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Joint Clustering and Blockchain for Real-Time Information Security Transmission at the Crossroads in C-V2X Networks
Abstract
The cellular vehicle-to-everything (C-V2X) networks support diverse kinds of services such as traffic management, road safety, and sharing data. However, the safety issues cannot be ignored in the process of information transmission. In this paper, a joint clustering and blockchain scheme is proposed for real-time information security transmission to prevent some vehicles from sending malicious messages to disrupt the traffic order at the crossroads in C-V2X networks. In this scheme, the dynamic stability of the cluster is maintained by updating the trust value of the vehicle nodes, which can improve the real-time and accuracy of the information transmission. The modified Webster algorithm is presented to divert the traffic flow so as to reduce the traffic jams at the crossroads. Meanwhile, the blockchain technology is utilized to establish a vehicle trust management mechanism in C-V2X, which can avoid malicious tampering of vehicle information during information sharing and ensure the safety of vehicle information communication. The simulation results of the Veins simulation platform are provided to demonstrate the effectiveness of the proposed algorithm and verify that the proposed scheme can guarantee the security of real-time information transmission.
... Furthermore, the clusters can be characterized by high cohesion and low coupling. Cluster analysis has been applied widely in business intelligence [1], image processing [2], Web search [3], Internet and security [4,5], and other fields. However, one of the main problems in clustering analysis is the similarity measure of data objects with mixed attributes due to the increasing complexity and diversity of application data. ...
... However, the important attribute dimension may be ignored in the similarity measure. The Formula (4) shows that when the attribute value is the same, ...
... Formula (4). Referring to Formula (4), using balance difference factor bvl, hybrid similarity measure is defined as follows: ...
In mixed attribute clustering, the similarity measure superposition is skewed due to the difference of measuring different attribute types. In this paper, a new clustering approach for mixed attribute data is proposed using hybrid similarity measure. Firstly, a hybrid similarity measure formula is defined using the information entropy, therefore the similarity difference among various attribute types is effectively reduced, and the inclination of similarity measure superposition is alleviated. Secondly, a calculation formula of similarity mean for mixed attributes is defined, which can describe the centralized trend of data distribution, and can be effectively used to merge of clustering clusters. Thus, artificial setting of similarity threshold parameters can be avoided. Thirdly, a novel clustering analysis algorithm for mixed attributes is proposed using hybrid similarity measure and allocation strategy of boundary data objects. In the end, experimental results validate that the algorithm performs well on clustering effect, scalability and anti-noise, as well as the stability and effectiveness of the similarity mean by using UCI, artificial data sets and stellar spectral data sets.
... The simulated records can be employed to overcome the limitations of the grid during the bottlenecks of the network. DLT is used to broadcast the availability and resiliency of the grid resources while BCn technology ensures the security and clarity of the transmitted network [107]. 9. Blockchain technology-enabled smart grid energy system. ...
... In the present BCn-enabled SGES authentication, it is important to give access to legal nodes or persons only. At the time of the authentication process, attackers may access the personal information of that user and hamper the protocol of the network [107,133]. It can be solved by a process in which the users have to go through some authentication processes like solving some cryptographic functions or encryption processes etc. ...
... Because of the centralization of the mining power, the chances of a 51% attack are high in there. The chances of data mutability, fake trading, and double spending can also be increased [107]. Also, because of the dependency on the 3rd party authority in various operations like inter-chain operation, smart contract execution, price standardization, etc., it puts the system under threat of security breach. ...
Technological advancements in smart grid energy systems (SGESs) are introducing sustainable
frameworks to meet the demand for the fourth industrial energy revolution. These frameworks
are planned to be used in the forthcoming future to maintain the energy network operation
with optimization, energy trading, grid automation, and so on. Blockchain (BCn), developing
after passing a diverse period of the research journey, comes to the mind of researchers and its
integration in SGES paves the way to reach the goal of energy demand. However, still of interest
is ongoing in the improvement of BCn features which can be regarded as the next-generation
blockchain framework. This paper exhibits the technical framework of the next-generation BCn
framework and explores its benefits and challenges in performing the emerging aspects of SGES.
This framework enables some advanced features for the sustainable operation of SGES like smart
metering, peer-to-peer (P2P) energy trading, self-operation, and transparency. The technical
explanation of this BCn technology established on essential features and requisites is also
presented in this paper from various points of view which include smart mechanism, intelligent
storage system, and interoperability. We also highlight the recent progress and limitations of
the current BCn framework in SGES. Finally, some challenges towards integrating the nextgeneration
BCn technology in SGES are reported. This work can provide extended support for
the practitioner and researcher in the context of BCn technology and SGES.
... Grouping vehicles based on feature similarities improves communication reliability and facilitates secure data exchange. Mobility, user behaviors, geolocations, and security are some of the most popular features due to the security-critical and mobile nature of IoV networks (Ghimire and Rawat, 2021), (Xiao et al., 2021), (Habbal et al., 2024). ...
... In Xiao et al. (2021), the joint clustering algorithm and blockchain for information security transmission have been proposed in cellular vehicle-to-everything C-V2X crossroads scenarios. The clustering can dynamically classify vehicles by updating the trust value of vehicle nodes. ...
The development of the Internet of Vehicles (IoV) has been made possible through a variety of communication
technologies and advanced AI techniques. In this context, ensuring stable and efficient communication for IoV
is extremely important. It addresses several challenges related to security issues, high dynamism, constant
connection outages, and the expected high traffic density. To overcome these challenges, vehicle clustering is
a viable strategy for a reliable communication environment. The majority of current research focuses on solving
the problem of cluster stability and efficiency by utilizing one or multiple factors, particularly vehicle location,
mobility, and behavior. This article introduces an efficient Multifactor Clustering Scheme for IoV (MFCS-IoV).
MFCS-IoV includes two stages: cluster formation and cluster head selection. The cluster formation is based
on the improved K-means algorithm, considering both the vehicle mobility and final destination within the
driving zone. While, a weighted cluster fitness function that includes mobility, behavior, dynamic location,
and security is used to optimally select the Cluster Head (CH). Blockchain technology has been integrated into
the model to safeguard the privacy of information like destination and other vehicle parameters. Simulation
results demonstrate the success of MFCS-IoV in partitioning the vehicles into stable clusters and selecting the
optimal cluster heads based on the proposed parameters. The effectiveness of MFCS-IoV is demonstrated by
simulating different scenarios of 50 to 300 vehicles in the driving area. A comparison with related works
shows that MFCS-IoV outperforms other schemes regarding average node-to-node delay, packet delivery rate,
and throughput. Additionally, the proposed MFCS-IoV increases communication reliability by providing stable
clusters while maintaining security measures.
... Ref [103] proposed a solution for the internet of energy by providing Hierarchical key generation as well as a Practical Byzantine Fault Tolerance (PBFT) consensus algorithm with an additional smart contract for energy transfer. In [113], a joint clustering and blockchain scheme for real-time information security transmission is designed to prevent traffic at the crossroads in C-V2X networks. ...
... Internet of things (IoT) [6,15,17,[19][20][21]24,27,32,33,45,48,[53][54][55][56]61,69,73,78,80,82,86,87,90,[99][100][101]104,105,107,110,114,115,117,118,129,138] Healthcare [35,39,62,64,94,98,101,106,108,[117][118][119]122,125,126,130,138] Industry [30,31,34,49,60,61,97,132,137] Energy [83,88,93,103,120,121,139] Transportation [16,22,37,44,71,113,123] Other Smart projects (Agriculture/city/home) [36,38,67,69,127,131] Cloud/Fog/Edge Computing [39][40][41]53,68,76,98,109,116,119,136] Unmanned Arial Vehicle (UAV) [28,42,69,101,114] Telecommunication (5G/6G/satellite/sensor network) [26,40,52,86,91,100] Software Defined Networking (SDN) [6,53,96,129] ...
