A new current limiting and overload protection scheme for distributed inverters in microgrids under grid faults
- Authors: Li, Zilin , Hu, Jiefeng , Chan, Ka Wing
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Industry Applications Vol. 57, no. 6 (2021), p. 6362-6374
- Full Text: false
- Reviewed:
- Description: Unlike a synchronous generator that could withstand a large overcurrent, an inverter-based distributed generation (DG) has low thermal inertia, and the inverter is likely damaged by overcurrents during grid faults. In this article, a new strategy, namely positive-And negative-sequence limiting with stability enhanced P-f droop control (PNSL-SEPFC), is proposed to limit the output currents and active power of droop-controlled inverters in islanded microgrids. This strategy is easy to implement in the inverter controller and does not require any fault detection. Inverter stability is analyzed mathematically, which gives guidelines to design the parameters of the PNSL-SEPFC strategy. PSCAD/EMTDC simulation based on a four-DG microgrid shows that the proposed PNSL-SEPFC can limit inverter output currents and powers with better performance under both symmetrical and asymmetrical faults. Furthermore, hardware experiments demonstrate that the proposed PNSL-SEPFC can ensure the inverters riding through grid faults safely and stably. (A video of experimental waveforms is attached.). © 1972-2012 IEEE.
The rise of ransomware and emerging security challenges in the internet of things
- Authors: Yaqoob, Ibrar , Ahmed, Ejaz , Rehman, Muhammad , Ahmed, Abdelmuttlib , Imran, Muhammad
- Date: 2017
- Type: Text , Journal article
- Relation: Computer Networks Vol. 129, no. (2017), p. 444-458
- Full Text: false
- Reviewed:
- Description: With the increasing miniaturization of smartphones, computers, and sensors in the Internet of Things (IoT) paradigm, strengthening the security and preventing ransomware attacks have become key concerns. Traditional security mechanisms are no longer applicable because of the involvement of resource-constrained devices, which require more computation power and resources. This paper presents the ransomware attacks and security concerns in IoT. We initially discuss the rise of ransomware attacks and outline the associated challenges. Then, we investigate, report, and highlight the state-of-the-art research efforts directed at IoT from a security perspective. A taxonomy is devised by classifying and categorizing the literature based on important parameters (e.g., threats, requirements, IEEE standards, deployment level, and technologies). Furthermore, a few credible case studies are outlined to alert people regarding how seriously IoT devices are vulnerable to threats. We enumerate the requirements that need to be met for securing IoT. Several indispensable open research challenges (e.g., data integrity, lightweight security mechanisms, lack of security software's upgradability and patchability features, physical protection of trillions of devices, privacy, and trust) are identified and discussed. Several prominent future research directions are provided. © 2017 Elsevier B.V. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**
Ignition delay, combustion and emission characteristics of diesel engine fueled with biodiesel
- Authors: Shahabuddin, M. , Liaquat, A. , Masjuki, H. , Kalam, M. , Mofijur, M.
- Date: 2013
- Type: Text , Journal article
- Relation: Renewable & sustainable energy reviews Vol. 21, no. (2013), p. 623-632
- Full Text: false
- Reviewed:
- Description: Biodiesels are gaining more importance as a promising alternative energy resource. Engine performance and emission characteristics of unmodified biodiesel fueled diesel engines are highly influenced by its ignition and combustion behavior. This review article presents the literature review on ignition delay (ID), combustion and emission characteristics of biodiesel fueled diesel engine. More than hundred articles report which have been published mostly in the last decade are reviewed in this paper. The investigation results report that the combustion characteristics of bio fueled engine is slightly different from the engine running with petroleum diesel. Most of the investigation results have reported that as compared to diesel, biodiesel has early start of combustion (SOC) and shorter ID of between 1–5° and 0.25–1.0°, respectively. Higher cetane number (CN), lower compressibility and fatty acid composition of biodiesel have been identified as the main elements for early SOC and shorter ID. In addition, it is also found that, the heat release rate (HRR) of biodiesel is slightly lower than diesel owing to the lower calorific value, lower volatility, shorter ID and higher viscosity.
