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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the development of Internet of things technology, intelligent terminals of the Internet of things have gained popularity. At present, there are many security vulnerabilities in the firmware of the Internet of things terminal, and it is very inconvenient to use manual methods to detect the vulnerabilities of the Internet of things terminal equipment. The intelligent fuzzing technology based on genetic algorithms is mainly used, and the firmware to be tested is automatically tested using random variation data. Aiming at the low efficiency of the existing fuzzing technology based on genetic algorithms, this paper proposes an intelligent fuzzing model based on multiple reinforcement learning algorithms. In this model, reinforcement learning algorithms are used to optimize the mutation operator selection strategy of fuzzing and the code coverage of fuzzing is improved by intelligently selecting different mutation operators for different test cases. This paper compares the performance of DDQN, DDPG, TRPO, and PPO algorithms in the model through comparative experiments on LAVA datasets and traditional fuzzing methods. The results show that in the fuzzing environment, there are significant differences in the performance of different algorithms for different target programs and the fuzzing method based on reinforcement learning is obviously superior to the traditional fuzzing method, proving the proposed model' s availability and effectiveness. [ABSTRACT FROM AUTHOR]

In view of the security and privacy protection issues faced in sharing sensing information data between sensing devices in the internet of things, this study proposes a blockchain based multi domain data sharing mechanism. The mechanism treats the data sharing problem as a data transaction problem and introduces a trusted regulator to guarantee the interests of both buyers and sellers of the transaction. The mechanism provides a data publishing method in which buyers can securely publish data to a cloud storage service and do not disclose information about the data during the publishing process. A ring signature is used to sign and verify the buyer's licence in order to protect the buyer's privacy from being compromised during the data transaction. In addition, the mechanism provides an anonymous evaluation method that allows buyers to evaluate transactions and monitor seller behaviour without revealing their identity. The security analysis and simulation experiments results show that the mechanism is secure and efficient, meets the requirements for secure data sharing in the internet of things. [ABSTRACT FROM AUTHOR]

To address the issues associated with traditional inspection methods for slag slow cooling fields, which rely on manual operations and are characterized by high safety risks, low efficiency, high costs, low accuracy, and harsh environments, an intelligent inspection system for slag slow cooling fields has been developed and applied. This system uses a gimbal infrared thermal imager to capture thermal and visible light images of slag package and cooling water spray. By applying infrared thermal imaging temperature algorithms and AI-based intelligent recognition, the system accurately monitors temperature changes in slag package and the operational status of the spray, automatically identifying anomalies and issuing timely warnings. Since its implementation, the system has demonstrated high stability and precision, enabling staff to address various on-site inspection issues, thereby ensuring production safety and stability. This system offers an efficient and intelligent inspection solution for the metal smelting industry, promoting digital transformation and intelligent upgrades within the sector. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things technology (IoT) has been developed extremely rapidly, and has been widely applied in the medicine field. Chronic obstructive pulmonary disease (COPD) is one of the most common chronic respiratory diseases in China, and it is worth exploring how IoT can be used to improve the clinical practice of COPD management. We summarized the research progress of IoT technology in COPD management at home and abroad and found that the current research on the application of IoT in COPD management is still in the exploratory stage and lacks high-quality, large-sample studies by reviewing the literature. Real-world studies are needed to conduct systematic evaluation of designed mature models or products in the future, especially the impacts on long-term clinical outcomes of patients, quality of life and health economics evaluation. Products design should be tailored to the characteristics and target of COPD and the characteristics of users, and emphasis should also be placed on qualitative researches to understand the experiences, opinions or attitudes of users toward such products.

Aiming at the lack of effective means for detecting and localizing malicious nodes in the data flow transmission link in Internet of things (IoT), a zero trust management method of data flow between IoT edge nodes based on software defined network (SDN) was proposed. This method applied the architecture of SDN to the process of data flow transmission between edge nodes. A fixed-length header overhead was used for zero trust management of data flow, nodes, and paths to achieve lightweight packet forwarding verification and malicious node localization functions. In the forwarding path, the security verification of packets was performed by the switching node, and the verification information was counted to ensure the security of the data flow transmission and the consistency of the path. Based on the type of abnormal packets, the controller adopted dichotomous method to mark the switching node that performed the verification operation to gradually narrow down the scope of malicious nodes, and realized the localization of multiple types of malicious nodes. Finally, the proposed method was simulated and evaluated. The experimental results show that the method introduces a forwarding delay of less than 10% and a throughput loss of less than 8%.

