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Intelligent Computing, Networking, Security and Robustness in Internet of Things
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Intelligent Computing, Networking, Security and Robustness in Internet of Things

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 89205

Special Issue Editors

Prof. Dr. Tie Qiu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Tianjin University, No.135 Yaguan Road, Haihe Education Park, Tianjin 300050, China
Interests: internet of things; big data; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Klaus Moessner

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Institute for Communication Systems, University of Surrey, Guildford GU2 7XH, Surrey, UK
Interests: cognitive networks; IoT deployments; sensor data based knowledge generation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chen Chen

E-Mail Website
Guest Editor
School of Telecommunication Engineering, Xidian University, Xi’an 710071, China
Interests: Internet of Things; intelligent transportation systems; satellite communications; space-air-ground cooperation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) plays an important role in the current and future generation of information, network, and communication developing and applications. Smart IoT is an exciting emerging research field that has great potential to transform both our understanding of fundamental computer science principles and our standard of living. IoT is being employed in more and more areas making “Everything Smart”, such as smart home, smart city, intelligent transportation, environment monitoring, security systems, and advanced manufacturing.

The conferences of IEEE SmartIoT, Qshine and ICDH have been established as the primary venue for researchers and practitioners to disseminate exchange and discuss all recent advances related to Internet of Things, particularly for quality, experience, reliability, security, robustness, intelligent computing and networking.

Subject Coverage

This special issue is intended to collect recent research results that address key issues and topics related to Intelligent Computing, Networking, Security and Robustness in Internet of Things (Especially from IEEE SmartIoT 2018, ICDH 2018, Qshine 2017 and SmartIoT 2019).

Topics of interest include:

  • Social networks, multimedia and mobile computing
  • Smart cities and internet of vehicles
  • Big data analysis, Machine learning and Evolutionary Computing
  • Computer Vision and Image Processing
  • Multimedia Signal Processing
  • Quality-of-Service (QoS) provisioning: architectures, protocols, mechanisms
  • Quality of Experience (QoE): user-perceived quality, cost-sensitive experience
  • Reliability and Scalability: performance, adaptation, cognition, extensibility
  • Security and Privacy: system/network security, user privacy
  • Robustness: fault/disruption/disaster-tolerance, resilience, survivability
  • Cross-Layer Design, Optimization and Implementation: spectrum/energy efficiency
  • Social and Economic Perspectives: incentive, pricing, billing, governance
  • New Applications and Communication Technologies: IoTs, cloud, smart grid, and SDN

Prof. Dr. Tie Qiu
Prof. Dr. Klaus Moessner
Prof. Dr. Chen Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (27 papers)

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Research

17 pages, 2735 KiB  
Article
BeiDou Satellite Positioning Method Based on IoT and Edge Computing
by Lina Wang and Rui Qiu
Sensors 2020, 20(3), 889; https://doi.org/10.3390/s20030889 - 07 Feb 2020
Cited by 5 | Viewed by 3711
Abstract
The BeiDou navigation satellite system (BDS) developed by China can provide users with high precision, as well as all-weather and real-time positioning and navigation. It can be widely used in many applications. However, new challenges emerge with the development of 5G communication system [...] Read more.
The BeiDou navigation satellite system (BDS) developed by China can provide users with high precision, as well as all-weather and real-time positioning and navigation. It can be widely used in many applications. However, new challenges emerge with the development of 5G communication system and Internet of Things (IoT) technologies. The BDS needs to be suitable for the large-scaled terminal scenario and provides higher positioning precision. In this paper, a BeiDou differential positioning method based on IoT and edge computing is proposed. The computational pressure on the data center is offloaded to the edge nodes when the massive positioning requests of IoT terminals need to be processed. To ensure the load balancing of the edge nodes, the resource allocation of the terminal positioning requests is performed with the improved genetic algorithm, thereby reducing the service delay of the entire edge network. Moreover, the optimized unscented Kalman filter based on the edge node (EUKF) algorithm is used to improve the positioning precision of IoT terminals. The results demonstrate that the proposed positioning method has better positioning performance which can provide the real-time positioning service for the large-scale IoT terminals. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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19 pages, 3282 KiB  
Article
A Malicious Behavior Awareness and Defense Countermeasure Based on LoRaWAN Protocol
by Shu-Yang Gao, Xiao-Hong Li and Mao-De Ma
Sensors 2019, 19(23), 5122; https://doi.org/10.3390/s19235122 - 22 Nov 2019
Cited by 8 | Viewed by 2668
Abstract
Low power wide area network (LoRaWAN) protocol has been widely used in various fields. With its rapid development, security issues about the awareness and defense against malicious events in the Internet of Things must be taken seriously. Eavesdroppers can exploit the shortcomings of [...] Read more.
Low power wide area network (LoRaWAN) protocol has been widely used in various fields. With its rapid development, security issues about the awareness and defense against malicious events in the Internet of Things must be taken seriously. Eavesdroppers can exploit the shortcomings of the specification and the limited consumption performance of devices to carry out security attacks such as replay attacks. In the process of the over-the-air-activation (OTAA) for LoRa nodes, attackers can modify the data because the data is transmitted in plain text. If the user’s root key is leaked, the wireless sensor network will not be able to prevent malicious nodes from joining the network. To solve this security flaw in LoRaWAN, we propose a countermeasure called Secure-Packet-Transmission scheme (SPT) which works based on the LoRaWAN standard v1.1 to prevent replay attacks when an attacker has obtained the root key. The proposed scheme redefines the format of join-request packet, add the new One Time Password (OTP) encrypted method and changes the transmission strategy in OTAA between LoRa nodes and network server. The security evaluation by using the Burrows-Abadi-Needham logic (BAN Logic) and the Scyther shows that the security goal can be achieved. This paper also conducts extensive experiments by simulations and a testbed to perform feasibility and performance analysis. All results demonstrate that SPT is lightweight, efficient and able to defend against malicious behavior. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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16 pages, 5269 KiB  
Article
Zero-Error Coding via Classical and Quantum Channels in Sensor Networks
by Wenbin Yu, Zijia Xiong, Zanqiang Dong, Siyao Wang, Jingya Li, Gaoping Liu and Alex X. Liu
Sensors 2019, 19(23), 5071; https://doi.org/10.3390/s19235071 - 20 Nov 2019
Cited by 2 | Viewed by 2285
Abstract
Today’s sensor networks need robustness, security and efficiency with a high level of assurance. Error correction is an effective communicational technique that plays a critical role in maintaining robustness in informational transmission. The general way to tackle this problem is by using forward [...] Read more.