A blockchain-based solution for enhancing security and privacy in smart factory
- Authors: Wan, Jafu , Li, Jiapeng , Imran, Muhammad , Li, Di
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 15, no. 6 (2019), p. 3652-3660
- Full Text: false
- Reviewed:
- Description: Through the Industrial Internet of Things (IIoT), a smart factory has entered the booming period. However, as the number of nodes and network size become larger, the traditional IIoT architecture can no longer provide effective support for such enormous system. Therefore, we introduce the Blockchain architecture, which is an emerging scheme for constructing the distributed networks, to reshape the traditional IIoT architecture. First, the major problems of the traditional IIoT architecture are analyzed, and the existing improvements are summarized. Second, we introduce a security and privacy model to help design the Blockchain-based architecture. On this basis, we decompose and reorganize the original IIoT architecture to form a new multicenter partially decentralized architecture. Then, we introduce some relative security technologies to improve and optimize the new architecture. After that we design the data interaction process and the algorithms of the architecture. Finally, we use an automatic production platform to discuss the specific implementation. The experimental results show that the proposed architecture provides better security and privacy protection than the traditional architecture. Thus, the proposed architecture represents a significant improvement of the original architecture, which provides a new direction for the IIoT development. © 2005-2012 IEEE.
An effective density-based clustering and dynamic maintenance framework for evolving medical data streams
- Authors: Al-Shammari, Ahmed , Zhou, Rui , Naseriparsaa, Mehdi , Liu, Chengfei
- Date: 2019
- Type: Text , Journal article
- Relation: International Journal of Medical Informatics Vol. 126, no. (2019), p. 176-186
- Full Text: false
- Reviewed:
- Description: Background: Medical data stream clustering has become an integral part of medical decision systems since it extracts highly-sensitive information from a tremendous flow of medical data. However, clustering and maintaining of medical data streams is still a challenging task. That is because the evolving of medical data streams imposes various challenges for clustering such as the ability to discover the arbitrary shape of a cluster, the ability to group data streams without a predefined number of clusters, and the ability to maintain the data clusters dynamically. Objective: To support the online medical decisions, there is a need to address the clustering challenges. Therefore, in this paper, we propose an effective density-based clustering and dynamic maintenance framework for grouping the patients with similar symptoms into meaningful clusters and monitoring the patients’ status frequently. Methods: For clustering, we generate a set of initial medical data clusters based on the combination of Piece-wise Aggregate Approximation and the density-based spatial clustering of applications with noise called (PAA+DBSCAN) algorithm. For maintenance, when new medical data streams arrive, we maintain the initially generated medical data clusters dynamically. Since the incremental cluster maintenance is time-consuming, we further propose an Advanced Cluster Maintenance (ACM) approach to improve the performance of the dynamic cluster maintenance. Results: The experimental results on real-world medical datasets demonstrate the effectiveness and efficiency of our proposed approaches. The PAA+DBSCAN algorithm is more efficient and effective than the exact DBSCAN algorithm. Moreover, the ACM approach requires less running time in comparison with the Baseline Cluster Maintenance (BCM) approach using different tuning parameter values in all datasets. That is because the BCM approach tracks all the data points in the cluster. Conclusion: The proposed framework is capable of clustering and maintaining the medical data streams effectively by means of grouping the patients who share similar symptoms and tracking the patients status that naturally tends to be changing over time. © 2019 Elsevier B.V.
A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing
- Authors: Li, Xiaomin , Wan, Jiafu , Dai, Hong-Ning , Imran, Muhammad , Xia, Min , Celesti, Antonio
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 15, no. 7 (2019), p. 4225-4234
- Full Text: false
- Reviewed:
- Description: At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing. © 2005-2012 IEEE.