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Fiber Inspection / Zhongguo Xian-Jian is the property of China Fiber Inspection and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To explore the management of big data for tobacco drying based on the Internet of Things. A total of 2 346 Internet of Things drying rooms in Jiangxi tobacco-growing areas were selected as the research objects. Big data analysis software was used to analyze 12 685 baking process data. The results showed that the fluctuation of the drying period of tobacco leaves in various regions of Jiangxi was greater than that of the baking period. The harvesting-curing cycles was 52-54 days, the harvest frequency was about five times, there were fewer drying rooms for farmers. There were certain differences in the stable time of the key temperature points, the total baking time, and the three-stage time ratio in different production areas. The total curing time of lower, middle and upper tobacco leaves was mainly in ranges of 133-144 h, 144- 155 h and 159-162 h. The control of the dry-bulb temperature points in Jiangxi production areas was relatively consistent, while there were some differences in the control of the wet-bulb temperature. The durations of stem drying stage could be appropriately reduced, and the wet bulb temperature should be reasonably regulated to avoid drastic fluctuation, the use of big data analysis software can quickly realize the analysis of baking execution status, baking time, and key temperature and humidity control in tobacco drying. It can also analyze the baking habits of tobacco farmers, which can achieve the management of big data for tobacco drying and continuously promote the high-quality development of tobacco drying. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Piezoelectrics & Acoustooptics is the property of Piezoelectric & Acoustooptic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the development of Industry 4.0 and the Industrial Internet, the mining industry has also begun to move towards intelligence. Faced with the current situation of complex access terminals, diverse communication protocols, access networks and heterogeneous multi-source data in the coal mine industry, an intelligent mine IoT edge data gateway technology is proposed. By constructing an edge gateway management architecture, the northbound uplink connects to the cloud platform using transmission protocols such as TCP, HTTP, WebSocket, MQTT, etc., and the southbound downlink supports multiple communication interfaces to connect perception layer devices, achieving protocol conversion of different types of networks and unified access of multi-source heterogeneous data from various systems. The research results have been applied for data collection and forwarding automation related equipment in multiple coal mines. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The compression strategy plays an important role in the performance of IoT time series data storage system. However, the current compression strategies can not adapt to the characteristics of NVM and IoT time series data. This paper proposes a polymorphic cooperative compression strategy for IoT time-series data based on NVM. Firstly, the overall structure of IoT time series data is given. Then, to address the consistent patterns in IoT time series data and the different granularity between user-space and kernel-space operations on NVM, a dual-compression strategy is devised. Initially, a lightweight compression method is applied directly as IoT time series data is received in user-space. This method efficiently reduces the volume of data for storage, while minimizing the impact on the timeliness of data storage. Moreover, a deep compression algorithm is designed for the kernel-space, primarily focusing on querying and analyzing anomalous time series data. Additionally, to address the competition for NVM bandwidth between deep compression and data storage, a dynamic adjustment algorithm that guarantees write bandwidth is proposed. Finally, a prototype of the polymorphic cooperative compression strategy is implemented and YCSB-TS is used to evaluate the results. The results show that the proposed method can effectively improve the write throughput of IoT time-series data by up to 161.3% and reduce the storage space by up to 14.6%, compared with InfluxDB, OpenTSDB, KairosDB and TVStore. [ABSTRACT FROM AUTHOR]

A large-scale street lights send data to the cloud platform, causing load imbalance among nodes of the server in high concurrency, resulting in communication failure. In view of this problem, based on microservices, this study proposes a dynamic weight load balancing algorithm based on the smart street lamp cloud platform. The algorithm calculates the respective weight coefficients and the initial weights of each node according to the hardware performance of each server during initialization, and dynamically adjusts the server weights according to the idle rate of CPU(Central Processing Unit) and network bandwidth during the request process to realize load optimization. By setting the minimum threshold and comparing it with the calculated remaining load rate, the weight of the server that reaches the upper limit of the load is set to 0 to prevent the server from being overloaded. The test results show that the proposed algorithm has a better load balancing effect than the minimum number of connections algorithm and the smooth weighted round robin algorithm in the experimental environment. Compared with the dynamic weight algorithm, the average response time and the actual number of concurrent connections of the proposed method are also improved. [ABSTRACT FROM AUTHOR]

The widespread application of Internet of Things (IoT) devices has brought forth the challenge of ensuring data security and integrity. This research proposed a blockchain-based IoT edge offloading strategy focused on data protection. The strategy involved uploading IoT device data to the blockchain, utilizing its immutability and traceability to enhance data security. Given the high computational demands of the blockchain's proof of work (PoW) consensus algorithm for data verification and block addition, edge computing technology was employed to offload the PoW consensus process to edge servers. Furthermore, it developed a multi-objective edge offloading algorithm(MEOA) to identify the optimal offloading strategy and dynamically adjusted the PoW consensus difficulty, balancing system security with operational efficiency. Simulation results show that this strategy outperforms others in enhancing the chaining efficiency of IoT device data and reducing time and energy costs, while ensuring data security and integrity. [ABSTRACT FROM AUTHOR]

