31 results on '"Xiong, Neal"'
Search Results
2. ADTO: A Trust Active Detecting-based Task Offloading Scheme in Edge Computing for Internet of Things.
- Author
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Yang, Xuezheng, Zeng, Zhiwen, Liu, Anfeng, Xiong, Neal N., and Zhang, Shaobo
- Subjects
TRUST ,EDGE computing ,INTERNET of things ,BUSINESS revenue - Abstract
In edge computing, Internet of Things devices with weak computing power offload tasks to nearby edge servers for execution, so the task completion time can be reduced and delay-sensitive tasks can be facilitated. However, if the task is offloaded to malicious edge servers, then the system will suffer losses. Therefore, it is significant to identify the trusted edge servers and offload tasks to trusted edge servers, which can improve the performance of edge computing. However, it is still challenging. In this article, a trust Active Detecting-based Task Offloading (ADTO) scheme is proposed to maximize revenue in edge computing. The main innovation points of our work are as follows: (a) The ADTO scheme innovatively proposes a method to actively get trust by trust detection. This method offloads microtasks to edge servers whose trust needs to be identified, and then quickly identifies the trust of edge servers according to the completion of tasks by edge servers. Based on the identification of the trust, tasks can be offloaded to trusted edge servers, to improve the success rate of tasks. (b) Although the trust of edge servers can be identified by our detection, it needs to pay a price. Therefore, to maximize system revenue, searching the most suitable number of trusted edge servers for various conditions is transformed into an optimization problem. Finally, theoretical and experimental analysis shows the effectiveness of the proposed strategy, which can effectively identify the trusted and untrusted edge servers. The task offloading strategy based on trust detection proposed in this article greatly improves the success rate of tasks, compared with the strategy without trust detection, the task success rate is increased by 40.27%, and there is a significant increase in revenue, which fully demonstrates the effectiveness of the strategy. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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3. Networked Fault Detection of Field Equipment from Monitoring System Based on Fusing of Motion Sensing and Appearance Information
- Author
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Wu, Chunxue, Guo, Shengnan, Wu, Yan, Ai, Jun, and Xiong, Neal N.
- Published
- 2020
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4. An efficient intelligent control algorithm for drying rack system.
- Author
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Zhang, Shiwen, Hu, Wang, Liang, Wei, Lei, Changjian, and Xiong, Neal N.
- Subjects
INTELLIGENT control systems ,PROGRAMMABLE controllers ,SWARM intelligence ,ALGORITHMS ,REMOTE control ,WIND speed - Abstract
With the development of the drying rack system, users with limited time tend to use the fully functional drying rack system to realize various intelligent control functions. However, the existing control methods for drying rack system has some defects such as, low intelligence and system delay, which are unsuitable for most users. In this paper, an efficient intelligent control algorithm based on the back‐propagation (BP) neural network is proposed. In this system, STM32F103 is first utilized as the central controller and multiple sensors are used to collect environmental information. Then, the remote control, voice keywords, and buttons can be used to achieve intelligent control. Subsequently, a motor drive intelligence control algorithm based on the BP neural network (MCBP) is proposed to improve the accuracy of the intelligent control. Next, an Application (APP) that can display environmental data such as wind speed, temperature, and humidity is developed. The APP can realize various intelligent control functions such as lifting, panning, rotating, and harvesting. Finally, MCBP is compared with normal control and programmable logic controller control. The accuracy of the MCBP is higher than other two control methods. The final extensive experiments confirm the accuracy and efficiency of the proposed intelligent control algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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5. Securely Computing Protocol of Set Intersection under the Malicious Model.
- Author
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Liu, Xin, Chen, Weitong, Xiong, Neal, Luo, Dan, Xu, Gang, and Chen, Xiubo
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INTERNET of things - Abstract
Private set intersection (PSI) is a valuable technique with various practical applications, including secure matching of communication packets in the Internet of Things. However, most of the currently available two-party PSI protocols are based on the oblivious transfer (OT) protocol, which is computationally expensive and results in significant communication overhead. In this paper, we propose a new coding method to design a two-party PSI protocol under the semi-honest model. We analyze possible malicious attacks and then develop a PSI protocol under the malicious model using the Paillier cryptosystem, cut-and-choose, zero-knowledge proof, and other cryptographic tools. By adopting the real/ideal model paradigm, we prove the protocol's security under the malicious model, which is more efficient compared to the existing related schemes. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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6. PA-WuRES: A green pre-awake routing protocol for wake-up radio enable sensor networks.
- Author
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Zeng, Zhiwen, He, Bingtang, Liu, Anfeng, Zhang, Shaobo, and Xiong, Neal N.