The role of big data analytics in internet of things
- Authors: Ahmed, Ejaz , Yaqoob, Ibrar , Hashem, Ibrahim , Khan, Imran , Imran, Muhammad
- Date: 2017
- Type: Text , Journal article
- Relation: Computer Networks Vol. 129, no. (2017), p. 459-471
- Full Text: false
- Reviewed:
- Description: The explosive growth in the number of devices connected to the Internet of Things (IoT) and the exponential increase in data consumption only reflect how the growth of big data perfectly overlaps with that of IoT. The management of big data in a continuously expanding network gives rise to non-trivial concerns regarding data collection efficiency, data processing, analytics, and security. To address these concerns, researchers have examined the challenges associated with the successful deployment of IoT. Despite the large number of studies on big data, analytics, and IoT, the convergence of these areas creates several opportunities for flourishing big data and analytics for IoT systems. In this paper, we explore the recent advances in big data analytics for IoT systems as well as the key requirements for managing big data and for enabling analytics in an IoT environment. We taxonomized the literature based on important parameters. We identify the opportunities resulting from the convergence of big data, analytics, and IoT as well as discuss the role of big data analytics in IoT applications. Finally, several open challenges are presented as future research directions. © 2017 Elsevier B.V. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**
Model compression for IoT applications in industry 4.0 via multiscale knowledge transfer
- Authors: Fu, Shipeng , Li, Zhen , Liu, Kai , Din, Sadia , Imran, Muhammad , Yang, Xiaomin
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 16, no. 9 (2020), p. 6013-6022
- Full Text: false
- Reviewed:
- Description: Recently, Industry 4.0 has attracted much attention. It has close relations with the Internet of Things (IoT). On the other hand, convolutional neural networks (CNNs) have shown promising performance in many foundational services of the IoT applications. For the IoT applications with high-speed data streams and the requirement of time-sensitive actions, fast processing is demanded on small-scale platforms or even on IoT devices themselves. Therefore, it is inappropriate to employ cumbersome CNNs in IoT applications, making the study of model compression necessary. In knowledge transfer, it is common to employ a deep, well-trained network, called teacher, to guide a shallow, untrained network, called student, to have better performance. Previous works have made many attempts to transfer single-scale knowledge from teacher to student, leading to degradation of generalization ability. In this article, we introduce multiscale representations to knowledge transfer, which facilitates the generalization ability of student. We divide student and teacher into several stages. Student learns from multiscale knowledge provided by teacher at the end of each stage. Extensive experiments demonstrate the effectiveness of our proposed method both on image classification and on single image super-resolution. The huge performance gap between student and teacher is significantly narrowed down by our proposed method, making student suitable for IoT applications. © 2005-2012 IEEE.
A holistic power management strategy of microgrids based on model predictive control and particle swarm optimization
- Authors: Shan, Yinghao , Hu, Jiefeng , Liu, Huashan
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 18, no. 8 (2022), p. 5115-5126
- Full Text: false
- Reviewed:
- Description: Power control and optimization are both crucial for the proper operation of a microgrid. However, in existing research, they are usually studied separately. Active and reactive powers are either maintained to constant values at device level or optimized at system level without considering frequency and voltage control of distributed converters. In this article, a holistic power control and optimization strategy is proposed for microgrids. Specifically, a model predictive control incorporated with the droop method is developed at device level to achieve load sharing and flexible power dispatching among distributed energy resources, which is feasible for both islanded and grid-connected modes. In addition, an evolutionary particle swarm optimization algorithm is designed at system level to generate the optimal active and reactive power setpoints, which are then sent to the device level for controlling inverters. The proposed power optimization scheme is able to mitigate voltage deviations and minimize the operational cost of the microgrid. Comprehensive case studies and real-time simulator test are provided to demonstrate the feasibility and efficacy of the proposed power control and optimization strategy. © 2005-2012 IEEE.
Thermoelastic fracture analysis of functionally graded materials using the scaled boundary finite element method
- Authors: Iqbal, M. , Birk, Carolin , Ooi, Ean Tat , Pramod, Aladurthi , Natarajan, Sundararajan , Gravenkamp, Hauke , Song, Chongmin
- Date: 2022
- Type: Text , Journal article
- Relation: Engineering Fracture Mechanics Vol. 264, no. (2022), p.