... Ensuring the confidentiality, integrity, and availability of data and communications in IoV requires robust security mechanisms and protocols [67]. To name a few examples researchers are exploring blockchainbased solutions to address security and privacy challenges [75] and joint optimization approaches to enhance trust management in IoV systems [76]. 4) Spectrum Allocation: Spectrum allocation is a crucial challenge in IoV, as the increasing number of connected vehicles demands efficient use of the limited available spectrum [77]. ...
... [36]- [38] propose satellite/terrestrial solutions for coverage in rural areas. [95] investigates the use of intersection management algorithms to improve resilience, [96] and [34] [66], [67], [75], [76] Spectrum Allocation [23], [77]- [79] Resource Management [54], [80]- [83] Interoperability [28], [72], [84]- [86] Openness and Customization [28], [60], [70], [87], [88] Green Computing, Communications and Networking [45], [79], [89], [90] Big Data Analytics [7], [55], [60], [91], [92] Availability, Resilience and Always-on Services [10], [34]- [38], [94]- [96] Reliability, Flexibility, Redundancy and Self-Healing [79], [95], [97]- [99] Non Technical Challenges Regulations and standards [100], [28] User acceptance and adoption [90], [101], [102] Business models and sustainability [33], [90] Service Models [88], [103] discuss the application of modeling techniques and visible light communication, respectively, to enhance the reliability of the network. 11) Reliability, Flexibility, Redundancy and Self-Healing Capability: IoV systems must possess reliability, flexibility, redundancy, and self-healing capabilities to maintain functionality under diverse conditions. ...
This paper presents a comprehensive review of emerging technologies in V2X communication and Vehicular Edge Computing (VEC) for sustainable Internet of Vehicles (IoV) in the 6G era. The paper employs the PRISMA method to systematically identify, screen, select, synthesize, and report relevant literature. The objective is to identify and discuss key technologies, assess challenges and opportunities, analyze potential impacts on energy efficiency and environmental sustainability , and provide recommendations for future research directions. Challenges analysed include both technical and non-technical challenges and oppportunities include both societal benefits as well as industry opportunities. Finally and based on these findings, future research directions are proposed for striving towards sustainable IoV.
... spectrum utilization and increasing demand, the FCC authorized the opportunistic use of licensed spectrum [5] [6], including TV white spaces managed by joint clustering powered by Neural Networks [7]. TV white spaces may use a portion of the licensed spectrum [8] [9]. This problemsolving technique generates innovative network topologies, such as the cognitive radio network [10] [11]. ...
Cognitive Radio Ad-hoc Networks (CRAHNs) are under constant attacks from compromised primary & secondary nodes. These attacks focus on bandwidth manipulation, internal configuration manipulation, and selective spoofing, which can disturb the normal working of the CRAHNs. Researchers propose various security models to mitigate these attacks, each with limitations. Most of these models have higher complexity, while others cannot be used to mitigate multiple attack types. To overcome these issues while maintaining higher security and Quality of Service (QoS) under attacks, this text proposes a design of a novel blockchain-based security model for improving attack resilience in CRAHNs. The model initially collects multiple information sets from different cognitive radio controllers and creates active & redundant miners for the storage of these sets. The number of active & redundant miners is decided via a Mayfly Optimizer (MO) Model, which assists in improving resource utilization while reducing deployment costs. Cognitive rules and configurations are stored on these nodes and updated via a secure blockchain verification. Due to this, the proposed model demonstrated significant improvements in cognitive radio communications across various metrics, even under different attack scenarios. It reduced communication delay by up to 18.5%, increased communication throughput by up to 19.5%, and improved the Packet Delivery Ratio (PDR) by up to 19.4% when compared with existing models such as SRC [2], Prob Less [4], and DDQL [13]. Additionally, the model achieved energy savings of up to 12.5%. These enhancements were made possible by the optimized selection of miner nodes, enabling quicker mining for high-speed communication, low-energy mining tasks for prolonged use, and high-performance mining for consistency. The results affirm the model’s suitability for various real-time cognitive radio scenarios. Due to the integration of the MO Model, the CRAHN showcases better communication speed, lower energy consumption, higher throughput, and higher packet delivery performance when compared with existing methods under real-time scenarios.
... Various chains are tasked with recording diverse environmental data to enable the federated learning process, thereby facilitating knowledge sharing. Xiao et al. [41] introduced a combined cluster and blockchain scheme for the secure transmission of real-time information. They devised a distributed consensus mechanism built on the Byzantine fault-tolerance algorithm to guarantee the security of vehicle information communication. ...
With the rapid development of the Internet of Vehicles (IoV), traffic sign detection plays an indispensable role in advancing autonomous driving and intelligent transportation. However, current road traffic sign detection technologies face challenges in terms of information privacy protection, model accuracy verification, and result sharing. To enhance system sustainability, this paper introduces blockchain technology. The decentralized, tamper-proof, and consensus-based features of blockchain ensure data privacy and security among vehicles while facilitating trustworthy validation of traffic sign detection algorithms and result sharing. Storing model training data on distributed nodes reduces the system computational resources, thereby lowering energy consumption and improving system stability, enhancing the sustainability of the model. This paper introduces an enhanced GGS-YOLO model, optimized based on YOLOv5. The model strengthens the feature extraction capability of the original network by introducing a coordinate attention mechanism and incorporates a BiFPN feature fusion network to enhance detection accuracy. Additionally, the newly designed GGS convolutional module not only improves accuracy but also makes the model more lightweight. The model achieves an enhanced detection accuracy rate of 85.6%, with a reduced parameter count of 0.34×107. In a bid to broaden its application scope, we integrate the model with blockchain technology for traffic sign detection in the IoV. This method demonstrates outstanding performance in traffic sign detection tasks within the IoV, confirming its feasibility and sustainability in practical applications.
... Here are some technical details [191,192] of how blockchain technology can help C-V2X as per 3GPP: 1. Data Sharing: Blockchain technology can be used to securely and transparently share data between connected vehicles and infrastructure, ensuring the authenticity and integrity of the data [193]. For example, vehicle location data and traffic flow data can be shared on the blockchain to improve traffic management and reduce congestion. ...
... We set buses as CHVs to ensure high reliability, computing, and storage capacity [23]. It must be noted that clusters are temporary and updated overtime because of the high mobility of vehicles and the dynamic topology changes of communication networks [24]. Figure 3 visually depicts the configuration of a unitary intersection. ...
Internet of Vehicles (IoV) is a novel technology to enhance the safety, intelligence, and efficiency of traffic systems, where vehicles can exchange critical information with other vehicles, roadside units, pedestrians, and cloud platforms. However, the dynamic network topology, high speed, and exposed communication links inevitably pose security threats to IoV. It is pivotal to establish a trust management and trust-sharing mechanism between vehicles to guarantee the safety of IoV. This paper proposes a distributed trust management scheme to discriminate malicious vehicles utilizing the machine learning technology Random Forest (RF). With the help of the sliding time window technology, the trust degree of vehicles can be comprehensively evaluated through the CART trees according to the current and historical records. To further improve the security of communication processes, we also introduce a lightweight cryptography mechanism. In addition, a trust-sharing mechanism based on path prediction algorithm is proposed to guarantee the consistency of trust information in the network. Finally, extensive simulations are conducted to demonstrate the feasibility and efficiency of the proposed scheme.
