Robert Ighodaro Ogie

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

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.

Spatio-topological network analysis of hydrological infrastructure as a decision support tool for flood mitigation in coastal mega-cities

Hydrological infrastructure components such as pumps, floodgates, and flood gauges are invaluable assets for mitigating flooding, which threatens millions of lives and damages property worth billions of dollars in coastal mega-cities around the world. By improving the understanding of how these hydrological infrastructure components are both spatially and topologically connected through waterways (rivers, canals, streams, etc.) within coastal mega-cities, more precise decisions can be made regarding the most appropriate hydrological infrastructure components required to mitigate flooding during emergency conditions. This paper explores the use of graph theory to create a spatio-topological model of a real world hydrological infrastructure network for one of the most representative coastal mega-cities—Jakarta, Indonesia. The network is modeled as a directed multigraph, with hydrological infrastructure represented as network nodes and waterways as edges. The article demonstrates how the network model can be used as a real-time decision support tool for responding to flooding events by alerting decision makers to the occurrence of rising water levels in any given area and, suggesting the most appropriate infrastructure components to engage in order to prevent a given area from flooding.

Participation Patterns and Reliability of Human Sensing in Crowd-Sourced Disaster Management

Over the last ten years, there has been a significant increase in crowd-sourcing applications for disaster management. Their success depends heavily on the behaviour of social media users, acting as human sensors during disaster monitoring and emergency response. Unlike their technological counterparts, human sensors are complex social entities, contributing in different ways to their collective task and creating varying participation patterns through social media. Failing to understand these participation patterns limits our capacity to evaluate the reliability of human sensing in different contexts. Based on an analysis of flood-related information contributed by Twitter users in Jakarta during the 2014/2015 and 2015/2016 monsoonal seasons, this study establishes four categories of human sensors and their respective levels of reliability for disaster management. The results have significant implications for how we frame expectations and develop reliance on the use of social media for disaster management. Importantly, the results will serve as a useful guide for understanding levels of incentive that may be required to motivate members of the different categories of social media users during emergencies and disasters.

Disaster Risk Communication in Culturally and Linguistically Diverse Communities: The Role of Technology

Migrants, ethnic minorities and people from culturally and linguistically diverse (CALD) communities are often more vulnerable to natural disasters due to cultural barriers and limited proficiency in the dominant language, which sometimes undermine their ability to access, interpret and respond to warnings. Technology can assist in engendering culturally and linguistically appropriate communication with CALD communities if key challenges are identified. This study contributes by reviewing relevant literature with the aim of ascertaining the most pressing challenges requiring technological interventions. Three broad issues (i.e., trust, message tailoring, and message translation) are identified and discussed, and potential solutions for addressing these issues are recommended.

Crowdsourced social media data for disaster management: Lessons from the PetaJakarta.org project

Abstract The application of crowdsourced social media data in flood mapping and other disaster management initiatives is a burgeoning field of research, but not one that is without challenges. In identifying these challenges and in making appropriate recommendations for future direction, it is vital that we learn from the past by taking a constructively critical appraisal of highly-praised projects in this field, which through real-world implementations have pioneered the use of crowdsourced geospatial data in modern disaster management. These real-world applications represent natural experiments, each with myriads of lessons that cannot be easily gained from computer-confined simulations. This paper reports on lessons learnt from a 3-year implementation of a highly-praised project- the PetaJakarta.org project. The lessons presented derive from the key success factors and the challenges associated with the PetaJakarta.org project. To contribute in addressing some of the identified challenges, desirable characteristics of future social media-based disaster mapping systems are discussed. It is envisaged that the lessons and insights shared in this study will prove invaluable within the broader context of designing socio-technical systems for crowdsourcing and harnessing disaster-related information.

Cyber Security Incidents on Critical Infrastructure and Industrial Networks

National critical infrastructure and industrial processes are heavily reliant on automation, monitoring and control technologies, including the widely used Supervisory Control and Data Acquisition (SCADA) systems. The growing interconnection of these systems with corporate networks exposes them to cyber attacks, with several security incidents reported over the last few decades. This study provides a classification scheme for categorising security incidents related to critical infrastructure and industrial control systems. The classification scheme is applied to analyse 242 security incidents on critical infrastructure and industrial control networks, which were reported between 1982 and 2014. The results show interesting patterns, with key points highlighted for the purpose of improving the way we plan for and direct efforts toward protecting critical infrastructure and industrial networks.