Route Planning Through Distributed Computing by Road Side Units

This paper proposed a mobility-aware multimedia data transfer mechanism using Multipath TCP in Vehicular Network. Since high transmission rate and low latency are the two key factors for multimedia data transmission that could provide stable video streaming services, therefore, we first adopted Multipath Transport Control Protocol which can transfer data concurrently for improving transmission rate and designed Quality-aware Data Distribution to dynamically allocate the data to different subflows. Moreover, in Vehicular Network, the mobile terminal, which means the vehicle, can communicate with remote server through roadside unit (RSU). However, the communication link between terminals and remote server will disrupt while the vehicle exceeding the communication range of roadside unit; and there also exists the situation that the vehicle is in the communication range of several RSU. Accordingly, we exploited a mobility-aware distance measurement for checking whether the vehicle has moved out of the communication range of any RSU or it is in multi-RSU's communication range. Afterwards, we designed a handover mechanism which transfer data to connected path (4G) for stable transmission using MPTCP when the vehicle exceeded the communication range of RSU; while one mobile can communicate with more than one RSU, we exploited a mechanism which can trigger new path for multipath data transmission and non-corporation Nash Equilibria was employed for solving the problem of fairness in the same kind of network technology multipath transmission. Simulation results show how mobility-aware multimedia data transfer mechanism improve the performance of transmission comparison with state-of-art solution.

The Internet of Things (IoT) is impacting the world’s connectivity landscape. More and more IoT devices are connected, bringing many benefits to our daily lives. However, the influx of IoT devices poses non-trivial challenges for the existing cloud-based computing paradigm. In the cloud-based architecture, a large amount of IoT data is transferred to the cloud for data management, analysis, and decision making. It could not only cause a heavy workload on the cloud but also result in unacceptable network latency, ultimately undermining the benefits of cloud-based computing. To address these challenges, researchers are looking for new computing models for the IoT. Edge computing, a new decentralized computing model, is valued by more and more researchers in academia and industry. The main idea of edge computing is placing data processing in near-edge devices instead of remote cloud servers. It is promising to build more scalable, low-latency IoT systems. Many studies have been proposed on edge computing and IoT, but a comprehensive survey of this crossover area is still lacking. In this survey, we first introduce the impact of edge computing on the development of IoT and point out why edge computing is more suitable for IoT than other computing paradigms. Then, we analyze the necessity of systematical investigation on the edge-computing-driven IoT (ECDriven-IoT) and summarize new challenges occurring in ECDriven-IoT. We categorize recent advances from bottom to top, covering six aspects of ECDriven-IoT. Finally, we conclude lessons learned and propose some challenging

The application of the Internet of Things (IoT), vehicles to infrastructure (V2I) communication and intelligent roadside units (RSU) are promising paradigms to improve road traffic safety. However, for the RSUs to communicate with the vehicles and transmit the data to the remote location, RSUs require enough power and good network quality. Recent advances in technology have improved lithium-ion battery capabilities. However, other complementary methodologies including battery management systems (BMS) have to be developed to provide an early warning sign of the battery's state of health. In this paper, we have evaluated the impact of the received signal strength indication (RSSI) and the current consumption at different transmission frequencies on a static battery-based RSU that depends on the global system for mobile communications (GSM)/general packet radio services (GPRS). Machine learning (ML) models, for instance, Random Forest (RF) and Support Vector Machine (SVM), were employed and tested on the collected data and later compared using the coefficient of determination (R2). The models were used to predict the battery current consumption based on the RSSI of the location where the RSUs were imposed and the frequency at which the RSU transmits the data to the remote database. The RF was preferable to SVM for predicting current consumption with an R2 of 98% and 94%, respectively. It is essential to accurately forecast the battery health of RSUs to assess their dependability and running time. The primary duty of the BMS is to estimate the status of the battery and its dynamic operating limits. However, achieving an accurate and robust battery state of charge remains a significant challenge. Referring to that can help road managers make alternative decisions, such as replacing the battery before the RSU power source gets drained. The proposed method can be deployed in other remote WSN and IoT-based applications.