... Sharing the supply chain information can improve the ''information island'' status of independent business content information in the supply chain. For example, this ERP system, e-ERP system, or even some other integrated information systems, etc., realize the information sharing of some business links of the supply chain and the integration of related businesses [3], [4]. However, it is difficult to share information to respond to changing demands or to support decision-making, and there is no integration and coordination of information and business from the perspective of the overall supply chain [5], [6]. ...
This paper proposes a blockchain-based supply chain information operation method to solve the current shortage of business information sharing in the supply chain. These approaches include the idea of blockchain application to realize the sharing of supply chain information, the supply chain architecture of production enterprises based on blockchain, and the hierarchical model of supply chain information flow based on blockchain. This paper proposes the information block recording method of internal and external data sources in the supply chain, including the composition structure and the analysis of the multisource data inside and outside the supply chain. In addition, the multisource data information block recording method is also proposed. Building a blockchain-based supply chain system can improve the integration and reconstruction capabilities of the supply chain. The information recording of the internal data of the supply chain and the external related multisource data in the block can improve the learnability of the supply chain. The internal and external information block connection of the supply chain system is the further integration and reconstruction of the supply chain information resources in the blockchain system. The information storage and access control of the blockchain-based supply chain is an information security guarantee system in the supply chain information sharing environment. Through the method of this study, the system can form a business architecture system based on blockchain-based supply chain information sharing.
... Recently, a lot of work has been performed on the integration of V2X and blockchain technologies. Literature [15] proposed a joint cluster and blockchain scheme for real-time information security transmission to prevent some vehicles from sending malicious messages to disrupt traffic order at C-V2X network intersections. e scheme keeps the dynamic stability of the cluster by updating the trust value of the vehicle nodes so as to improve the real time and accuracy of information transmission. ...
The development of 5G network communication has brought technological innovation to smart city communication, making the realization of V2X (vehicle to everything) technology possible. Vehicles wirelessly communicate with other vehicles, sensors, pedestrians, and roadside units, raising data security issues while driving. In order to ensure driving safety, the risk map cognitive model is established with the help of blockchain technology. In this model, the key map data and personal privacy information are encrypted and uploaded to form a blockchain, and the smart contract technology is used for automatic script processing. Then, according to different risk scenarios, cognitive learning is carried out for different risk levels, the cognitive results and corresponding operations are fed back to the intelligent vehicle, and these operations ensure the safe operation of the vehicle according to the intelligent vehicle. Finally, the feasibility of the model was verified by comparing different dangerous scenarios. The experimental results show that this risk cognition model can cognize the data of the intelligent vehicle according to different danger scenarios, and the model can transmit acceleration, deceleration, braking, and other behaviors to the intelligent vehicle to ensure smart city driving safety.
... By considering that some vehicles can introduce false information in IoV applications in order to disrupt the traffic order, a blockchain-based vehicle trust management scheme is proposed in [79]. Therein, the validity of the sent message is evaluated through blockchain technology. ...
The conceptualisation of the sixth generation of mobile wireless networks (6G) has already started with some potential disruptive technologies resonating as enablers for driving the emergence of a number of innovative applications. Particularly, 6G will be a prominent supporter for the evolution towards a truly Intelligent Transportation System and the realisation of the Smart City concept by fulfilling the limitations of 5G, once vehicular networks are becoming highly dynamic and complex with stringent requirements on ultra-low latency, high reliability, and massive connections. More importantly, providing security and privacy to such critical systems should be a top priority as vulnerabilities can be catastrophic, thus there are huge concerns regarding data collected from sensors, people and their habits. In this paper, we provide a timely deliberation of the role that promissory 6G enabling technologies such as artificial intelligence, network softwarisation, network slicing, blockchain, edge computing, intelligent reflecting surfaces, backscatter communications, terahertz links, visible light communications, physical layer authentication, and cell-free massive multiple-input multiple-output (MIMO) will play on providing the expected level of security and privacy for the Internet of Vehicles.
... By considering that some vehicles can introduce false information in IoV applications in order to disrupt the traffic order, a blockchain-based vehicle trust management scheme is proposed in [79]. Therein, the validity of the sent message is evaluated through blockchain technology. ...
Blockchain technology is a promising technology that attracts popularity among researchers. However, it was first introduced with digital currencies, particularly Bitcoin, but nowadays, it is also known as one of the most frequently used techniques for securing networks. This systematic review research identifies studies that use blockchain for their security challenges. In addition, different fields in blockchain usage, blockchain categorization type, consensus mechanism, smart contract usage, and integration with other software-based algorithms are also investigated. Our results maintain that the Internet of Things (IoT) is the main field in which blockchain provides security.
This work aims to improve the transmission and sharing efficiency of intelligent transportation data and promote the further development of intelligent transportation and smart city. In this work, an EEMR (Energy Efficient Multi-hop Routing) is designed for the intelligent transportation wireless sensor network. In the EEMR algorithm, the base station runs the IAP clustering algorithm for network clustering after receiving the information of all surviving nodes. A method called ACRR (Adaptive Cluster-head Round Robin) is proposed for local dynamic election of cluster heads. In addition, the deep learning-based stochastic gradient descent algorithm and its evolution algorithm are sorted out, and its application in practical scenarios is analyzed. Due to the disadvantages of the adaptive algorithm in the current data processing process, the gradient optimization algorithm based on deep learning is adopted and the concept of adaptive friction coefficient is applied to the Adam algorithm to obtain a new adaptive algorithm (TAdam). The simulation experiment reveals that the proportion of network surviving nodes of the EEMR algorithm is still as high as 90% in 1,500 rounds of data collection. This shows that the EEMR algorithm achieves the energy balance of the network nodes as much as possible while minimizing the system energy consumption. In application scenario 1, when the network surviving nodes of the four data collection algorithms compares dropped below 40%, the number of surviving nodes in the EEMR algorithm is still as high as 97.6%. On the PTB (Penn Tree Bank) test data set, the TAdam algorithm shows the fastest convergence speed and the best generalization performance. The TAdam algorithm based on deep learning discussed in this work was of great significance for improving the transmission and sharing efficiency of intelligent transportation data.
Industrial Internet of Things (IIoT) technology is widely used in modern industrial fields like transportation, but data security remains a major challenge. The blockchain-based access control mechanism can address the data security issue by preventing unauthorized devices from accessing limited IIoT resources. However, most existing blockchain-based access control mechanism for IIoT still has scalability and privacy issues. To deal with the above problems, we propose a new scalable and secure strategy for the blockchain-based access control framework for IIoT via sharding, which consists of two components. First, the Network Sharding Scheme based on Access Frequency Set (N2SAF) is designed to (1) improve the scalability of our proposed strategy by transaction sharding to reduce the storage pressure on nodes; and (2) increase the transaction processing speed on a three-layer architecture based on cloud-edge-device in IIoT. Second, the Privacy Protection scheme based on Bloom Filter (P2BF) is designed to deal with the privacy leakage problem caused by anonymous address clustering. Simulation results show that our proposed strategy improves the scalability of the system compared to existing methods while ensuring the security of the shard network, especially in large-scale IIoT environments.
