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Delta regions represent unique settings characterized by a combination of dynamic hydrological environments and livelihood opportunity. They are sites of intensive human activity and infrastructure development aimed at managing the environment and ameliorating hazards such as riverbank erosion. In this paper, we present a case study from the Meghna River delta highlighting livelihood dynamics in the context of riverbank erosion and the recent construction of a protective concrete revetment. To account for the hydrological, socioeconomic, and infrastructural dynamics of the delta environment, we characterize our setting as a hydrosocial territory, and we draw from interviews with local residents to document key dimensions of delta life within the Meghna estuary. Our findings show that the delta environment provides opportunity for local residents, but that riverbank erosion has led to significant displacement and is a source of anxiety for many. We also find that both the nature of the hazard and the limited extent of the new embankment have led to an uneven hydrosocial territory characterized by social and spatial inequality. Despite ongoing challenges, our study shows that riverside dwellers are active agents who manage to craft unique hybrid livelihoods from within the Meghna floodplains. 

A resilient positioning, navigation, and timing (PNT) system is a necessity for the robust navigation of autonomous vehicles (AVs). A global navigation satellite system (GNSS) provides satellite-based PNT services. However, a spoofer can tamper the authentic GNSS signal and could transmit wrong position information to an AV. Therefore, an AV must have the capability of real-time detection of spoofing attacks related to PNT receivers, whereby it will help the end-user (the AV in this case) to navigate safely even if the GNSS is compromised. This paper aims to develop a deep reinforcement learning (RL)-based turn-by-turn spoofing attack detection method using low-cost in-vehicle sensor data. We have utilized the Honda Research Institute Driving Dataset to create attack and non-attack datasets to develop a deep RL model and have evaluated the performance of the deep RL-based attack detection model. We find that the accuracy of the deep RL model ranges from 99.99% to 100%, and the recall value is 100%. Furthermore, the precision ranges from 93.44% to 100%, and the f1 score ranges from 96.61% to 100%. Overall, the analyses reveal that the RL model is effective in turn-by-turn spoofing attack detection. 

Abstract Riverbank erosion is a common hazard in Bangladesh, posing a significant threat to homes, properties, and livelihoods. In recent years, the government of Bangladesh has intensified efforts to mitigate riverbank erosion by hardening shorelines, including the building of concrete revetments, but the local dynamics of these interventions are not well documented. To address this, we present results from a study of community-level response to a 2-mile long concrete revetment recently constructed in the administrative district of Ramgati, in the lower Meghna River basin of Bangladesh. Our study combines quantitative analysis of data from a household survey with qualitative data from semi-structured interviews to assess resident perceptions of the new revetment and its effect on the landscape of riverbank erosion hazard. The study finds, firstly, that hazard awareness is widespread but may be influenced by livelihood factors related to the dynamics of displacement and resettlement. Second, we find that that the negative impacts of riverbank erosion vary spatially. Hazard perception in Ramgati is significantly influenced by the physical location of the household, with those residing closer to the river and in unprotected zones north and south of the revetment expressing much greater worry that they will lose their homes, and believing that this will happen much sooner than residents further away or in the zone now protected by the embankment. As an empirically grounded case study, our findings add to the literature on riverbank erosion in Bangladesh and perception studies focused on water-based hazards in similar settings globally. 

As camera quality improves and their deployment moves to areas with limited bandwidth, communication bottlenecks can impair real-time constraints of an intelligent transportation systems application, such as video-based real-time pedestrian detection. Video compression reduces the bandwidth requirement to transmit the video which degrades the video quality. As the quality level of the video decreases, it results in the corresponding decreases in the accuracy of the vision-based pedestrian detection model. Furthermore, environmental conditions, such as rain and night-time darkness impact the ability to leverage compression by making it more difficult to maintain high pedestrian detection accuracy. The objective of this study is to develop a real-time error-bounded lossy compression (EBLC) strategy to dynamically change the video compression level depending on different environmental conditions to maintain a high pedestrian detection accuracy. We conduct a case study to show the efficacy of our dynamic EBLC strategy for real-time vision-based pedestrian detection under adverse environmental conditions. Our strategy dynamically selects the lossy compression error tolerances that maintain a high detection accuracy across a representative set of environmental conditions. Analyses reveal that for adverse environmental conditions, our dynamic EBLC strategy increases pedestrian detection accuracy up to 14% and reduces the communication bandwidth up to 14 × compared to the state-of-the-practice. Moreover, we show our dynamic EBLC strategy is independent of pedestrian detection models and environmental conditions allowing other detection models and environmental conditions to be easily incorporated. 

Connected vehicle (CV) application developers need a development platform to build, test, and debug real-world CV applications, such as safety, mobility, and environmental applications, in edge-centric cyber-physical system (CPS). The objective of this paper is to develop and evaluate a scalable and secure CV application development platform (CVDeP) that enables application developers to build, test, and debug CV applications in real-time while meeting the functional requirements of any CV applications. The efficacy of the CVDeP was evaluated using two types of CV applications (one safety and one mobility application) and they were validated through field experiments at the South Carolina Connected Vehicle Testbed (SC-CVT). The analyses show that the CVDeP satisfies the functional requirements in relation to latency and throughput of the selected CV applications while maintaining the scalability and security of the platform and applications.