Broadband Communications, Networks, and Systems

Elastic scaling in/out of operator parallelism degree is needed for processing real time dynamic data streams under low latency and high stability requirements. Usually the operator parallelism degree is set when a streaming application is submitted to a stream computing system and kept intact during runtime. This may substantially affect the performance of the system due to the fluctuation of input streams and availability of system resources. To address the problems brought by the static parallelism setting, we propose and implement a machine learning based elastic strategy for operator parallelism (named Me-Stream) in big data stream computing systems. The architecture of Me-Stream and its key models are introduced, including parallel bottleneck identification, parameter plan generation, parameter migration and conversion, and instances scheduling. Metrics of execution latency and process latency of the proposed scheduling strategy are evaluated on the widely used big data stream computing system Apache Storm. The experimental results demonstrate the efficiency and effectiveness of the proposed strategy.

Improving the transmission efficiency for small files over a wide area network is always challenging. Time may be wasted when waiting for transmission commands due to the design of transfer protocols, which in turn increases the Round-trip time (RTT). GridFTP is widely deployed as a transfer protocol in the grid era, where a concept of pipelining is proposed to improve the transmission efficiency for small files. Based on the GridFTP protocol, we design a smart data structure to classify files and propose a corresponding scheduling algorithm to tune the pipelining parameters, making them more reasonable and adaptive to different transmission scenarios. Bandwidth usage is optimized when a large number of small files are transferred with our strategy by combining the optimal pipelining and concurrency parameters. A method to optimizing the throughput for high-priority file transfer is also proposed. By adjusting the pipelining parameter dynamically, the throughput is increased by almost 10% compared with other methods. Moreover, our method achieves better performance even with a smaller concurrency setting. The favorable throughput is maintained when transferring high-priority files.

Docker containers’ privacy and data protection is a critical issue. Unfortunately, existing works overlook runtime scanning methods. This paper proposes a novel lightweight and rapid scanning model under a framework covering assertion techniques during the container’s runtime, defined as vulnerability scanning framework VSF. Our framework includes identifying vulnerability, scanning security exposures, conduct analysis, and call-back notifications to the requestor asynchronously. In addition, the proposed scanning model is compared against other tools of similar and complementary objectives. The framework is modeled using nmap scripting engine NSE for its active scanning building block. It applies network port scanning and security assertion techniques to rapidly discover security vulnerabilities in a running Docker container environment for a proactive testing approach as a security engine. Also, providing an active trust model developed for Docker containers whether containers are black-listed or grey-listed. It was developed over a framework for DevSecOps environments and DevOps teams as the persona on its adoption. The empirical case studies demonstrate the capability of our scanning model, including standalone, CI/CD pipelines, and security containerized environment. The case studies revealed no tangible difference in the performance but the flexibility driven by the modeled architecture. The experiments presented a velocity of \( 1.15 \frac{scans}{sec}\). However, the execution time is directly proportional to the complexity of the vulnerability on the Docker ecosystem and its related attack vector complexity. Its core capability resides on the artifacts developed as part of the Art per relevant CVE via nmap NSE scripts.

Digital Twin (DT) impacts significantly to both industries and research. It has emerged as a promising technology enabling us to add value to our lives and society. DT enables us to virtualize any physical systems and observe real-time dynamics of their status, processes, and functions by using the data obtained from the physical counterpart. This paper attempts to explore a new direction to enhance cyber resilience in the perspective of cybersecurity and Digital Twins. We enumerate definitions of the Digital Twin concept to introduce readers to this disruptive concept. We then explore the existing literature to develop a holistic analysis of the DT’s integration into cybersecurity. Our research questions develop a novel roadmap for a promising direction of research, which is worth exploring in the future and is validated by an extensive and systematic survey of recent works. Our research has aimed to properly illustrate the current research state in this area and can benefit both community and industry to further the integration of Digital Twins into Cybersecurity.