Blockchain has been witnessed a growing application in mobile communication. The application field ranges from the Internet of Things (IoT), Vehicle to Everything (V2X), Mobile Edge Computing (MEC), etc. The conjunction with blockchain can significantly avoid data tempering and protect user privacy. Although blockchain guarantees the security of mobile communication, the operation cost is increasing. The consensus algorithm of blockchain drives multiple untrust parties to keep data consistency, and it generates a large number of communication overheads. In this letter, we focus on the Byzantine Fault Tolerance (BFT) type of consensus and conduct a performance model. By utilizing the deterministic network calculus method, we analyze the delay bound of the Practical Byzantine Fault Tolerance (PBFT) and HotStuff consensus algorithm. The result of the simulation provides a valuable guideline for delay-sensitive blockchain-based applications.
Setting up the physical parameters of a simulator is key to achieving realistic results. Setting those parameters requires an understanding of how they affect the simulation results. Packet loss is a key performance metric in communication and networks fields. In this paper, we present an overview of how Veins VANET simulator simulates WiFi packet loss. This work explains the physical parameters set by the user in Veins and how Veins then uses these parameters to simulate the effects of attenuation and packet loss.
The smart device owning rate such as smart phone and smart watch is higher than ever before and mobile payment has become one of the major payment methods in many different areas. At the same time, blockchain-based cryptocurrency is becoming a nonnegligible type of currency and the total value of all types of cryptocurrency has reached USD 200 billion. Therefore, it is a natural demand to support cryptocurrency payment on mobile devices. Considering the poor infrastructure and low penetration of financial service in developing countries, this combination is especially attractive. The high storage cost and payment processing latency are the two main obstacles for mobile payment using cryptocurrency. We propose two different schemes for cryptocurrency mobile payment, one involves a centralized bank and the other one does not require any centralized party. We also provide a solution for the bank to meet KYC (know your customer)/AML (antimoney laundering) compliance requirements when it is involved in cryptocurrency mobile payment processing.
To overcome the limitations of Dedicated Short Range Communications (DSRC) with short range, non-supportability of high-density networks, unreliable broadcast services, signal congestion and connectivity disruptions, vehicle-to-everything (V2X) communication networks, standardized in 3rd Generation Partnership Project (3GPP) Release 14, have been recently introduced to cover broader vehicular communication scenarios including vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P) and vehicle-to-infrastructure/network (V2I/N). Motivated by the stringent connection reliability and coverage requirements in V2X, this paper presents the first comprehensive and tractable analytical framework for the uplink and downlink performance of cellular V2X networks, where the vehicles can deliver their information via V2N/cellular network (vehicle-to-base station, V2B communication link) or directly between vehicles in the sidelink, based on their distances, propagation environments and the bias factor. By practically modeling the vehicles on the roads using the doubly stochastic Cox process and the BSs, we derive new association probabilities of V2B and V2V links, new success probabilities of the V2B and V2V communications, and overall success probability of the V2X communication, which are validated by the simulations results. Our results reveal the benefits of V2X communication compared to V2V communication in terms of success probability.
Blockchain is a technology making the shared registry concept from distributed systems a reality for a number of application domains, from the cryptocurrency one to potentially any industrial system requiring decentralized, robust, trusted and automated decision making in a multi-stakeholder situation. Nevertheless, the actual advantages in using blockchain instead of any other traditional solution (such as centralized databases) are not completely understood to date, or at least there is a strong need for a vademecum guiding designers toward the right decision about when to adopt blockchain or not, which kind of blockchain better meets use-case requirements, and how to use it. In this article we aim at providing the community with such a vademecum, while giving a general presentation of blockchain that goes beyond its usage in Bitcoin and surveying a selection of the vast literature that emerged in the last few years. We draw the key requirements and their evolution when passing from permissionless to permissioned blockchains, presenting the differences between proposed and experimented consensus mechanisms, and describing existing blockchain platforms.
In the last century the automotive industry has arguably transformed society, being one of the most complex, sophisticated and technologically advanced industries, with innovations ranging from hybrid, electric and self-driving smart cars to the development of IoT-connected cars. Due to its complexity, it requires the involvement of many Industry 4.0 technologies, like robotics, advanced manufacturing systems, cyber-physical systems or augmented reality. One of the latest technologies that can benefit the automotive industry is blockchain, which can enhance its data security, privacy, anonymity, traceability, accountability, integrity, robustness, transparency, trustworthiness and authentication, as well as provide long-term sustainability and a higher operational efficiency to the whole industry. This review analyzes the great potential of applying blockchain technologies to the automotive industry emphasizing its cybersecurity features. Thus, the applicability of blockchain is evaluated after examining the state-of-the-art and devising the main stakeholders’ current challenges. Furthermore, the article describes the most relevant use cases, since the broad adoption of blockchain unlocks a wide area of short- and medium-term promising automotive applications that can create new business models and even disrupt the car-sharing economy as we know it. Finally, after a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, some recommendations are enumerated with the aim of guiding researchers and companies in future cyberresilient automotive industry developments.
Cooperative Intelligent Transport System (C-ITS) is a key enabler of future road traffic management systems. The core component of C-ITS includes vehicles, road side units and traffic command centers. They generate a large amount of traffic that is made up of both mobility and service-related data. To extract useful and relevant information out of this data, data analytics will play a crucial role in future C-ITS applications. We present a review of how data analytics can benefit C-ITS applications. We describe the various types of data generated by C-ITS applications and potential dissemination techniques using various wireless technologies. We demonstrate how meaningful results from this data could be beneficial to C-ITS. We also demonstrate the improved reliability of C-ITS applications that can be achieved with data analytics using simulation results. Finally, we discuss future possible applications of data analytics in C-ITS.
In the last two decades a plethora of energy efficient protocols based on LEACH protocol have been proposed by researchers. LEACH is one of the most prominent protocol for wireless sensor network, which is a self-configurable and energy efficient cluster based protocol. LEACH protocol uses certain approximations and assumptions. In this paper we tried to find, what happens if these basic assumptions and approximations of LEACH are not considered. We have found that EelecRx plays a vital role in deciding optimum number of clusters and EelecTx has no effect on number of clusters in the network. We have also found that multi-clustering might not be required at some point and at some point number of optimum clusters could be as high as N (number of live nodes in the network).
The drastically increasing volume and the growing trend on the types of data have brought in the possibility of realizing advanced applications such as enhanced driving safety, and have enriched existing vehicular services through data sharing among vehicles and data analysis. Due to limited resource of vehicles, mobile edge computing integrated with vehicular networks gives rise to Vehicular Edge COmputing and Networks (VECONs) for providing powerful computing and massive storage resources. However, vehicular edge computing servers consisted of roadside units cannot be fully trusted, which may result in serious security and privacy challenges. We exploit consortium blockchain and smart contract technologies to achieve secure data storage and sharing in vehicular edge networks. These technologies efficiently prevent data sharing without authorization. In addition, we propose a reputation based data sharing scheme to ensure high-quality data sharing among vehicles. A three-weight subjective logic model is utilized for precisely managing reputation of the vehicles. Numerical results based on a real dataset show that our schemes achieve reasonable efficiency and high-level security for data sharing in VECONs.
The use of sensors and actuators as a form of controlling cyber-physical systems in resource networks has been integrated and referred to as the Internet of Things (IoT). However, the connectivity of many stand-alone IoT systems through the Internet introduces numerous cybersecurity challenges as sensitive information is prone to be exposed to malicious users. This paper focuses on the improvement of IoT cybersecurity from an ontological analysis, proposing appropriate security services adapted to the threats. The authors propose an ontology-based cybersecurity framework using knowledge reasoning for IoT, composed of two approaches: (1) design time, which provides a dynamic method to build security services through the application of a model-driven methodology considering the existing enterprise processes; and (2) run time, which involves monitoring the IoT environment, classifying threats and vulnerabilities, and actuating in the environment ensuring the correct adaptation of the existing services. Two validation approaches demonstrate the feasibility of our concept. This entails an ontology assessment and a case study with an industrial implementation.