Vehicular Ad-hoc Networks (VANETs) have high requirements for real-time data processing and security of message authentication. In order to solve the computing power asymmetry between vehicles and road side units (RSUs) in VANETs under high-density traffic, accelerate the processing speed of message authentication, and solve the problems of high computational overhead and long message authentication time caused by the use of bilinear pairing encryption technology in similar message-batch-authentication schemes, we propose introducing the concept of edge computing (EC) into VANETs and using idle nodes’ resources to assist the RSU in quickly authenticating messages to achieve computing power load balancing under multiple traffic flows. We propose introducing the idea of edge computing (EC) into VANETs and using idle nodes’ resources to assist RSUs in quickly authenticating messages. This scheme performs two identity-based message authentications based on the identity signature constructed by elliptic curve cryptography (ECC). One of them is the batch authentication of the vehicle sending messages by the RSU-authenticated vehicles with free resources, as temporary edge computing nodes (TENs), and the other is the authentication of the temporary TEN messages by the fixed-edge-node RSUs. The resources of the TEN are used to reduce the computational burden of RSUs and message authentication time, thereby improving the efficiency of system authentication of messages. We performed a security analysis of the scheme to prove its security properties and compared it with other schemes in terms of performance. The experimental results show that our scheme has a transmission overhead of 2400 bytes when there are four TENs, and the number of verification message requests reaches 20, which outperforms other methods. The gap will be more evident as the numbers of TEN and message verification requests increase.

Edge computing is a type of distributed computing that was designed especially for Internet of Things (IoT) users to provide computational resources and data management nearby to users' devices. Introducing edge computing for IoT networks has reduced the bandwidth and latency issue while handling real-time applications. The major benefit of edge computing is that it reduces the communication overhead between IoT user and server. Integrating IoT in our daily life has attracted researchers towards its performance management such as complexity minimization, latency minimization, memory management, energy consumption minimization, etc. The paper focuses on deep reinforcement learning to minimize the computational complexity at IoT user end. The task offloading decision process is designed using Q-Learning that minimizes the system cost and curse of high dimensional data.

Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power, and thus, is not well-suited for ML tasks. Consequently, this article aims to combine edge and cloud computing for IoT data analytics by taking advantage of edge nodes to reduce data transfer. In order to process data close to the source, sensors are grouped according to locations, and feature learning is performed on the close by edge node. For comparison reasons, similarity-based processing is also considered. Feature learning is carried out with deep learning - the encoder part of the trained autoencoder is placed on the edge and the decoder part is placed on the cloud. The evaluation was performed on the task of human activity recognition from sensor data. The results show that when sliding windows are used in the preparation step, data can be reduced on the edge up to 80% without significant loss in accuracy.

The incorporation of electronics by embedding the relevant sensors in the physical devices in home and office, vehicles of all types, buildings in the smart cities and in all possible spheres of life form a network of devices termed as internet of things (IoT). It is being realized that vehicular ad-hoc networks (VANETs) which are responsible for the reliable and secure communication among vehicles is a primary area of research in IoT and hence ensuring security in this area is essential. Thus, this work introduces a novel approach to improve the existing authentication support to VANETs. In this proposed framework, first an anonymous authentication approach for preserving the privacy is proposed which not only performs the vehicle user’s anonymous authentication but preserves the message integrity of the transmitting messages as well. Although many anonymous authentication schemes have been proposed in VANETs until now, the existing schemes suffer from a high computation cost during the signature and certificate verification process which leads to delayed authentication. Consequently, the vehicles and roadside units (RSUs) cannot authenticate more number of vehicles per second in VANETs. Second, an efficient anonymous group key distribution protocol is proposed in this paper for securely distributing the group key to the group of vehicles in the communication range of an RSU. The RSUs can send location based information to the group of vehicles in a secure manner using this group key. Experimental analysis portrays that the results of this new privacy preserving anonymous authentication and key management schemes are promising and efficient with regard to signature verification cost and computational cost in comparison with the existing schemes.

