Emergency message dissemination schemes based on congestion avoidance in VANET and vehicular FoG computing

With the rapid growth in connected vehicles, FoG-assisted vehicular ad hoc network (VANET) is an emerging and novel field of research. For information sharing, a number of messages are exchanged in various applications, including traffic monitoring and area-specific live weather and social aspects monitoring. It is quite challenging where vehicles' speed, direction, and density of neighbors on the move are not consistent. In this scenario, congestion avoidance is also quite challenging to avoid communication loss during busy hours or in emergency cases. This paper presents emergency message dissemination schemes that are based on congestion avoidance scenario in VANET and vehicular FoG computing. In the similar vein, FoG-assisted VANET architecture is explored that can efficiently manage the message congestion scenarios. We present a taxonomy of schemes that address message congestion avoidance. Next, we have included a discussion about comparison of congestion avoidance schemes to highlight the strengths and weaknesses. We have also identified that FoG servers help to reduce the accessibility delays and congestion as compared to directly approaching cloud for all requests in linkage with big data repositories. For the dependable applicability of FoG in VANET, we have identified a number of open research challenges. © 2013 IEEE

Monitoring Bicycle Safety through GPS data and Deep Learning Anomaly Detection

Cycling has always been considered a sustainable and healthy mode of transport. Moreover, during Covid-19 period, cycling was further appreciated. by citizens as an individual opportunity of mobility. As a counterpart of the growth in the num.ber ofbicyclists and of riding k:ilometres, bicyclist safety has become a challenge as the unique road transport mode with an increasing trend of crash fatalities in EU (Figure 1). When compared to the traditional road safety network screening. availability of suitable data for crashes involving bicyclists is more difficult because of underreporting and traffic flow issues. In such framework, new technologies and digital transformation in smart cities and communities is offering new opportunities of data availability which requires also different approaches for collection and analysis. An experimental test was carried out to collect data ftom different users with an instrumented bicycle equipped with Global Navigation Satellite Systems (GNSS) and cameras. A panel of experts was asked to review the collected data to identify and score the severity of the safety critical events (CSE) reaching a good consensus. Anyway, manual observation and classi.fication of CSE is a time consu.ming and unpractical approach when large amount of data must be analysed. Moreover, due to the complex correlation between precrash driving behaviour and due to high dimensionality of the data, traditional statistical methods might not be appropriate in t.bis context. Deep learning-based model have recently gained significant attention in the lit.erature for time series data analysis and for anomaly detection, but generally applied to vehicles' mobility and not to micro-mobility. We present and discuss data requirements and treatment to get suitable infonnation from the GNSS devices, the development of an experimental :framework: where convolutional neural networks (CNN) is applied to integrate multiple GPS data streams of bicycle kinematics to detect the occurrence of a CSE

Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network

Internet of vehicles (IoVs) allows millions of vehicles to be connected and share information for various purposes. The main applications of IoVs are traffic management, emergency messages delivery, E-health, traffic, and temperature monitoring. On the other hand, IoVs lack in location awareness and geographic distribution, which is critical for some IoVs applications such as smart traffic lights and information sharing in vehicles. To support these topographies, fog computing was proposed as an appealing and novel term, which was integrated with IoVs to extend storage, computation, and networking. Unfortunately, it is also challenged with various security and privacy hazards, which is a serious concern of smart cities. Therefore, we can formulate that Fog-assisted IoVs (Fa-IoVs), are challenged by security threats during information dissemination among mobile nodes. These security threats of Fa-IoVs are considered as anomalies which is a serious concern that needs to be addressed for smooth Fa-IoVs network communication. Here, smooth communication refers to less risk of important data loss, delay, communication overhead, etc. This research work aims to identify research gaps in the Fa-IoVs network and present a deep learning-based dynamic scheme named CAaDet (Convolutional autoencoder Aided anomaly detection) to detect anomalies. CAaDet exploits convolutional layers with a customized autoencoder for useful feature extraction and anomaly detection. Performance evaluation of the proposed scheme is done by using the F1-score metric where experiments are carried out by exploiting a benchmark dataset named NSL-KDD. CAaDet also observes the behavior of fog nodes and hidden neurons and selects the best match to reduce false alarms and improve F1-score. The proposed scheme achieved significant improvement over existing schemes for anomaly detection. Identified research gaps in Fa-IoVs can give future directions to researchers and attract more attention to this new era

Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living

Physical activity is essential for physical and mental health, and its absence is highly associated with severe health conditions and disorders. Therefore, tracking activities of daily living can help promote quality of life. Wearable sensors in this regard can provide a reliable and economical means of tracking such activities, and such sensors are readily available in smartphones and watches. This study is the first of its kind to develop a wearable sensor-based physical activity classification system using a special class of supervised machine learning approaches called boosting algorithms. The study presents the performance analysis of several boosting algorithms (extreme gradient boosting—XGB, light gradient boosting machine—LGBM, gradient boosting—GB, cat boosting—CB and AdaBoost) in a fair and unbiased performance way using uniform dataset, feature set, feature selection method, performance metric and cross-validation techniques. The study utilizes the Smartphone-based dataset of thirty individuals. The results showed that the proposed method could accurately classify the activities of daily living with very high performance (above 90%). These findings suggest the strength of the proposed system in classifying activity of daily living using only the smartphone sensor’s data and can assist in reducing the physical inactivity patterns to promote a healthier lifestyle and wellbeing

Monitoring Bicycle Safety through GPS data and Deep Learning Anomaly Detection

Cycling has always been considered a sustainable and healthy mode of transport. Moreover, during Covid-19 period, cycling was further appreciated. by citizens as an individual opportunity of mobility. As a counterpart of the growth in the num.ber ofbicyclists and of riding k:ilometres, bicyclist safety has become a challenge as the unique road transport mode with an increasing trend of crash fatalities in EU (Figure 1). When compared to the traditional road safety network screening. availability of suitable data for crashes involving bicyclists is more difficult because of underreporting and traffic flow issues. In such framework, new technologies and digital transformation in smart cities and communities is offering new opportunities of data availability which requires also different approaches for collection and analysis. An experimental test was carried out to collect data ftom different users with an instrumented bicycle equipped with Global Navigation Satellite Systems (GNSS) and cameras. A panel of experts was asked to review the collected data to identify and score the severity of the safety critical events (CSE) reaching a good consensus. Anyway, manual observation and classi.fication of CSE is a time consu.ming and unpractical approach when large amount of data must be analysed. Moreover, due to the complex correlation between precrash driving behaviour and due to high dimensionality of the data, traditional statistical methods might not be appropriate in t.bis context. Deep learning-based model have recently gained significant attention in the lit.erature for time series data analysis and for anomaly detection, but generally applied to vehicles' mobility and not to micro-mobility. We present and discuss data requirements and treatment to get suitable infonnation from the GNSS devices, the development of an experimental :framework: where convolutional neural networks (CNN) is applied to integrate multiple GPS data streams of bicycle kinematics to detect the occurrence of a CSE

Monitoring Bicycle Safety through GPS data and Deep Learning Anomaly Detection

Cycling has always been considered a sustainable and healthy mode of transport. Moreover, during Covid-19 period, cycling was further appreciated. by citizens as an individual opportunity of mobility. As a counterpart of the growth in the num.ber ofbicyclists and of riding k:ilometres, bicyclist safety has become a challenge as the unique road transport mode with an increasing trend of crash fatalities in EU (Figure 1). When compared to the traditional road safety network screening. availability of suitable data for crashes involving bicyclists is more difficult because of underreporting and traffic flow issues. In such framework, new technologies and digital transformation in smart cities and communities is offering new opportunities of data availability which requires also different approaches for collection and analysis. An experimental test was carried out to collect data ftom different users with an instrumented bicycle equipped with Global Navigation Satellite Systems (GNSS) and cameras. A panel of experts was asked to review the collected data to identify and score the severity of the safety critical events (CSE) reaching a good consensus. Anyway, manual observation and classi.fication of CSE is a time consu.ming and unpractical approach when large amount of data must be analysed. Moreover, due to the complex correlation between precrash driving behaviour and due to high dimensionality of the data, traditional statistical methods might not be appropriate in t.bis context. Deep learning-based model have recently gained significant attention in the lit.erature for time series data analysis and for anomaly detection, but generally applied to vehicles' mobility and not to micro-mobility. We present and discuss data requirements and treatment to get suitable infonnation from the GNSS devices, the development of an experimental :framework: where convolutional neural networks (CNN) is applied to integrate multiple GPS data streams of bicycle kinematics to detect the occurrence of a CSE

Detection of anomalies in cycling behavior with convolutional neural network and deep learning