A fixed-timed signal-controlled traffic system for 4-leg atgrade intersection was designed for two candidate intersections namely the ‘SRS’ and ‘B-Division’ intersections in
Makurdi town using Webster’s traffic signal design models,
and performances assessed. Geometric layouts of the
intersections were measured and manual traffic count carried
out to obtain average travel demand (pcu/h) for base year
(2017) and projected year (2027) using traffic growth rate of
3%. Design of 4-phased traffic control system for both
intersections revealed failed performance for the base year.
Though increasing number of lanes from 1 to 2 on major
approaches of the SRS junction as permitted by its right-ofway
improved its performance slightly from Level of Service
(LOS) E to D. Performance of the B-Division junction at base
year failed with degree of saturation ratio of unity. Since rightof-
way and land-use features at the B-Division intersection do
not permit increasing its number of lanes, the redesign and
construction of roundabout, grade separated or continuous
flow intersection as suggested by other researchers for failed
cross-intersection were recommended for improved
performance of the intersections. Also, implementation of
annotated pavement markings to guide motorist for improve
lane discipline as well as stringent enforcement policies to
check on-street parking around intersections for improve lane
capacity were recommended for efficient operations of crossintersections.
Vehicular networks enable vehicles to generate and broadcast messages in order to improve traffic safety and efficiency. However, due to the non-trusted environments, it is difficult for vehicles to evaluate the credibilities of received messages. In this paper, we propose a decentralized trust management system in vehicular networks based on blockchain techniques. In this system, vehicles can validate the received messages from neighboring vehicles using Bayesian Inference Model. Based on the validation result, the vehicle will generate a rating for each message source vehicle. With the ratings uploaded from vehicles, Roadside Units (RSUs) calculate the trust value offsets of involved vehicles and pack these data into a “block”. Then, each RSU will try to add their “blocks” to the trust blockchain which is maintained by all the RSUs. By employing the joint Proof-of-Work and Proof-of-Stake consensus mechanism, the more total value of offsets (stake) is in the block, the easier RSU can find the nonce for the hash function (proof-of-work). In this way, all RSUs collaboratively maintain an updated, reliable, and consistent trust blockchain. Simulation results reveal that the proposed system is effective and feasible in collecting, calculating, and storing trust values in vehicular networks.
With the attractive feature of guaranteeing maximum network throughput, backpressure routing has been widely used in wireless communication networks. Motivated by the backpressure routing, in this paper, we propose a delay-based traffic signal control algorithm in transportation networks. We prove that this delay-based control achieves optimal throughput performance, same to the queue-based traffic signal control in literature. However, a vehicle at a lane whose queue length remains very small may be excessively delayed under queue- based signal control. Our delay-based backpressure control can deal with the excessive delays, and achieve better fairness with respect to delay, while still guaranteeing throughput optimality. Moreover, a general weighted control scheme combining the queue-based and delay-based schemes is also investigated, to provide a more flexible control according to the quality of service requirements. Numerical results explore their performance under both homogeneous and heterogeneous traffic scenarios.
The increasing amount of renewable energy sources in the energy system calls for new market approaches to price and distribute the volatile and decentralized generation. Local energy markets, on which consumers and prosumers can trade locally produced renewable generation directly within their community, balance generation and consumption locally in a decentralized approach. We present a comprehensive concept, market design and simulation of a local energy market between 100 residential households. Our approach is based on a distributed information and communication technology, i.e. a private blockchain, which underlines the decentralized nature of local energy markets. Thus, we provide energy prosumers and consumers with a decentralized market platform for trading local energy generation without the need of a central intermediary. Furthermore, we present a preliminary economic evaluation of the market mechanism and a research agenda for the technological evaluation of blockchain technology as the local energy market’s main information and communication technology.
Smart grids equipped with bi-directional communication flow are expected to provide more sophisticated consumption monitoring and energy trading. However, the issues related to the security and privacy of consumption and trading data present serious challenges. In this paper we address the problem of providing transaction security in decentralized smart grid energy trading without reliance on trusted third parties.We have implemented a proof-of-concept for decentralized energy trading system using blockchain technology, multi-signatures, and anonymous encrypted messaging streams, enabling peers to anonymously negotiate energy prices and securely perform trading transactions. We conducted case studies to perform security analysis and performance evaluation within the context of the elicited security and privacy requirements.
Device-to-device (D2D) communication enables direct communication between
proximate devices thereby improving the overall spectrum utilization and
offloading traffic from cellular networks. This paper develops a new spatial
model for D2D networks in which the device locations are modeled as a Poisson
cluster process. Using this model, we study the performance of a typical D2D
receiver in terms of coverage probability under two realistic content
availability setups: (i) content of interest for a typical device is available
at a device chosen uniformly at random from the same cluster, which we term
uniform content availability, and (ii) content of interest is available at the
$k^{th}$ closest device from the typical device inside the same cluster, which
we term $k$-closest content availability. Using these coverage probability
results, we also characterize the area spectral efficiency (ASE) of the whole
network for the two setups. A key intermediate step in this analysis is the
derivation of the distributions of distances from a typical device to both the
intra- and inter-cluster devices. Our analysis reveals that an optimum number
of D2D transmitters must be simultaneously activated per cluster in order to
maximize ASE. This can be interpreted as the classical tradeoff between more
aggressive frequency reuse and higher interference power. The optimum number of
simultaneously transmitting devices and the resulting ASE increase as the
content is made available closer to the receivers. Our analysis also quantifies
the best and worst case performance of clustered D2D networks both in terms of
coverage and ASE.
Clustering in vehicular ad hoc networks is an effective approach to make dynamic wireless vehicular sensor networks more manageable and stable. To make vehicle clustering applicable everywhere regardless of the provided infrastructure, vehicles must rely only on themselves and must not take any supporting services, such as location or external communication services, for granted. In this paper, we propose a new clustering metric and a clustering algorithm with multihoming support. It relies only on the vehicle's ability to send and receive wireless packets which identify the vehicle relationship. Clusters are created with redundant connections between nodes to increase the communication reliability in case of topological changes and the cluster creation process is also inverted compared to other algorithms. The presented solution is verified and compared to MOBIC with the use of ns-3 and SUMO simulation tools. Simulation results have confirmed the expected behavior and show that our algorithm achieves better node connectivity and cluster stability than the former.
Vehicle to Vehicle (V2V) communication offers great potential as far as information dissemination in VANETs is concerned. Research has shown that clustering vehicles and relaying information through cluster-heads (CHs) has several advantages over allowing all the vehicles to broadcast the information. Forming and maintaining stable clusters as well as ensuring good QoS in intra-cluster communications has always been a great challenge. In this paper we present a VANET Cluster-on-Demand (CoD) Minimum Spanning Tree (MST) Prim algorithm which clusters vehicles taking into consideration the intra-cluster QoS. Matlab simulation results of the algorithm applied to real traffic data has shown the algorithm's ability to successfully form clusters with good QoS. The algorithm has also been shown to compare very well with Dijkstra's algorithm which is one of the best clustering algorithms.