- Subjects
SENSOR networks ,SMART cities ,INTERNET of things ,ENERGY consumption ,ENVIRONMENTAL monitoring ,NETWORK routing protocols - Abstract
More and more sensor nodes which are deployed in smart cities for data collection and analysis form the ubiquitous urban Internet of Things. Energy saving and rapid data collection play a key role in environmental monitoring, military search, medical care, and general city management in smart cities. Therefore, it is a very meaningful and challenging work to design an energy efficient and fast routing protocol to collect data. Although the widely used duty cycle mechanism can effectively save energy, it brings greater delay to routing. Using wake-up radio enable nodes can effectively reduce the delay, but it will bring a large network deployment cost. So how to reduce the deployment cost and quickly route data is a challenge issue for sensor-based system. In this paper, Pre-Awake for Wake-up Radio Enable Sensor based system (PA-WuRES) routing protocol is proposed to reduce delay and deployment cost. The main contribution of our work are as follows: (a) A novel PA-WuRES protocol is proposed which requires much less WuR(WuR,Wake-up Radio) enable nodes, so its deployment cost is lower. (b) Using WuR enable nodes will increase a certain amount of energy consumption. Therefore, this paper proposes a differentiated service data routing mechanism to minimize energy consumption. (c) Full theoretical analysis and experimental results show that the proposed PA-WuRES protocol reduces the deployed WuR enable nodes by 80%, but sensitive-data routing delay is reduced by 48.4%. [ABSTRACT FROM AUTHOR]
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- 2023
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7. An Intelligent Epileptic Prediction System Based on Synchrosqueezed Wavelet Transform and Multi-Level Feature CNN for Smart Healthcare IoT.
- Author
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Song, Kunpeng, Fang, Jiajia, Zhang, Lei, Chen, Fangni, Wan, Jian, and Xiong, Neal
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ELECTROENCEPHALOGRAPHY ,WAVELET transforms ,CONVOLUTIONAL neural networks ,PEOPLE with epilepsy ,MEDICAL care ,INTERNET of things - Abstract
Epilepsy is a common neurological disease worldwide, characterized by recurrent seizures. There is currently no cure for epilepsy. However, seizures can be controlled by drugs and surgeries in about 70% of epileptic patients. A timely and accurate prediction of seizures can prevent injuries during seizures and improve the patients' quality of life. In this paper, we proposed an intelligent epileptic prediction system based on Synchrosqueezed Wavelet Transform (SWT) and Multi-Level Feature Convolutional Neural Network (MLF-CNN) for smart healthcare IoT network. In this system, we used SWT to map EEG signals to the frequency domain, which was able to measure the energy changes in EEG signals caused by seizures within a well-defined Time-Frequency (TF) plane. MLF-CNN was then applied to extract multi-level features from the processed EEG signals and classify the different seizure segments. The performance of our proposed system was evaluated with the publicly available CHB-MIT dataset and our private ZJU4H dataset. The system achieved an accuracy of 96.99% and 94.25%, a sensitivity of 96.48% and 97.76%, a specificity of 97.46% and 94.07% and a false prediction rate (FPR/h) of 0.031 and 0.049 FPR/h on the CHB-MIT dataset and the ZJU4H dataset, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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8. ADCC: An effective adaptive duty cycle control scheme for real time big data in Green IoT.
- Author
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Bai, Jing, Zeng, Zhiwen, Abualnaja, Khamael M., and Xiong, Neal N.
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REAL-time control ,INTERNET of things ,NETWORK performance ,BIG data ,ENERGY consumption - Abstract
Currently, large number of devices have been connected to the Internet of Things (IoT). Ubiquitous IoT devices encounter emergencies and generate much data which may lead to congestion and urgency to be processed timely. To alleviate it, many approaches have been proposed and Adaptive Duty Cycle Control (ADCC) scheme is an effective one. The main contributions of this paper are as follows: (a) Unlike previous studies that mostly the efficiency of congestion control and real-time processing is experiment-oriented, specially, this paper theoretically gives a targeted optimization of duty cycle ratio, which can effectively guide the design of ADCC scheme for real time big data. (b) A general design principle is proposed to guide the scheme designing in order to improve the efficiency and performance of networks. (c) A comprehensive congestion avoidance and real-time processing scheme combining dynamic duty cycle adjustment and full utilization of residual energy is proposed. Thus, these ideas can meet the concept of Green IoT. Through our extensively theoretical and experimental analysis, the principle can effectively guide the design of ADCC scheme, and can reduce the delay, data drop ratio by 20.95% − 77.85% and 29.63% − 100% respectively, without affecting network lifetime, compared with previous scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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9. Ensuring Cryptography Chips Security by Preventing Scan-Based Side-Channel Attacks With Improved DFT Architecture.
- Author
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Wang, Weizheng, Wang, Xiangqi, Wang, Jin, Xiong, Neal N., Cai, Shuo, and Liu, Peng
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CRYPTOGRAPHY ,SMART power grids ,SHIFT registers ,DISCRETE Fourier transforms ,WIRELESS channels ,INTERNET of things ,SECURITY management - Abstract
Cryptography chips are often used in some applications, such as smart grids and Internet of Things (IoT) to ensure their security. Cryptographic chips must be strictly tested to guarantee the correctness of the encryption and decryption. Scan-based design-for-testability (DFT) provides high test quality. However, it can also be misused to steal the cipher key of cryptographic chips by hackers. In this article, we present a new scan design methodology that can resist scan-based side-channel attacks by the dynamical obfuscation of scan input data and scan output data. The scan test is managed by a test password, which consists of load password and scan password. When the chip enters into the test mode, it is required to apply the test password via some external input ports. Once the correct load password is delivered, the scan password can be loaded into a special shift register. If the scan password is also correct, the chip testing can proceed normally. In case the load password or the scan password is wrong, the data in scan chains cannot be propagated correctly. Specifically, some elusory bits are sneaked into scan chains dynamically. The advantage of the proposed method is that it has no negative impact on design performance and test flow when powerfully protecting cryptographic chips. The area penalty is also acceptably low compared with other schemes. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
10. SPPS: A Search Pattern Privacy System for Approximate Shortest Distance Query of Encrypted Graphs in IIoT.
- Author
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Ge, Xinrui, Yu, Jia, Zhang, Hanlin, Bai, Jianli, Fan, Jianxi, and Xiong, Neal N.