- Full Text: false
- Reviewed:
- Description: The scaled boundary finite element method is extended to model fracture in functionally graded materials (FGM) under coupled thermo-mechanical loads. The governing equations of coupled thermo-mechanical equilibrium are discretized using scaled boundary shape functions enriched with the thermal load terms. The material gradient is modeled as a series of power functions, and the stiffness matrix is calculated semi-analytically. Stress intensity factors and T−stress are directly calculated from their definition without any need for additional post-processing techniques. Arbitrary-sided polygon elements are employed for flexible mesh generation. Several numerical examples for isotropic and orthotropic FGMs are presented to validate the proposed technique. © 2022 Elsevier Ltd
A novel real-time deterministic scheduling mechanism in industrial cyber-physical systems for energy internet
- Authors: Peng, Yuhuai , Jolfaei, Alireza , Yu, Keping
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 18, no. 8 (2022), p. 5670-5680
- Full Text: false
- Reviewed:
- Description: As an effective distributed renewable energy utilization paradigm, a microgrid is expected to realize the high integration of the industrial cyber-physical systems (CPS), which has attracted extensive attention from academia and industry. However, the real-time interaction and feedback loop between physical systems and cyber systems have posed severe challenges to the reliability, determinacy, and energy efficiency of the multiway flow of information and communication transmission. In order to solve the problem of slot scheduling and data transmission (SSDT) in the microgrid, a novel real-time deterministic scheduling (RTDS) scheme for industrial CPS is proposed in this article. First, the SSDT is formulated as a multiway flow scheduling problem, and it is theoretically proved that the SSDT problem is NP-hard. Then, the RTDS scheme designs two heuristic algorithms: scheduling request preprocessing and greedy-based multichannel time slot allocation for an optimal scheduling solution. Practical experimental results demonstrate that the proposed RTDS scheme has significant advantages in packet loss rate, deadline guarantee rate, and energy consumption compared with the traditional schemes, and thus, is more suitable for deployment in microgrid systems. © 2005-2012 IEEE.
Time-frequency filter bank: A simple approach for audio and music separation
- Authors: Yang, Ning , Usman, Muhammad , He, Xiangjian , Jan, Mian Ahmad , Zhang, Liming
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE access Vol. 5, no. (2017), p. 27114-27125
- Full Text: false
- Reviewed:
- Description: Blind Source Separation techniques are widely used in the field of wireless communication for a very long time to extract signals of interest from a set of multiple signals without training data. In this paper, we investigate the problem of separation of the human voice from a mixture of human voice and sounds from different musical instruments. The human voice may be a singing voice in a song or may be a part of some news, broadcast by a channel with background music. This paper proposes a generalized Short Time Fourier Transform (STFT)-based technique, combined with filter bank to extract vocals from background music. The main purpose is to design a filter bank and to eliminate background aliasing errors with best reconstruction conditions, having approximated scaling factors. Stereo signals in time-frequency domain are used in experiments. The input stereo signals are processed in the form of frames and passed through the proposed STFT-based technique. The output of the STFT-based technique is passed through the filter bank to minimize the background aliasing errors. For reconstruction, first an inverse STFT is applied and then the signals are reconstructed by the OverLap-Add method to get the final output, containing vocals only. The experiments show that the proposed approach performs better than the other state-of-the-art approaches, in terms of Signal-to-Interference Ratio (SIR) and Signal-to-Distortion Ratio (SDR), respectively.