Permissioned blockchain such as Hyperledger fabric enables a secure supply chain model in Industrial Internet of Things (IIoT) through multichannel and private data collection mechanisms. However, the existing data sharing and querying mechanism in Hyperledger fabric is not suitable for supply chain environment in IIoT because the queries are evaluated on actual data stored on ledger which consists of sensitive information such as business secrets, and special discounts offered to retailers and individuals. To solve this problem, we propose a differential privacy-based permissioned blockchain using Hyperledger fabric to enable private data sharing in supply chain in IIoT (DH-IIoT). We integrate differential privacy into the chaindcode (smart contract) of Hyperledger fabric to achieve privacy preservation. As a result, the query response consists of perturbed data which protects the sensitive information in the ledger. We evaluate and compare our differential privacy integrated chaincode of Hyperledger fabric with the default chaincode setting of Hyperledger fabric for supply chain scenario. The results confirm that the proposed work maintains 96.15% of accuracy in the shared data while guarantees the protection of sensitive ledger’s data.

User matching is one of the most essential features that allows users to identify other people by comparing the attributes of their profiles and finding similarities. While this facility enables the exploration of friends in the same network, it poses serious security concerns over the privacy of the users as the prevalence of modern cloud computing services, companies outsource computational power to untrusted cloud service providers and confidential data of the users can be exposed as the data storage is transparent in the remote host server. Encryption can hide the user data, but it is difficult to compare the encrypted profiles. While solutions utilising the homomorphic encryption can overcome such limitations, they incur significant performance overhead, which is impractical for large networks. To overcome these problems, we propose an efficient privacy-preserving user matching protocol with Intel SGX. Other techniques such as oblivious data structure and searchable encryption are deployed to resolve security issues that Intel SGX has suffered. Our construction relies on secure hardware which guarantees the integrity and confidentiality of the code execution, which enables the computation of similarities between the profiles of the users. Moreover, our protocol is designed to provide protection against several types of side-channel attacks. The security analysis and experimental results presented in this paper indicate that our protocol is efficient, secure, practical and prevents side-channel attacks.

Educational timetabling is a fundamental problem impacting schools and universities’ effective operation in many aspects. Different priorities for constraints in different educational institutions result in the scarcity of universal approaches to the problems. Recently, COVID-19 crisis causes the transformation of traditional classroom teaching protocols, which challenge traditional educational timetabling. Especially for examination timetabling problems, as the major hard constraints change, such as unlimited room capacity, non-invigilator and diverse exam durations, the problem circumstance varies. Based on a scenario of a local university, this research proposes a conceptual model of the online examination timetabling problem and presents a conflict table for constraint handling. A modified Artificial Bee Colony algorithm is applied to the proposed model. The proposed approach is simulated with a real case containing 16,246 exam items covering 9,366 students and 209 courses. The experimental results indicate that the proposed approach can satisfy every hard constraint and minimise the soft constraint violation. Compared to the traditional constraint programming method, the proposed approach is more effective and can provide more balanced solutions for the online examination timetabling problems.

Reducing latency has become the focus of task scheduling research in distributed big data stream computing systems. Currently, most task schedulers in big data stream computing systems mainly focus on tasks assignment and implicitly ignore task topology which can have significant impact on the latency and energy efficiency. This paper proposes a topology-aware scheduling strategy to reduce the processing latency of stream processing systems. We construct the data stream graph as a directed acyclic graph and then, divide it using the graph Laplace algorithm. On the divided graph, tasks will be assigned with a low-latency scheduling strategy. We also provide a computing node selection strategy, which enables the system to run tasks on the topology with the least number of computing nodes. Based on this scheduling strategy, the tasks of the data stream graph can be redistributed and the scheduling mechanism can be optimized to minimize the system latency. The experimental results demonstrate the efficiency and effectiveness of the proposed strategy.

