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Collaborative Sensor Networks and Advanced Data Analytics for Urban Emergencies and Disaster Relief Efforts

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (23 September 2019) | Viewed by 17521

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Special Issue Editors

Dr. Sergio F. Ochoa

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Guest Editor

Computer Science Department, University of Chile, Santiago, Chile
Interests: computer-supported collaborative work; ad hoc communication and coordination; loosely-coupled collaborative work; emergency management; decision making; collaborative systems; software modeling tools and methodologies; software architecture; mobile and ubiquitous computing; internet of things
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Weiming Shen

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Guest Editor

1. National Research Council of Canada, Ottawa, ON, Canada
2. Department of Electrical and Computer Engineering, University of Western Ontario, London, ON, Canada
Interests: computer-supported collaborative work; modeling and implementation of decision support systems; agent-based systems; disaster management; distributed computing; wireless sensor networks; Internet of Things; smart cities; supply chain; manufacturing systems; process optimization and scheduling
Special Issues, Collections and Topics in MDPI journals

Dr. Roc Meseguer

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Guest Editor

Polytechnic University of Catalonia, Department of Computer Architecture, Barcelona, Spain
Interests: Large-scale peer-to-peer and cloud computing systems; mobile ad hoc networks; communication support in disaster scenarios; decentralized systems applied to ambient intelligence; computer-supported cooperative work and learning; socioeconomic-oriented distributed systems; community networks based on bottom-up initiatives

Special Issue Information

Dear Colleagues,

Every year the population density in cities increases and becomes more and more dependent on supporting systems (e.g., electricity, water, transportation, communication networks) which are steadily growing into complexity and interconnections. Therefore, when natural or human-made hazardous events hit urban areas, the consequences on the civilians is high, and the response and recovery processes are complex and expensive. In these scenarios, the effectiveness of the preparedness and response activities play a key role to mitigate the impact of these events. The research work in disaster management has identified the ICT technology and collaborative work as key pieces to conceive solutions that help address urban emergencies and disaster relief efforts.

This Special Issue aims at covering the state of the art and advancements in technologies, processes, and IT solutions that improve the strategies available to address preparedness, response, recovery, and learning from extreme events affecting urban areas.

Dr. Sergio F. Ochoa
Dr. Weiming Shen
Dr. Roc Meseguer
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Computer-supported collaborative work in emergency scenarios
Preparedness, response, recovery, and learning from urban extreme events
Digital communication in emergency scenarios
Wireless sensor networks and IoT applied to urban emergencies
Early detection of extreme events and warning delivery
Cyber–physical systems to prepare physical infrastructures
Advanced data analytics to forecast extreme events
Advanced data analytics to support decision-making during disaster management
Design aspects of emergency-supporting processes and tools
Security, authentication, robustness, scalability of supporting systems
Health aspects and management of victims
Social applications for civilians
Consciousness, awareness, and artificial intelligence for disaster relief

Published Papers (5 papers)

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Research

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24 pages, 501 KiB

Open AccessArticle

Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History

by Pere Millán, Carles Aliagas, Carlos Molina, Roc Meseguer, Sergio F. Ochoa and Rodrigo M. Santos

Sensors 2020, 20(1), 24; https://doi.org/10.3390/s20010024 - 19 Dec 2019

Cited by 3 | Viewed by 2878

Abstract

The mobile ad hoc communication in highly dynamic scenarios, like urban evacuations or search-and-rescue processes, plays a key role in coordinating the activities performed by the participants. Particularly, counting on message routing enhances the communication capability among these actors. Given the high dynamism of these networks and their low bandwidth, having mechanisms to predict the network topology offers several potential advantages; e.g., to reduce the number of topology propagation messages delivered through the network, the consumption of resources in the nodes and the amount of redundant retransmissions. Most strategies reported in the literature to perform these predictions are limited to support high mobility, consume a large amount of resources or require training. In order to contribute towards addressing that challenge, this paper presents a history-based predictor (HBP), which is a prediction strategy based on the assumption that some topological changes in these networks have happened before in the past, therefore, the predictor can take advantage of these patterns following a simple and low-cost approach. The article extends a previous proposal of the authors and evaluates its impact in highly mobile scenarios through the implementation of a real predictor for the optimized link state routing (OLSR) protocol. The use of this predictor, named OLSR-HBP, shows a reduction of 40–55% of topology propagation messages compared to the regular OLSR protocol. Moreover, the use of this predictor has a low cost in terms of CPU and memory consumption, and it can also be used with other routing protocols. Full article

(This article belongs to the Special Issue Collaborative Sensor Networks and Advanced Data Analytics for Urban Emergencies and Disaster Relief Efforts)

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20 pages, 10166 KiB

Open AccessArticle

Technology Support for Collaborative Preparation of Emergency Plans

by Nelson Baloian, Jonathan Frez, Jose A. Pino, Sergio Peñafiel, Gustavo Zurita and Alvaro Abarca

