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With the widespread use of smartphones and the continuous increase of their capabilities, a new sensing paradigm has emerged: mobile crowdsensing. The concept of crowdsensing implies the reliance on the crowd to perform sensing tasks and collect data about a phenomena of interest. Due to the benefits it offers in terms of time and cost savings in t...
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... the proposed approach, we deal with microcells that cover around one mile in diameter since our study addresses the urban roads. Figure 2 illustrates the road topology used in the simulated scenarios including the cell towers with hexagonal shape covering the case study area. If a data consumer requests the status of the road x presented in the figure, towers T2 and T3 are picked to capture the vehicles movement. ...
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... (vehicles/mile) k (vehicles/305 meters) By analyzing the flow condition on road x in Figure 2, we can find that the length of the road is 305 meters, and therefore the relation between the traffic condition and the number of vehicles is calculated and provided in Table 2. ...
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... simulator adopted to generate such movement traces is VanetMobiSim [16], which is widely used and could replace the need of collecting real sensory data from mobile phones. We used in our experiments the road topology of Figure 2 generated from VanetMobiSim and simulated four cell towers to cover the entire region. Each tower has 1 mile of diameter that covers the area shown in hexagonal shape. ...
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... some scenarios, the targeted roads could be covered by two towers (i.e. road x in Figure 2) or even more. In the conducted experiments, we consider the case where the targeted roads (roads y in Figure 2) are fully located in the coverage area of only one tower. ...
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... x in Figure 2) or even more. In the conducted experiments, we consider the case where the targeted roads (roads y in Figure 2) are fully located in the coverage area of only one tower. ...
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... the conducted experiments, we consider that each sensing request falls into different towers in the studied area (e.g. in Figure 2, one request for a road covered by cell #3, while another covered by cell #1, and so on). We also consider that these requests are sent in parallel, so the platform will be performing the whole communication and data processing simultaneously. ...
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... geographic location, 2. User availability, 3. Node battery level, 4. User reputation, 5. Node sensing capability, 6. Node data transfer accuracy). Figures 12 and 13 show respectively the matching error and response time of 2, 3, 4 and 6 selected criteria. ...
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... matching error in Figure 12 is calculated as: [1 -(the number of cars selected by the matching algorithm) / (the actual number of cars found on the targeted road)] * 100. We use the simulation traces of VanetMobiSim visualization to acquire the actual number of cars at the time studying the traffic condition. ...
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... the proposed approach, we deal with microcells that cover around one mile in diameter since our study addresses the urban roads. Figure 2 illustrates the road topology used in the simulated scenarios including the cell towers with hexagonal shape covering the case study area. If a data consumer requests the status of the road x presented in the figure, towers T2 and T3 are picked to capture the vehicles movement. ...
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... (vehicles/mile) k (vehicles/305 meters) By analyzing the flow condition on road x in Figure 2, we can find that the length of the road is 305 meters, and therefore the relation between the traffic condition and the number of vehicles is calculated and provided in Table 2. ...
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... simulator adopted to generate such movement traces is VanetMobiSim [16], which is widely used and could replace the need of collecting real sensory data from mobile phones. We used in our experiments the road topology of Figure 2 generated from VanetMobiSim and simulated four cell towers to cover the entire region. Each tower has 1 mile of diameter that covers the area shown in hexagonal shape. ...
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... some scenarios, the targeted roads could be covered by two towers (i.e. road x in Figure 2) or even more. In the conducted experiments, we consider the case where the targeted roads (roads y in Figure 2) are fully located in the coverage area of only one tower. ...
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... x in Figure 2) or even more. In the conducted experiments, we consider the case where the targeted roads (roads y in Figure 2) are fully located in the coverage area of only one tower. ...
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... the conducted experiments, we consider that each sensing request falls into different towers in the studied area (e.g. in Figure 2, one request for a road covered by cell #3, while another covered by cell #1, and so on). We also consider that these requests are sent in parallel, so the platform will be performing the whole communication and data processing simultaneously. ...
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... geographic location, 2. User availability, 3. Node battery level, 4. User reputation, 5. Node sensing capability, 6. Node data transfer accuracy). Figures 12 and 13 show respectively the matching error and response time of 2, 3, 4 and 6 selected criteria. ...
