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Communities in Scholarship @ UWindsor

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• Papers, presentations and abstracts of conferences held at the University of Windsor, in person and virtually.
• Digitized local items from the collections of the Leddy Library, University of Windsor, and community partners.
• Open Access Faculty publications, reports and working papers from academic departments at the University of Windsor.
• Formal graduate original research from the University of Windsor's Masters and Doctoral programs.

Recent Submissions

• 
  Item type: Item , Access status: Open Access ,
  Balancing Awareness and Congestion in Vehicular Networks Using Variable Transmission Power
  (Multidisciplinary Digital Publishing Institute
  ,
  2021-08-08
  )
  Vehicular ad Hoc networks (VANETs) support a variety of applications ranging from critical safety applications to “infotainment” or “comfort” applications. In North America, 75 MHz of the spectrum in the 5.9 GHz band has been allocated for vehicular communication. Safety applications rely on event-driven “alert” messages as well as the periodic broadcast of Basic Safety Messages (BSMs) containing critical information, e.g., position, speed, and heading from participating vehicles. The limited channel capacity and high message rates needed to ensure an adequate level of awareness make the reliable delivery of BSMs a challenging problem for VANETs. In this paper, we propose a decentralized congestion control algorithm that uses variable transmission power levels to reduce the channel busy ratio while maintaining a high level of awareness for nearby vehicles. The simulation results indicate that the proposed approach is able to achieve a suitable balance between awareness and bandwidth usage.
• 
  Item type: Item , Access status: Open Access ,
  Geo-Team Formation Model for Impromptu Activities
  (PubPub
  ,
  2021-06-08
  )
  Maryam MahdavyRad
  This research provides a new model for team formation problems for Impromptu task activities in geo-social networks, called Geo-team formation. The team formation problem (TFP) is the process of dedicating the users from Social Networks to activities as teams in a collaborative functioning environment for an effective outcome based on their skills. It was proven to be an NP-hard problem. Impromptu activities deployment requires users with required skills who are socially close to each other and spatially close to the activity location. Most existing researches tackle the geo-team formation problem as a single social constraint or skills constraints query while optimizing the spatial closeness. To cover this gap, we present a model that efficiently narrows down the search space and then applies the required constraint. Efficient processing of the geo-team formation model is challenging considering (1) required skills of users, (2) weight of user skills, (3) the maximum contribution of users skills, (4) social cohesiveness between users, and (5) spatial closeness, which needs to be carefully examined and made timely invitations. The weight of user's skills helps the model dedicate users with high expertise for each team's required activities. In this approach, the time and the search cost of the team formation process are efficiently restricted. Our preliminary results on real-world datasets determine that the proposed algorithms can efficiently apply the geo-team formation model under various parameter settings. the results confirm the effectiveness of the proposed method.
• 
  Item type: Item , Access status: Open Access ,
  Machine Learning Based Misbehaviour Detection in VANET Using Consecutive BSM Approach
  (Institute of Electrical and Electronics Engineers
  ,
  2021-12-24
  )
  Aekta Sharma; Arunita Jaekel
  Vehicular ad-hoc network (VANET) is an emerging technology for vehicle-to-vehicle communication vital for reducing road accidents and traffic congestion in an Intelligent Transportation System (ITS). VANET communication is vulnerable to various attacks and cryptographic techniques are commonly used for message integrity and authentication of vehicles. However, cryptograhpic techniques alone may not be sufficient to protect against insider attacks. Many VANET safety applications rely on periodic broadcast of basic safety messages (BSMs) from surrounding vehicles that contain important status information about a vehicle such as its position, speed, and heading. If an attacker (misbehaving vehicle) injects false position information in a BSM, it can lead to serious consequences including traffic congestion or even accidents. Therefore, it is imperative to accurately detect and identify such attackers to ensure safety in the network. This paper presents a novel data-centric approach to detect position falsification attacks, using machine learning (ML) algorithms. Unlike existing techniques, the proposed approach combines information from 2 consecutive BSMs for training and testing. Simulations using the Vehicular Reference Misbehavior (VeReMi) dataset demonstrate that the proposed model clearly outperforms existing approaches for identifying a range of different attack types.
• 
  Item type: Item , Access status: Open Access ,
  A network-based drug repurposing method via non-negative matrix factorization
  (Oxford University Press
  ,
  2021-12-01
  )
  Shaghayegh Sadeghi; Jianguo Lü; Alioune Ngom
  MOTIVATION: Drug repurposing is a potential alternative to the traditional drug discovery process. Drug repurposing can be formulated as a recommender system that recommends novel indications for available drugs based on known drug-disease associations. This article presents a method based on non-negative matrix factorization (NMF-DR) to predict the drug-related candidate disease indications. This work proposes a recommender system-based method for drug repurposing to predict novel drug indications by integrating drug and diseases related data sources. For this purpose, this framework first integrates two types of disease similarities, the associations between drugs and diseases, and the various similarities between drugs from different views to make a heterogeneous drug-disease interaction network. Then, an improved non-negative matrix factorization-based method is proposed to complete the drug-disease adjacency matrix with predicted scores for unknown drug-disease pairs. RESULTS: The comprehensive experimental results show that NMF-DR achieves superior prediction performance when compared with several existing methods for drug-disease association prediction. AVAILABILITY AND IMPLEMENTATION: The program is available at https://github.com/sshaghayeghs/NMF-DR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
• 
  Item type: Item , Access status: Open Access ,
  Blockchain-Based Pseudonym Management Scheme for Vehicular Communication
  (Multidisciplinary Digital Publishing Institute
  ,
  2021-06-30
  )
  Sonia Alice George; Steffie Maria Stephen; Arunita Jaekel
  A vehicular ad hoc network (VANET) consists of vehicles, roadside units, and other infrastructures that communicate with each other with the goal of improving road safety, reducing accidents, and alleviating traffic congestion. For safe and secure operation of critical applications in VANET, it is essential to ensure that only authenticated vehicles can participate in the network. Another important requirement for VANET communication is that the privacy of vehicles and their users must be protected. Privacy can be improved by using pseudonyms instead of actual vehicle identities during communication. However, it is also necessary to ensure that these pseudonyms can be linked to the real vehicle identities if needed, in order to maintain accountability. In this paper, we propose a new blockchain-based decentralized pseudonym management scheme for VANET. This allows the vehicles to maintain conditional anonymity in the network. The blockchain is used to maintain a record of each vehicle and all of its pseudo-IDs. The information in the blockchain can only be accessed by authorized entities and is not available to all vehicles. The proposed distributed framework maintains an immutable record of the vehicle data, which is not vulnerable to a single point of failure. We compared the performance of the proposed approach with a traditional PKI scheme and shown that it significantly reduces the authentication delay.