In order to study and explore the role of automated monitoring technology in subway construction and maintenance, this paper adopted a combination of theoretical analysis and engineering cases. In terms of theoretical analysis, on the basis of introducing the application background and significance of automated monitoring technology in the subway industry, the main methods and technical means of automatic monitoring of subway deformation were summarized by analyzing cutting-edge sensor technology and data processing methods, including monitoring methods based on sensor technology, data management based on the Internet of Things technology, and artificial intelligence platforms based on big data. In terms of engineering cases, this paper demonstrated the progressiveness of automated monitoring based on the case of a subway bridge with monitoring duration 2 h per time, and the monthly personnel input can be reduced by 420 man days with obvious economic benefits. The results show that, introducing advanced technology for traditional industy could enhance automated data management and research platform, and improve the automation level and efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Metal Working (1674-165X) is the property of Metal Working Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increasingly dense deployment of base stations in the internet of things (IoT), the importance of interference management becomes ever more pronounced. In IoT environments, devices often employ random access, connecting to channels in a distributed manner. In scenarios involving massive numbers of devices, severe interference may arise between nodes, leading to significant degradation in the throughput performance of the network. To address interference control issues in networks with random access, a multi-base station slotted Aloha network based on cooperative reception was considered, the reinforcement learning techniques was leveraged to design adaptive transmission algorithms that effectively managed interference, optimized network throughput performance, and enhanced network fairness. Firstly, an adaptive transmission algorithm were devised based on Q-learning, which was verified to maintain high network throughput performance under varying traffic conditions through simulation. Secondly, to improve network fairness, the penalty function method was employed to refine the adaptive transmission algorithm. Simulations confirm that the fairness-optimized algorithm significantly enhances network fairness while preserving satisfactory network throughput performance.

The 5G era has brought opportunities for the rapid development of the Internet of things. Identity authentication is the basis for ensuring the security of the Internet of things. However, in the face of quantum attacks due to the large number of nodes in the Internet of things, the signcrypt-based identity authentication scheme will consume a lot of resources to generate node keys, which is difficult to meet the low cost requirements of the Internet of things. Therefore, a lightweight lattice-based group signcryption authentication scheme was designed. In the key generation stage, the improved trapdoor diagonal matrix was designed to optimize the original image sampling algorithm required for key generation and reduce the overall time required for generating a large number of keys. In the identity authentication stage, based on the residual Hash lemma and the signcryptic property, the message was signed and encrypted in one logical step at the same time, and the interaction flow of dot multiplication and Hash operation with low frequency was proposed to complete the access authentication performed by the group leader on behalf of the group members. Simulation experiments show that this scheme reduces the number of interactions during the access of Internet of things devices, reduces the computing cost of identity authentication stage, and reduces the total cost of signcryption and decryption by more than 7% compared with the existing schemes. Furthermore, the simulation results prove that this scheme can resist quantum attacks in the Internet of things.

In order to realize agricultural modernization, to promote the structural reform of the agricultural supply side, and to ensure highquality and efficient wheat production, we collected and analyzed the data of soil moisture, seedling growth, photosynthesis, insects and meteorology in wheat planting process, so as to provide data and planting options for decision making of agricultural technicians and producers. Taking wheat production of Ningjin County as an example, we used the Internet of Things, UAV remote sensing and big data platform processing to design a big data platform system for wheat growth monitoring. This system could be divided to 3 modules, including 1 big data platform and 5 field monitoring point. The system collected insect pest situation, soil moisture and meteorological data every 30 min, provided monitoring picture of wheat growth at the site all through 24 h. The data were transferred to the database of the big data platform through the internet. The platform summarized the data to form images and reports, so as to provide more suitable planting schemes for agricultural experts and agricultural producers and operators. [ABSTRACT FROM AUTHOR]