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... matching error in Figure 12 is calculated as: [1 -(the number of cars selected by the matching algorithm) / (the actual number of cars found on the targeted road)] * 100. We use the simulation traces of VanetMobiSim visualization to acquire the actual number of cars at the time studying the traffic condition. ...
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... the proposed approach, we deal with microcells that cover around one mile in diameter since our study addresses the urban roads. Figure 2 illustrates the road topology used in the simulated scenarios including the cell towers with hexagonal shape covering the case study area. If a data consumer requests the status of the road x presented in the figure, towers T2 and T3 are picked to capture the vehicles movement. ...
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... analyzing the flow condition on road x in Figure 2, we can find that the length of the road is 305 meters, and therefore the relation between the traffic condition and the number of vehicles is calculated and provided in Table 2. ...
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... simulator adopted to generate such movement traces is VanetMobiSim [16], which is widely used and could replace the need of collecting real sensory data from mobile phones. We used in our experiments the road topology of Figure 2 generated from VanetMobiSim and simulated four cell towers to cover the entire region. Each tower has 1 mile of diameter that covers the area shown in hexagonal shape. ...
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... some scenarios, the targeted roads could be covered by two towers (i.e. road x in Figure 2) or even more. In the conducted experiments, we consider the case where the targeted roads (roads y in Figure 2) are fully located in the coverage area of only one tower. ...
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... x in Figure 2) or even more. In the conducted experiments, we consider the case where the targeted roads (roads y in Figure 2) are fully located in the coverage area of only one tower. ...
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... the conducted experiments, we consider that each sensing request falls into different towers in the studied area (e.g. in Figure 2, one request for a road covered by cell #3, while another covered by cell #1, and so on). We also consider that these requests are sent in parallel, so the platform will be performing the whole communication and data processing simultaneously. ...
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Autonomous vehicles (AV) are game-changing innovations that promise a safer, more convenient, and environmentally friendly mode of transportation than traditional vehicles. Therefore, understanding AV technologies and their impact on society is critical as we continue this revolutionary journey. Generally, there needs to be a detailed study available to assist a researcher in understanding AV and its challenges. This research presents a comprehensive survey encompassing various aspects of AVs, such as public adoption, driverless city planning, traffic management, environmental impact, public health, social implications, international standards, safety, and security. Furthermore, it presents emerging technologies such as artificial intelligence (AI), integration of cloud computing, and solar power usage in automated vehicles. It also presents forensics approaches, tools used, standards involved, and challenges associated with conducting digital forensics in the context of autonomous vehicles. Moreover, this research provides an overview of cyber attacks affecting autonomous vehicles, attack management, traditional security devices, threat modeling, authentication schemes, over-the-air updates, zero-trust architectures, data privacy, and the corresponding defensive strategies to mitigate such risks. It also presents international standards, guidelines, and best practices for AVs. Finally, it outlines the future directions of AVs and the challenges that must be addressed to achieve widespread adoption.
... On-site ML [13], where some ML operations are moved to devices with powerful resources, has improved to solve such issues. [15] 2.2 Distributed On-Site Learning As the hazards of transferring data to a central organization increase, the demand for real-time intelligence is pushing distributed on-site ML, where training, predictions, and inferences are based on live-streaming data [14]. Instead of transmitting a request along with sensitive user data to the cloud, onsite ML leverages the server to install a pre-trained or generic ML model on the devices. ...
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... It can provide a robust operating environment for localization systems [25,26]. With the surging of smartphone sensing and wireless networking techniques, crowdsensing has become a promising paradigm for cross-space and large-scale sensing [27][28][29][30][31][32][33]. Gupta A, et al. used crowdsourcing methods to estimate road gradients from multiple sources or vehicles to improve the accuracy and robustness of the system [34]. ...