- Subjects
DATA structures ,CLOUD computing ,PRIVACY ,INTERNET of things ,RANDOM numbers - Abstract
In recent years, Industrial Internet of Things (IIoT) has gradually attracted the attention of the industry owing to its accurate time synchronization, communication accuracy, and high adaptability. As an important data structure, graphs are widely used in IIoT applications, where entities and their relationships can be expressed in the form of graphs. With the widespread adoption of IIoT and cloud computing, an increasing number of individuals or organizations are outsourcing their IIoT graph data to cloud servers to enjoy the unlimited storage space and fast computing service. To protect the privacy of graph data, graphs are usually encrypted before being outsourced. In this article, we propose a search pattern privacy system for approximate shortest distance query of encrypted graphs in IIoT. To realize search pattern privacy, we adopt two noncolluded cloud servers to accomplish different tasks. We leverage the first server to store the encrypted data and perform query operations, and use the second one to rerandomize the contents and shuffle the locations of the queried records. Before queries, we generate the trapdoors by using different random numbers. After queries, we ask the second server to rerandomize the contents of the records that the first server touched. In addition, we shuffle the physical locations of original records by inserting some fake records. In this way, all contents and physical locations of the touched records change, so that the first server cannot distinguish whether two queries are the same or not. To enhance the efficiency on the user side, we further improve this system by moving some heavy workloads from the user to the cloud. The security analysis and the performance evaluation show that our work is secure and efficient. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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11. Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series.
- Author
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Yin, Chunyong, Zhang, Sun, Wang, Jin, and Xiong, Neal N.
- Subjects
ANOMALY detection (Computer security) ,TIME series analysis ,DEEP learning ,ARTIFICIAL neural networks ,FEATURE extraction ,CONVOLUTIONAL neural networks - Abstract
Internet of Things (IoT) realizes the interconnection of heterogeneous devices by the technology of wireless and mobile communication. The data of target regions are collected by widely distributed sensing devices and transmitted to the processing center for aggregation and analysis as the basis of IoT. The quality of IoT services usually depends on the accuracy and integrity of data. However, due to the adverse environment or device defects, the collected data will be anomalous. Therefore, the effective method of anomaly detection is the crucial issue for guaranteeing service quality. Deep learning is one of the most concerned technology in recent years which realizes automatic feature extraction from raw data. In this article, the integrated model of the convolutional neural network (CNN) and recurrent autoencoder is proposed for anomaly detection. Simple combination of CNN and autoencoder cannot improve classification performance, especially, for time series. Therefore, we utilize the two-stage sliding window in data preprocessing to learn better representations. Based on the characteristics of the Yahoo Webscope S5 dataset, raw time series with anomalous points are extended to fixed-length sequences with normal or anomaly label via the first-stage sliding window. Then, each sequence is transformed into continuous time-dependent subsequences by another smaller sliding window. The preprocessing of the two-stage sliding window can be considered as low-level temporal feature extraction, and we empirically prove that the preprocessing of the two-stage sliding window will be useful for high-level feature extraction in the integrated model. After data preprocessing, spatial and temporal features are extracted in CNN and recurrent autoencoder for the classification in fully connected networks. Empiric results show that the proposed model has better performances on multiple classification metrics and achieves preferable effect on anomaly detection. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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12. A UAV-Assisted Ubiquitous Trust Communication System in 5G and Beyond Networks.
- Author
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Huang, Mingfeng, Liu, Anfeng, Xiong, Neal N., and Wu, Jie
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TELECOMMUNICATION systems ,5G networks ,DATA collection platforms ,SENSE data ,WIRELESS communications ,INTERNET of things ,DRONE aircraft - Abstract
UAV-assisted wireless communications facilitate the applications of Internet of Things (IoT), which employ billions of devices to sense and collect data with an on-demand style. However, there are numerous malicious Mobile Data Collectors (MDCs) mixing into the network, stealing or tampering with data, which greatly damages IoT applications. So, it is urgent to build a ubiquitous trust communication system. In this paper, a UAV-assisted Ubiquitous Trust Evaluation (UUTE) framework is proposed, which combines the UAV-assisted global trust evaluation and the historical interaction based local trust evaluation. We first propose a global trust evaluation model for data collection platforms. It can accurately eliminate malicious MDCs and create a clean data collection environment, by dispatching UAVs to collect baseline data to validate the data submitted by MDCs. After that, a local trust evaluation model is proposed to help select credible MDCs for collaborative data collection. By letting UAVs distribute the data verification hash codes to MDCs, the MDCs can verify whether the exchanged data from the interacted MDCs is reliable. Extensive experiments conduct on a real-life dataset demonstrate that our UUTE system outperforms the existing trust evaluation systems in terms of accuracy and cost. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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13. Privacy Protection for Medical Images Based on DenseNet and Coverless Steganography.
- Author
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Yun Tan, Jiaohua Qin, Hao Tang, Xuyu Xiang, Ling Tan, and Xiong, Neal N.