Abstract Background Cycling has always been considered a sustainable and healthy mode of transport. With the increasing concerns of greenhouse gases and pollution, policy makers are intended to support cycling as commuter mode of transport. Moreover, during Covid-19 period, cycling was further appreciated by citizens as an individual opportunity of mobility. Unfortunately, bicyclist safety has become a challenge with growing number of bicyclists in the 21st century. When compared to the traditional road safety network screening, availability of suitable data for bicycle based crashes is more difficult. In such framework, new technologies based smart cities may require new opportunities of data collection and analysis. Methods This research presents bicycle data requirements and treatment to get suitable information by using GPS device. Mainly, this paper proposed a deep learning-based approach “BeST-DAD” to detect anomalies and spot dangerous points on map for bicyclist to avoid a critical safety event (CSE). BeST-DAD follows Convolutional Neural Network and Autoencoder (AE) for anomaly detection. Proposed model optimization is carried out by testing different data features and BeST-DAD parameter settings, while another comparison performance is carried out between BeST-DAD and Principal Component Analysis (PCA). Result BeST-DAD over perform than traditional PCA statistical approaches for anomaly detection by achieving 77% of the F-score. When the trained model is tested with data from different users, 100% recall is recorded for individual user’s trained models. Conclusion The research results support the notion that proper GPS trajectory data and deep learning classification can be applied to identify anomalies in cycling behavior

Fog-assisted congestion avoidance scheme for internet of vehicles

Recently, Internet of Vehicles (IoVs) is getting growing interest because of their suitability for a wide range of emerging applications. Most of these applications require vehicles to continuously update their information to a centralized location in order to gain various services. However, frequent transmission of messages by an abundance number of vehicles may not only overwhelm a centralized server but also causes a huge congestion which might disrupt various services including emergency situations. The aim of this research is to minimize congestion and messaging delay. This paper presents a fog-assisted congestion avoidance scheme for IoV named Energy Efficient Message Dissemination (E2MD). To capitalize the merits of fog computing and minimize latency, E2MD opts a distributed approach by employing a fog server to complement services in IoVs. In E2MD, vehicles continuously update their status to a fog server either directly or through intermediate nodes. The performance of the proposed scheme is validated through NS 2.35 simulations. Simulation results confirm the performance supremacy of E2MD compared to contemporary schemes in terms of end-to-end delay and messaging cost. © 2018 IEEE

Congestion avoidance through fog computing in internet of vehicles

Recently, internet of vehicles (IoVs) is captivating a lot of interest due to a wide range of applications in various domains. These applications rely on up-to-date information of vehicles for provisioning various services. However, frequent message transmissions by a sheer number of vehicles may not only engulf a centralized server but may also cause a severe congestion which is not suitable for ongoing services specially in emergency situations. The aim of this study is to reduce congestion and messaging overhead. This paper presents a fog-assisted congestion avoidance scheme for IoV named energy efficient message dissemination (E2MD). Unlike most of the existing schemes, E2MD capitalizes the merits of fog computing to minimize communication cost and manage services. Each vehicle is required to update their status to a fog server frequently, either directly or through intermediate nodes. In case of an emergency, the fog server will timely inform upcoming traffic to slow down, dispatch rescue teams to provide necessary services, and coordinate patrolling missions to clear the road. Moreover, failure detection and recovery mechanisms are also presented to ensure availability of the fog server. The performance of the proposed scheme is validated through NS 2.35 simulations. Simulation results confirm the performance reign of E2MD compared to contemporary schemes in terms of latency and communication cost. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature

Novel congestion avoidance scheme for Internet of Drones

Internet of Drones (IoDs) is getting growing interest of researchers due to its applicability in wide range of applications for transportation, weather monitoring, emergency monitoring for flood, earth quake, healthcare and road hazards. To update the data about emergency situation, a real-time data sharing is mandatory. However, regular message transmission by various drones may not only overwhelm a central server but it also causes congestion on the network. It is mandatory to reduce messaging cost and congestion. This paper presents a fog-assisted congestion avoidance approach for Smooth Message Dissemination (SMD). We present a message forwarding algorithm for congestion avoidance to select the appropriate next-hop node using layered model. This model is based on various layers having drones. In first phase, it looks for an appropriate drone in a layer near the fog server for message forwarding. In next step, the drone is identified in nearby layers to forward the emergency message to next-hop to further locate the group head as per priority. It is a drone that has less distance towards fog server and inform in its one-hop circle. It can stop forwarding message after delivering it to fog server. Finally, the fog server disseminates information timely towards upper layers for necessary actions for emergency situations. The performance of the proposed approach is validated through extensive simulations using NS 2.35. Results prove the dominance of SMD over counterparts in terms of messaging overhead, packet delivery ratio, throughput, energy consumption and average delay. Proposed SMD improves PDR by 85% and message overhead cost by 91% as compared to counterparts