Performance evaluation of high definition video streaming over mobile ad hoc networks
- Authors: Usman, Muhammad , Jan, Mian Ahmad , He, Xiangjian , Alam, Muhammad
- Date: 2018
- Type: Text , Journal article
- Relation: Signal processing Vol. 148, no. (2018), p. 303-313
- Full Text: false
- Reviewed:
- Description: A unique combinational approach of QoS and QoE for multimedia traffic in MANETs is proposed in this paper.•A feedback-based multi-path transmission strategy is adopted to minimize the data loss during HD video streaming.•An EC technique based on multi-threading-based parallel processing is used to recover the lost video frames.•We use multiple objective evaluation metrics to test the performance of the EC technique on recovered video frames.•Subjective analysis is combined with objective evaluation using subjective scores and statistical analysis to maintain QoE. Video Service Providers (VSPs) can collect and analyze an enormous amount of multimedia data from various cloud storage centers using real-time big data systems for supporting various online customers. The infrastructure-less nature of Mobile Ad Hoc Networks (MANETs) makes the video streaming a challenging task for VSPs. High packet-loss probability in MANETs can create a notable distortion in the received video quality. In this paper, High Definition (HD) videos are streamed over MANETs. First, a transmission model is designed followed by a distortion model to estimate network distortions, such as packet-loss rate and end-to-end delay. Based on the proposed models, a video streaming framework is designed to efficiently utilize the available bandwidth in MANETs, minimize the network distortions, and improve Quality of Service (QoS). Later, an Error Concealment (EC) technique is used to conceal the lost/dropped video frames to improve the Quality of Experience (QoE). Experimental results show that our proposed video streaming framework outperforms the state-of-the-art routing protocols designed for MANETs, such as Destination-Sequenced Distance Vector (DSDV) and Optimized Link Sate Routing (OLSR) protocols. In the end, both subjective and objective evaluations are performed to evaluate the perceptual quality of the concealed video data.
Frame interpolation for cloud-based mobile video streaming
- Authors: Usman, Muhammad , Xiangjian, He , Kin-Man, Lam , Min, Xu , Bokhari, Syed , Jinjun, Chen
- Date: 2016
- Type: Text , Journal article
- Relation: IEEE transactions on multimedia Vol. 18, no. 5 (2016), p. 831-839
- Full Text: false
- Reviewed:
- Description: Cloud-based High Definition (HD) video streaming is becoming popular day by day. On one hand, it is important for both end users and large storage servers to store their huge amount of data at different locations and servers. On the other hand, it is becoming a big challenge for network service providers to provide reliable connectivity to the network users. There have been many studies over cloud-based video streaming for Quality of Experience (QoE) for services like YouTube. Packet losses and bit errors are very common in transmission networks, which affect the user feedback over cloud-based media services. To cover up packet losses and bit errors, Error Concealment (EC) techniques are usually applied at the decoder/receiver side to estimate the lost information. This paper proposes a time-efficient and quality-oriented EC method. The proposed method considers H.265/HEVC based intra-encoded videos for the estimation of whole intra-frame loss. The main emphasis in the proposed approach is the recovery of Motion Vectors (MVs) of a lost frame in real-time. To boost-up the search process for the lost MVs, a bigger block size and searching in parallel are both considered. The simulation results clearly show that our proposed method outperforms the traditional Block Matching Algorithm (BMA) by approximately 2.5 dB and Frame Copy (FC) by up to 12 dB at a packet loss rate of 1%, 3%, and 5% with different Quantization Parameters (QPs). The computational time of the proposed approach outperforms the BMA by approximately 1788 seconds.
VANET–LTE based heterogeneous vehicular clustering for driving assistance and route planning applications
- Authors: Ahmad, Iftikhar , Noor, Rafidah , Ahmedy, Ismail , Shah, Syed , Imran, Muhammad
- Date: 2018
- Type: Text , Journal article
- Relation: Computer Networks Vol. 145, no. (2018), p. 128-140
- Full Text: false
- Reviewed:
- Description: The Internet of vehicles incorporates multiple access networks and technologies to connect vehicles on roads. These vehicles usually require the use of individual long-term evolution (LTE) connections to send/receive data to/from a remote server to make smart decisions regarding route planning and driving. An increasing number of vehicles on the roads may not only overwhelm LTE network usage but also incur added cost. Clustering helps minimize LTE usage, but the high speed of vehicles renders connections unstable and unreliable not only among vehicles but also between vehicles and the LTE network. Moreover, non-cooperative behavior among vehicles within a cluster is a bottleneck in sharing costly data acquired from the Internet. To address these issues, we propose a novel destination- and interest-aware clustering (DIAC) mechanism. DIAC primarily incorporates a strategic game-theoretic algorithm and a self-location calculation algorithm. The former allows vehicles to participate/cooperate and enforces a fair-use policy among the cluster members (CMs), whereas the latter enables CMs to calculate their location coordinates in the absence of a global positioning system under an urban topography. DIAC strives to reduce the frequency of link failures not only among vehicles but also between each vehicle and the 3G/LTE network. The mechanism also considers vehicle mobility and LTE link quality and exploits common interests among vehicles in the cluster formation phase. The performance of the DIAC mechanism is validated through extensive simulations, whose results demonstrate that the performance of the proposed mechanism is superior to that of similar and existing approaches. © 2018 Elsevier B.V.
