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Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.

Mobile crowdsensing is a burgeoning concept that allows smart cities to leverage the sensing power and ubiquitous nature of mobile devices in order to capture and map phenomena of common interest. At the core of any successful mobile crowdsensing application is active user participation, without which the system is of no value in sensing the phenomenon of interest. A major challenge militating against widespread use and adoption of mobile crowdsensing applications is the issue of how to identify the most appropriate incentive mechanism for adequately and efficiently motivating participants. This paper reviews literature on incentive mechanisms for mobile crowdsensing and proposes the concept of SPECTRUM as a guide for inferring the most appropriate type of incentive suited to any given crowdsensing task. Furthermore, the paper highlights research challenges and areas where additional studies related to the different factors outlined in the concept of SPECTRUM are needed to improve citizen participation in mobile crowdsensing. It is envisaged that the broad range of factors covered in SPECTRUM will enable smart cities to efficiently engage citizens in large-scale crowdsensing initiatives. More importantly, the paper is expected to trigger empirical investigations into how various factors as outlined in SPECTRUM can influence the type of incentive mechanism that is considered most appropriate for any given mobile crowdsensing initiative.

The irreversible marriage between digital technology and physical urban infrastructure has given rise to the concept of smart infrastructure. The potential benefits of smart infrastructure are significant; however, their realisation will depend on society's ability to address pressing issues, such as the need to develop a common language to describe terms and processes. This paper aims to lay out the foundations of such a common language. First, the authors review academic literature in order to outline key characteristics of so-called smart infrastructure systems. Importantly, the authors define and differentiate between smart and intelligent infrastructure systems. Then, the authors use an LVP framework to describe the levels (L), values (V) and principles (P) of inherently smart infrastructure systems. Finally, the authors argue that the study of smart infrastructure is a multidisciplinary field of research that reaches beyond traditional engineering and information technology disciplines. The authors expect that eliminating ambiguity and fragmentation in the definition of smart infrastructure systems will enhance research on and practice of these systems.

Natural hazards pose significant threats to different communities and various places around the world. Failing to identify and support the most vulnerable communities is a recipe for disaster. Many studies have proposed social vulnerability indices for measuring both the sensitivity of a population to natural hazards and its ability to respond and recover from them. Existing techniques, however, have not accounted for the unique strengths that exist within different communities to help minimize disaster loss. This study proposes a more balanced approach referred to as the strength-based social vulnerability index (SSVI). The proposed SSVI technique, which is built on sound sociopsychological theories of how people act during disasters and emergencies, is applied to assess comparatively the social vulnerability of different suburbs in the Wollongong area of New South Wales, Australia. The results highlight suburbs that are highly vulnerable, and demonstrates the usefulness of the technique in improving understanding of hotspots where limited resources should be judiciously allocated to help communities improve preparedness, response, and recovery from natural hazards.