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DIAGNOSTIC imaging ,CRYPTOGRAPHY ,DATA privacy ,PRIVACY ,INTERNET of things - Abstract
With the development of the internet of medical things (IoMT), the privacy protection problem has become more and more critical. In this paper, we propose a privacy protection scheme for medical images based on DenseNet and coverless steganography. For a given group of medical images of one patient, DenseNet is used to regroup the images based on feature similarity comparison. Then the mapping indexes can be constructed based on LBP feature and hash generation. After mapping the privacy information with the hash sequences, the corresponding mapped indexes of secret information will be packed together with the medical images group and released to the authorized user. The user can extract the privacy information successfully with a similar method of feature analysis and index construction. The simulation results show good performance of robustness. And the hiding success rate also shows good feasibility and practicability for application. Since the medical images are kept original without embedding and modification, the performance of crack resistance is outstanding and can keep better quality for diagnosis compared with traditional schemes with data embedding. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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- View/download PDF
14. Adaptive Positioning Systems Based on Multiple Wireless Interfaces for Industrial IoT in Harsh Manufacturing Environments.
- Author
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Batalla, Jordi Mongay, Mavromoustakis, Constandinos X., Mastorakis, George, Xiong, Neal Naixue, and Wozniak, Jozef
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INDOOR positioning systems ,DYNAMIC positioning systems ,INDUSTRIAL buildings ,INTERNET of things ,CAPITAL investments - Abstract
As the industrial sector is becoming ever more flexible in order to improve productivity, legacy interfaces for industrial applications must evolve to enhance efficiency and must adapt to achieve higher elasticity and reliability in harsh manufacturing environments. The localization of machines, sensors and workers inside the industrial premises is one of such interfaces used by many applications. Current localization-based systems are unable to deal with highly variable conditions, meaning that the solutions working well in stationary systems suffer from considerable difficulties in harsh environments, such as factories. As a result, the precision of localization techniques is not satisfactory in most industrial applications. This paper fills in the existing gap between static approaches and dynamic indoor positioning systems, by presenting a solution adapting the system to highly changeable conditions. The proposed solution makes use of a Machine Learning-based feedback loop that learns the variability of the environment. This feedback makes continuous fingerprint calibration feasible even in the presence of different machines and Industrial Internet of Things sensors that introduce variations to the electromagnetic environment. This paper also presents a comprehensive indoor positioning system solution that reduces complexity of hardware, meaning that a multi-standard-transceiver infrastructure may be adopted with reduced capital and operational expenditures. We have developed the system from scratch and have conducted an extensive range of testbed experiments showing that the multi-technology transceiver feature is capable of increasing positioning accuracy, as well as of introducing permanent fingerprints calibration at harsh industrial premises. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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15. A Dual-Chaining Watermark Scheme for Data Integrity Protection in Internet of Things.
- Author
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Baowei Wang, Weiwen Kong, Wei Li, and Xiong, Neal N.
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DATA integrity ,INTERNET of things ,SYNCHRONIZATION software ,SENSORY perception ,DATA analysis - Abstract
Chaining watermark is an effective way to verify the integrity of streaming data in wireless network environment, especially in resource-constrained sensor networks, such as the perception layer of Internet of Things applications. However, in all existing single chaining watermark schemes, how to ensure the synchronization between the data sender and the receiver is still an unsolved problem. Once the synchronization points are attacked by the adversary, existing data integrity authentication schemes are difficult to work properly, and the false negative rate might be up to 50 percent. And the additional fixed group delimiters not only increase the data size, but are also easily detected by adversaries. In this paper, we propose an effective dual-chaining watermark scheme, called DCW, for data integrity protection in smart campus IoT applications. The proposed DCW scheme has the following three characteristics: (1) In order to authenticate the integrity of the data, fragile watermarks are generated and embedded into the data in a chaining way using dynamic grouping; (2) Instead of additional fixed group delimiters, chained watermark delimiters are proposed to synchronize the both transmission sides in case of the synchronization points are tampered; (3) To achieve lossless integrity authentication, a reversible watermarking technique is applied. The experimental results and security analysis can prove that the proposed DCW scheme is able to effectively authenticate the integrity of the data with free distortion at low cost in our smart meteorological Internet of Things system. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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16. RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization.
- Author
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Yuling Fang, Qingkui Chen, Xiong, Neal N., Deyu Zhao, and Jingjuan Wang
- Subjects
INTERNET of things ,COMPUTER networks ,GRAPHICS processing units ,COMPUTER graphics equipment ,HIGH performance computing - Abstract
This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes' diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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17. An Effective Massive Sensor Network Data Access Scheme Based on Topology Control for the Internet of Things.
- Author
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Meng Yi, Qingkui Chen, and Xiong, Neal N.
- Subjects
WIRELESS sensor networks ,INTERNET of things ,DATA libraries ,SELF-organizing maps ,BEES algorithm - Abstract
This paper considers the distributed access and control problem of massive wireless sensor networks' data access center for the Internet of Things, which is an extension of wireless sensor networks and an element of its topology structure. In the context of the arrival of massive service access requests at a virtual data center, this paper designs a massive sensing data access and control mechanism to improve the access efficiency of service requests and makes full use of the available resources at the data access center for the Internet of things. Firstly, this paper proposes a synergistically distributed buffer access model, which separates the information of resource and location. Secondly, the paper divides the service access requests into multiple virtual groups based on their characteristics and locations using an optimized self-organizing feature map neural network. Furthermore, this paper designs an optimal scheduling algorithm of group migration based on the combination scheme between the artificial bee colony algorithm and chaos searching theory. Finally, the experimental results demonstrate that this mechanism outperforms the existing schemes in terms of enhancing the accessibility of service requests effectively, reducing network delay, and has higher load balancing capacity and higher resource utility rate. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
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18. TDTA: A truth detection based task assignment scheme for mobile crowdsourced Industrial Internet of Things.
