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Special Issue "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: 31 December 2019.

Special Issue Editors

Prof. Dr. Tie Qiu
E-Mail Website
Guest Editor
School of Computer Science and Technology, Tianjin University, China
Interests: Internet of Things; smart cities and intelligent transportation; social networks and mobile computing; big data and cloud computing
Special Issues and Collections in MDPI journals
Prof. Dr. Klaus Moessner
E-Mail Website
Guest Editor
Institute for Communication Systems (ICS), University of Surrey, Guildford GU2 7XH, UK
Interests: cognitive networks; IoT deployments and sensor data based knowledge generation; reconfiguration and resource management
Prof. Dr. Chen Chen
E-Mail Website
Guest Editor
Xidian University, Xi'an 710071, China
Interests: short range wireless communication; Internet of Things; Vehicular Ad Hoc Networks; Operating Systems; Network Simulation

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 2018ICDH 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 papers will be 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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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 (19 papers)

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Research

Open AccessArticle
An LSTM-Method-Based Availability Prediction for Optimized Offloading in Mobile Edges
Sensors 2019, 19(20), 4467; https://doi.org/10.3390/s19204467 - 15 Oct 2019
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
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Open AccessArticle
Conditional Artificial Potential Field-Based Autonomous Vehicle Safety Control with Interference of Lane Changing in Mixed Traffic Scenario
Sensors 2019, 19(19), 4199; https://doi.org/10.3390/s19194199 - 27 Sep 2019
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
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Open AccessArticle
Time-Efficient Allocation Mechanisms for Crowdsensing Tasks with Precedence Constraints
Sensors 2019, 19(11), 2456; https://doi.org/10.3390/s19112456 - 29 May 2019
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
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Open AccessArticle
Authentication and Delegation for Operating a Multi-Drone System
Sensors 2019, 19(9), 2066; https://doi.org/10.3390/s19092066 - 03 May 2019
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
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Open AccessArticle
Design and Analysis of a Lightweight Context Fusion CNN Scheme for Crowd Counting
Sensors 2019, 19(9), 2013; https://doi.org/10.3390/s19092013 - 29 Apr 2019
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
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Open AccessArticle
Parallel Transmission of Distributed Sensor Based on SCTP and TCP for Heterogeneous Wireless Networks in IoT
Sensors 2019, 19(9), 2005; https://doi.org/10.3390/s19092005 - 29 Apr 2019
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
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Open AccessArticle
Improved Real-Time Facial Expression Recognition Based on a Novel Balanced and Symmetric Local Gradient Coding
Sensors 2019, 19(8), 1899; https://doi.org/10.3390/s19081899 - 22 Apr 2019
Cited by 1
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
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Open AccessArticle
Household Power Demand Prediction Using Evolutionary Ensemble Neural Network Pool with Multiple Network Structures
Sensors 2019, 19(3), 721; https://doi.org/10.3390/s19030721 - 10 Feb 2019
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
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Open AccessArticle
Dynamically Reconfigurable Encryption and Decryption System Design for the Internet of Things Information Security
Sensors 2019, 19(1), 143; https://doi.org/10.3390/s19010143 - 03 Jan 2019
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
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Open AccessArticle
A Randomness Detection Method of ZigBee Protocol in a Wireless Sensor Network
Sensors 2018, 18(11), 3962; https://doi.org/10.3390/s18113962 - 15 Nov 2018
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
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Open AccessArticle
Multivariate-Time-Series-Driven Real-time Anomaly Detection Based on Bayesian Network
Sensors 2018, 18(10), 3367; https://doi.org/10.3390/s18103367 - 09 Oct 2018
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
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Open AccessArticle
EasyLB: Adaptive Load Balancing Based on Flowlet Switching for Wireless Sensor Networks
Sensors 2018, 18(9), 3060; https://doi.org/10.3390/s18093060 - 12 Sep 2018
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
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Open AccessArticle
New Multi-Keyword Ciphertext Search Method for Sensor Network Cloud Platforms
Sensors 2018, 18(9), 3047; https://doi.org/10.3390/s18093047 - 12 Sep 2018
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
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Open AccessArticle
Efficient Interference Estimation with Accuracy Control for Data-Driven Resource Allocation in Cloud-RAN
Sensors 2018, 18(9), 3000; https://doi.org/10.3390/s18093000 - 07 Sep 2018
Cited by 1
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
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Open AccessArticle
An Intelligent Computing Method for Contact Plan Design in the Multi-Layer Spatial Node-Based Internet of Things
Sensors 2018, 18(9), 2852; https://doi.org/10.3390/s18092852 - 29 Aug 2018
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
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Open AccessArticle
A Dynamic Estimation of Service Level Based on Fuzzy Logic for Robustness in the Internet of Things
Sensors 2018, 18(7), 2190; https://doi.org/10.3390/s18072190 - 07 Jul 2018
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
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Open AccessArticle
Access Control Model Based on Time Synchronization Trust in Wireless Sensor Networks
Sensors 2018, 18(7), 2107; https://doi.org/10.3390/s18072107 - 30 Jun 2018
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
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Open AccessArticle
An Exception Handling Approach for Privacy-Preserving Service Recommendation Failure in a Cloud Environment
Sensors 2018, 18(7), 2037; https://doi.org/10.3390/s18072037 - 26 Jun 2018
Cited by 21
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
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Open AccessArticle
Design of Event-Triggered Fault-Tolerant Control for Stochastic Systems with Time-Delays
Sensors 2018, 18(6), 1929; https://doi.org/10.3390/s18061929 - 13 Jun 2018
Cited by 3
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
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