Lightweight searchable encryption protocol for industrial internet of things
- Authors: Zhang, Ke , Long, Jiahuan , Wang, Xiaofen , Dai, Hong-Ning , Liang, Kaitai , Imran, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 17, no. 6 (2021), p. 4248-4259
- Full Text: false
- Reviewed:
- Description: Industrial Internet of Things (IoT) has suffered from insufficient identity authentication and dynamic network topology, thereby resulting in vulnerabilities to data confidentiality. Recently, the attribute-based encryption (ABE) schemes have been regarded as a solution to ensure data transmission security and the fine-grained sharing of encrypted IoT data. However, most of existing ABE schemes that bring tremendous computational cost are not suitable for resource-constrained IoT devices. Therefore, lightweight and efficient data sharing and searching schemes suitable for IoT applications are of great importance. To this end, In this article, we propose a light searchable ABE scheme (namely LSABE). Our scheme can significantly reduce the computing cost of IoT devices with the provision of multiple-keyword searching for data users. Meanwhile, we extend the LSABE scheme to multiauthority scenarios so as to effectively generate and manage the public/secret keys in the distributed IoT environment. Finally, the experimental results demonstrate that our schemes can significantly maintain computational efficiency and save the computational cost at IoT devices, compared to other existing schemes. © 2005-2012 IEEE.
Reconfigurable smart factory for drug packing in healthcare industry 4.0
- Authors: Wan, Jiafu , Tang, Shenglong , Li, Di , Imran, Muhammad , Zhang, Chunhua
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 15, no. 1 (2019), p. 507-516
- Full Text: false
- Reviewed:
- Description: Industry 4.0, which exploits cyber-physical systems and represents digital transformation of manufacturing, is deeply affecting healthcare as well as other traditional production sector. To accommodate the increasing demand of agility, flexibility, and low cost in healthcare sector, a data-driven reconfigurable production mode of Smart Factory for pharmaceutical manufacturing is proposed in this paper. The architecture of the Smart Factory is consisted of three primary layers, namely perception layer, deployment layer, and executing layer. A Manufacturing's Semantics Ontology based knowledgebase is introduced in the perception layer, which is responsible for plan scheduling of pharmaceutical production. The reconfigurable plans are generated from the production demand of drugs as well as the information statement of low-level machine resources. To further functionality reconfiguration and low-level controlling, the IEC 61499 standard is also introduced for functionality modeling and machine controlling. We verify the proposed method with an experiment of demand-based drug packing production, which reflects the feasibility and adequate flexibility of the proposed method. © 2005-2012 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran" is provided in this record**
A joint scheduling and power control scheme for hybrid I2V/V2V networks
- Authors: Nguyen, Bach , Ngo, Duy , Dao, Minh , Duong, Quang-Thang , Okada, Minoru
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Vehicular Technology Vol. 69, no. 12 (2020), p. 15668-15681
- Full Text: false
- Reviewed:
- Description: In automotive infotainment systems, vehicles using the applications are serviced via continuous infrastructure-to-vehicle (I2V) communications. Additionally, the I2V communications can be combined with vehicle-to-vehicle (V2V) connectivity owing to the small area covered by road side units (RSUs). However, dozens of vehicles have to compete for limited bandwidth when they request service simultaneously in the covered area. In this paper, we propose a joint scheduling and power control scheme for I2V and V2V links in the RSUs' coverage range. Mapping the I2V and V2V links to tuple-links, we formulate a mixed-integer nonlinear programming (MINLP) problem where frequency scheduler and power controller for those tuple-links are jointly designed. Then, we employ the delayed column generation technique and the transmission pattern definition to decompose the MINLP problem into a transmission pattern scheduling problem, as well as a power control problem. Therein, the transmission pattern scheduling problem is solved by linear programming while a greedy power control algorithm is developed. Simulation results with practical parameter settings show that our proposed scheme outperforms several conventional schemes in terms of service disruption and achieved throughput while maintaining throughput fairness among the requesting vehicles. In particular, a high channel number, a small power level number, and a large buffer size at the requesting vehicles are shown to be helpful for our proposed scheme. © 1967-2012 IEEE.
