A new methodology to support group decision-making for IoT-based emergency response systems

Abstract

An emergency response system (ERS) can assist a municipality or government in improving its capabilities to respond urgent and severe events. The responsiveness and effectiveness of an ERS relies greatly on its data acquisition and processing system, which has been evolved with information technology (IT). With the rapid development of sensor networks and cloud computing, the emerging Internet of things (IoT) tends to play an increasing role in ERSs; the networks of sensors, public services, and experts are able to interact with each other and make scientific decisions to the emergencies based on real-time data. When group decision making is required in an ERS, one critical challenge is to obtain the good understanding of massive and diversified data and make consensus group decisions under a high-level stress and strict time constraint. Due to the nature of unorganized data and system complexity, an ERS depends on the perceptions and judgments of experts from different domains; it is challenging to assess the consensus of understanding on the collected data and response plans before appropriate decisions can be reached for emergencies. In this paper, the group decision-making to emergency situations is formulated as a multiple attribute group decision making (MAGDM) problem, the consensus among experts is modeled, and a new methodology is proposed to reach the understanding of emergency response plans with the maximized consensus in course of decision-making. In the implementation, the proposed methodology in integrated with computer programs and encapsulated as a service on the server. The objectives of the new methodology are (i) to enhance the comprehensive group cognizance on emergent scenarios and response plans and (ii) to accelerate the consensus for decision making with an intelligent clustering algorithm, (iii) to adjust the experts' opinions without affecting the reliability of the decision when the consensus cannot be reached from the preliminary decision-making steps. Partitioning Around Medoids (PAM) has been applied as the clustering algorithm, Particle Swarm Optimization (PSO) is deployed to adjust evaluation values automatically. The methodology is applied in a case study to illustrate its effectiveness in converging group opinions and promoting the consensus of understanding on emergencies.

Robust Platelet Logistics Planning in Disaster Relief Operations Under Uncertainty: a Coordinated Approach

“…An integrated model that captures the interactions between operations of multiple 2 Literature review Table 1 summarizes the detailed specifications of some of the most recent related research in the field of emergency disaster management. To coordinate relief organizations, various structures (Raju & Becker, 2013;Raju & Niekerk, 2013), mechanisms (Balcik et al, 2010), and information technology-based platforms have been developed (Rossel et al, 2016;Poblet et al, 2017;Li et al, 2014). Balcik et al (2010), Schulz and Blecken (2010), Ozdamar and Ertem (2014), Caunhye et al (2012), and Altay and Green (2006) widely surveyed the existing literature regarding humanitarian disaster management.…”

An industrial information integration approach to in-orbit spacecraft

“…Thanks to the advancement of the semantic web, the ontologies have been proliferated in recent years, and they have been widely adopted in many fields such as adapting e-learning (Cristea 2004;Pattuelli 2008), complex product designs (Li et al 2015;Niu, Xu, and Bi 2013;Bi, Xu, and Wang 2014;Bi 2011;) and emergency response systems (Malizia et al 2010;Li et al 2014;Xu 2014). In this paper, ontologies will be utilised to establish the database for the status detection of spacecraft to promote resource sharing, interacting and reasoning.…”

“…In particular, various solutions will be integrated in the framework of industrial information integration engineering first coined in Xu (2015) to synergise resources, analyse realtime data and assess the health conditions of a spacecraft effectively (Zhang et al 2012;Zhang and Wang 2006). A number of emergency response systems or fault detection systems have been developed for other applications (Li et al 2014;Pan et al 2014).…”

Multiagent Semantical Annotation Enhancement Model for IoT-Based Energy-Aware Data

“…As a rule, either the preprocessing monitoring based on KDD or the information mining advances should be updated for IoT that can deliver a lot of information. Semantic Web-based systems on top of agents can be taken into account to resolve huge information recovery for IoT [13][14][15][16].…”