtained by a LiDAR, i.e., the coordinate conversion of LiDAR data in combination with UAV motion parameters, is completed. Then, the LiDAR data graph at the current moment is generated by the image noisy point filtering algorithm. After that, the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm is used for image clustering of intrusions to obtain the LiDAR time-domain cumulative graph in a certain detection time. Finally, the motion recognition and location detection of each cluster are completed. The experiment results verify the effectiveness of the proposed algorithm in identifying the moving state of the intrusions.

With the development of software-defined network (SDN), there will be a large number of devices to access network, which may cause an incalculable burden to the communication network. In addition, due to the high bandwidth in the fifth-generation (5G) era, innovation will occur in different fields. There are not only strict requirements on the communication capability of SDN for these application scenarios but also a lot of computing resources. For massive access devices, it is difficult for the traditional service resource scheduling and the allocation system to meet user demand growth. To address the above-stated problems, an artificial intelligence agent (AI Agent) system is put forth in this article. AI Agents can be deployed in different layers of the SDN, thus realizing functions like network service prediction and resource scheduling. A brand new AI Agent framework is designed, and an AI algorithm is adopted to replace the traditional service prediction and resource scheduling strategies. In the meantime, a relevant agent deployment scheme is put forward. Finally, an AI Agent-based simulation experiment for resource scheduling is designed, and the accuracy in network service prediction and rationality in resource allocation based on this framework are tested. The experimental result showed that the operation efficiency of the SDN can be effectively improved, and the resource hit ratio and user service quality may be improved with AI-agent-based traffic prediction and resource allocation model.

Along with the continuing evolution of the Internet and its applications, Content Delivery Networks (CDNs) have become a hot topic with both opportunities and challenges. CDNs were mainly proposed to solve content availability and download time issues by delivering content through edge cache servers deployed around the world. In our previous work, we presented a novel CDN architecture based on a Fog computing environment as a promising solution for real-time applications. In such architecture, we proposed to use a name-based routing protocol following the Information Centric Networking (ICN) approach, with a popularity-based caching strategy to guarantee overall delivery performance. To validate our design principle, we have implemented the proposed Fog-based CDN architecture with its major protocol components and evaluated its performance, as shown through this article. On the one hand, we have extended the Optimized Link-State Routing (OLSR) protocol to be content aware (CA-OLSR), i.e., so that it uses content names as routing labels. Then, we have integrated CA-OLSR with the popularity-based caching strategy, which caches only the most popular content (MPC). On the other hand, we have considered two similar architectures for conducting performance comparative studies. The first is pure Fog-based CDN implemented by the original OLSR (IP-based routing) protocol along with the default caching strategy. The second is a classical cloud-based CDN implemented by the original OLSR. Through extensive simulation experiments, we have shown that our Fog-based CDN architecture outperforms the other compared architectures. CA-OLSR achieves the highest packet delivery ratio (PDR) and the lowest delay for all simulated numbers of connected users. Furthermore, the MPC caching strategy shows higher cache hit rates with fewer numbers of caching operations compared to the existing default caching strategy, which caches all the pass-by content.

Performances evaluation, reproducibility and benchmarking represent crucial aspects for assessing the practical impact of research results in the computer science eld. In spite of all the benets (e.g., increasing impact, increasing visibility, improving the research quality) that can be gained from performing extensive experimental evaluation or providing reproducible software artifacts and detailed description of experimental setup, the required eort for achiev

With the rising demands on cloud services, the electricity consumption has been increasing drastically as the main operational expenditure (OPEX) to data center providers. The geographical heterogeneity of electricity prices motivates us to study the task placement problem over geo-distributed data centers. We exploit the dynamic frequency scaling technique and formulate an optimization problem that minimizes OPEX while guaranteeing the quality-of-service, i.e, the expected response time of tasks. Furthermore, an optimal solution is discovered for this formulated problem. The experimental results show that our proposal achieves much higher cost-efficiency than the traditional resizing scheme, i.e, by activating/deactivating certain servers in data centers.