Artificial intelligence (AI) and edge computing have truly advanced in vehicular networks encouraging assessment of real‐time traffic conditions using kinetic information of autonomous vehicles with the help of road side units (RSUs). However, regardless of numerous improvements in sensor fusion technologies the existing vision/LIDAR‐based systems have found severe difficulties during perception on roads. In addition, the inter‐vehicular communications are hampered due to inefficient RSU placement techniques causing high‐latency issues during transmission of messages. Therefore, this article presents an AI‐driven vision‐based self driver assistance system (VSDAS) comprising a joint RSU deployment mechanism that utilizes enhanced memetic architecture‐based optimal RSU placement (MARP) method and an object detection model that implements an improved Haar‐cascade object detection algorithm for speedy identification of object. We have designed two varieties of genetic algorithm (GA) to solve optimal placement problem of RSUs: genetic architecture‐based with random restart hill climbing (GARRH) and the other is MARP for efficient placement of RSUs. After our experimental results, we see that the MARP algorithm shows best possible RSU locations over different generations achieving significantly better fitness scores than the GAHRC and GA ascribing to its local search process. In addition, Haar‐cascade achieves better mean average precision than local binary pattern and histogram of oriented gradients by selecting key frames. The experimental outcomes of our model reveals that the proposed enhanced memetic algorithm reduces the transmission delay to a greater extent. Additionally, the outcomes of our investigations on two public datasets (KITTI and Panasonic) showed that our improved algorithm clearly enhances the object detection performance.

In order to support the development of 5G technologies, researchers are actively engaged in addressing the challenges accompanying the emerging 5G applications. Unquestionably, an eminent technology gaining significant research attention is edge computing. Vehicular edge computing brings data storage and computing capabilities as well as hosting support applications that comprise emerging vehicular services and applications which demand low-delay processing, to the edge closer to the vehicles, reducing response time and increasing reliability, therefore achieving the holistic vision of the tactile Internet. In this context, this paper considers a vehicular network with edge computing capabilities deployed at road side units, and addresses the problem of workload offloading as well as scheduling of computation tasks on the computing resources available at the edge. The challenge here is the high mobility of the vehicles and hence their short residence time within the coverage range of the road side units hosting the edge computing resources. A joint problem considering the communication and computation resources, as well as the latency requirements of the workload is formulated and the scheduling is shown to be NP-Hard. Subsequently, efficient solutions based on Lagrangian relaxation are derived and presented. We evaluate numerically the proposed methods and show their closeness to the optimal solutions.

Recent advancements in technology now allow for the generation of massive quantities of data. There is a growing need to transmit this data faster and more securely such that it cannot be accessed by malicious individuals. Edge computing has emerged in previous research as a method capable of improving data transmission times and security before the data ends up in the cloud. Edge computing has an impressive transmission speed based on fifth generation (5G) communication which transmits data with low latency and high bandwidth. While edge computing is sufficient to extract important features from the raw data to prevent large amounts of data requiring excessive bandwidth to be transmitted, cloud computing is used for the computational processes required for developing algorithms and modeling the data. Edge computing also improves the quality of the user experience by saving time and integrating quality of life (QoL) features. QoL features are important for the healthcare sector by helping to provide real-time feedback of data produced by healthcare devices back to patients for a faster recovery. Edge computing has better energy efficiency, can reduce the electricity cost, and in turn help people reduce their living expenses. This paper will take a detailed look into edge computing applications around Internet of Things (IoT) devices, smart city infrastructure, and benefits to healthcare.