- Author
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Zhang, Rui, Li, Zeyuan, Xiong, Neal N., Zhang, Shaobo, and Liu, Anfeng
- Subjects
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INTERNET of things , *INDUSTRIALISM , *INTEGRITY , *CROWDSENSING , *TRUST , *QUALITY of service - Abstract
Mobile Crowdsensing (MCs), a promising strategy for intelligent data collection and task computing, is conducive to constructing industrial system and providing industrial services in the industrial Internet of Things (IIoT). How to ensure data reliability in an industrial environment is a challenging problem. In this article, we proposed a Truth Detection based Task Assignment (TDTA) scheme to assign micro-tasks to reliable workers and establish a credible task execution environment for crowdsourced IIoT. The TDTA scheme mainly contains three methods for different application scenarios: (1) Direct truth detection method. The Edge Node (EN) will calculate partial micro-tasks results as true results when idle, and compares them with the results reported by workers to detect their reliability directly. (2) Indirect truth detection method. After the platform obtains some trusted workers through the direct method, it distributes the same micro-tasks to workers with unknown credibility and trusted workers, and compares their results to verify reliability indirectly. (3) Post audit truth detection method. When idle, the platform recalculates historical reporting results for suspicious workers to verify their credibility. And the effect of time decay on credibility is considered. Moreover, the TDTA scheme also considers the factors of integrity and delay of the results to calculate the Quality of Service (QoS) value. Then the most active and credible workers are chosen to execute the task. Theoretical analysis and experiment results demonstrate the effectiveness of our proposed scheme, which has higher EN resource utilization, task result accuracy, and malicious worker recognition rate. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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19. An efficient emerging network and secured hopping scheme employed over the unsecured public channels.
- Author
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Shahzad, Aamir, Zhang, Kaiwen, Landry, René, Xiong, Neal, and Kim, Young-Gab
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TELECOMMUNICATION systems ,WIRELESS communications ,COMMUNICATION models ,INFORMATION technology security ,WIRELESS Internet ,INTERNET of things - Abstract
With the emergence of new smart technologies, including the Internet of Things, wireless media are playing an important role to connect numerous devices to fulfill the requirements of newly developed communication systems. The massive connectivity, therefore, made the wireless spectrum too crowded and gave several challenges to resisting against potential wireless jammers. Note that, the two main challenges that have always been a part of any communication system, especially in the case of wireless communication, are information security and information jamming. Carefully considering the given challenges, this study uses a new advanced anti-jamming approach, a modulation technique based on the frequency-hopping spread spectrum, which has notably high resistance accounted against various potential jammers. The objective of this study is two-fold. First, the physical channel properties are considered, and the random bits are transmitted, employing a cryptographic secured hoping-spread pattern, having a set of carrier frequencies, known at both sides of the transmission. Second, the hashing code is computed only for the key, and transmitted along the original hopset, but with distinct frequencies set. The deployed practical anti-jamming approach, therefore, computed a high efficiency to examine the information secrecy well and primarily the connection availability even in the presence of the jammers. Moreover, this study considered and modeled a communication system and evaluated the proposed system's performance, applying the theories of Shannon's entropy and Wyner's entropy (i.e. Wyner's wiretap channel), to anticipate the system's perfect secrecy, even in the worst case when jammer has unlimited computational capabilities. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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20. Enabling wireless communication and networking technologies for the internet of things [Guest editorial].
- Author
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Vinel, Alexey, Chen, Wen-Shyen Eric, Xiong, Neal N., Rho, Seungmin, Chilamkurti, Naveen, and Vasilakos, Athanasios V.
- Abstract
The Internet of Things (IoT) is enabling ubiquitous computing with a novel design paradigm to integrate global physical objects, cyber and social spaces, and machines. It may be envisaged as a web of trillions of machines that will communicate with each other. The major enabling technologies that are giving a flying kickstart to IoT are ad hoc and wireless sensor networks, short-range wireless communications, real-time systems, low power and energy harvesting, radio frequency identification, machine type communication, resource-constrained networks, and embedded software. [ABSTRACT FROM PUBLISHER]
- Published
- 2016
- Full Text
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21. LIAA: A listen interval adaptive adjustment scheme for green communication in event-sparse IoT systems.
- Author
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Wang, Han, Liu, Wei, Xiong, Neal N., Zhang, Shaobo, and Wang, Tian
- Subjects
- *
END-to-end delay , *WIRELESS sensor networks , *INTERNET of things , *LISTENING , *PHENOMENOLOGICAL theory (Physics) - Abstract
Due to the development of microprocessor technology, there are more than 20 billion sensor-based devices connected to the Internet of Things (IoT) to monitor physical phenomena and events. To reduce the energy used by idle listening, a low-duty cycle is often used in an event-sparse wireless sensor network. However, low-duty cycles bring large end-to-end delays. In this paper, a listen interval adaptive adjustment (LIAA) scheme is proposed to adjust the listen interval (LI) of a node to reduce end-to-end delays while maintaining the long lifetime of a network. The key idea is to make full use of the energy consumption imbalance in a network, to allow nodes away from the sink to use the residual energy to add listen intervals. The LIAA scheme has 3 sub-strategies. One is the basic add listen interval (BALI) strategy, in which the parent node adds listen intervals in the fixed active slots of its child nodes. Another strategy is the consecutive listen (CL) scheme, which is based on BALI, and listen intervals are added consecutively. The third strategy is the random add listen interval (RALI) scheme, which uses the idea of randomness to add listen intervals. The extensive theoretical analysis and experimental results show that the LIAA scheme proposed in this paper has better performance. Compared with the traditional scheme, the delays in the BALI scheme, CL scheme and RALI scheme were reduced by 24.03%, 23.45% and 39.41%, respectively, while the lifetime of the network was maintained. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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22. A trustworthiness-based vehicular recruitment scheme for information collections in Distributed Networked Systems.