In a distributed stream processing system, elastic resource provisioning/scheduling is the main factor that affects system performance and limits system applications. However, in the data stream computing platform, resource allocation is often suboptimal due to the large fluctuations of the data stream rate, which creates a performance bottleneck for the cluster. In this paper, we propose a data stream prediction strategy (Dp-Stream) for elastic computing system to mitigate the resource allocation issue. First, we establish a back propagation (BP) neural network prediction model based on genetic simulated annealing algorithm to predict the trend of the data stream rate in the next time window of the cluster; second, according to the time latency, the estimation model adjusts the resources allocated to the critical operations of the critical path in the Directed Acyclic Graph (DAG) and finally, the resource communication cost is optimized. We evaluate the prediction accuracy and system latency of the proposed scheduling strategy in Storm. The experimental results prove the feasibility and effectiveness of the proposed strategy.

The prevalence of both documented incidents and anecdotal evidence perpetuate mistrust in video collected via Law Enforcement body worn recording devices. This paper examines the application of blockchain technology for the management of high volumes of video produced every day during the course of a police field officers’ duties. We apply a comprehensive blockchain system developed specifically for law enforcement video collection to the body worn scenario and examine the protection level offered whilst considering the specific requirements and limitations of this mobile platform. Specific scenarios are examined and shown to offer a compelling level of assurance to mobile body worn video collection operations.

The drivers’ anger caused by the influence of external environment leads to excessive aggressive driving behavior which brings great potential danger to traffic safety. This paper proposes a method using face recognition technology to design an emotional intelligence model of road rage with a high accuracy rate. Firstly, making a homemade emotion data set of road rage according to the definition of road rage and labeling the information of road rage in the data set. Secondly, using a sliding window combined with emotional intelligence scale to determine road rage emotion of drivers, so as to regulate driving behavior. Finally, the correctness and effectiveness of road anger emotional intelligence model were verified by the experimental scenes. It is of great practical significance to reduce the impact of road rage on road safety. Demos URL: https://​b23.​tv/​CnMw6M.

Drip irrigation, a type of micro-irrigation system, has been applied in agriculture, forestry, and urban greening. In order to cut down the labor cost and improve agricultural efficiency, modern technology, such as communication methods, or computer science, has been used in drip irrigation for irrigating a wide area. The Internet of Things (IoT) used computing, intelligent mobiles, and mobile app to perform remote monitoring and control tasks. The 5G network is a new generation technology standard that is helpful to massive expand today’s IoT technology. This paper proposes a frame structure for a drip irrigation remote control system (DIRCS) using 5G-IoT technology and mobile app. The system can be operated by people who are anywhere in the world using a mobile device. We utilize 5G-IoT technology to realize data storage and sharing in the platform. Moreover, we design layered software architecture to the presented IoT platform as an alternative technique to manage all the systems. Therefore, the drip irrigation system can be controlled remotely to overcome the previous problems like distance problem, range problem. The prototype demonstrates the effectiveness and efficiency of the design in the result.

The multipath transmission becomes the recognized alternative for traditional Quality of Service architectures. Recently, the multipath version of TCP protocol and its modern replacement – QUIC – has been proposed. The paper presents the dynamic properties of the data transfer between physical systems, engaging the multipath version of QUIC protocol (MPQUIC) which inherits the properties of its predecessors. The advantages and weaknesses of the transmission are emphasized and compared to the singlepath QUIC. While QUIC is designed to convey HTTP traffic, in the paper, general-purpose networking is investigated. Based on the measurements, the use recommendations are given together with the directions of improvements.