Today’s sensor networks need robustness, security and efficiency with a high level of assurance. Error correction is an effective communicational technique that plays a critical role in maintaining robustness in informational transmission. The general way to tackle this problem is by using forward error correction (FEC) between two communication parties. However, by applying zero-error coding one can assure information fidelity while signals are transmitted in sensor networks. In this study, we investigate zero-error coding via both classical and quantum channels, which consist of n obfuscated symbols such as Shannon’s zero-error communication. As a contrast to the standard classical zero-error coding, which has a computational complexity of O ( 2 n ) , a general approach is proposed herein to find zero-error codewords in the case of quantum channel. This method is based on a n-symbol obfuscation model and the matrix’s linear transformation, whose complexity dramatically decreases to O ( n 2 ) . According to a comparison with classical zero-error coding, the quantum zero-error capacity of the proposed method has obvious advantages over its classical counterpart, as the zero-error capacity equals the rank of the quantum coefficient matrix. In particular, the channel capacity can reach n when the rank of coefficient matrix is full in the n-symbol multilateral obfuscation quantum channel, which cannot be reached in the classical case. Considering previous methods such as low density parity check code (LDPC), our work can provide a means of error-free communication through some typical channels. Especially in the quantum case, zero-error coding can reach both a high coding efficiency and large channel capacity, which can improve the robustness of communication in sensor networks. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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27 pages, 1348 KiB  
Article
Mobility Aware Duty Cycling Algorithm (MADCAL) A Dynamic Communication Threshold for Mobile Sink in Wireless Sensor Network
by Craig Thomson, Isam Wadhaj, Zhiyuan Tan and Ahmed Al-Dubai
Sensors 2019, 19(22), 4930; https://doi.org/10.3390/s19224930 - 12 Nov 2019
Cited by 7 | Viewed by 2392
Abstract
The hotspot issue in wireless sensor networks, with nodes nearest the sink node losing energy fastest and degrading network lifetime, is a well-referenced problem. Mobile sink nodes have been proposed as a solution to this. They do not completely remove the hotspot problem [...] Read more.
The hotspot issue in wireless sensor networks, with nodes nearest the sink node losing energy fastest and degrading network lifetime, is a well-referenced problem. Mobile sink nodes have been proposed as a solution to this. They do not completely remove the hotspot problem though, with nodes the sink passes most closely still expending more energy than others. This study proposes a lightweight algorithm, located in the media access control (MAC) layer of static nodes and utilising knowledge of predictable sink node mobility. This is in order to create a dynamic communication threshold between static nodes and the sink, within which static nodes awaken, lessening competition for sink communication between nodes. In utilising predictable mobility and factors already known to the static node, such as location and interference range, there is no need for energy-consuming messaging. Analysis and simulation results, tested on a lightweight implementation of a carrier-sense multiple-access-based MAC protocol, show a significant improvement in energy consumption in both controlled and random environments, with frame delivery improved to the point where sink speed is negated. This is when compared to the existing duty cycling approach. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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19 pages, 738 KiB  
Article
Data Anomaly Detection for Internet of Vehicles Based on Traffic Cellular Automata and Driving Style
by Nan Ding, Haoxuan Ma, Chuanguo Zhao, Yanhua Ma and Hongwei Ge
Sensors 2019, 19(22), 4926; https://doi.org/10.3390/s19224926 - 12 Nov 2019
Cited by 5 | Viewed by 3111
Abstract
The data validity of safe driving in the Internet of Vehicles (IoV) is the basis of improving the safety of vehicles. Different from a traditional information systems, the data anomaly analysis of vehicle safety driving faces the diversity of data anomaly and the [...] Read more.
The data validity of safe driving in the Internet of Vehicles (IoV) is the basis of improving the safety of vehicles. Different from a traditional information systems, the data anomaly analysis of vehicle safety driving faces the diversity of data anomaly and the randomness and subjectivity of the driver’s driving behavior. How to combine the characteristics of the IOV data with the driving style analysis to provide effective real-time anomaly data detection has become an important issue in the IOV applications. This paper aims at the critical safety data analysis, considering the large computing cost generated by the real-time anomaly detection of all data in the data package. We preprocess it through the traffic cellular automata model which is built to achieve the ideal abnormal detection effect with limited computing resources. On the basis of this model, the Anomaly Detection based on Driving style (ADD) algorithm is proposed to realize real-time and online detection of anomaly data related to safe driving. Firstly, this paper designs the driving coefficient and proposes a driving style quantization model to represent the driving style of the driver. Then, based on driving style quantization model and vehicle driving state information, a data anomaly detection algorithm is developed by using Gaussian mixture model (GMM). Finally, combining with the application scenarios of multi-vehicle collaboration in the Internet of Vehicles, this paper uses real data sets and simulation data sets to analyze the effectiveness of the proposed ADD algorithm. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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19 pages, 7991 KiB  
Article
A Joint Power and Channel Scheduling Scheme for Underlay D2D Communications in the Cellular Network
by Zefang Lin, Hui Song and Daru Pan
Sensors 2019, 19(21), 4799; https://doi.org/10.3390/s19214799 - 04 Nov 2019
Cited by 6 | Viewed by 2986
Abstract
Device-to-device (D2D) communication, as one of the promising candidates for the fifth generation mobile network, can afford effective service of new mobile applications and business models. In this paper, we study the resource management strategies for D2D communication underlying the cellular networks. To [...] Read more.