- Author
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Li, Ting, Liu, Anfeng, Xiong, Neal N., Zhang, Shaobo, and Wang, Tian
- Subjects
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ACQUISITION of data , *INFORMATION technology security , *REWARD (Psychology) , *INTERNET of things , *MACHINE learning - Abstract
Because of high mobility, large number of vehicles are utilized to achieve timely and quality-based information in the smart Internet of Things, which has formulated into a dynamic Distributed Networked Systems (DNS). However, designing a vehicular recruitment scheme to enhance a security-based DNS is challenging since it is hard to judge trustworthiness values of vehicular sensors. Therefore, in this paper, a novel vehicular trust evaluation scheme is proposed to analyze and supervise the data collected by vehicular sensors with a trust and low-cost style. To obtain vehicular trusts, the proposed scheme that considers time factor and gap between sensed data and real data is designed to calculate trustworthiness of vehicles. Moreover, sensing data in the vehicle sparse regions has more contributions because of its rareness. Thus, to inspire vehicles to sense data in the vehicle sparse regions, a trustworthiness-based gradient pricing method is designed to pay rewards for the vehicular sensors. Finally, with real vehicular GPS datasets, simulation results demonstrate that the proposed scheme can improve accuracy rate of data sensing by 37.72% and can improve data quality by 76.95%. By incentive pricing method, coverage ratio of data sensing is improved by 13.1%. In general, performances of the proposed scheme can be improved by 19. 39% to 22.32% approximately. Future works focus on improving information security by advanced machine learning methods in the dynamic DNS. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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23. A digital twin-based edge intelligence framework for decentralized decision in IoV system.
- Author
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El Azzaoui, Abir, Jeremiah, Sekione Reward, Xiong, Neal N., and Park, Jong Hyuk
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DIGITAL twins , *TECHNOLOGICAL innovations , *SMART cities , *INFORMATION & communication technologies , *INTERNET of things - Abstract
• The Internet of Vehicle (IoV) is an emerging technology for the development of future smart cities. • The world climate institution has reported that the carbon emission from transportation system is one-fifth of global carbon dioxide with a sum of around 24% of energy. The communication between EV and Roadside Unit (RSU), and the continuous computational power required to support IoV system is also energy harvesting. • In this paper, we propose a decentralized trust management solution for IoV systems to reduce both carbon print and offload the computation power required. • Our solution resides in developing a Digital Twin of the vehicle on an intelligent edge environment to simulate the physical vehicle and process the required data processing. The Internet of Vehicle (IoV) is an emerging technology for the development of future smart cities. With the fast and exponential growing rate of Internet of Things (IoT), the smart transportation field is ushering in a revolutionary advancement. Smart transportation systems facilitate better informed, more coordinated, and smarter use of transport networks, with the use of advanced information and communication technologies applied to vehicles to help improve traffic management, minimize congestion, improve safety, and ultimately provide a more intelligent use of transport networks. Smart transportation is an integral part of modern-day smart city projects. However, the world climate institution has reported that carbon emissions from the overall transportation system accounts for one-fifth of global carbon dioxide with a sum of around 24% of energy. Electric vehicles (EV) represent a solution for this issue, yet, it is not sustainable. The communication between EVs and a roadside unit (RSU), and the continuous computational power required to support an IoV system also requires a reliance on energy harvesting. With this in mind, in this paper, we propose a decentralized trust management solution for IoV systems to reduce both carbon footprint and offload the computation power required. Our solution resides in developing the digital twin of vehicles on an intelligent edge environment to simulate the physical vehicle and handle the required data processing. Also, we implement a smart contract model for fast, secure, and sustainable on-road battery recharging between EVs. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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24. JOET: Sustainable Vehicle-assisted Edge Computing for IoT devices.
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Huang, Wei, Zeng, Zhiwen, Xiong, Neal N., and Mumtaz, Shahid
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EDGE computing , *NONLINEAR programming , *INTERNET of things , *INTEGER programming , *ENERGY consumption - Abstract
Network-accessible task offloading is for low latency; offline task offloading is for low cost. Offline devices cannot directly access service nodes due to lack of resources. Accordingly, the latter involves more steps and optimization variables such as: where to offload tasks, how to allocate computation resources, how to adjust offloading ratio and transmit power, and such optimization variables and hybrid combination features are highly coupled with each other. In this paper, we first formulate a Mixed Integer Nonlinear Programming Problem (MINLP) for such task offloading under energy and delay constraints. Furthermore, we decompose it into two subproblems so as to efficiently solve the formulated MINLP, and design a low-cost and low-complexity Joint Optimization for Energy Consumption and Task Processing Delay (JOET) algorithm to optimize selection decisions, resource allocation, offloading ratio and transmit power adjustment. We carry out extensive simulation experiments to validate JOET. Simulation results demonstrate that JOET outperforms many representative existing schemes in quickly converge and effective reduction of energy consumption and delay. Specifically, the average energy consumption and the average delay have been reduced by 15.93% and 13.70%, respectively, and the load balancing efficiency has increased by 10.20%. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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25. SG-PBFT: A secure and highly efficient distributed blockchain PBFT consensus algorithm for intelligent Internet of vehicles.