As an important cornerstone of the fourth industrial revolution, the industrial Internet transforms isolated industrial systems into connected networks and is an important expansion direction for digital industrialization. Anomaly detection in the industrial Internet of things environment is of great significance for automated decision-making. To address the problem that most existing methods fail to effectively consider the complex unknown topological relationships between sensors and the multi-scale patterns inherent in the industrial IoT temporal data, an unsupervised anomaly detection method MSTSAD with multi-scale spatiotemporal feature fusion for industrial IoT temporal data is proposed, which firstly constructs a novel bi-directional spatiotemporal feature extraction module to sequentially captures the correlation and bidirec- tional dependency between multiple time series. Secondly, a multi- scale gated temporal convolutional neural network is designed to adaptively extract multi-scale temporal features of time series, and a dual affine projection is introduced to realize the cross- fusion of multi-scale temporal features and spatiotemporal features to enhance the feature extraction of the model on the original data. Finally, a variational self-encoder combined with adversarial training is proposed to amplify the reconstruction error of anomalies and enhance the anti-interference ability of the model to train data noise, which enhances the ability of model to discriminate anomalous data. Experiments are conducted on four publicly available datasets, GPW, ECG5000, Occupancy and SWaT, in comparison with five state-of-the-art methods, and the experimental results show that F1 scores of MSTSAD are enhanced by 0.015-0.047. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The cumulative length of subways and tunnels in China has reached nearly 25 000 kilometers, bringing huge demand for underground construction. The monitoring for construction safety has become more significant, due to complicated environment and rapid-onset natural disasters, which raises the frequency of accidents in underground construction. The informatization engineering of monitoring and early-warning on natural disasters is built to achieve construction safety, through promoting integrated monitoring on multi-hazard and disaster chains, and practicing intelligent algorithms on comprehensive quick accurate perception and early-warning of construction hazards. The integrated network architecture for intelligent safety monitoring with edge-computing ability is established, realizing the visualization of monitoring data analysis through researches on AD hoc transmission based on directional antenna network bridge, development of IoT edge computing software and researches on accessing to IoT platform for multiple types of sensors, and development of the front-end and back-end of the monitoring system. The introduction of IoT and cloud computing sustains accessing to platform for huge amount of sensors and full exploiting of big-data mining, improving intelligent safety hazard identification, and reducing safety risks in construction. Based on the instant response of edge devices and interval data acquisition of sensors, and the capability for more than 100 000 sensors to access the system simultaneously, the complete monitoring system for underground constructions with second-level warning is established. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on the systematic study of digital construction of assembled bridges, an overall system of digital construction of the whole process of assembled bridges based on BIM + GIS + IoT is proposed. The digital construction process is divided into four stages, including the three-dimensional design, digital production, intelligent transportation and intelligent assembly. The three-dimensional design of assembled bridges is carried out through fine BIM modeling. The digital production concept and component complete production process are applied to the factory production of prefabricated components of assembled bridges. The intelligent transportation body with GIS and IoT as the core is used to provide intelligent transportation control of the whole process of components. Based on IoT intelligent assembly, this research supports realizing the digital construction of prefabricated bridges in the field of bridge engineering. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology & Material is the property of Automobile Technology & Material Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mobile edge computing (MEC) provides users with low latency and low energy consumption services by unloading computing tasks to MEC servers, overcoming the shortcomings of traditional cloud computing. In mobile edge computing, how to make unloading decision is one of the key technologies to provide low latency and low energy consumption services. In addition, due to the limited bandwidth resources of wireless channel, unreasonable bandwidth allocation will increase the energy consumption and delay of user devices. Therefore, how to reasonably allocate resource is also the key to the implementation of edge computing. To jointly optimize delay, energy consumption, and computational resources, this paper proposes a multi-channel search algorithm based on Monte Carlo tree search (MCS-MCTS). Firstly, with the cost of delay and energy consumption as the optimization objective, the decision on computing resource allocation and transmission power modeling is modeled as a convex optimization problem. The optimal transmission power allocation problem is solved by using gradient descent method, and the optimal computational resource allocation problem is solved by means of Lagrange multiplier method and Karush-Kuhn-Tucker (KKT) condition. Subsequently, the MCS-MCTS algorithm is used to deal with the binary unloading decision problem. Besides, the simulated annealing algorithm is introduced to avoid the search results falling into local optimum. The numerical results show that MCS-MCTS algorithm can obtain near optimal unloading decision and resource allocation decision in linear coherent time. Compared with existing heuristic search algorithms, this algorithm can achieve near optimal performance while reducing time complexity and improving system energy efficiency. [ABSTRACT FROM AUTHOR]

The design and development of electrocardiogram（ECG） monitoring cloud platform based on the Internet of Things（IoT） electrocardiograph is introduced. The platform is mainly composed of ECG acquisition module, algorithm module, diagnostic model comparison module, warning module, positioning module and medical aid system. The ECG acquisition module collects ECG signals of patients and displays them in real time on the mobile terminals. Then they are uploaded to the ECG monitoring cloud platform through the IoT. The algorithm module and the diagnostic model comparison module mark, process, analyze and diagnose the ECG. Meanwhile, the ECG diagnosis and warning results are pushed to patients and 120 emergency centers through the IoT. Furthermore, the cloud platform will guide patients to self-rescue and the emergency platform will open the green channel to save patients in time.The platform realizes from the onset to diagnosis and treatment in one step, and saves lives against time.