Device-to-device (D2D) communication, as one of the promising candidates for the fifth generation mobile network, can afford effective service of new mobile applications and business models. In this paper, we study the resource management strategies for D2D communication underlying the cellular networks. To cater for green communications, our design goal is to the maximize ergodic energy efficiency (EE) of all D2D links taking into account the fact that it may be tricky for the base station (BS) to receive all the real-time channel state information (CSI) while guaranteeing the stability and the power requirements for D2D links. We formulate the optimization problem which is difficult to resolve directly because of its non-convex nature. Then a novel maximum weighted ergodic energy efficiency (MWEEE) algorithm is proposed to solve the formulated optimization problem which consists of two sub-problems: the power control (PC) sub-problem which can be solved by employing convex optimization theory for both cellular user equipment (CUE) and D2D user equipment (DUE) and the channel allocation (CA) sub-problem which can be solved by obtaining the weighted allocation matrix. In particular, we shed light into the impact on EE metric of D2D communication by revealing the nonlinear power relationship between CUE and DUE and taking the QoS of CUEs into account. Furthermore, simulation results show that our proposed algorithm is superior to the existing algorithms. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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19 pages, 1333 KiB  
Article
A SINS/DVL Integrated Positioning System through Filtering Gain Compensation Adaptive Filtering
by Xiaozhen Yan, Yipeng Yang, Qinghua Luo, Yunsai Chen and Cong Hu
Sensors 2019, 19(20), 4576; https://doi.org/10.3390/s19204576 - 21 Oct 2019
Cited by 9 | Viewed by 2968
Abstract
Because of the complex task environment, long working distance, and random drift of the gyro, the positioning error gradually diverges with time in the design of a strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated positioning system. The use of velocity information [...] Read more.
Because of the complex task environment, long working distance, and random drift of the gyro, the positioning error gradually diverges with time in the design of a strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated positioning system. The use of velocity information in the DVL system cannot completely suppress the divergence of the SINS navigation error, which will result in low positioning accuracy and instability. To address this problem, this paper proposes a SINS/DVL integrated positioning system based on a filtering gain compensation adaptive filtering technology that considers the source of error in SINS and the mechanism that influences the positioning results. In the integrated positioning system, an organic combination of a filtering gain compensation adaptive filter and a filtering gain compensation strong tracking filter is explored to fuse position information to obtain higher accuracy and a more stable positioning result. Firstly, the system selects the indirect filtering method and uses the integrated positioning error to model the navigation parameters of the system. Then, a filtering gain compensation adaptive filtering method is developed by using the filtering gain compensation algorithm based on the error statistics of the positioning parameters. The positioning parameters of the system are filtered and information on errors in the navigation parameters is obtained. Finally, integrated with the positioning parameter error information, the positioning parameters of the system are solved, and high-precision positioning results are obtained to accurately position autonomous underwater vehicles (AUVs). The simulation results show that the SINS/DVL integrated positioning method, based on the filtering gain compensation adaptive filtering technology, can effectively enhance the positioning accuracy. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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13 pages, 7729 KiB  
Article
Mobile Phone Passive Positioning through the Detection of Uplink Signals for Search and Rescue
by Yuhui Gao, Zhongliang Deng, Yao Zhang, Shihua Sun and Zhen Li
Sensors 2019, 19(20), 4526; https://doi.org/10.3390/s19204526 - 17 Oct 2019
Cited by 5 | Viewed by 3329
Abstract
To satisfy the service requirements of high accuracy and efficient life detection and location for search and rescue (SAR) missions after a disaster, we developed a passive positioning method to locate mobile phones by capturing the random access preamble, which can be applied [...] Read more.
To satisfy the service requirements of high accuracy and efficient life detection and location for search and rescue (SAR) missions after a disaster, we developed a passive positioning method to locate mobile phones by capturing the random access preamble, which can be applied to fourth-generation (4G) and even fifth-generation (5G) communication systems. We analyzed the characteristics of the random access procedure of a communication system and established a way to detect mobile phones by combining the time-difference-of-arrival (TDOA) estimation to determine the location. Then, we performed an experiment and a simulation of preamble sequence acquisition, and the results proved that the method is feasible and has high detection accuracy in high-noise conditions. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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19 pages, 1047 KiB  
Article
An LSTM-Method-Based Availability Prediction for Optimized Offloading in Mobile Edges
by Chaoxiong Cui, Ming Zhao and Kelvin Wong
Sensors 2019, 19(20), 4467; https://doi.org/10.3390/s19204467 - 15 Oct 2019
Cited by 5 | Viewed by 2473
Abstract
Mobile edge computing (MEC) can augment the computation capabilities of a vehicle terminal (VT) through offloading the computational tasks from the VT to the mobile edge computing-enabled base station (MEC-BS) covering them. However, due to the limited mobility of the vehicle and the [...] Read more.
Mobile edge computing (MEC) can augment the computation capabilities of a vehicle terminal (VT) through offloading the computational tasks from the VT to the mobile edge computing-enabled base station (MEC-BS) covering them. However, due to the limited mobility of the vehicle and the capacity of the MEC-BS, the connection between the vehicle and the MEC-BS may be intermittent. If we can expect the availability of MEC-BS through cognitive computing, we can significantly improve the performance in a mobile environment. Based on this idea, we propose a offloading optimization algorithm based on availability prediction. We examine the admission control decision of MEC-BS and the mobility problem, in which we improve the accuracy of availability prediction based on Empirical Mode Decomposition(EMD) and LSTM in deep learning. Firstly, we calculate the availability of MEC, completion time, and energy consumption together to minimize the overall cost. Then, we use a game method to obtain the optimal offloading decision. Finally, the experimental results show that the algorithm can save energy and shorten the completion time more effectively than other existing algorithms in the mobile environment. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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21 pages, 4419 KiB  
Article
Conditional Artificial Potential Field-Based Autonomous Vehicle Safety Control with Interference of Lane Changing in Mixed Traffic Scenario
by Kai Gao, Di Yan, Fan Yang, Jin Xie, Li Liu, Ronghua Du and Naixue Xiong
Sensors 2019, 19(19), 4199; https://doi.org/10.3390/s19194199 - 27 Sep 2019
Cited by 30 | Viewed by 4191
Abstract
Car-following is an essential trajectory control strategy for the autonomous vehicle, which not only improves traffic efficiency, but also reduces fuel consumption and emissions. However, the prediction of lane change intentions in adjacent lanes is problematic, and will significantly affect the car-following control [...] Read more.
