Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities

Abstract

Smart cities contain intelligent things which can intelligently automatically and collaboratively enhance life quality, save people's lives, and act a sustainable resource ecosystem. To achieve these advanced collaborative technologies such as drones, robotics, artificial intelligence, and Internet of Things (IoT) are required to increase the smartness of smart cities by improving the connectivity, energy efficiency, and quality of services (QoS). Therefore, collaborative drones and IoT play a vital role in supporting a lot of smartcity applications such as those involved in communication, transportation, agriculture,safety and security, disaster mitigation, environmental protection, service delivery, energy saving, e-waste reduction, weather monitoring, healthcare, etc. This paper presents a survey of the potential techniques and applications of collaborative drones and IoT which have recently been proposed in order to increase the smartness of smart cities. It provides a comprehensive overview highlighting the recent and ongoing research on collaborative drone and IoT in improving the real-time application of smart cities. This survey is different from previous ones in term of breadth, scope, and focus. In particular, we focus on the new concept of collaborative drones and IoT for improving smart-city applications. This survey attempts to show how collaborative drones and IoT improve the smartness of smart cities based on data collection, privacy and security, public safety, disaster management, energy consumption and quality of life in smart cities. It mainly focuses on the measurement of the smartness of smart cities, i.e., environmental aspects, life quality, public safety, and disaster management.

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

“…AI paradigms Machine learning [26]; [63]; [67][68][69][70]; Probabilistic methods [70]; [73][74][75]; [77]; [80][81][82]; [87]; [90]; [93,94]; [96][97][98][99][100][101]; [112][113][114]; [134]; [136][137][138] Knowledge-based [26]; [63]; [67][68][69][70][71]; [73]; [77,78]; [82]; [92]; [98]; [100]; [112]; [136]; [139][140][141][142]…”

“…Advanced AI surveillance technologies, enabled by motion detection, predictive analytics, drones, and other autonomous devices, can be used to monitor urban areas, recognize threats, such as crime [72,93,100,148], fraud [109], accidents, and fire [101,123,160]. On a broader scale, AI can be used to monitor communication networks and recognize potential terrorist threats, trafficking, crime syndicates, and other illegal behavior [100].…”

“…This would give decision-makers and other authorities more time to respond to threats such as natural disasters [132], house fires [123], or other incidents. Furthermore, AI can be used to assess the extent of damage caused by these events helping authorities better respond to, and mitigate any damage caused [101,132].…”

Deployment of an aerial platform system for rapid restoration of communications links after a disaster: a machine learning approach

“…Second, during a forest fire in Cyprus, aerial platforms are deployed to cover a 60 km diameter in a multi cellular layout. Authors in [32] investigate the challenges of WiMAX, WiFi, LTE, ZigBee, and XBee wireless technologies for enabling aerial drone platform in Alpine environments, in order to support short term winter events and provide a novel but viable solution in emergency and rescue situations in a hostile environment. The candidacy of an omni-directional antenna type is considered in this case and WiMAX has been suggested as a suitable wireless technology for drone communications for several reasons: flexibility, safety, QoS, interference, throughput, installation, and coverage area.…”

“…Considering aerial platforms in any emergency situations with an optimized propagation model is scarcely reported in the literature, let alone considering QoS and GoS. This section aims at first to introduce our physical model for re-establishing communications with aerial [14,18,25] MIMO limited consideration for diversity gain [38,44] QoS Various techniques to minimize power consumption [17,32,35] Trade-off between pathloss and power consumption to guarantee long lifespan connection for WSN [35,45] GoS -Lack of consideration for aerial platforms platforms in the immediate aftermath of a natural or man-made disaster and then set up an initial, non-optimized, ATG propagation model. Figure 1 illustrates through the use of the 3D Remcom Wireless InSite software tool our physical model in action.…”