Adaptive transmission optimization in SDN-based industrial internet of things with edge computing
- Authors: Li, Xiaomin , Li, Di , Wan, Jiafu , Liu, Chengliang , Imran, Muhammad
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 5, no. 3 (2018), p. 1351-1360
- Full Text: false
- Reviewed:
- Description: In recent years, smart factory in the context of Industry 4.0 and industrial Internet of Things (IIoT) has become a hot topic for both academia and industry. In IIoT system, there is an increasing requirement for exchange of data with different delay flows among different smart devices. However, there are few studies on this topic. To overcome the limitations of traditional methods and address the problem, we seriously consider the incorporation of global centralized software defined network (SDN) and edge computing (EC) in IIoT with EC. We propose the adaptive transmission architecture with SDN and EC for IIoT. Then, according to data streams with different latency constrains, the requirements can be divided into two groups: 1) ordinary and 2) emergent stream. In the low-deadline situation, a coarse-grained transmission path algorithm provided by finding all paths that meet the time constrains in hierarchical Internet of Things (IoT). After that, by employing the path difference degree (PDD), an optimum routing path is selected considering the aggregation of time deadline, traffic load balances, and energy consumption. In the high-deadline situation, if the coarse-grained strategy is beyond the situation, a fine-grained scheme is adopted to establish an effective transmission path by an adaptive power method for getting low latency. Finally, the performance of proposed strategy is evaluated by simulation. The results demonstrate that the proposed scheme outperforms the related methods in terms of average time delay, goodput, throughput, PDD, and download time. Thus, the proposed method provides better solution for IIoT data transmission. © 2018 IEEE.
SAMS: A seamless and authorized multimedia streaming framework for wmsn-based iomt
- Authors: Jan, Mian Ahmad , Usman, Muhammad , He, Xiangjian , Ur Rehman, Ateeq
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE internet of things journal Vol. 6, no. 2 (2019), p. 1576-1583
- Full Text: false
- Reviewed:
- Description: An Internet of Multimedia Things (IoMT) architecture aims to provide a support for real-time multimedia applications by using wireless multimedia sensor nodes that are deployed for a long-term usage. These nodes are capable of capturing both multimedia and nonmultimedia data, and form a network known as Wireless Multimedia Sensor Network (WMSN). In a WMSN, underlying routing protocols need to provide an acceptable level of Quality of Service (QoS) support for multimedia traffic. In this paper, we propose a Seamless and Authorized Streaming (SAMS) framework for a cluster-based hierarchical WMSN. The SAMS uses authentication at different levels to form secured clusters. The formation of these clusters allows only legitimate nodes to transmit captured data to their Cluster Heads (CHs). Each node senses the environment, stores captured data in its buffer, and waits for its turn to transmit to its CH. This waiting may result in an excessive packet-loss and end-to-end delay for multimedia traffic. To address these issues, a channel allocation approach is proposed for an intercluster communication. In the case of a buffer overflow, a member node in one cluster switches to a neighboring CH provided that the latter has an available channel for allocation. The experimental results show that the SAMS provides an acceptable level of QoS and enhances security of an underlying network.