Car-following is an essential trajectory control strategy for the autonomous vehicle, which not only improves traffic efficiency, but also reduces fuel consumption and emissions. However, the prediction of lane change intentions in adjacent lanes is problematic, and will significantly affect the car-following control of the autonomous vehicle, especially when the vehicle changing lanes is only a connected unintelligent vehicle without expensive and accurate sensors. Autonomous vehicles suffer from adjacent vehicles’ abrupt lane changes, which may reduce ride comfort and increase energy consumption, and even lead to a collision. A machine learning-based lane change intention prediction and real time autonomous vehicle controller is proposed to respond to this problem. First, an interval-based support vector machine is designed to predict the vehicles’ lane change intention utilizing limited low-level vehicle status through vehicle-to-vehicle communication. Then, a conditional artificial potential field method is used to design the car-following controller by incorporating the lane-change intentions of the vehicle. Experimental results reveal that the proposed method can estimate a vehicle’s lane change intention more accurately. The autonomous vehicle avoids collisions with a lane-changing connected unintelligent vehicle with reliable safety and favorable dynamic performance. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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18 pages, 900 KiB  
Article
Time-Efficient Allocation Mechanisms for Crowdsensing Tasks with Precedence Constraints
by Xiaocan Wu, Yu-E Sun, He Huang, Yang Du and Danlei Huang
Sensors 2019, 19(11), 2456; https://doi.org/10.3390/s19112456 - 29 May 2019
Cited by 2 | Viewed by 2577
Abstract
Crowdsensing has emerged as an efficient and inexpensive way to perform specialized tasks by leveraging external crowds. In some crowdsensing systems, different tasks may have different requirements, and there may be precedence constraints among them, such as the Unmanned Aerial Vehicle (UAV) crowdsensing [...] Read more.
Crowdsensing has emerged as an efficient and inexpensive way to perform specialized tasks by leveraging external crowds. In some crowdsensing systems, different tasks may have different requirements, and there may be precedence constraints among them, such as the Unmanned Aerial Vehicle (UAV) crowdsensing systems. Moreover, minimizing the total execution time is a regular target for finishing the crowdsensing tasks with precedence constraints. As far as we know, only a few existing studies consider the precedence constraints among crowdsensing tasks, and none of them can minimize the total execution time simultaneously. To tackle this challenge, an efficient allocation mechanism for tasks with precedence constraints is first proposed, which can minimize the total execution time. Then, a case study is given to show how to fit our mechanism in the UAV crowdsensing system. Finally, the simulation results show that the proposed mechanisms have good approximate optimal ratios under different parameter settings and are efficient for the UAV crowdsensing system as well. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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19 pages, 1133 KiB  
Article
Authentication and Delegation for Operating a Multi-Drone System
by Mungyu Bae and Hwangnam Kim
Sensors 2019, 19(9), 2066; https://doi.org/10.3390/s19092066 - 03 May 2019
Cited by 15 | Viewed by 2994
Abstract
As the era of IoT comes, drones are in the spotlight as a mobile medium of Internet of Things (IoT) devices and services. However, drones appear to be vulnerable to physical capture attacks since they usually operate far from operators. If a drone [...] Read more.
As the era of IoT comes, drones are in the spotlight as a mobile medium of Internet of Things (IoT) devices and services. However, drones appear to be vulnerable to physical capture attacks since they usually operate far from operators. If a drone is illegally captured, some important data will be exposed to the attacker. In this paper, we propose a saveless-based key management and delegation system for a multi-drone control system. The proposed system enables a multi-drone control system to highly resist physical capture by minimizing exposure of confidential data. In addition, when the drone leaves the formation for performing another mission or by a natural environment, the system can allow the drone to securely re-participate in the formation with the help of the ground control station (GCS) when it comes back. The analysis result shows that the proposed system can reduce storage space usage and require less computational overhead. From the result, we expect that the system can guarantee the resistance of physical capture and secure key management to the drones as well as many mobile IoT devices. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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17 pages, 6284 KiB  
Article
Design and Analysis of a Lightweight Context Fusion CNN Scheme for Crowd Counting
by Yang Yu, Jifeng Huang, Wen Du and Naixue Xiong
Sensors 2019, 19(9), 2013; https://doi.org/10.3390/s19092013 - 29 Apr 2019
Cited by 7 | Viewed by 3162
Abstract
Crowd counting, which is widely used in disaster management, traffic monitoring, and other fields of urban security, is a challenging task that is attracting increasing interest from researchers. For better accuracy, most methods have attempted to handle the scale variation explicitly. which results [...] Read more.
