Towards a Social-media Driven Multi-Drone Tasking platform

Abstract

We present a framework for utilising social-media for tasking drone swarms in search and rescue missions. As elaborated in this work, social-media are a source of timely and valuable information regarding the occurrence, the evolution and the damage produced in disasters. Exploiting this source of knowledge can be very valuable in planning search and rescue missions and identifying tasks to be conducted. In this work a novel multi-drone search and rescue framework is developed that considers social-media signals in the creation of search tasks. The key design challenges of implementing the proposed framework are discussed and a simulated scenario based on a real-life Twitter data set is presented as a means of providing evidence of the effectiveness of the proposed framework.

A survey on social-physical sensing: An emerging sensing paradigm that explores the collective intelligence of humans and machines

“…Due to the integrated nature of the social and physical sensors in SPS, one critical challenge in SPS applications is to efficiently address the privacy issues of end users of SPS applications (Liu et al, 2019). Figure 17 illustrates an SAS application where UAVs need to be dispatched based on locations derived from social media (Terzi et al, 2020). However, due to concerns about privacy, a good proportion of users refrain from sharing their GPS data, due to which the UAVs would be unable to determine the locations where to fly to.…”

“…Social airborne sensing (SAS) is progressing as a new SPS application domain where social signals are used to dispatch unmanned aerial vehicles (UAVs) for perceiving anomalous occurrences in time-sensitive applications (e.g., disaster response, wildfire monitoring) (Rashid and Wang 2022b). Figure 2 in Section 1 illustrates the concept of representative SAS schemes (Terzi et al, 2020). SAS is motivated by the agility and empirical sensing capabilities of UAVs fitted with physical sensors (e.g., camera, LiDAR, and thermal scanner) (Casbeer et al, 2005) and the ubiquity of social data platforms (i.e., social media and crowdsensing).…”

“…An SAS system collects and analyzes data from social media and crowdsensing platforms to locate probable events of interest (e.g., a person injured on a roadside, an area getting flooded, or buildings damaged by an earthquake) (Terzi et al, 2020; Rashid et al, 2019b). Afterward, UAVs are selectively dispatched to the extracted locations using various resource management policies (e.g., game theory, supply chain management, and reinforcement learning) to verify the authenticity of the event reports using their onboard physical sensors and augment the knowledge acquisition.…”

“…Afterward, UAVs are selectively dispatched to the extracted locations using various resource management policies (e.g., game theory, supply chain management, and reinforcement learning) to verify the authenticity of the event reports using their onboard physical sensors and augment the knowledge acquisition. Examples of SAS frameworks from recent literature include: (i) a path cheapest arc-based SAS scheme that incorporates calls for help from Twitter and dispatches UAVs for search and rescue missions (Terzi et al, 2020); (ii) a semantic web and machine learning-based SAS design for disaster management in urban areas (Sukmaningsih et al, 2020); (iii) a correlation-driven SAS solution for conducting disaster damage assessment in the aftermath of hurricanes (Yuan and Liu 2018); and (iv) a spatiotemporal-aware SAS framework that identifies latent correlations among reported event locations to dispatch UAVs selectively (Rashid et al, 2019a).…”

A Survey on Social-Physical Sensing: An Emerging Sensing Paradigm that Explores the Collective Intelligence of Humans and Machine

“…By exploiting the collective benefits of social sensing and satellitebased remote sensing, the above schemes hence facilitate in obtaining a fine-grained interpretation of earth's geological features. [32], [126]. Figure 2 in Section I illustrates the architecture of representative SAS schemes.…”