Recent popularization of the Internet of Vehicles (IoVs) and vehicles-to-everything (V2X) enables the emergence of real-time vehicular applications, posing challenges to resourcelimited vehicles. Toward this end, vehicle edge computing (VEC) has been proposed to alleviate the computational burden on vehicles by leveraging resources from roadside units (RSUs) and VEC servers. While existing works mainly focus on the task requirement for either vehicles or RSUs, the joint task offloading for both V2X and RSUs-to-everything (R2X) has not been fully studied. In this paper, we aim at optimizing the task offloading strategies for both vehicles and RSUs, and adopt a multi-hop task offloading manner to fully utilize the VEC network resources. This problem introduces a severe state-action space shift issue with varying dimensions and representation, which poses challenges for conventional DRL approaches. To address it, we propose a Bidirectional Encoder Representations from Transformers (Bert)-based matching Q-network (BMQN) algorithm. First, we design the BMQN model to efficiently capture correlations among all vehicles and RSUs through bidirectional attention. Then, we propose type-embedded grouped attention and available action embedding to mitigate the overfitting sequence length issue, thereby enhancing generalization capacity. Moreover, we propose to address the state-action space shift issue through a matching-based manner, which can significantly enhance the task offloading ability by matching the states among devices. Simulation results demonstrate that: 1) the BMQN can achieve much better performance than other approaches in scenarios comprising various numbers of vehicles and RSUs as well as diverse road lengths; 2) the BMQN has sufficient generalization capacity to adapt to inexperienced scenarios through matching-based architecture and available action embedding.

Edge computing has gradually availed itself as one of the most important technologies in the Internet of Things (IoT) to influence the processing, management, and utilization of data.A major advantage that is offered by edge computing by bringing computation and storage resources closer to IoT devices is that it minimizes latency as well as conserves bandwidth and offers greater capabilities for real time decision making.These changes from massive cloud processing to multiple neighboring edge nodes solve IoT architectural challenges common in earlier types of design, including those with low-latency responses and offline capabilities of applications such as smart cities, self-driving cars, and industrial control systems.The potential advantages of edge computing in IoT include privacy and security, because the collected information can be analyzed on the fly on the endpoint without sending it to the cloud; scalability, given that a great amount of IoT devices can be monitored by edge nodes without putting extra load on the centralized facilities.However, the use of edge computing in IoT also poses some challenges for example restricted resources at edge nodes, issue of the network infrastructure and more so the security that has to be enhanced.This abstract discusses the existing works done on architecture of edge computing IoT, the advantages and the limitation of integrating edge computing IoT system and indicates how it is capable of revolutionizing industries and enable a new IoT generation application system equipped with intelligence and decentralization.

Research Article| March 01, 2016 The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation Frank Sepulveda; Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Jay Pulliam Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Publisher: Seismological Society of America First Online: 14 Jul 2017 Online Issn: 1938-2057 Print Issn: 0895-0695 © 2016 by the Seismological Society of America Seismological Research Letters (2016) 87 (2A): 345–357. https://doi.org/10.1785/0220150234 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Frank Sepulveda, Jay Pulliam; The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation. Seismological Research Letters 2016;; 87 (2A): 345–357. doi: https://doi.org/10.1785/0220150234 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The proliferation of commercial Internet of Things (IoT) devices is raising consumers’ awareness of the benefits of enhancing everyday objects with the ability to communicate, sense, and process information. Commercial‐off‐the‐shelf (COTS) versions of the embedded technologies responsible for the rise of the IoT are easy‐to‐use, inexpensive, and relatively powerful. IoT Makers, technological do‐it‐yourself enthusiasts, utilize these technologies and the IoT ecosystem to create IoT devices that range from casual hobbyist to entrepreneurial in nature.In an effort to develop a “no‐engineer‐needed” Internet of Geophysical Things (IoGT) device, we integrated a COTS embedded computer with a geoscience‐related COTS data acquisition system (DAS). Using skills common to geoscientists, we integrated the Raspberry Pi System‐On‐Chip with the REF TEK (a Trimble brand) 130‐01 DAS. This Raspberry Pi enhanced REF TEK (RaPiER) provides IoGT Makers a platform for the development of geoscientific sensor node or network enhancements. IoGT Makers can use the RaPiER to tinker with IoT capabilities while simultaneously acquiring research‐quality data. Geoscientific (i.e., seismic) application areas that may benefit from RaPiER, particularly RaPiER nodal processing, include earthquake and engineering seismology.In this article, we discuss the emerging technologies that allow IoGT Makers to build their own IoGT device. We review our selection of the Raspberry Pi and REF TEK 130‐01 for IoGT device integration. We provide a RaPiER system integration guide and REF TEK interface software compiled for the Raspberry Pi ( available in the electronic supplement to this article). We present methods to expand RaPiER capabilities using the Raspberry Pi’s ecosystem. Last, we discuss our evaluation of RaPiER’s performance and its suitability for realistic field deployments. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