Timely and reliable information sharing among autonomous vehicles (AVs) provides a promising approach for reducing traffic congestion and improving the efficiency of future transportation systems. In this paper, we consider millimeter-wave (mmWave) based multi-hop vehicle-to-vehicle (V2V) communications to facilitate ultra-reliable low-latency information sharing among AVs. We propose a novel framework for performance analysis and design of relay selections in mmWave multi-hop V2V communications, while taking into account the mmWave signal propagation characteristics, road topology, and traffic conditions. In particular, considering the minimum tracking distance requirement of road traffic, the headway, i.e., the distance between adjacent AVs, is modeled as shifted-exponential distribution. Moreover, to capture the characteristics of mmWave signal propagation in urban grid roads, we model the communication path losses using the Manhattan distance metric in the taxicab geometry, which is more accurate than conventional Euclidean distance geometry. Based on the proposed model, we investigate the latency and reliability of mmWave multi-hop V2V communications for three widely adopted relay selection schemes, i.e., the random with forward progress (RFP), the most forward with fixed radius (MFR), and the nearest with forward progress (NFP), respectively. Furthermore, we propose a novel relay selection scheme for joint optimization of the single-hop forward progress (FP) and single-hop latency according to the AVs' instantaneous locations and an estimate of the residual multi-hop latency. Simulation results show that, by exploiting the location information of AVs within the transmission range of each hop and considering the residual multi-hop latency for the multi-hop V2V network, the proposed relay selection scheme significantly outperforms the MFR, NFP and RFP in both multi-hop transmission latency and reliability of mmWave V2V communications.
Vehicles equipped with sensors can participate in mobile crowdsourcing applications. Vehicular Ad Hoc Networks (VANETs) based on Dedicated Short Range Communication (DSRC) are used to carry sensing data. However, multi-hop transmissions for gathering data to Road Side Units (RSUs) in VANETs suffer from low data rate and long end-to-end delay, which can hardly meet the QoS requirements of delay-sensitive services. This triggers the consideration of constituting a DSRC and Cellular-Vehicle-to-Everything (C-V2X) hybrid vehicular network. Nevertheless, using cellular links to carry traffic can cause high cellular bandwidth costs. In this paper, we propose a Traffic Differentiated Clustering Routing (TDCR) mechanism in a Software Defined Network (SDN)-enabled hybrid vehicular network. The proposed mechanism includes a centralized one-hop clustering approach and a data delivery optimization method. Particularly, the optimization is to make a tradeoff between cellular bandwidth cost and end-to-end delay, for Cluster Heads (CHs) delivering their aggregated data either by multi-hop Vehicle-to-Vehicle (V2V) transmissions or by cellular networks. Since the problem is proven to be NP-hard, a two-stage heuristic algorithm is designed. We carry out simulations to evaluate the performance of our data collection scheme and the results show that it performs better than traditional mechanisms.
Blockchain technology has been deemed to be an ideal choice for strengthening existing computing systems in varied manners. As one of the network-enabled technologies, cloud computing has been broadly adopted in the industry through numerous cloud service models. Fusing blockchain technology with existing cloud systems has a great potential in both functionality/ performance enhancement and security/ privacy improvement. The question remains on how blockchain technology inserts into current deployed cloud solutions and enables the reengineering of cloud datacenter. This survey addresses this issue and investigates recent efforts in the technical fusion of blockchain and clouds. Three technical dimensions roughly are covered in this work. First, we concern the service model and review an emerging cloud-relevant blockchain service model, Blockchain-as-a-Service (BaaS); second, security is considered a key technical dimension in this work and both access control and searchable encryption schemes are assessed; finally, we examine the performance of cloud datacenter with supports/ participance of blockchain from hardware and software perspectives. Main findings of this survey will be theoretical supports for future reference of blockchain-enabled reengineering of cloud datacenter.
The vehicular social networks (VSNs) supports diverse kinds of services such as traffic management, road safety, and sharing data (videos, audios, roads photos, air quality, and so on). However, its complex, large-scale and dynamic network structure poses new security challenges. Among these challenges, secure data transmission has turned to be a spotlight. Ciphertext-policy attribute-based encryption (CP-ABE) may be adopted to realize one-to-many data sharing in VSNs. In traditional CP-ABE schemes, access policy is stored and granted by the could, which lacks credibility due to centralization. In this paper, we propose a secure and verifiable one-to-many data sharing scheme to solve the above problem. we use blockchain to record the access policy, realizing user self-certification and cloud non-repudiation. Considering the computing capabilities of the vehicular user, we propose an effective scheme for certificating. Meanwhile, considering the sensitive information included in the access policy, we propose a policy hiding scheme. Our scheme also supports data revocation when a vehicular user no longer wants to share the data in VSNs. Finally, security analysis and simulation show our scheme is both secure and efficient.
Different studies have recently emphasized the importance of deploying clustering schemes in Vehicular ad hoc Network (VANET) to overcome challenging problems related to scalability, frequent topology changes, scarcity of spectrum resources, maintaining clusters stability, and rational spectrum management. However, most of these studies addressed the clustering problem using conventional performance metrics while spectrum shortage, and the combination of spectrum trading and VANET architecture have not been tackled so far. Thus, this paper presents a new fuzzy logic based clustering control scheme to support scalability, enhance the stability of the network topology, motivate spectrum owners to share spectrum and provide efficient and cost-effective use of spectrum. Unlike existing studies, our context-aware scheme is based on multi-criteria decision making where fuzzy logic is adopted to rank the multi-attribute candidate nodes for optimizing the selection of cluster heads (CH)s. Criteria related to each candidate node include: received signal strength, speed of vehicle, vehicle location, spectrum price, reachability, and stability of node. Our model performs efficiently, exhibits faster recovery in response to topology changes and enhances the network efficiency life time.
Cell transmission model (CTM) is useful for evaluating the performance of urban traffic networks due to its mathematical formalism. This paper explores the application of CTM to develop control strategies for dispersion accident-induced traffic jams and evaluates the efficiency of these strategies. On the other hand, Timed Petri nets (TPNs) perform in discrete event systems because they provide a balance between modeling power and analyzability. This paper focuses on using the developed TPNs to model variable traffic light control systems. One advantage of the proposed methods is that the traffic light behavior is clearly represented in terms of conditions and events, which result in changes in the pre-emption phase. Moreover, this paper also proposes a new control strategy to eliminate the traffic jam propagation by preventing the traffic flow heading in the direction of an accident. The analysis is performed to demonstrate how the models enforce the phase of traffic transitions by a reachability graph with time information. The liveness and reversibility of the proposed model are verified. To the best of our knowledge, this is the first paper to use TPNs to model a variable phase traffic light control system and identify its accident scenarios for the purpose of their complete avoidance. This helps the latest advanced technology in traffic safety related to the intersection of roadways.
Blockchain offers an innovative approach to storing information, executing transactions, performing functions, and establishing trust in an open environment. Many consider blockchain as a technology breakthrough for cryptography and cybersecurity, with use cases ranging from globally deployed cryptocurrency systems like Bitcoin, to smart contracts, smart grids over the Internet of Things, and so forth. Although blockchain has received growing interests in both academia and industry in the recent years, the security and privacy of blockchains continue to be at the center of the debate when deploying blockchain in different applications. This article presents a comprehensive overview of the security and privacy of blockchain. To facilitate the discussion, we first introduce the notion of blockchains and its utility in the context of Bitcoin-like online transactions. Then, we describe the basic security properties that are supported as the essential requirements and building blocks for Bitcoin-like cryptocurrency systems, followed by presenting the additional security and privacy properties that are desired in many blockchain applications. Finally, we review the security and privacy techniques for achieving these security properties in blockchain-based systems, including representative consensus algorithms, hash chained storage, mixing protocols, anonymous signatures, non-interactive zero-knowledge proof, and so forth. We conjecture that this survey can help readers to gain an in-depth understanding of the security and privacy of blockchain with respect to concept, attributes, techniques, and systems.