In order to realize the diagnosis of slit lamp in cross-regional patients and improve the real-time and convenience of diagnosis, a remote slit lamp diagnosis platform based on Internet of Things (IoT) technology is designed. Firstly, the feasibility of remote slit lamp is analyzed. Secondly, the IoT platform architecture of doctor/server/facility (D/S/F) is proposed and a remote slit lamp is designed. Finally, the performance of the remote slit lamp diagnostic platform is tested. The platform solves the communication problem of distributed slit lamps and realizes respectively numerical control of multi-area slit lamp by multi-eye experts. The test results show that the remote control delay of the platform is less than 20 ms, which supports multiple experts to diagnose multiple patients separately.

To solve the network access problem of low data rate and delay-insensitive services in remote areas, a wide-area Internet of things (IoT) information transmission method based on mobile piggyback was proposed. Mobile carriers were used to provide senseless access support for IoT devices and expand network coverage. In areas without base station signal coverage, the mobile carrier established a connection with the sensor, obtained sensor data and stored it. When 5G access conditions were met, the sensing information was offloaded to achieve information access to the network. A data caching strategy based on cost function was proposed to improve the efficiency of information piggybacking. The coverage performance of the mobile piggyback scheme was analyzed and a hardware implementation scheme based on LoRa was designed. Simulation results show that the wide-area IoT information transmission method based on mobile piggyback can improve the efficiency of IoT information piggyback, and expand the coverage area of 5G network by 4.98 times. Field tests verify the effectiveness of the proposed method, and mobile piggyback nodes can achieve communication coverage with a radius of 4 km.

As an important technology for the deployment of Internet of things (IoT), backscatter communication performs low-power passive communication and energy harvesting by applying the incident signals, which is expected to achieve energy self-sustainability of the IoT node.In order to deepen its understanding and promote the development of related fields, a review of existing research was conducted and the future development prospects were presented.Firstly, the basic principle of backscatter communication and its architecture evolution were elaborated.Secondly, in terms of different communication architectures, the existing research works were surveyed.On this basis, the hybrid active-passive mutualistic symbiotic backscatter communication was proposed, and preliminary research and simulation analysis were conducted.Finally, future research directions of the proposed communication architecture were explored based on the existing research.

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems in the Practical Byzantine Fault Tolerance Algorithm (PBFT), such as large communication overhead, high delay and inability to reasonably divide according to the differences of scenarios and devices to meet the multi-scenario application of Internet of Things (IoT) in the integration of IoT and blockchain, this paper proposed an improved Practical Byzantine Fault Tolerance consensus algorithm based on Comprehensive Evaluation (CE-PBFT). Firstly, it conducted comprehensive evaluation of nodes based on the weighted performance and reputation value to screen out nodes that meet the needs of specific scenarios; after that, it conducted clustering based on the comprehensive evaluation of nodes to form a two-layer network architecture; finally, it divided the consensus process into sub-cluster consensus and main cluster consensus. The experimental results show that CE-PBFT has high fault tolerance and scenario adaptability, and when the number of scenario nodes reaches 100, it respectively has 93.9% and 87.8% performance optimisation over PBFT in terms of communication overhead and consensus delay. [ABSTRACT FROM AUTHOR]

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As smart homes become more prevalent, users expect to control smart devices through natural language commands and desire personalized smart home services. However, existing challenges include the interoperability of smart devices and a comprehensive understanding of the user environment. To address these issues, this paper proposed a framework supporting personalized smart home service recommendations for device-end users. Firstly, it constructed a runtime knowledge graph to reflect contextual information in specific smart homes and generated scenario-based sentences. Secondly, it trained a general recommendation model using pre-collected natural language instructions and corresponding scenario-based sentence representations from users in common scenarios. Finally, users interacted with smart home devices and services through natural language on the device end while fine-tuning the weights of the general model through feedback to obtain a personal model. Experimental results on three datasets-basic instruction set, paraphrase set, and scenario instruction set show that the personal model achieves an accuracy improvement of 6.5% to 30% compared to word embedding methods and 2.4% to 25% compared to the Sentence-BERT model, which validates that the device-end deep learning-based smart home service framework has a high service recommendation accuracy and effectively manages smart home devices and services. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Water Conservancy Science & Techonlogy & Economy is the property of Water Conservancy Science & Technology & Economy Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)