Lightning localization is of great significance to weather forecasting, forest fire prevention, aviation, military, and other aspects. Traditional lightning localization requires the deployment of base stations and expensive measurement equipment. With the development of IoT technology and the continuous expansion of application scenarios, IoT devices can be interconnected through sensors and other technical means to ultimately achieve the goal of automatic intelligent computing. Therefore, this paper proposes a low-cost distributed thunder-localization system based on IoT smart devices, namely ThunderLoc. The main idea of ThunderLoc is to collect dual-microphone data from IoT smart devices, such as smartphones or smart speakers, through crowdsourcing, turning the localization problem into a search problem in Hamming space. We studied the dual microphones integrated with smartphones and used the sign of Time Difference Of Arrival (TDOA) as measurement information. Through a simple generalized cross-correlation method, the TDOA of thunderclaps on the same smartphone can be estimated. After quantifying the TDOA measurement from the smartphone node, thunder localization was performed by minimizing the Hamming distance between the binary sequence and the binary vector measured in a database. The ThunderLoc system was evaluated through extensive simulations and experiments (a testbed with 30 smartphone nodes). The extensive experimental results demonstrate that ThunderLoc outperforms the main existing schemes in terms of effectively locating position and good robustness.
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The development of vehicular intelligence and networking has led to the emergence of vehicular crowdsensing as a new perception paradigm. By integrating edge computing, vehicular intelligence, and Internet of Vehicles technologies, vehicular crowdsensing is poised to have far-reaching implications in the domains of intelligent transportation, industrial sensing, and smart cities. In urban sensing scenarios, recruiting a large number of users can provide a lot of useful data, yet is costly due to expected financial incentive plans. As a solution, sparse mobile crowdsensing techniques have been proposed to collect data at a subset of sensing grids for data inference. However, the majority of these methods rely solely on explicit connections between sensing grids and do not consider implicit relations, which are crucial for accurate data inference. To achieve both high-quality data inference and cost reduction, we propose a cost-efficient vehicular crowdsensing scheme based on implicit relation-aware graph attention networks (CVC-IRGAT), which combines missing data inference with active grid selection. First, we design the IRGAT model to capture implicit and explicit relations between grids through a dual-channel mechanism of relation-aware and graph attention. Then, we design a method to assess the inferred data based on the Gaussian mixture model. Given the assessment values, a deviation information score function is proposed to measure the importance of the inferred values and model errors. Finally, we introduce active learning iterations to select the grids in accordance with this function. Extensive experiments have been conducted on real-world datasets, which demonstrate the superiority of the proposed CVC-IRGAT.
Task offloading combined with reinforcement learning (RL) is a promising research direction in edge computing. However, the intractability in the training of RL and the heterogeneity of network devices have hindered the application of RL in large-scale networks. Moreover, traditional RL algorithms lack mechanisms to share information effectively in a heterogeneous environment, which makes it more difficult for RL algorithms to converge due to the lack of global information. This article focuses on the task offloading problem in a heterogeneous environment. First, we give a formalized representation of the Lyapunov function to normalize both data and virtual energy queue operations. Subsequently, we jointly consider the computing rate and energy consumption in task offloading and then derive the optimization target leveraging Lyapunov optimization. A Deep Deterministic Policy Gradient(DDPG)-based multiple continuous variable decision model is proposed to make the optimal offloading decision in edge computing. Considering the heterogeneous environment, we improve Hetero Federated Learning (HFL) by introducing Kullback-Leibler (KL) divergence to accelerate the convergence of our DDPG based model. Experiments demonstrate that our algorithm accelerates the search for the optimal task offloading decision in heterogeneous environment.
The implementation of the toll free during holidays makes a large number of traffic jams on the expressway. Real-time and accurate holiday traffic flow forecasts can assist the traffic management department to guide the diversion and reduce the expressway's congestion. However, most of the current prediction methods focus on predicting traffic flow on ordinary working days or weekends. There are fewer studies for festivals and holidays traffic flow prediction, it is challenging to predict holiday traffic flow accurately because of its sudden and irregular characteristics. Therefore, we put forward a data-driven expressway traffic flow prediction model based on holidays. Firstly, Electronic Toll Collection (ETC) gantry data and toll data are preprocessed to realize data integrity and accuracy. Secondly, after Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) processing, the preprocessed traffic flow is sorted into trend terms and random terms, and the spatial-temporal correlation and heterogeneity of each component are captured simultaneously using the Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN) model. Finally, the fluctuating traffic flow of holidays is predicted using Fluctuation Coefficient Method (FCM). Through experiments of real ETC gantry data and toll data in Fujian Province, this method is superior to all baseline methods and has achieved good results. It can provide reference for future public travel choices and further road network operation.
