Continuous and Responsive D2D Victim Localization for Post-Disaster Emergencies

One of the most challenging tasks in a disaster scenario is the detection and localization of victims with high accuracy and minimum delay, especially in out-of-coverage areas. In the event of a disaster that disrupts the cellular network infrastructure, emergency calls can be relayed to the core network via multi-hop D2D communications. In this paper, a localization system is proposed that uses radio measurements obtained through such D2D multi-hop assisted emergency calls to localize in-coverage and out-of-coverage devices. To address the uncertainty and gradual reception of data in real-time in this scenario, a dynamic constraint satisfaction-based Multi Victim Localization Algorithm (MVLA) is proposed. This algorithm locates multi-hop devices in a progressive propagation manner to provide fast and accurate updates on victim locations. Additionally, three modes of <inline-formula><tex-math notation="LaTeX">${MVLA}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>M</mml:mi><mml:mi>V</mml:mi><mml:mi>L</mml:mi><mml:mi>A</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="basnayake-ieq1-3336353.gif"/></alternatives></inline-formula>, namely <inline-formula><tex-math notation="LaTeX">${MVLA}_{recent}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow><mml:mi>M</mml:mi><mml:mi>V</mml:mi><mml:mi>L</mml:mi><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="basnayake-ieq2-3336353.gif"/></alternatives></inline-formula>, <inline-formula><tex-math notation="LaTeX">${MVLA}_{seq}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow><mml:mi>M</mml:mi><mml:mi>V</mml:mi><mml:mi>L</mml:mi><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="basnayake-ieq3-3336353.gif"/></alternatives></inline-formula>, and <inline-formula><tex-math notation="LaTeX">${MVLA}_{all}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow><mml:mi>M</mml:mi><mml:mi>V</mml:mi><mml:mi>L</mml:mi><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="basnayake-ieq4-3336353.gif"/></alternatives></inline-formula> are proposed. Simulation results demonstrate that <inline-formula><tex-math notation="LaTeX">${MVLA}_{all}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow><mml:mi>M</mml:mi><mml:mi>V</mml:mi><mml:mi>L</mml:mi><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="basnayake-ieq5-3336353.gif"/></alternatives></inline-formula> has a lower localization error compared to <inline-formula><tex-math notation="LaTeX">${MVLA}_{recent}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow><mml:mi>M</mml:mi><mml:mi>V</mml:mi><mml:mi>L</mml:mi><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="basnayake-ieq6-3336353.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">${MVLA}_{seq}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow><mml:mi>M</mml:mi><mml:mi>V</mml:mi><mml:mi>L</mml:mi><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="basnayake-ieq7-3336353.gif"/></alternatives></inline-formula>. Moreover, <inline-formula><tex-math notation="LaTeX">${MVLA}_{all}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow><mml:mi>M</mml:mi><mml:mi>V</mml:mi><mml:mi>L</mml:mi><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="basnayake-ieq8-3336353.gif"/></alternatives></inline-formula>, is compared with an existing particle filtering-based localization algorithm called RSSI Monte-Carlo Boxed Localization (RSSI-MCL) under an increasing number of emergency user devices and functional gNodeBs. Results show that <inline-formula><tex-math notation="LaTeX">${MVLA}_{all}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow><mml:mi>M</mml:mi><mml:mi>V</mml:mi><mml:mi>L</mml:mi><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="basnayake-ieq9-3336353.gif"/></alternatives></inline-formula> significantly outperforms the RSSI-MCL method in terms of localization accuracy and computational delay.