To reconstruct the target-induced attenuation image keeping consistent with the observed measurement data, this paper explores the use of a new horizontal distance attenuation-based elliptical weighting model in building an attenuation image, where a horizontal distance attenuation factor and a vertical distance attenuation factor are introduced, respectively, which is able to clear the difference of the voxel weightings perpendicular to the line-of-sight (LOS) direction, as well as the difference of the voxel weightings parallel to the LOS direction. Compared with the existing model, the proposed model can additively reflect the occlusion effect of the radio frequency signal when the target is close to the transceiver nodes. Besides, the Tikhonov-ℓ_p-norm regularization is incorporated into the image reconstruction, which makes full use of the sparse ability of the ℓ_p-norm (0 < p < 1) to further reduce the noise interference. The experimental studies on indoor and outdoor scenarios with radio tomographic imaging are presented to validate the effectiveness of the proposed approach.

With the development of photovoltaic technology, the number of building integrated photovoltaic (BIPV) systems is increasing. Differing from the traditional design of BIPV systems based on the experience of experts, which suffers from high cost and non-maximum efficiency of equipment due to the information lack of buildings, this paper proposes a novel calculation approach based on building information modeling (BIM) technology. Taking a BIPV building located in Hainan, China as a example, the modelling process is given, which description is 1:1 to the real system. Besides, the geographic information attribute of Hainan and the thermal radiation of the building are considered, respectively. Numerical simulation validates the effectiveness of the proposed approach with advantages of high-accuracy and practicability.

The rise of the artificial intelligence (AI) brings golden opportunity to accelerate the development of the intelligent transportation system (ITS). The platoon control of connected autonomous vehicle (CAV) as the key technology exhibits superior for improving traffic system. However, there still exist some challenges in multi-objective platoon control and multi-agent interaction. Therefore, this paper proposed a connected autonomous vehicle latoon control approach with multi-agent deep reinforcement learning (MADRL). Finally, the results in stochastic mixed traffic flow based on SUMO (simulation of urban mobility) platform demonstrate that the proposed method is feasible, effective and advanced.

Lithium-ion batteries have been extensively used worldwide for energy storage and supply in electric vehicles and other devices. An accurate estimation of their state-of-charge (SoC) is essential to ensure their safety and protect them from the explosion caused by overcharge. Large amounts of training data are required for SoC estimation resulting in a great computational burden. Model-based observation method can effectively estimate battery SoC with a limited amount of data. This study applied a combined model, including a one-state hysteresis model and a resistor-capacitor (RC) model, to diminish the parameter estimation errors caused by the hysteresis phenomenon, increasing the estimation accuracy. The Luenberger observer was designed based on the hysteresis RC battery model and evaluated under dynamic stress test (DST) and federal urban driving schedule (FUDS). Our simulation results have shown that the hysteresis RC model has better performance in terms of SoC estimation accuracy using Luenberger observer. Additionally, after the investigation of communication technologies, 5G cellular network offers feasibility for real-time vehicle interaction.

In order to solve the problem of fire inducing and spread process with complex characteristics, this paper proposes a novel approach to realize fire dynamic simulation and evacuation optimization. Focusing on the inducing factors and spread, a fire source heat release rate and combustion model is established based on the technology of BIM and Pyrosim. And the evacuation settings and building environment are further concluded for the accurate dynamic simulation. For the evacuation optimization, the time of different evacuation path corresponding to specific evacuation exit is calculated and compared to achieve the optimal choice of the path in the case of building fire with complex environment.

In this paper, a novel discrete full-order terminal sliding mode (FTSM) control approach is proposed for a permanent magnet synchronous motor (PMSM) working in network transmission environment. By utilizing the vector control technology, the decoupled model of PMSM with the structure of double closed loop can be deduced. The discretization influence of network transmission is specially investigated by comparing the control performances in continuous domain and discrete domain, following the guaranteed stability condition when working in network transmission environment. In order to simulate the network transmission environment, a test platform based on OPC technology is established. Simulations validate the proposed approach.