Crowd counting, which is widely used in disaster management, traffic monitoring, and other fields of urban security, is a challenging task that is attracting increasing interest from researchers. For better accuracy, most methods have attempted to handle the scale variation explicitly. which results in huge scale changes of the object size. However, earlier methods based on convolutional neural networks (CNN) have focused primarily on improving accuracy while ignoring the complexity of the model. This paper proposes a novel method based on a lightweight CNN-based network for estimating crowd counting and generating density maps under resource constraints. The network is composed of three components: a basic feature extractor (BFE), a stacked à trous convolution module (SACM), and a context fusion module (CFM). The BFE encodes basic feature information with reduced spatial resolution for further refining. Various pieces of contextual information are generated through a short pipeline in SACM. To generate a context fusion density map, CFM distills feature maps from the above components. The whole network is trained in an end-to-end fashion and uses a compression factor to restrict its size. Experiments on three highly-challenging datasets demonstrate that the proposed method delivers attractive performance. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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23 pages, 2842 KiB  
Article
Parallel Transmission of Distributed Sensor Based on SCTP and TCP for Heterogeneous Wireless Networks in IoT
by Weifeng Sun, Shumiao Yu, Yuanxun Xing and Zhenquan Qin
Sensors 2019, 19(9), 2005; https://doi.org/10.3390/s19092005 - 29 Apr 2019
Cited by 4 | Viewed by 3012
Abstract
Sensors in the Internet of Things (IoT) generate large amounts of data, which requires high-speed data transmission. In order to achieve the parallel transmissions of the wireless sensor network on the transmission layer, the performance of stream control transmission protocol (SCTP) and transmission [...] Read more.
Sensors in the Internet of Things (IoT) generate large amounts of data, which requires high-speed data transmission. In order to achieve the parallel transmissions of the wireless sensor network on the transmission layer, the performance of stream control transmission protocol (SCTP) and transmission control protocol (TCP) in the wireless sensor network under different packet error rates was simulated and compared. A dynamic multipath handover method for SCTP (MS-SCTP) was proposed to improve the transmission performance, which selects the transmission path according to the packet error rate and the retransmission ratio in the sender’s buffer. The TCP and SCTP protocol switching method (TCP-SCTP) was proposed to detect the current network traffic and adjust the MS-SCTP or TCP method. Analysis and simulation results show that MS-SCTP and TCP-SCTP could improve network throughput and reduce packet loss rate. MS-SCTP and TCP-SCTP can be combined with other technologies and channel allocation algorithms to improve network traffic. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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13 pages, 5241 KiB  
Article
Improved Real-Time Facial Expression Recognition Based on a Novel Balanced and Symmetric Local Gradient Coding
by Jucheng Yang, Xiaojing Wang, Shujie Han, Jie Wang, Dong Sun Park and Yuan Wang
Sensors 2019, 19(8), 1899; https://doi.org/10.3390/s19081899 - 22 Apr 2019
Cited by 8 | Viewed by 2821
Abstract
In the field of Facial Expression Recognition (FER), traditional local texture coding methods have a low computational complexity, while providing a robust solution with respect to occlusion, illumination, and other factors. However, there is still need for improving the accuracy of these methods [...] Read more.
In the field of Facial Expression Recognition (FER), traditional local texture coding methods have a low computational complexity, while providing a robust solution with respect to occlusion, illumination, and other factors. However, there is still need for improving the accuracy of these methods while maintaining their real-time nature and low computational complexity. In this paper, we propose a feature-based FER system with a novel local texture coding operator, named central symmetric local gradient coding (CS-LGC), to enhance the performance of real-time systems. It uses four different directional gradients on 5 × 5 grids, and the gradient is computed in the center-symmetric way. The averages of the gradients are used to reduce the sensitivity to noise. These characteristics lead to symmetric of features by the CS-LGC operator, thus providing a better generalization capability in comparison to existing local gradient coding (LGC) variants. The proposed system further transforms the extracted features into an eigen-space using a principal component analysis (PCA) for better representation and less computation; it estimates the intended classes by training an extreme learning machine. The recognition rate for the JAFFE database is 95.24%, whereas that for the CK+ database is 98.33%. The results show that the system has advantages over the existing local texture coding methods. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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19 pages, 2275 KiB  
Article
Household Power Demand Prediction Using Evolutionary Ensemble Neural Network Pool with Multiple Network Structures
by Songpu Ai, Antorweep Chakravorty and Chunming Rong
Sensors 2019, 19(3), 721; https://doi.org/10.3390/s19030721 - 10 Feb 2019
Cited by 40 | Viewed by 4628
Abstract
The progress of technology on energy and IoT fields has led to an increasingly complicated electric environment in low-voltage local microgrid, along with the extensions of electric vehicle, micro-generation, and local storage. It is required to establish a home energy management system (HEMS) [...] Read more.
The progress of technology on energy and IoT fields has led to an increasingly complicated electric environment in low-voltage local microgrid, along with the extensions of electric vehicle, micro-generation, and local storage. It is required to establish a home energy management system (HEMS) to efficiently integrate and manage household energy micro-generation, consumption and storage, in order to realize decentralized local energy systems at the community level. Domestic power demand prediction is of great importance for establishing HEMS on realizing load balancing as well as other smart energy solutions with the support of IoT techniques. Artificial neural networks with various network types (e.g., DNN, LSTM/GRU based RNN) and other configurations are widely utilized on energy predictions. However, the selection of network configuration for each research is generally a case by case study achieved through empirical or enumerative approaches. Moreover, the commonly utilized network initialization methods assign parameter values based on random numbers, which cause diversity on model performance, including learning efficiency, forecast accuracy, etc. In this paper, an evolutionary ensemble neural network pool (EENNP) method is proposed to achieve a population of well-performing networks with proper combinations of configuration and initialization automatically. In the experimental study, power demand predictions of multiple households are explored in three application scenarios: optimizing potential network configuration set, forecasting single household power demand, and refilling missing data. The impacts of evolutionary parameters on model performance are investigated. The experimental results illustrate that the proposed method achieves better solutions on the considered scenarios. The optimized potential network configuration set using EENNP achieves a similar result to manual optimization. The results of household demand prediction and missing data refilling perform better than the naïve and simple predictors. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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17 pages, 2974 KiB  
Article
Dynamically Reconfigurable Encryption and Decryption System Design for the Internet of Things Information Security
by Zhu Wang, Yan Yao, Xiaojun Tong, Qinghua Luo and Xiangyu Chen
Sensors 2019, 19(1), 143; https://doi.org/10.3390/s19010143 - 03 Jan 2019
Cited by 18 | Viewed by 4287
Abstract
Information security is the foundation for building trust between the Internet of Things (IoT) and its users. Due to the sharp increase of information quantity and the limitation of hardware resources, it is difficult to maintain the high performance of hardware equipment, while [...] Read more.
