Next Article in Journal
3D Contact Position Estimation of Image-Based Areal Soft Tactile Sensor with Printed Array Markers and Image Sensors
Previous Article in Journal
A QoE-Aware Energy Supply Scheme over a FiWi Access Network in the 5G Era
Previous Article in Special Issue
Edge Machine Learning for AI-Enabled IoT Devices: A Review
Open AccessArticle

Detecting and Tracking Criminals in the Real World through an IoT-Based System

1
Department of Computer Science, Technische Universität Darmstadt, 64289 Darmstadt, Germany
2
Fraunhofer SIT, Institute for Secure Information Technology, 64295 Darmstadt, Germany
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3795; https://doi.org/10.3390/s20133795
Received: 8 May 2020 / Revised: 2 July 2020 / Accepted: 3 July 2020 / Published: 7 July 2020
(This article belongs to the Special Issue Sensors and Smart Devices at the Edge: IOT Meets Edge Computing)
Criminals and related illegal activities represent problems that are neither trivial to predict nor easy to handle once they are identified. The Police Forces (PFs) typically base their strategies solely on their intra-communication, by neglecting the involvement of third parties, such as the citizens, in the investigation chain which results in a lack of timeliness among the occurrence of the criminal event, its identification, and intervention. In this regard, a system based on IoT social devices, for supporting the detection and tracking of criminals in the real world, is proposed. It aims to enable the communication and collaboration between citizens and PFs in the criminal investigation process by combining app-based technologies and embracing the advantages of an Edge-based architecture in terms of responsiveness, energy saving, local data computation, and distribution, along with information sharing. The proposed model as well as the algorithms, defined on the top of it, have been evaluated through a simulator for showing the logic of the system functioning, whereas the functionality of the app was assessed through a user study conducted upon a group of 30 users. Finally, the additional advantage in terms of intervention time was compared to statistical results. View Full-Text
Keywords: IoT; safety; smart city; simulation; crime detection; crime tracking; terrorism IoT; safety; smart city; simulation; crime detection; crime tracking; terrorism
Show Figures

Figure 1

MDPI and ACS Style

Tundis, A.; Kaleem, H.; Mühlhäuser, M. Detecting and Tracking Criminals in the Real World through an IoT-Based System. Sensors 2020, 20, 3795.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop