Identification of crowds using mobile crowd detection (MCS) and visualization with the DBSCAN algorithm for a Smart Campus environment

• URL: http://arxiv.org/abs/2410.12797v1
• Date: Sat, 28 Sep 2024 22:35:04 GMT
• Title: Identification of crowds using mobile crowd detection (MCS) and visualization with the DBSCAN algorithm for a Smart Campus environment
• Authors: Luis Chirinos-Apaza,
• Abstract summary: This paper explores the feasibility of using Mobile Crowd Sensing (MCS) and visualization algorithms to detect crowding on a university campus. Preliminary results suggest that the system is viable and could be a useful tool for the prevention of accidents due to crowding and for the management of public spaces.
• Score: 0.0
• License:
• Abstract: Multidisciplinary research, in conjunction with artificial intelligence (AI), the Internet of Things (IoT), Blockchain and Big Data analysis, has lowered barriers and made companies more productive, in other words, the joint work of these areas has promoted digital transformation in all areas, for example Artificial intelligence (AI) has made it possible to automate processes, and the Internet of Things (IoT) has connected devices and physical objects, enabling real-time data collection and analysis. Blockchain has provided a secure and transparent way to transact and store data. Big Data analysis has allowed companies to obtain valuable insights from large amounts of data. As these technologies continue to evolve, we can expect to see even more innovations and benefits in the future. This paper explores the feasibility of using Mobile Crowd Sensing (MCS) and visualization algorithms to detect crowding on a university campus. A survey was conducted to evaluate the university community's perception of a mobile application that provides information about crowds, and a detection scenario was simulated using randomly generated data and the DBSCAN algorithm for visualization. Preliminary results suggest that the system is viable and could be a useful tool for the prevention of accidents due to crowding and for the management of public spaces. The limitations of the study are discussed and future lines of research are proposed, such as crowd prediction, data privacy, and visualization optimization.

Related papers

• Systematic review, analysis, and characterisation of malicious industrial network traffic datasets for aiding Machine Learning algorithm performance testing [0.0]
  This paper systematically reviews publicly available network traffic capture-based datasets. It includes categorisation of contained attack types, review of metadata, and statistical as well as complexity analysis. It provides researchers with metadata that can be used to select the best dataset for their research question.
  arXiv  Detail & Related papers  (2024-05-08T07:48:40Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms [56.119374302685934]
  There have been severe concerns over the trustworthiness of AI technologies. Machine and deep learning algorithms depend heavily on the data used during their development. We propose a framework to evaluate the datasets through a responsible rubric.
  arXiv  Detail & Related papers  (2023-10-24T14:01:53Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey [67.7834898542701]
  We focus on reviewing privacy-preserving techniques of graph machine learning. We first review methods for generating privacy-preserving graph data. Then we describe methods for transmitting privacy-preserved information.
  arXiv  Detail & Related papers  (2023-07-10T04:30:23Z)
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
  There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
  arXiv  Detail & Related papers  (2023-02-13T16:44:44Z)
• A review of Federated Learning in Intrusion Detection Systems for IoT [0.15469452301122172]
  Intrusion detection systems are evolving into intelligent systems that perform data analysis searching for anomalies in their environment. Deep learning technologies opened the door to build more complex and effective threat detection models. Current approaches rely on powerful centralized servers that receive data from all their parties. This paper focuses on the application of Federated Learning approaches in the field of Intrusion Detection.
  arXiv  Detail & Related papers  (2022-04-26T17:00:07Z)
• Modelling and Optimisation of Resource Usage in an IoT Enabled Smart Campus [0.0]
  Internet of Things (IoT) technologies have opened up new opportunities for organisations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory.
  arXiv  Detail & Related papers  (2021-11-07T13:30:46Z)
• Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence [45.076180487387575]
  Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems.
  arXiv  Detail & Related papers  (2021-05-04T23:42:06Z)
• Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges [67.71975391801257]
  Much concern in mobile data analysis is related to human beings and their behaviours. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
  arXiv  Detail & Related papers  (2020-08-29T15:14:03Z)
• Understanding Crowd Behaviors in a Social Event by Passive WiFi Sensing and Data Mining [21.343209622186606]
  We propose a comprehensive data analysis framework to extract three types of patterns related to crowd behaviors in a large social event. First, trajectories of the mobile devices are extracted from probe requests to reveal the spatial patterns of the crowds' movement. Next, k-means and k-shape clustering algorithms are applied to extract temporal patterns visiting the crowds by days and locations.
  arXiv  Detail & Related papers  (2020-02-05T03:36:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.