1,721,222 research outputs found
Special issue on advances in data intelligence and modelling
No full textShakshuki, EM (corresponding author), Acadia Univ, Jodrey Sch Comp Sci, Wolfville, NS, Canada.
[email protected]; [email protected]
Internet of Things and Blockchain Technology in Apparel Manufacturing Supply Chain Data Management
No full textThe rapid changes in textile and clothing industry's operational environment in which apparel businesses are collaborating with their suppliers and customers have recognized interoperability of information systems as an important factor. The need to address this challenge becomes vital in the context of new paradigms such as the Internet of Things (IoT), and its ability to capture real-time information from different parts of textile and cloth manufacturing value chain by using Radio Frequency Identification (RFID) tags and sensors-based data communication networks. In this process, enterprise information system architecture plays an important role in storing, processing, and distributing data. Despite contributing to the rapid development of IoT applications, the current IoT-centric architecture has led to a myriad of isolated data silos. This paper presents a blockchain-based architecture for the IoT applications, which brings distributed data management to support transactions services within a multi-party apparel business supply chain network. (C) 2020 The Authors. Published by Elsevier B.V.Pal, K (corresponding author), City Univ London, Dept Comp Sci, London ECV 0HB, England.
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Applications of machine learning in pervasive systems
No full textShakshuki, EM (corresponding author), Acadia Univ, Jodrey Sch Comp Sci, Wolfville, NS, Canada.
[email protected]; [email protected]; [email protected]
Special issue on ubiquitous computing and NextGen context-fusion
No full textYasar, AU (reprint author), Hasselt Univ, Transportat Res Inst, Hasselt, Belgium.
[email protected]; [email protected]; [email protected]
Estimating Nonlinear Parameters Present in OFDM-based System Using Non-linear Least Squares
No full textAbstractWireless communication systems are omnipresent in our everyday life and high standards regarding capacity, re- liability, speed and power are naturally expected. In order to satisfy these requirements, more sophisticated and efficient technology is needed. This is especially true for the integrated amplifiers at the transmitter side, which are responsible for the majority of the power consumption. In order to obtain high gain at the low power levels, these amplifiers are operated into their nonlinear region. However, many communication schemes cannot handle the nonlinear distortion generated by such amplifiers. The nonlinear distortions generated at the transmitter side can be compensated through the use of digital predistortion. This method compensates the non-linear effects by first esti- mating a non-linear model for the amplifier, and then applying the inverse model onto the data during transmission such that the nonlinearities are canceled. The main advantage of this approach is that it allows to use the classical linear communication scheme once the precompensation is performed.In this work, Special attention goes to the estimation of the nonlinear model used for the predistortion. Most nonlinear models are not only nonlinear in behavior but also nonlinear in their parametrization, which requires spe- cial care during estimation of the model parameters. Performance of the simple linear least square estimator and nonlinear least square estimator is investigated using a National Instrument student setup
Special issue on ubiquitous computing in the IoT revolution
No full textShakshuki, EM (corresponding author), Acad Univ, Jodrey Sch Comp Sci, Wolfville, NS, Canada.
[email protected]; [email protected]; [email protected]
Cloud Acknowledgment Scheme for a Node Network
No full textRecently, wireless devices are rapidly added to existing networks. This growth is due to abrupt development in technology and change of lifestyle. Due to the distribution nature of these networks, it is essential to mention that there is a substantial increase in the number of attacks as the network is expanding. By virtue of such trend, we are interested in bringing back centralization of a network even in wireless networks to deal with such attacks. In this paper, we propose a scheme called Cloud ACKnowledgement Scheme (CACKS) to strengthen a wireless network by fetching cloud as a monitoring tool. To validate our proposed approach, we performed several experiments using OMNET++ 5.4.1. The outcome of these experiments shows that the proposed scheme strongly monitors and protects the network from attackers while supporting unrestricted mobility. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Conference Program Chairs.Kaja, S (corresponding author), Acadia Univ, Jodrey Sch Comp Sci, Wolfville, NS B4P 2R6, Canada.