Information security is the foundation for building trust between the Internet of Things (IoT) and its users. Due to the sharp increase of information quantity and the limitation of hardware resources, it is difficult to maintain the high performance of hardware equipment, while also enhancing information security. To solve the problem of high consumption and low flexibility of multiple cryptographic algorithms hardware implementation, we have designed the Dynamically Reconfigurable Encryption and Decryption System, which is based on Field Programmable Gate Array. Considering the functional requirements, the cryptographic algorithm reconfigurable module files stored in External Memory could be configured dynamically into the assigned on-chip Reconfigurable Partition, supported by Core Controller and the Reconfiguration Control Platform. The experiment results show that, compared with the Static Encryption and Decryption System, our design reduces the logic resources by more than 30% and completes the algorithm swapping at the configuration speed of 15,759.51 Bytes/ms. It indicates that our design could reduce logic resources consumption and improve utilization efficiency and system flexibility. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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11 pages, 1981 KiB  
Article
A Randomness Detection Method of ZigBee Protocol in a Wireless Sensor Network
by Yongli Tang, Huanhuan Lian, Lixiang Li, Xiaojun Wang and Xixi Yan
Sensors 2018, 18(11), 3962; https://doi.org/10.3390/s18113962 - 15 Nov 2018
Cited by 4 | Viewed by 2396
Abstract
This study investigates the randomness detection of cryptographic algorithms in network security. To effectively test and verify the security of ZigBee protocol in the Internet of Things, the study combines with the characteristics of ZigBee networks, and it rationally organizes and divides test [...] Read more.
This study investigates the randomness detection of cryptographic algorithms in network security. To effectively test and verify the security of ZigBee protocol in the Internet of Things, the study combines with the characteristics of ZigBee networks, and it rationally organizes and divides test modes based on the binary matrix rank theory test. Then this paper proposes a randomness detection method of ZigBee protocol in a wireless sensor network. The proposed method solves the one-sidedness that the binary matrix rank test simply assesses random sequences by linear correlation. The proposed assessment method can effectively appraise whether the ZigBee protocol has an encryption mechanism and encryption strength. Simulation results show that this method has the characteristics of fewer errors and high reliability. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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13 pages, 526 KiB  
Article
Multivariate-Time-Series-Driven Real-time Anomaly Detection Based on Bayesian Network
by Nan Ding, Huanbo Gao, Hongyu Bu, Haoxuan Ma and Huaiwei Si
Sensors 2018, 18(10), 3367; https://doi.org/10.3390/s18103367 - 09 Oct 2018
Cited by 34 | Viewed by 7122
Abstract
Anomaly detection is an important research direction, which takes the real-time information system from different sensors and conditional information sources into consideration. Based on this, we can detect possible anomalies expected of the devices and components. One of the challenges is anomaly detection [...] Read more.
Anomaly detection is an important research direction, which takes the real-time information system from different sensors and conditional information sources into consideration. Based on this, we can detect possible anomalies expected of the devices and components. One of the challenges is anomaly detection in multivariate-sensing time-series in this paper. Based on this situation, we propose RADM, a real-time anomaly detection algorithm based on Hierarchical Temporal Memory (HTM) and Bayesian Network (BN). First of all, we use HTM model to evaluate the real-time anomalies of each univariate-sensing time-series. Secondly, a model of anomalous state detection in multivariate-sensing time-series based on Naive Bayesian is designed to analyze the validity of the above time-series. Lastly, considering the real-time monitoring cases of the system states of terminal nodes in Cloud Platform, the effectiveness of the methodology is demonstrated using a simulated example. Extensive simulation results show that using RADM in multivariate-sensing time-series is able to detect more abnormal, and thus can remarkably improve the performance of real-time anomaly detection. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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15 pages, 716 KiB  
Article
EasyLB: Adaptive Load Balancing Based on Flowlet Switching for Wireless Sensor Networks
by Zhiqiang Guo, Xiaodong Dong, Sheng Chen, Xiaobo Zhou and Keqiu Li
Sensors 2018, 18(9), 3060; https://doi.org/10.3390/s18093060 - 12 Sep 2018
Cited by 1 | Viewed by 3313
Abstract
Load balancing is effective in reducing network congestion and improving network throughput in wireless sensor networks (WSNs). Due to the fluctuation of wireless channels, traditional schemes achieving load balancing in WSNs need to maintain global or local congestion information, which turn out to [...] Read more.
Load balancing is effective in reducing network congestion and improving network throughput in wireless sensor networks (WSNs). Due to the fluctuation of wireless channels, traditional schemes achieving load balancing in WSNs need to maintain global or local congestion information, which turn out to be complicated to implement. In this paper, we design a flowlet switching based load balancing scheme, called EasyLB, by extending OpenFlow protocol. Flowlet switching is efficient to achieve adaptive load balancing in WSNs. Nevertheless, one tricky problem lies in determining the flowlet timeout value, δ . Setting it too small would risk reordering issue, while setting it too large would reduce flowlet opportunities. By formulating the timeout setting problem with a stationary distribution of Markov chain, we give a theoretical reference for setting an appropriate timeout value in flowlet switching based load balancing scheme. Moreover, non-equal probability path selection and multiple parallel load balancing paths are considered in timeout setting problem. Experimental results show that, by setting timeout value following the preceding theoretical reference, EasyLB is adaptive to wireless channel condition change and achieves fast convergence of load balancing after link failures. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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11 pages, 1048 KiB  
Article
New Multi-Keyword Ciphertext Search Method for Sensor Network Cloud Platforms
by Lixia Xie, Ziying Wang, Yue Wang, Hongyu Yang and Jiyong Zhang
Sensors 2018, 18(9), 3047; https://doi.org/10.3390/s18093047 - 12 Sep 2018
Cited by 2 | Viewed by 2844
Abstract
This paper proposed a multi-keyword ciphertext search, based on an improved-quality hierarchical clustering (MCS-IQHC) method. MCS-IQHC is a novel technique, which is tailored to work with encrypted data. It has improved search accuracy and can self-adapt when performing multi-keyword ciphertext searches on privacy-protected [...] Read more.