In order to solve the problem of aging population and to relieve the massive impact on the pension service system, a design scheme of smart medical and nursing platform based on 5G technology is proposed. The model of participants and services related to the medical and nursing systems are established. Based on the information flow in the process of service, the intelligent vital signs monitoring system, pension service management system and decision-making system are introduced into the design of the smart medical and nursing platform. Specially, by utilizing 5G technology, the health information of the elderly, disease early warning and implementation of pension scheme are guaranteed by the perception layer, network layer and application layer, respectively. The proposed scheme can benefit the elderly health records, personalized pension plan, telemedicine diagnosis, etc.

For the polynomial lane change method, the lane change trajectory is planned only at the initial time, and it cannot cope with the problem that other traffic participants enter the driving environment during the lane change process. This paper decomposes the polynomial lane change method into lateral displacement planning and longitudinal velocity planning. The Pontryagin minimum principle is used to solve the optimal lane change duration meeting the requirements of different driving conditions, and the polynomial method is used to plan the lateral displacement trajectory. In the longitudinal direction, the variable acceleration motion equation is used to describe the trajectory, so as to establish a prediction model, the real-time driving environment information is obtained through the internet of vehicles to realize the speed rolling optimization, the trajectory dynamic planning is carried out during the driving process, and the slack variable is introduced to solve the problem that the vehicle suddenly increases speed beyond the constraint range. Through Matlab/Simulink and Prescan co-simulation verification, the trajectory planned in this paper not only meets the requirements of comfort and lane change efficiency, but also has better avoidance capabilities for other traffic participants and is easy to follow in real vehicles.

The minimum Hamming distance is not considered for the traditional rate-compatible shortened polar (RCSP) codes, which may cause performance degradations. In this paper we propose a hybrid algorithm to construct RCSP codes based on Reed-Muller (RM) code-aided. The shortened bits and pre-frozen bits are jointly designed by the row weight property of the common generator matrix \(G_N\) for the RM/Polar code. First, the selected shortened bits are guaranteed to be uniquely depended upon the pre-frozen bits, which makes them completely be known by the decoder. Second, the proposed construction method is designed in such way, so that the minimum row weight of \(G_N\) can be maximized. More specifically, when multiple candidate positions satisfy the conditions (weight-1 column constraint), those rows having less weights are deleted to form the shortened/pre-frozen bits, which can reduce the number of rows with small weight and naturally, make the resulting RCSP codes have larger minimum Hamming distance in average. Simulation results show that the proposed RCSP codes perform better than the traditional shortened codes at low code rates. While at high code rates, the proposed RCSP codes can achieve better performance than that of the quasi uniform punctured (QUP) polar codes, especially at large signal-to-noise ratio (SNR) region. The proposed RCSP codes can find applications in future communications, such as the beyond 5th generation (B5G) and 6th generation (6G) systems.

The doping rate is one of the important indexes to evaluate the quality grade and price of wheat. In order to accurately and quickly recognize impurities (wheat husk) in wheat grains, images of doped wheat were collected and Convolutional Neural Network (CNN) was used to realize the classification and recognition of grains and impurities in wheat grains. In this study, image segmentation and image enhancement were used to preprocess the acquired images to establish the image database of wheat grains and impurities. According to the characteristics of image data, the classic CNN, VGGNet and ResNet network models for wheat impurity images recognition were established. Simulation analysis shows that, compared with the classical CNN and VGGNet network models, the ResNet network model has the best recognition performance. The recognition accuracy of the test set is 96.94%, the recognition time is 5.60 ms.

As the key part of the Energy Internet (EI), the energy router (ER) needs to achieve the purpose of distribution and balance of power, making the entire power system more safe and stable. This paper proposes several energy management strategies for ER. Photovoltaic array is used as the basic power generation unit, wind power is used as the auxiliary unit, and energy storage unit realized the power balance through charging and recharging. At the same time, the maximum power tracking control and constant power of the photovoltaic power generation system and the wind power generation system are carried out, respectively. At last, simulation and control strategy are verified in the MATLAB simulation platform. The simulation results show the proposed management is effective and correct.