[email protected]
An Optimization Approach of IoD Deployment for Optimal Coverage Based on Radio Frequency Model
No full textRecently, Internet of Drones (IoD) has garnered significant attention due to its widespread applications. However, deploying IoD for area coverage poses numerous limitations and challenges. These include interference between neighboring drones, the need for directional antennas, and altitude restrictions for drones. These challenges necessitate the development of efficient solutions. This research paper presents a cooperative decision-making approach for an efficient IoD deployment to address these challenges effectively. The primary objective of this study is to achieve an efficient IoD deployment strategy that maximizes the coverage region while minimizing interference between neighboring drones. In deployment problem, the interference increases as the number of deployed drones increases, resulting in bad quality of communication. On the other hand, deploying a few drones cannot satisfy the coverage demand. To accomplish this, an enhanced version of a concise population-based meta-heuristic algorithm, namely Improved Particle Swarm Optimization (IPSO), is applied. The objective function of IPSO is defined based on the coverage probability, which is primarily influenced by the characteristics of the antennas and drone altitude. A radio frequency (RF) model is derived to evaluate the coverage quality, considering both Line of Sight (LOS) and Non-Line of Sight (NLOS) down-link coverage probabilities for ground communication. It is assumed that each drone is equipped with a directional antenna to optimize coverage in a given region. Extensive simulations are conducted to assess the effectiveness of the proposed approach. Results demonstrate that the proposed method achieves maximum coverage with minimum transmission power. Furthermore, a comparison is made against Collaborative Visual Area Coverage Approach (CVACA), and a game-based approach in terms of coverage quality and convergence speed. The simulation results reveal that our approach outperforms both CVACA and the gamebased schemes in terms of coverage and convergence speed. Comparisons validate the superiority of our approach over existing methods. To assess the robustness of the proposed RF model, we have considered two distinct ranges of noise: range1 spanning from -120 to -90 dBm, and range2 spanning from -90 to -70 dBm for different numbers of UAVs. In summary, this research presents a cooperative decision-making approach for efficient IoD deployment to address the challenges associated with area coverage and achieves an optimal coverage with minimal interference.This research was funded by Project Number INML2104 under the Interdisciplinary Center of Smart Mobility and Logistics at King Fahd University of Petroleum and Minerals. This study also was supported by the Special Research Fund BOF23KV17.
Authors at KFUPM would like to acknowledge the support received under University Funded Grant # INML2300. The author at Hasselt University acknowledges the support received from Special Research Fund (BOF) under Grant # BOF23KV17
Modelling Value of Time for Trip Chains in Daily Schedules
No full textAbstractThe decision about spending time on an activity, switching to the next activity and transport mode used to travel to the next activity location depends on money value of time; opportunity cost of time at activity. Optimal condition of transition between two activities occurs when their marginal utility of time is equal. The presented framework in this paper models the marginal utility of activity to express the money benefit earned by spending each unit of time at the given activity. The proposed model is generalized for the schedule with any number of activities as contrast to previous studies, where such models were used for schedules with fixed number of activities. This framework can be used to calculate the loss in value of time due decreased activity participation resulting from travel delays
Data Preparation to Simulate Public Transport in Micro-Simulations Using OSM and GTFS
No full textAbstractResearch on demand-responsive collective transportation facilities that can act as feeder services to time-table based public transportation (PT) requires detailed and accurate information about the PT infrastructure, including the attachment of bus stops to the appropriate network link. Due to the size of the infrastructure, the data integration shall be automated. This paper describes the effort to prepare data from publicly available OpenStreetMap (OSM) and General Transit Feed Specification (GTFS) sources. Procedures are proposed (i) to build a network derived from OSM suitable for simulations in transportation, (ii) to extract bus stops from GTFS and remove anomalies and (iii) to find candidate network links to attach them
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