Sensors 2019, 19(22), 5040; https://doi.org/10.3390/s19225040 - 19 Nov 2019

Cited by 3 | Viewed by 2997

Abstract

Preparing a plan for reaction to a grave emergency is a significant first stage in disaster management. A group of experts can do such preparation. Best results are obtained with group members having diverse backgrounds and access to different relevant data. The output of this stage should be a plan as comprehensive as possible, taking into account various perspectives. The group can organize itself as a collaborative decision-making team with a process cycle involving modeling the process, defining the objectives of the decision outcome, gathering data, generating options and evaluating them according to the defined objectives. The meeting participants may have their own evidences concerning people’s location at the beginning of the emergency and assumptions about people’s reactions once it occurs. Geographical information is typically crucial for the plan, because the plan must be based on the location of the safe areas, the distances to move people, the connecting roads or other evacuation links, the ease of movement of the rescue personnel, and other geography-based considerations. The paper deals with this scenario and it introduces a computer tool intended to support the experts to prepare the plan by incorporating the various viewpoints and data. The group participants should be able to generate, visualize and compare the outcomes of their contributions. The proposal is complemented with an example of use: it is a real case simulation in the event of a tsunami following an earthquake at a certain urban location. Full article

(This article belongs to the Special Issue Collaborative Sensor Networks and Advanced Data Analytics for Urban Emergencies and Disaster Relief Efforts)

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19 pages, 3071 KiB

Open AccessArticle

A Two-Level WiFi Fingerprint-Based Indoor Localization Method for Dangerous Area Monitoring

by Fei Li, Min Liu, Yue Zhang and Weiming Shen

Sensors 2019, 19(19), 4243; https://doi.org/10.3390/s19194243 - 29 Sep 2019

Cited by 12 | Viewed by 2557

Abstract

Localization technologies play an important role in disaster management and emergence response. In areas where the environment does not change much after an accident or in the case of dangerous areas monitoring, indoor fingerprint-based localization can be used. In such scenarios, a positioning system needs to have both a high accuracy and a rapid response. However, these two requirements are usually conflicting since a fingerprint-based indoor localization system with high accuracy usually has complex algorithms and needs to process a large amount of data, and therefore has a slow response. This problem becomes even worse when both the size of monitoring area and the number of reference nodes increase. To address this challenging problem, this paper proposes a two-level positioning algorithm in order to improve both the accuracy and the response time. In the off-line stage, a fingerprint database is divided into several sub databases by using an affinity propagation clustering (APC) algorithm based on Shepard similarity. The online stage has two steps: (1) a coarse positioning algorithm is adopted to find the most similar sub database by matching the cluster center with the fingerprint of the node tested, which will narrow the search space and consequently save time; (2) in the sub database area, a support vector regression (SVR) algorithm with its parameters being optimized by particle swarm optimization (PSO) is used for fine positioning, thus improving the online positioning accuracy. Both experiment results and actual implementations proved that the proposed two-level localization method is more suitable than other methods in term of algorithm complexity, storage requirements and localization accuracy in dangerous area monitoring. Full article

(This article belongs to the Special Issue Collaborative Sensor Networks and Advanced Data Analytics for Urban Emergencies and Disaster Relief Efforts)

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10 pages, 583 KiB

Open AccessArticle

Low-Cost Radon Detector with Low-Voltage Air-Ionization Chamber

by Filip Studnička, Jan Štěpán and Jan Šlégr

Sensors 2019, 19(17), 3721; https://doi.org/10.3390/s19173721 - 28 Aug 2019

Cited by 8 | Viewed by 4871

Abstract

This paper describes the design of a low-cost radon detector that can easily be fabricated in large quantities for the purposes of earthquake prediction. The described detector can also be used for monitoring radon levels in houses because high radon levels pose a great health risk. A very simple air-ionization chamber for alpha particles was used, considering the experimental results. Chamber current-sensing circuitry is also suggested, and an Internet of Things (IoT) sensor grid is described. The main advantages of this detector are the low cost, low power consumption, and complete elimination of high-voltage power sources. The minimum detectable activity achieved with the proposed detector for one measurement was around

50 Bq \cdot m^{- 3}

, with time of measurement comparable to that featured on commercial devices, while the price of the described detector is one order of magnitude lower. Full article

(This article belongs to the Special Issue Collaborative Sensor Networks and Advanced Data Analytics for Urban Emergencies and Disaster Relief Efforts)

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Review

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29 pages, 3621 KiB

Open AccessReview

Crowdsourcing as a Tool for Urban Emergency Management: Lessons from the Literature and Typology

by Ramon Chaves, Daniel Schneider, António Correia, Claudia L. R. Motta and Marcos R. S. Borges

Sensors 2019, 19(23), 5235; https://doi.org/10.3390/s19235235 - 28 Nov 2019

Cited by 13 | Viewed by 3725

Abstract

Recently, citizen involvement has been increasingly used in urban disaster prevention and management, taking advantage of new ubiquitous and collaborative technologies. This scenario has created a unique opportunity to leverage the work of crowds of volunteers. As a result, crowdsourcing approaches for disaster prevention and management have been proposed and evaluated. However, the articulation of citizens, tasks, and outcomes as a continuous flow of knowledge generation reveals a complex ecosystem that requires coordination efforts to manage interdependencies in crowd work. To tackle this challenging problem, this paper extends to the context of urban emergency management the results of a previous study that investigates how crowd work is managed in crowdsourcing platforms applied to urban planning. The goal is to understand how crowdsourcing techniques and quality control dimensions used in urban planning could be used to support urban emergency management, especially in the context of mining-related dam outages. Through a systematic literature review, our study makes a comparison between crowdsourcing tools designed for urban planning and urban emergency management and proposes a five-dimension typology of quality in crowdsourcing, which can be leveraged for optimizing urban planning and emergency management processes. Full article

(This article belongs to the Special Issue Collaborative Sensor Networks and Advanced Data Analytics for Urban Emergencies and Disaster Relief Efforts)

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