Internet of Things Enabled Policing Processes

TL;DR

This paper presents iCOP, an IoT-enabled system that enhances police investigations by integrating real-time data collection, knowledge extraction, and process analysis to support complex, data-driven law enforcement activities.

Contribution

It introduces a scalable, extensible IoT data analytics pipeline and a novel system, iCOP, for augmenting police investigations with smart device integration and knowledge management.

Findings

Successful implementation of iCOP system for evidence collection

Enhanced data integration from IoT devices in policing scenarios

Improved decision-making support for investigators

Abstract

The Internet of Things (IoT) has the potential to transform many industries. This includes harnessing real-time intelligence to improve risk-based decision making and supporting adaptive processes from core to edge. For example, modern police investigation processes are often extremely complex, data-driven and knowledge-intensive. In such processes, it is not sufficient to focus on data storage and data analysis; as the knowledge workers (e.g., police investigators) will need to collect, understand and relate the big data (scattered across various systems) to process analysis. In this thesis, we analyze the state of the art in knowledge-intensive and data-driven processes. We present a scalable and extensible IoT-enabled process data analytics pipeline to enable analysts ingest data from IoT devices, extract knowledge from this data and link them to process execution data. We focus on a motivating scenario in policing, where a criminal investigator will be augmented by smart devices to collect data and to identify devices around the investigation location, to communicate with them to understand and analyze evidence. We design and implement a system (namely iCOP, IoT-enabled COP) to assist investigators collect large amounts of evidence and dig for the facts in an easy way.