Presenting the Proper Data to the Crisis Management Operator: A Relevance Labelling Strategy

Lollini

et al. 2018

IEEE Systems Journal

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Abstract-A dramatic shift in system complexity is occurring, bringing monolithic system designs to be progressively replaced by modular approaches. In the latest years this trend has been emphasized by the System of Systems (SoS) concept, in which a complex system or application is the result of the integration of many independent, autonomous Constituent Systems (CS), brought together in order to satisfy a global goal under certain rules of engagement. The overall behavior of the SoS, emerging from such complex interactions and dependencies, poses several threats in terms of dependability, timeliness and security, due to the challenging operating and environmental conditions caused by mobility, wireless connectivity, and the use of off-the-shelf components. Referring to our experience in mobile safety-critical applications gained from three different research projects, in this paper we illustrate the challenges and benefits posed by the adoption of an SoS approach in designing, developing and maintaining mobile safety-critical applications, and we report on some possible solutions.

Section: B Resilient Monitoringmentioning

confidence: 99%

System-of-Systems to Support Mobile Safety Critical Applications: Open Challenges and Viable Solutions

Bondavalli

Lollini

et al. 2018

IEEE Systems Journal

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“…This paper is an extended version of a previous study, where a preliminary version of the relevance labelling strategy is presented. The main novel contributions of this paper are the following.…”

Section: Introductionmentioning

confidence: 99%

“…The evaluation of our strategy is performed showing 3 real crisis scenarios happened in Italy, where data is collected and elaborated in Secure!. This paper is an extended version of a previous study, 18 where a preliminary version of the relevance labelling strategy is presented.…”

mentioning

confidence: 99%

“…Scenarios and parameters details We use the same setup for each scenario: in a previous study,18 the strategy was not able to provide a relevance labelling score for all the scenarios, but with the current modifications, this setup of the parameters is suitable for all the listed scenarios. Lastly, we remark that due to the geographical dimension of the considered areas, localization uncertainty of the events (which may be up to several meters42 ) is not significant for our analysis and consequently not considered in this work.6.2 | Relevance labelling strategy for historical data…”

mentioning

confidence: 99%

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Labelling relevant events to support the crisis management operator

Zoppi

J Software Evolu Process

Piccolo

et al. 2017

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Thanks to the large availability of portable devices and the growing interest in the Internet of Things, during crises, social networks, or alerts sent through mobile devices or sensor networks are available and can be matched each other to perform situational analysis. However, the inclusion of multiple heterogeneous sources in situational analyses leads to 2 main issues: (1) a source could deliver (voluntarily or erroneously) wrong data damaging the integrity and the correctness of the analysis, and (2) a significant amount of heterogeneous data need to be processed. As a consequence, the crisis management operator faces a large amount of potentially unreliable data.In this paper, we present a relevance labelling strategy to process information gathered from heterogeneous data streams to select the most relevant events. These are presented to the crisis management operator with the highest priority. Our strategy is evaluated using events collected by the Secure! crisis management system, considering 3 real crisis scenarios happened in Italy in 2015. Results show that our strategy is able to correctly identify sets of relevant events, supporting the activities of the crisis management operator. Recently, the interest in researching and developing CMSs is growing significantly, mainly due to an increasing number of available information. 3,4 This includes information provided by physical sensors and humans, both citizens and trained personnel, which generate information (eg, using their smartphones 5,6 ) accessible through social networks. 7,8 On one side, having multiple information sources is a benefit for the analysis of a crisis scenario, but on the other side, the risk is that the crisis management operator is overloaded of potentially unreliable information, negatively affecting the response process. For this reason, the information needs to be processed and filtered to become readable and trusted for the human operator.Crisis data from the citizens generate Volunteered Geographic Information (VGI 6 ) that is shared for example through SMS, 9 Social Media, 10 or dedicated applications. 11 This activity involves crowd-sourcing 12 and crowd-sensing 13 techniques: crowd-sourcing is the process of obtaining needed services, ideas, or content by soliciting contributions from a crowd of people (eg, online communities), while crowd-sensing refers to the involvement of a large group of participants in retrieving reliable data from a specific field. Considering "human sensors" ------------------------------------------------------This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

“…Information on the impact of the disaster was initially collected from online sources, including social media channels like Facebook and Twitter, and alerts received by SMS sent from citizens that wanted to signal their most urgent needs and location. Information coming from all sources was geolocated to build a crisis map; ten days after the earthquake, the Head of the US Federal Emergency Management Association (FEMA) recognized the map as the most comprehensive and up to date map available to the humanitarian community [13].…”

Section: Introductionmentioning

confidence: 99%

Identification of critical situations via Event Processing and Event Trust Analysis

Itria

Kocsis-Magyar²,

et al. 2016

Knowl Inf Syst

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In crisis management systems, situational awareness is usually at the basis of guiding the intervention process, and it is required to rapidly process data acquired from information sources on the field such as sensors or even humans. Given the variety and heterogeneity of sources and the amount of information that can be collected, together with the urgency of taking decisions, such information needs to be rapidly collected, filtered and aggregated in a form that can be used in subsequent machine-assisted decision support processes. At the same time, uncertainties in the input data or approximations in the processing phase may lead to an incorrect interpretation of the real situation in progress, which may generate mismanagements and severe consequences. This paper presents an event processor for crisis management systems that combines heterogeneous input sources to detect a critical situation. Complex event processing technology is applied for correlating data and creating events that describe the critical situation. Anomaly detection techniques are then used to analyze such events and detect possible anomalies, i.e. events not pertaining to the identified critical situation. The devised event processor creates trusted events that describe a critical situation merging inputs from heterogeneous and potentially untrusted sources. A prototype of the solution has been implemented and exercised within the crisis management system developed during the Secure! project. The experimental validation activities performed make use of different input sources, such as Twitter and sensors deployed on field (a doppler radar for people detection and accelerometers for vibrations detection). The objective of the experimental campaign is to show i) the adequacy of the solution to rapidly process the information and describe the critical situation, and ii) its capability in detecting anomalous events that could impair the accuracy of the description of the critical situation.