This paper proposed a multi-keyword ciphertext search, based on an improved-quality hierarchical clustering (MCS-IQHC) method. MCS-IQHC is a novel technique, which is tailored to work with encrypted data. It has improved search accuracy and can self-adapt when performing multi-keyword ciphertext searches on privacy-protected sensor network cloud platforms. Document vectors are first generated by combining the term frequency-inverse document frequency (TF-IDF) weight factor and the vector space model (VSM). The improved quality hierarchical clustering (IQHC) algorithm then generates document vectors, document indices, and cluster indices, which are encrypted via the k-nearest neighbor algorithm (KNN). MCS-IQHC then returns the top-k search result. A series of experiments proved that the proposed method had better searching efficiency and accuracy in high-privacy sensor cloud network environments, compared to other state-of-the-art methods. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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19 pages, 828 KiB  
Article
Efficient Interference Estimation with Accuracy Control for Data-Driven Resource Allocation in Cloud-RAN
by Yanchao Zhao, Jie Wu, Wenzhong Li and Sanglu Lu
Sensors 2018, 18(9), 3000; https://doi.org/10.3390/s18093000 - 07 Sep 2018
Cited by 5 | Viewed by 2651
Abstract
The emerging edge computing paradigm has given rise to a new promising mobile network architecture, which can address a number of challenges that the operators are facing while trying to support growing end user’s needs by shifting the computation from the base station [...] Read more.
The emerging edge computing paradigm has given rise to a new promising mobile network architecture, which can address a number of challenges that the operators are facing while trying to support growing end user’s needs by shifting the computation from the base station to the edge cloud computing facilities. With such powerfully computational power, traditional unpractical resource allocation algorithms could be feasible. However, even with near optimal algorithms, the allocation result could still be far from optimal due to the inaccurate modeling of interference among sensor nodes. Such a dilemma calls for a measurement data-driven resource allocation to improve the total capacity. Meanwhile, the measurement process of inter-nodes’ interference could be tedious, time-consuming and have low accuracy, which further compromise the benefits brought by the edge computing paradigm. To this end, we propose a measurement-based estimation solution to obtain the interference efficiently and intelligently by dynamically controlling the measurement and estimation through an accuracy-driven model. Basically, the measurement cost is reduced through the link similarity model and the channel derivation model. Compared to the exhausting measurement method, it can significantly reduce the time cost to the linear order of the network size with guaranteed accuracy through measurement scheduling and the accuracy control process, which could also balance the tradeoff between accuracy and measurement overhead. Extensive experiments based on real data traces are conducted to show the efficiency of the proposed solutions. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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18 pages, 3391 KiB  
Article
An Intelligent Computing Method for Contact Plan Design in the Multi-Layer Spatial Node-Based Internet of Things
by Cui-Qin Dai, Qingyang Song and Lei Guo
Sensors 2018, 18(9), 2852; https://doi.org/10.3390/s18092852 - 29 Aug 2018
Cited by 1 | Viewed by 2991
Abstract
Computational Intelligence (CI) has been addressed as a great challenge in recent years, particularly the aspects of routing, task scheduling, and other high-complexity issues. Especially for the Contact Plan Design (CPD) that schedules contacts in dynamic and resource-constrained networks, a suitable CI algorithm [...] Read more.
Computational Intelligence (CI) has been addressed as a great challenge in recent years, particularly the aspects of routing, task scheduling, and other high-complexity issues. Especially for the Contact Plan Design (CPD) that schedules contacts in dynamic and resource-constrained networks, a suitable CI algorithm can be exchanged for a high-quality Contact Plan (CP) with the appropriate computational overhead. Previous works on CPD mainly focused on the optimization of satellite network connectivity, but most of them ignored network topology characteristics. In this paper, we study the CPD issue in the spatial node based Internet of Things (IoT), which enables the spatial nodes to deliver data cooperatively via intelligent networking. Specifically, we first introduce a Multi-Layer Space Communication Network (MLSCN) model consisting of satellites, High Altitude Platforms (HAPs), Unmanned Aerial Vehicles (UAVs), and ground stations, on which a Time-Evolving Graph (TEG) is used to illustrate the CPD process. Then, according to the characteristics of each layer in the MLSCN, we design the corresponding CPs for the inter-layer contacts and intra-layer contacts. After that, a CI algorithm named as Multidirectional Particle Swarm Optimization (MPSO) is proposed for inter-layer CPD, which utilizes a grid-based initialization strategy to expand the diversity of individuals, in which a quaternary search method and quaternary optimization are introduced to improve efficiency of particle swarms in iterations and to ensure the continuous search ability, respectively. Furthermore, an optimized scheme is implemented for the intra-layer CPD to reduce congestion and improve transmission efficiency. Simulation results show that the proposed CPD scheme can realize massive data transmission with high efficiency in the multi-layer spatial node-based IoT. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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13 pages, 431 KiB  
Article
A Dynamic Estimation of Service Level Based on Fuzzy Logic for Robustness in the Internet of Things
by Bing Jia, Lifei Hao, Chuxuan Zhang and Dong Chen
Sensors 2018, 18(7), 2190; https://doi.org/10.3390/s18072190 - 07 Jul 2018
Cited by 8 | Viewed by 3066
Abstract
The Internet of things (IoT) technology is developing rapidly, and the IoT services are penetrating broadly into every aspect of people’s lives. As the large amount of services grows dramatically, how to discover and select the best services dynamically to satisfy the actual [...] Read more.
