IoT RSSI Pattern Analysis for Intruder Detection
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Solution Overview
Problem
In incident investigations, investigators often fail to establish the presence of intruders at a crime scene when physical evidence is lacking, as intruders may not leave traces of their presence, especially if they move objects back to their original positions or if changes in object positions are not significant.
Innovation Solution
A method and system using IoT devices to capture and analyze Received Signal Strength Indication (RSSI) information over time to identify suspicious object movements by generating patterns based on historical RSSI values, indicating potential intruder activity even without physical evidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If investigators rely on physical evidence to establish intruder presence, then evidence reliability is improved, but detection capability deteriorates when intruders leave no traces
Solution Approach 1:
The system performs preliminary action by continuously collecting and storing RSSI data from IoT devices before incidents occur. Historical RSSI values are captured and archived, enabling investigators to analyze object movement patterns retrospectively even when no physical evidence remains at the scene.
Solution Approach 2:
The patent replaces mechanical/physical evidence collection methods with electronic wireless signal analysis. Instead of relying on physical traces left by intruders, the system uses RSSI measurements from IoT devices to detect and reconstruct object movements through electronic data analysis.
2Measurement precision
If investigators document object positions manually, then measurement precision is improved for significant changes, but detection capability deteriorates for minor or restored movements
Solution Approach 1:
The system ensures continuity of useful action by continuously monitoring and recording RSSI values from IoT devices over time. This continuous electronic monitoring captures all object movements regardless of significance, unlike manual documentation which only records noticeable changes. The continuous data stream enables detection of minor movements and movements that were later restored.
Solution Approach 2:
The patent creates electronic copies of object position information through RSSI measurements. Instead of relying on physical evidence or manual notes, the system generates digital replicas of spatial data that can be analyzed computationally to reconstruct movement patterns, including those that occurred briefly or were subsequently reversed.
3Device complexity
If no wireless monitoring system is deployed, then device complexity is reduced, but information loss increases regarding object movements
Solution Approach 1:
The system achieves universality by leveraging existing multi-functional IoT devices already deployed in environments. These devices serve their primary purposes while simultaneously functioning as wireless monitoring sensors for object detection. This approach minimizes additional hardware complexity while maximizing information capture capability.
Solution Approach 2:
The system implements self-service by utilizing the inherent wireless communication capabilities of IoT devices for their intended purposes. The same wireless interfaces used for device operation automatically provide movement detection data, eliminating the need for separate dedicated monitoring hardware and reducing overall system complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the detection of suspicious object movements and unidentified object movements relative to IoT devices, providing valuable clues in incident investigations by analyzing RSSI patterns, thereby aiding in reconstructing the incident scene and identifying areas of intruder presence.
Implementation Method 1
A method and system using IoT devices to capture and analyze Received Signal Strength Indication (RSSI) information over time to identify suspicious object movements
Data Source
AI summary
A process of identifying suspicious object movements in an incident location. An electronic computing device obtains incident information identifying a time of occurrence and a location of an incident. The electronic computing device identifies internet-of-things (IoT) devices deployed in the incident location and accesses received signal strength indication (RSSI) information associated with a selected IoT device. The RSSI information includes historical RSSI values that were captured at the IoT device during the time of occurrence of the incident. The electronic computing device generates a suspicious object movement pattern corresponding to the at least one IoT device based on variations within the historical RSSI values that were captured at the at least one IoT device during the first time period. A display or audio output device provides a corresponding visual or audio output indicating the suspicious object movement pattern corresponding to the IoT device.