In this paper we discussed an efficient statistical method with PoW consensus approach for cloud and fog computing. With this method, solution with precise probability in minimal time is realized. We have used the expectation maximization algorithm and polynomial matrix factorization. The advantages of this statistical method are the less iteration to converge to the consensus solution and easiness to configure the complete mathematical model as per the requirement. Moreover, the energy and memory consumption are also less which make this approach appealing for cloud and fog computing. The experimental results also show that the proposed approach is significantly efficient in terms of time and memory consumption. This novel approach seems beneficial for Internet-of-Things (IoTs), one of the most fast-growing technologies in network computing.
To support diversified quality of service (QoS) demands and dynamic resource requirements of users in 5G driven VANETs, network resources need flexible and scalable resource allocation strategies. Current heterogeneous vehicular networks are designed and deployed with a connection-centric mindset with fixed resource allocation to a cell regardless of traffic conditions, static coverage and capacity. In this paper, we propose a Hybrid-Fuzzy Logic guided Genetic Algorithm (H-FLGA) approach for the SDN controller, to solve a multi-objective resource optimization problem for 5G driven VANETs. Realizing the service oriented view, the proposed approach formulates five different scenarios of network resource optimization in 5G VANETs. Furthermore, the proposed Fuzzy Inference system (FIS) is used to optimize weights of multi-objectives, depending on the Type of Service (ToS) requirements of customers. The proposed approach shows the minimized value of multi-objective cost function when compared with the GA. The simulation results show the minimized value of end-to-end delay as compared to other schemes. The proposed approach will help the network service providers to implement a customer-centric network infrastructure , depending on dynamic customer needs of users.
Effective route planning is the key to improving transportation efficiency. By leveraging the in-depth knowledge of road topology and traffic trends, experienced drivers (e.g., taxi drivers) can usually find near-optimal routes. However, existing online route planning services can hardly acquire this domain knowledge, so they just provide the fastest/shortest route based on current traffic conditions. These seemingly optimal routes may attract numerous vehicles and then become extremely congested. To solve this problem and actually improve transportation efficiency, we propose a double rewarded value iteration network (VIN) to fully learn the experienced drivers' routing decisions which are based on their implicitly estimated traffic trends. First, the global traffic status and routing actions are chronologically extracted from large-scale taxicab trajectories. Then, to model the knowledge of traffic trends, a long short-term memory (LSTM) network is trained. Being expert at learning long-term planning involved functions, the VIN is utilized to model the policy function from both current and predicted future traffic status to an experienced driver's routing action. Finally, the performance of our proposed model is evaluated on real map and taxicab trajectories in Beijing, China. The experimental results demonstrate that the proposed model can achieve human-like performance in most cases, with high success rate and less commuting time.
Both clustering and cluster-head vehicles (CHVs) cooperative communication have been employed for reducing traffic congestion to improve road traffic efficiency in cooperative vehicular networks. In this paper, an iterative optimization k-means clustering algorithm with lower complexity than previous algorithms is proposed. It can automatically generate multiple clusters according to the number of vehicles and quickly find the CHVs by avoiding delays caused by complex calculations. Moreover, a new optimization power allocation strategy with bidirectional incremental hybrid decode-amplify-forward protocol focusing on reducing the total power consumption of CHVs is proposed. This strategy can set the signal to noise ratio threshold as the critical point for selecting dynamically the bidirectional incremental amplify-and-forward or decoding and forwarding protocol with a lower outage probability to transmit information. Thus, the proposed power allocation strategy is capable of minimizing the total transmission power while ensuring a lower outage probability than previous approaches. Finally, the numerical results are provided for corroborating the theoretical results and demonstrate the efficiency of the proposed approaches. Note that through the numerical simulations we can find the critical point of the outage probability for the aforementioned protocols under different relay locations. This assists vehicles to select "relays" with the optimal cooperative position for vehicular cooperative communication system.
The infrastructure to vehicle (I2V) communication boosts a large number of prevailing vehicular services, which can provide vehicles with external information, storage and computing power located at both mobile edge server (MES) and remote cloud. However, vehicle distribution is imbalanced due to the spatial inhomogeneity and temporal dynamics. As a consequence, the communication load for MES is imbalanced and vehicles may suffer from poor I2V communications where the MES is overloaded. In this paper, we propose a novel proactively load balancing approach that enables efficient cooperation among MESs, which is referred to as end-to-end load balancer (E2LB). E2LB schedules the cached data among MESs based on the predicted road traffic situation. Firstly, a convolutional neural network (CNN) is applied to efficiently learn the spatio-temporal correlation in order to predict the road traffic situation. Then, we formulate the load balancing problem as a nonlinear programming (NLP) problem and a novel framework based on CNN is adopted to approximate the NLP optimization. Finally, we connect the above neural networks into an end-toend neural network to jointly optimize the performance, where the input is the historical traffic situation while the output is the balanced scheduling solution. E2LB can guarantee the real-time scheduling, since the calling of a well-trained neural network only requires a small number of simple operations. Experiments on the trajectories of taxis and buses in Beijing demonstrate the efficiency and effectiveness of E2LB.
Intra-vehicle Wireless Sensor Networks (WSNs) require reliable and real-time data delivery. The Time-Slotted Channel Hopping (TSCH) mode of the IEEE 802.15.4 standard provides a reliable solution for low-power networks through guaranteed medium access and channel diversity. However, satisfying the stringent requirements of dense in-vehicle networks demands for special consideration in network formation and TSCH scheduling. This paper targets convergecast in dense in-vehicle WSNs in which all nodes can potentially directly reach the sink node. A cross-layer Low-Latency Topology management and TSCH scheduling (LLTT) technique is proposed that provides a very high timeslot utilization for the TSCH schedule and minimizes communication latency. Two techniques, namely grouped retransmission and periodic aggregation, are also exploited to increase the performance of the TSCH communications. The experimental results show that LLTT reduces the end-to-end communication latency compared to other approaches, while keeping the communications reliable by using dedicated links and grouped retransmissions.
Blockchain and the Internet of Things (IoT) are key technologies that will have a huge impact in the next 10 years for companies in the industrial market. This article describes how these two technologies will improve efficiencies, provide new business opportunities, address regulatory requirements, and improve transparency and visibility.
The objective of this paper is to present a new analytical tool that predicts highway congestion in real time by utilizing a macroscopic traffic flow model, and to investigate a data collection strategy that is adaptable to the quality of traffic information. A stochastic Lagrangian traffic flow model is proposed to capture the transition into traffic jam and randomness in the traffic flow. To calibrate the model, vehicles in a traffic flow are divided into cells, and only the first and last vehicles in each cell are probed. Model parameters and traffic information are updated in real time by the unscented Kalman filter, and an advance warning is provided for stop-and-go traffic jam. Adaptive data collection is done by adjusting the probing cell size based on the variance of the prediction from the stochastic model. By validating the model with empirical highway traffic data, the proposed stochastic model shows a 20% improvement in predicting the one-step-ahead traffic state, comparing it to the result from the deterministic model. The 3-sec prediction of traffic status, which may be applied to compensate for the latency of data processing in real-time applications, can be obtained with a 15% error. The model parameter can be used to warn the drivers 6.76 sec before entering a traffic jam. The scenario with low penetration rate and longer sample time interval is also demonstrated with traffic data collected by smartphones. The results from adaptive probing suggest that it can efficiently use less data to provide higher prediction accuracy than using non-adaptive probing.