The Internet of things (IoT) technology is developing rapidly, and the IoT services are penetrating broadly into every aspect of people’s lives. As the large amount of services grows dramatically, how to discover and select the best services dynamically to satisfy the actual needs of users in the IoT service set, the elements of which have the same function, is an unavoidable issue. Therefore, for the robustness of the IoT system, evaluating the quality level of the IoT service to provide a reference for the users choosing the most appropriate service has become a hot topic. Most of the current methods just use some static data to evaluate the quality of the service and ignore the dynamic changing trend of the service performance. In this paper, an estimation mechanism for the quality level of the IoT service based on fuzzy logic is conducted to grade the quality of the service. Specifically, the comprehensive factors are taken into account according to the defined level changing rules and the effect of the service in the previous execution process, so that it can provide users with an effective reference. Experiments are carried out by using a simulated service set. It is shown that the proposed algorithm can estimate the quality level of the service more comprehensively and reasonably, which is evidently superior to the other two common methods, i.e., the estimating method by a Randomization Test (RT) and the estimating method by a Single Test in Steps (STS). Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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15 pages, 2918 KiB  
Article
Access Control Model Based on Time Synchronization Trust in Wireless Sensor Networks
by Zhaobin Liu, Qiang Ma, Wenzhi Liu, Victor S. Sheng, Liang Zhang and Gang Liu
Sensors 2018, 18(7), 2107; https://doi.org/10.3390/s18072107 - 30 Jun 2018
Cited by 5 | Viewed by 3418
Abstract
Internal reliability and external safety of Wireless Sensor Networks (WSN) data transmission have become increasingly outstanding issues with the wide applications of WSN. This paper proposes a new method for access control and mitigation of interfering noise in time synchronization environments. First, a [...] Read more.
Internal reliability and external safety of Wireless Sensor Networks (WSN) data transmission have become increasingly outstanding issues with the wide applications of WSN. This paper proposes a new method for access control and mitigation of interfering noise in time synchronization environments. First, a formal definition is given regarding the impact interference noise has on the clock skew and clock offset of each node. The degree of node interference behavior is estimated dynamically from the perspective of time-stamp changes caused by the interference noise. Secondly, a general access control model is proposed to resist invasion of noise interference. A prediction model is constructed using the Bayesian method for calculating the reliability of neighbor node behavior in the proposed model. Interference noise, which attacks the time synchronization, is regarded as the key factor for probability estimation of the reliability. The result of the calculations determines whether it is necessary to initiate synchronization filtering. Finally, a division of trust levels with bilinear definition is employed to lower interference noise and improve the quality of interference detection. Experimental results show that this model has advantages in system overhead, energy consumption and testing errors, compared to its counterparts. When the disturbance intensity of a WSN increases, the proposed optimized algorithm converges faster with a lower network communication load. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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11 pages, 1575 KiB  
Article
An Exception Handling Approach for Privacy-Preserving Service Recommendation Failure in a Cloud Environment
by Lianyong Qi, Shunmei Meng, Xuyun Zhang, Ruili Wang, Xiaolong Xu, Zhili Zhou and Wanchun Dou
Sensors 2018, 18(7), 2037; https://doi.org/10.3390/s18072037 - 26 Jun 2018
Cited by 49 | Viewed by 3466
Abstract
Service recommendation has become an effective way to quickly extract insightful information from massive data. However, in the cloud environment, the quality of service (QoS) data used to make recommendation decisions are often monitored by distributed sensors and stored in different cloud platforms. [...] Read more.
Service recommendation has become an effective way to quickly extract insightful information from massive data. However, in the cloud environment, the quality of service (QoS) data used to make recommendation decisions are often monitored by distributed sensors and stored in different cloud platforms. In this situation, integrating these distributed data (monitored by remote sensors) across different platforms while guaranteeing user privacy is an important but challenging task, for the successful service recommendation in the cloud environment. Locality-Sensitive Hashing (LSH) is a promising way to achieve the abovementioned data integration and privacy-preservation goals, while current LSH-based recommendation studies seldom consider the possible recommendation failures and hence reduce the robustness of recommender systems significantly. In view of this challenge, we develop a new LSH variant, named converse LSH, and then suggest an exception handling approach for recommendation failures based on the converse LSH technique. Finally, we conduct several simulated experiments based on the well-known dataset, i.e., Movielens to prove the effectiveness and efficiency of our approach. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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15 pages, 2194 KiB  
Article
Design of Event-Triggered Fault-Tolerant Control for Stochastic Systems with Time-Delays
by Yi Gao, YunJi Li, Li Peng and Junyu Liu
Sensors 2018, 18(6), 1929; https://doi.org/10.3390/s18061929 - 13 Jun 2018
Cited by 10 | Viewed by 3108
Abstract
This paper proposes two novel, event-triggered fault-tolerant control strategies for a class of stochastic systems with state delays. The plant is disturbed by a Gaussian process, actuator faults, and unknown disturbances. First, a special case about fault signals that are coupled to the [...] Read more.
This paper proposes two novel, event-triggered fault-tolerant control strategies for a class of stochastic systems with state delays. The plant is disturbed by a Gaussian process, actuator faults, and unknown disturbances. First, a special case about fault signals that are coupled to the unknown disturbances is discussed, and then a fault-tolerant strategy is designed based on an event condition on system states. Subsequently, a send-on-delta transmission framework is established to deal with the problem of fault-tolerant control strategy against fault signals separated from the external disturbances. Two criteria are provided to design feedback controllers in order to guarantee that the systems are exponentially mean-square stable, and the corresponding H∞-norm disturbance attenuation levels are achieved. Two theorems were obtained by synthesizing the feedback control gains and the desired event conditions in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are provided to illustrate the effectiveness of the proposed theoretical results. Full article
(This article belongs to the Special Issue Intelligent Computing, Networking, Security and Robustness in Internet of Things)
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