- Author
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Xu, Guangquan, Bai, Hongpeng, Xing, Jun, Luo, Tao, Xiong, Neal N., Cheng, Xiaochun, Liu, Shaoying, and Zheng, Xi
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DISTRIBUTED algorithms , *BLOCKCHAINS , *ALGORITHMS , *INTERNET , *INTERNET of things , *FAULT tolerance (Engineering) , *INTELLIGENT transportation systems - Abstract
• Our algorithm is more efficient, has less communication overhead, and has greater throughput than the original PBFT algorithm. • We propose a Vehicle - Service Provider - Roadside Unit architecture to ensure the orderliness and safety of vehicles. • We propose an identity authentication scheme based on blockchain. • Experimental results demonstrate that our solution is better than PBFT, G-PBFT and CPBFT. As an application of Internet of Things (IoT) technology, the Internet of Vehicles (IoV) faces two main security issues: (1) the central server of the IoV may not be powerful enough to support the centralized authentication of the rapidly increasing connected vehicles, (2) the IoV itself may not be robust enough to single-node attacks. To address these issues, in this paper, we propose SG-PBFT (Score Grouping-PBFT), a secure and efficient distributed consensus algorithm for blockchain applications in the IoV. The distributed structure can reduce the pressure on the central server and decrease the risk of single-node attacks. The SG-PBFT consensus algorithm improves the traditional practical Byzantine fault tolerance (PBFT) consensus algorithm by optimizing the PBFT consensus process and using a score grouping mechanism to achieve a higher consensus efficiency. The experimental results show that the method can greatly improve the consistency efficiency and effectively prevent single-node attacks. Specifically, when the number of consensus nodes reaches 1000, the consensus time of our algorithm is only about 27% of what is required for the state-of-the-art PBFT consensus algorithm. Our proposed SG-PBFT algorithm is versatile and can be used in other application scenarios which require high consensus efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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26. RDIC: A blockchain-based remote data integrity checking scheme for IoT in 5G networks.
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Wang, Huaqun, He, Debiao, Yu, Jia, Xiong, Neal N., and Wu, Bin
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DATA integrity , *5G networks , *INTERNET of things , *RSA algorithm , *MODULAR arithmetic , *BIG data , *DIGITAL signatures , *BLOCKCHAINS - Abstract
Internet of things (IoT) is one of the main application scenarios of 5th generation mobile networks (5G). Along with the rapid development of 5G, IoT terminal devices will create big data. Generally, IoT terminal devices are lightweight user equipments, for example, wearable devices. In order to take use of these lightweight terminal devices, it is a feasible way to outsource these created big data to the public cloud. When these data are out of the client's control, it is of crucial importance to ensure the remote data integrity. To solve the weaknesses of the existing remote data integrity checking schemes, we propose the concept of blockchain-based remote data integrity checking (RDIC) scheme for big data. The new concept makes use of blockchain technique which greatly improves the efficiency and security of RDIC. First, the system model and security definition are given for the proposed RDIC scheme by using blockchain. Then, by using efficient modular arithmetic, RSA digital signature, blockchain, etc , we construct a lightweight blockchain-based RDIC scheme. Finally, we analyze its security and performance. The detailed analysis shows that our scheme is provably secure and lightweight. • The concept of blockchain-based RDIC for IoT in 5G Networks. • The first concrete scheme for the model. • Detailed performance analysis and experimental results. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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27. A trust active and Trace back based trust Management system about effective data collection for mobile IoT services.
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Zhang, Rui, Liu, Anfeng, Wang, Tian, Xiong, Neal N., and Vasilakos, Athanasios V.
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TRUST , *ACQUISITION of data , *TELECOMMUNICATION satellites , *DRONE aircraft , *INTERNET of things - Abstract
Mobile Crowdsensing (MCs) is a widely applicable and inexpensive data-obtaining method that leverages mobile devices to sense and report data without deploying sensors. The rapid development of the Integration of IoT systems, as well as the widespread use of communication satellites, make MCs further develop and receive extensive attention from researchers. One key issue is how to ensure the reliability of the reported data to avoid the presence of malicious participants leading to poor IoT service quality. In this paper, we proposed an Active and Trace Back based Trust Management (ATBTM) algorithm to evaluate the trust of participants and handle malicious participants. The main innovations are: (1) An active trust evaluation approach for MCs is proposed, which uses an Unmanned Aerial Vehicle (UAV) to collect data as a baseline to verify the reliability of the data reported by participants. Then, the data reported by high reputation participants can also be used as a sub-baseline to verify the trust of some participants. (2) A traceback based trust evaluation method is also proposed. In this approach, some reliable devices provide historical sensing data that has been collected but not reported. Then the system compares it to real data with corresponding timestamps to evaluate the trust of other participants. Sufficient theoretical analysis and experimental results demonstrate that the proposed ATBTM framework can effectively identify the malicious workers, and conquer the drawbacks of lacking trust evaluation method in existed MCs. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
28. DTC-MDD: A spatiotemporal data acquisition technology for privacy-preserving in MCS.
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Liang, Runfu, Chen, Lingyi, Liu, Anfeng, Xiong, Neal N., Zhang, Shaobo, and Vasilakos, Athanasios V.