The new era of the Internet of Things is promoting the evolution of self-driving vehicles into connected and autonomous vehicles (CAVs), which enable modern intelligent transportation systems. CAVs provide connectivity for vehicles by integrating the capabilities of new generation wireless technologies such as dedicated short-range communication (DSRC) and cellular networks (e.g., LTE, 5G, and potential future 6G developments). The success of CAVs and their applications is dependent on the performance of their underlying networks, known as vehicular networks, which can be compromised due to the traffic dynamics and limited transmission range of communication devices. This dissertation focuses on the optimization of message coverage and dissemination that improves the overall performance of vehicular networks. The coverage of message, from a performance perspective, can be studied in terms of dissemination distance, the size of inter-vehicle networks, and the delay of message dissemination. We study the message coverage in infrastructure-based vehicular networks by focusing on the problem of optimal utilization of roadside units (RSUs) in urban environments. Specifically, we develop efficient algorithms, which by considering the driving dynamics and delay constraint of applications find the candidate sites for RSUs deployment to achieve the maximum spatiotemporal message coverage and minimum delay of message dissemination. We also present a disseminator selection algorithm for infrastructure-based environments, which aims at improving message forwarding in the presence of RSUs. Effective clustering is crucial to mitigate routing scalability and reliability issues in vehicular networks. Clustering provides a hierarchical network architecture as a result of grouping the vehicles. We propose an adaptive clustering scheme to maximize the stability of clusters. To that end, we define a novel concept, named stability degree of vehicles, by taking into consideration the driving dynamics over a prediction horizon. We then formulate the clustering problem as an optimization problem, which is used within a rolling horizon framework in the cluster formation process. Our scheme also provides insight into the optimally of clustering algorithms. We present a heterogeneous vehicular network architecture, which supports DSRC- and Device-to-Device-based communications for vehicle-to-vehicle to improve routing reliability. We prove that the optimal clustering problem is NP-hard. Therefore, we develop a heuristic algorithm, which by taking into consideration the stability degree of vehicles over the prediction horizon, finds a near-optimal solution. We propose a hybrid routing protocol based on our adaptive clustering scheme to achieve a high packet delivery ratio and low delay. Our hybrid routing protocol is composed of generic

To make people's car driving experience safer and more comfortable, the architecture of intrusion recognition model based on the K-nearest neighbour classification Deep Neural Networks (K-DNN) is proposed to classify and identify various network intrusion factors, thereby strengthening the security level of Internet of Vehicles (IoV) and alleviating the hardware resource scarcity for IoV. Then, a secure collaborative download system based on edge computing is proposed, which can accurately and timely collect the information of roads, vehicles and nearby infrastructure in the driving process and facilitate people to safely and quickly download the required content. In the proposed system, the vehicle encrypts the content download request and sends it to the Roadside Unit (RSU) and the content server, respectively. The content server sends the content corresponding to the download request to the vehicle through the RSU.