Lost Silence: An emergency response early detection service through continuous processing of telecommunication data streams

TL;DR

The paper addresses the challenge of swiftly detecting catastrophic events by leveraging real-time, semantically annotated telecom data. It proposes Lost Silence, an approach that encodes phone location and status as RDF streams and uses Geo-Pixel-based spatial aggregation with C-SPARQL queries on a $30$-minute window, alerting when unReachable counts exceed $10$ with a $5$-second evaluation cadence. The methodology is demonstrated on a ship capsizing scenario, showing that early warnings can be produced and scaled through a thread-per-geo-pixel architecture, while acknowledging false positives from blind zones. The work relies on the TOCO ontology to harmonize heterogeneous data and points toward future enhancements with machine learning and real network data for improved robustness and applicability to diverse incidents.

Abstract

Early detection of significant traumatic events, e.g. a terrorist attack or a ship capsizing, is important to ensure that a prompt emergency response can occur. In the modern world telecommunication systems could play a key role in ensuring a successful emergency response by detecting such incidents through significant changes in calls and access to the networks. In this paper a methodology is illustrated to detect such incidents immediately (with the delay in the order of milliseconds), by processing semantically annotated streams of data in cellular telecommunication systems. In our methodology, live information about the position and status of phones are encoded as RDF streams. We propose an algorithm that processes streams of RDF annotated telecommunication data to detect abnormality. Our approach is exemplified in the context of a passenger cruise ship capsizing. However, the approach is readily translatable to other incidents. Our evaluation results show that with a properly chosen window size, such incidents can be detected efficiently and effectively.

Figures (4)

• Figure 1: Classes of the TOUCON Ontology used in the Lost Silence scenario. The solid block denotes a class, while the hollow block denotes a data. The solid lines denote object properties, and dash lines denote datatype properties. The prefixes adopted in lost silence are: net: $<$http://home.eps.hw.ac.uk/$\scriptsize{\sim}$qz1/ontologies/wirelessnetwork_networkResource.owl/$>$; core: $<$http://home.eps.hw.ac.uk/$\scriptsize{\sim}$qz1/ontologies/wirelessnetwork.owl/$>$; geo: $<$http://www.w3.org/2003/01/geo/wgs84_pos/$>$.
• Figure 2: The Process of disaster early detection in lost silence.
• Figure 3: The total number of lost phones in the shipwreck area and the query results in the geo-pixel (329.863, 246.792), where the shipwreck took place, at each query step for the three experiments with the query steps of 5 seconds, 20 seconds, and 30 seconds, respectively. Fig. a) and b) correspond to the experiment with the query step of 5 seconds, Fig. c) and d) correspond to 20 seconds, and Fig. e) and f) 30 seconds. Fig. a), c), and e) illustrate the total number of phones lost signal at the water area of city Jianli at each experiment. The number of phones lost signal scales with the colour and radius of the circle, e.g., the brighter colour and larger radius of a circle denotes a larger number of lost phones. Fig. b), d), and f) show the number of detected lost phones in the geo-pixel (329.863, 246.792), at each query step from the beginning of the query.
• Figure 4: Query result in the pressure test scenario in which massive phones lost signal in multiple geo-pixels along the river.