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ACQUISITION of data , *CROWDSENSING , *FALSIFICATION of data , *INTERNET of things , *DATA quality , *INTERNET privacy - Abstract
Some attackers in the Internet of Things submit falsified high-quality data to cause harm to users. To prevent malicious workers from reporting untruthful data for skyline computation in Mobile Crowd Sensing, we propose a double trust check-based spatiotemporal data acquisition scheme, DTC-MDD. In DTC-MDD, worker trust uses four-way validation to obtain reliable worker trust evaluations. Then, based on Probabilistic Skyline Calculation, we propose a worker selection algorithm to select high-trust, high-quality workers for data reporting. We also introduce the Non-Interactive Encrypted Integer Comparison Protocol to safeguard privacy between workers and users from malicious attacks. Finally, through extensive simulations on real datasets, DTC-MDD effectively enhances the quality and security of spatiotemporal data acquisition. DTC-MDD improved the data quality and reliability of candidate worker sets by 16.2% and 49.1%, respectively, and the data quality and reliability of the first skyline worker by 21.4% and 320.0%, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
29. STMTO: A smart and trust multi-UAV task offloading system.
- Author
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Guo, Jialin, Huang, Guosheng, Li, Qiang, Xiong, Neal N., Zhang, Shaobo, and Wang, Tian
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EXPECTED utility , *PURCHASING agents , *TASKS , *INTERNET of things , *ENERGY consumption - Abstract
As one of promising distributed multi-robot system, Unmanned Aerial Vehicles (UAVs) can collaborate to offload complex tasks in edge networks. A Smart and Trust Multi-UAV Task Offloading (STMTO) system is established to offload tasks from Internet of Thing (IoT) devices to edge severs through UAVs with a trust style. In STMTO system, first, a group of UAVs is dispatched to relay tasks from devices to edge servers with rich computing resource. A collaborative task collection scheme is proposed to minimize energy consumption and the task processing delay by dividing working area for each UAV and designing the flight trajectory. Secondly, a many-to-many task double auction model is established for devices and edge servers to maximize the offloading utility, where devices act as buyers, edge servers as sellers, and UAVs as auctioneers. Last, to resist attack of malicious edge servers and ensure the task security, a novel trust evaluation method based on the comparison of true utility and expected utility is integrated in auction mechanism. The theoretical analysis and implementation results show that the proposed STMTO system not only achieve the best utility for devices and edge servers simultaneously, but also identify the malicious edge servers and protect task from attacks. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
30. A novel IoT network intrusion detection approach based on Adaptive Particle Swarm Optimization Convolutional Neural Network.
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Kan, Xiu, Fan, Yixuan, Fang, Zhijun, Cao, Le, Xiong, Neal N., Yang, Dan, and Li, Xuan
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CONVOLUTIONAL neural networks , *DENIAL of service attacks , *PARTICLE swarm optimization , *INTERNET of things , *COMPUTER network security , *STATISTICAL accuracy - Abstract
In the field of network security, it is of great significance to accurately detect various types of Internet of Things (IoT) network intrusion attacks which launched by the attacker-controlled zombie hosts. In this paper, we propose a novel IoT network intrusion detection approach based on Adaptive Particle Swarm Optimization Convolutional Neural Network (APSO-CNN). In particular, the PSO algorithm with change of inertia weight is used to adaptively optimize the structure parameters of one-dimensional CNN. The cross-entropy loss function value of the validation set, which is obtained from the first training of CNN, is taken as the fitness value of PSO. Especially, we define a new evaluation method that considers both the prediction probability assigned to each category and prediction label to compare the proposed APSO-CNN algorithm with CNN set parameters manually (R-CNN). Meanwhile, the comprehensive performance of proposed APSO-CNN and other three well known algorithms are compared in the five traditional evaluation indicators and the accuracy statistical characteristics of 10 times independent experiments. The simulation results show that the multi-type IoT network intrusion attack detection task based on APSO-CNN algorithm is effective and reliable. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
31. DDSR: A delay differentiated services routing scheme to reduce deployment costs for the Internet of Things.
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Liu, Xiaohuan, Liu, Anfeng, Zhang, Shaobo, Wang, Tian, and Xiong, Neal N.
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INTERNET of things , *WIRELESS sensor nodes , *WIRELESS sensor networks , *WIRELESS Internet , *COST - Abstract
Low-delay and low-cost communication are the main concerns of the wireless sensor-based Internet of Things (IoT), which is widely deployed in various kinds of applications. Adopting wake-up radio (WuR)-enabled wireless sensor nodes can effectively reduce delay, but the deployment costs of the network increase. In this paper, we propose a delay differentiated services routing (DDSR) scheme to reduce the deployment costs for WuR-enabled wireless sensor networks (WSNs). Urgent data prefer to select the awake non-WuR node to forward data. If there is no available awake non-WuR node to forward data at the instant, it will awaken WuR nodes alternatively to maintain a relatively low delay. However, normal data are forwarded by the awake non-WuR node to reduce the number of deployed WuR nodes. Our theoretical analysis results demonstrate that the DDSR scheme can effectively reduce the deployment costs by 93.63%–98.91% while meeting the delay requirement of forwarding urgent data and maintaining a long lifetime. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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