Given a set of events and a set of robots, the dispatch problem is to allocate one robot for each event to visit it. In a single round, each robot may be allowed to visit only one event (matching dispatch), or several events in a sequence (sequence dispatch). In a distributed setting, each event is discovered by a sensor and reported to a robot. Here, we present novel algorithms aimed at overcoming the shortcomings of several existing solutions. We propose pairwise distance based matching algorithm (PDM) to eliminate long edges by pairwise exchanges between matching pairs. Our sequence dispatch algorithm (SQD) iteratively finds the closest event-robot pair, includes the event in dispatch schedule of the selected robot and updates its position accordingly. When event-robot distances are multiplied by robot resistance (inverse of the remaining energy), the corresponding energy-balanced variants are obtained. We also present generalizations which handle multiple visits and timing constraints. Our localized algorithm MAD is based on information mesh infrastructure and local auctions within the robot network for obtaining the optimal dispatch schedule for each robot. The simulations conducted confirm the advantages of our algorithms over other existing solutions in terms of average robot-event distance and lifetime.

The ever-growing cellular traffic demand has laid a heavy burden on cellular networks. The recent rapid development in vehicle-to-vehicle communication techniques makes vehicular delay-tolerant network (VDTN) an attractive candidate for traffic offloading from cellular networks. In this paper, we study a bulk traffic offloading problem with the goal of minimizing the cellular communication cost under the constraint that all the subscribers receive their desired whole content before it expires. It needs to determine the initial offloading points and the dissemination scheme for offloaded traffic in a VDTN. By novelly describing the content delivery process via a contact-based flow model, we formulate the problem in a linear programming (LP) form, based on which an online offloading scheme is proposed to deal with the network dynamics (e.g., vehicle arrival/departure). Furthermore, an offline LP-based analysis is derived to obtain the optimal solution. The high efficiency of our online algorithm is extensively validated by simulation results.

Vehicular Cyber-Physical System (VCPS) provides CPS services via exploring the sensing, computing and communication capabilities on vehicles. VCPS is deeply influenced by the performance of the underlying vehicular network with intermittent connections, which make existing routing solutions hardly to be applied directly. Epidemic routing, especially the one using random linear network coding, has been studied and proved as an efficient way in the consideration of delivery performance. Much pioneering work has tried to figure out how epidemic routing using network coding (ERNC) performs in VCPS, either by simulation or by analysis. However, none of them has been able to expose the potential of ERNC accurately. In this paper, we present a stochastic analytical framework to study the performance of ERNC in VCPS with intermittent connections. By novelly modeling ERNC in VCPS using a token-bucket model, our framework can provide a much more accurate results than any existing work on the unicast delivery performance analysis of ERNC in VCPS. The correctness of our analytical results has also been confirmed by our extensive simulations.

Malware is pervasive in networks, and poses a critical threat to network security. However, we have very limited understanding of malware behavior in networks to date. In this paper, we investigate how malware propagates in networks from a global perspective. We formulate the problem, and establish a rigorous two layer epidemic model for malware propagation from network to network. Based on the proposed model, our analysis indicates that the distribution of a given malware follows exponential distribution, power law distribution with a short exponential tail, and power law distribution at its early, late and final stages, respectively. Extensive experiments have been performed through two real-world global scale malware data sets, and the results confirm our theoretical findings.

One of the main advantages of the cloud computing paradigm is that it simplifies the time-consuming processes of hardware provisioning, hardware procurement and software deployment. Currently, we are witnessing a proliferation in the number of cloud-hosted applications. However, one of the important challenges of the cloud computing paradigm that may hurt the growth of this technology is the interoperability and portability between cloud platforms. The developers and cloud users may lock-in to the first cloud they choose, or face difficulties when they have to move their data or software from one cloud platform to another. In this paper, we focus on the challenge of data portability between different cloud-based data storage services. In particular, we propose a common data model and a standardized API for the new generation of cloud-based NoSQL databases. The initial implementation of our framework covers three of the most popular NoSQL systems, namely, Google Datastore, Amazon SimpleDB and MongoDB. However, our framework is designed in a flexible way that it can be easily extended to support other NoSQL systems. Furthermore, our framework is equipped with tools that support the conversion, transformation and exchange of the data which is stored on the supported NoSQL databases of the framework. Finally, we describe the design and the proof-of-concept implementation of our framework using a case study.