I would like to jump on the blockchain bandwagon. I would like to be able to say that blockchain is the solution to the longstanding problem of secure identity on the Internet. I would like to say that everyone in the world will soon have a digital identity. Put yourself on the blockchain and never again ask yourself, Who am I? - you are your blockchain address.
One particular trend observed in healthcare is the progressive shift of data and services to the cloud, partly due to convenience (e.g. availability of complete patient medical history in real-time) and savings (e.g. economics of healthcare data management). There are, however, limitations to using conventional cryptographic primitives and access control models to address security and privacy concerns in an increasingly cloud-based environment. In this paper, we study the potential to use the Blockchain technology to protect healthcare data hosted within the cloud. We also describe the practical challenges of such a proposition and further research that is required.
The vehicular announcement network is one of the most promising utilities in the communications of smart vehicles and in the smart transportation systems. In general, there are two major issues in building an effective vehicular announcement network. First, it is difficult to forward reliable announcements without revealing users' identities. Second, users usually lack the motivation to forward announcements. In this paper, we endeavor to resolve these two issues through proposing an effective announcement network called CreditCoin, a novel privacy-preserving incentive announcement network based on Blockchain via an efficient anonymous vehicular announcement aggregation protocol. On the one hand, CreditCoin allows nondeterministic different signers (i.e., users) to generate the signatures and to send announcements anonymously in the nonfully trusted environment. On the other hand, with Blockchain, CreditCoin motivates users with incentives to share traffic information. In addition, transactions and account information in CreditCoin are tamper-resistant. CreditCoin also achieves conditional privacy since Trace manager in CreditCoin traces malicious users' identities in anonymous announcements with related transactions. CreditCoin thus is able to motivate users to forward announcements anonymously and reliably. Extensive experimental results show that CreditCoin is efficient and practical in simulations of smart transportation.
A new vehicle positioning system is proposed using unscented Kalman filter for the data fusion of global positioning system and inertial navigation system, and a multi-hypothesis algorithm for map matching. The study presents a method to evaluate whether the results of the multi-hypothesis map matching algorithm can be used for feedback, and a strategy to increase the positioning accuracy based on this feedback. As the number of hypothesis nodes in the multi-hypothesis map matching algorithm grows exponentially with time, which costs lots of computation time and memory, several methods are proposed to reduce the number of hypotheses nodes by improving the generation method of hypothesis nodes, pruning the branches of multi-hypothesis tree, eliminating and merging the redundant nodes. Field test results indicate that the system can achieve much higher accuracy with the feedback from map matching, and can greatly save the computation time and memory.
Abstract—Efficient data dissemination is critical for enabling emerging applications in vehicular ad-hoc networks (VANETs). As a typical traffic scenario, the bidirectional road scenario of highways bring unique challenges on well exploiting the benefit of vehicle-to-vehicle (V2V) communication for data sharing among vehicles driving in opposite directions. This work is dedicated to investigating the characteristics of data services in such a scenario and exploring new opportunities for enhancing overall system performance. Specifically, we present a system architecture to enable the road-side unit (RSU) assisted data scheduling via vehicle-to-infrastructure (V2I) communication. Then, we give a theoretical analysis on the opportunity of successful data sharing among vehicles driving in opposite directions based on the analysis of Signal-to-Interference-Noise-Ratio (SINR) of V2V communication. On this basis, we propose a clustering mechanism based on the design of a time division policy and the derivation of the optimal cluster length. In addition, a cluster association strategy is designed to enable vehicles to dynamically join or leave a cluster based on their real-time velocities. Further, a two-phase backoff mechanism is designed for distributed data sharing based on V2V communication, and a cooperative scheduling algorithm is proposed for selecting sender vehicles as well as the corresponding data items for broadcasting. Finally, we build the simulation model and give a comprehensive simulation study, which demonstrates that the proposed solutions can effectively improve the overall system performance.
In this paper, a novel approach for improving vehicular positioning is presented. This method is based on the cooperation of the vehicles by communicating their measured information about their position. This method consists of two steps. In the first step, we introduce our cooperative map matching method. This map matching method uses the V2V communication in a vehicular ad hoc network (VANET) to exchange global positioning system (GPS) information between vehicles. Having a precise road map, vehicles can apply the road constraints of other vehicles in their own map matching process and acquire a significant improvement in their positioning. After that, we have proposed the concept of a dynamic base station DGPS (DDGPS), which is used by vehicles in the second step to generate and broadcast the GPS pseudorange corrections that can be used by newly arrived vehicles to improve their positioning. The DDGPS is a decentralized cooperative method that aims to improve the GPS positioning by estimating and compensating the common error in GPS pseudorange measurements. It can be seen as an extension of DGPS where the base stations are not necessarily static with an exact known position. In the DDGPS method, the pseudorange corrections are estimated based on the receiver's belief on its positioning and its uncertainty and then broadcasted to other GPS receivers. The performance of the proposed algorithm has been verified with simulations in several realistic scenarios.
Intelligent transportation systems are currently attracting the attention of the research community and the automotive industry, which both aim to provide not only more safety in the transportation systems but other high-quality services and applications for their customers as well. In this paper, we propose a cooperative traffic transmission algorithm in a joint vehicular ad hoc network-Long Term Evolution Advanced (LTE Advanced) hybrid network architecture that elects a gateway to connect the source vehicle to the LTE Advanced infrastructure under the scope of vehicle-to-infrastructure (V2I) communications. The originality of the proposed fuzzy quality-of-service (QoS)-balancing gateway selection (FQGwS) algorithm is the consideration of QoS traffic class constraints for electing the gateway. Our algorithm is a multicriteria and QoS-based scheme optimized by performing the fuzzy logic to make the decision on the appropriate gateway. Criteria are related to the received signal strength (RSS) and load of the cluster head (CH) and gateway candidates (GwCs), as well as the vehicle-to-vehicle link connectivity duration (LCD). Simulation results demonstrate that our algorithm gets better results than the deterministic scheme for gateway selection. Moreover, results show the efficiency of the FQGwS algorithm as it adapts its gateway selection decision to the cluster density and the relative velocity of the source node.
In this paper, we propose to use vehicular ad hoc networks (VANETs) to collect and aggregate real-time speed and position information on individual vehicles to optimize signal control at traffic intersections. We first formulate the vehicular traffic signal control problem as a job scheduling problem on processors, with jobs corresponding to platoons of vehicles. Under the assumption that all jobs are of equal size, we give an online algorithm, referred to as the oldest job first (OJF) algorithm, to minimize the delay across the intersection. We prove that the OJF algorithm is 2-competitive, implying that the delay is less than or equal to twice the delay of an optimal offline schedule with perfect knowledge of the arrivals. We then show how a VANET can be used to group vehicles into approximately equal-sized platoons, which can then be scheduled using OJF. We call this the two-phase approach, where we first group the vehicular traffic into platoons and then apply the OJF algorithm, i.e., the oldest arrival first (OAF) algorithm. Our simulation results show that, under light and medium traffic loads, the OAF algorithm reduces the delays experienced by vehicles as they pass through the intersection, as compared with vehicle-actuated methods, Webster's method, and pretimed signal control methods. Under heavy vehicular traffic load, the OAF algorithm performs the same as the vehicle-actuated traffic method but still produces lower delays, as when compared with Webster's method and the pretimed signal control method.
Cellular V2X as the essential enabler of superior global connected transportation services
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A blockchain-based trusted data management scheme in edge computing
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