The efficient deployment of Big Data processing tasks in cloud environments is the basic core function of Big Data processing, which refers to the effective deployment of tasks to the computing resources of cloud platforms, achieving high-performance and high-throughput data processing. In this process, task deployment needs to consider load balancing on the cloud platform to ensure that tasks can be evenly deployed to each computing node. However, currently in the process of providing services on cloud platforms, the available resources of all hosts will be automatically and dynamically readjusted, and it cannot be guaranteed that each task will be deployed to the host with the most remaining resources. This load imbalance in the platform will result in computational results that cannot be returned to users in a timely and effective manner. So, a heuristic multi-task efficient deployment approach for Big Data processing based on QoS awareness and Bayesian classification in cloud environments called QBC is proposed. The QBC first performs long-run QoS awareness on hosts in the cloud; Then, based on user task requirements, selects host nodes that meet QoS constraints to form a candidate set, and performs Bayesian classification to find the host node which has highest a posteriori probability to serve as the clustering center; thirdly, designs an objective function based on Euclidean spatial distance to acquire the optimum host clustering set in the candidate set; Finally, deploys the user's tasks to this optimal host cluster set. The experimental results show that this approach implements optimization of long-run load balancing in Big Data cloud platforms with minimal resource consumption, enhances the ability of the cloud platform to provide external support, and thus promotes efficient deployment of multitasking in Big Data processing under cloud computing. The proposed QBC based framework reduces the overall Energy Consumption by an average of 47.98%, MakeSpan by an average of 24.42%, Total Cost by an average of 30.17%, Average Waiting Time by an average of 36.92%, and the Throughput is increased by an average of 41.93% as compared to the existing algorithms.

The Industrial Internet of Things (IIoT), a typical Internet of Things (IoT) application, integrates the global industrial system with other advanced computing, analysis, and sensing technologies through Internet connectivity. Due to the limited storage and computing capacity of edge and IIoT devices, data sensed and collected by these devices are usually stored in the cloud. Encryption is commonly used to ensure privacy and confidentiality of IIoT data. However, the key used for data encryption and decryption is usually directly stored and managed by users or third-party organizations, which has security and privacy implications. To address this potential security and privacy risk, we propose a Shamir threshold cryptography scheme for IIoT data protection using blockchain: STCChain. Specifically, in our solution, the edge gateway uses a symmetric key to encrypt the data uploaded by the IoT device and stores it in the cloud. The symmetric key is protected by a private key generated by the edge gateway. To prevent the loss of the private key and privacy leakage, we use a Shamir secret sharing algorithm to divide the private key, encrypt it, and publish it on the blockchain. We implement a prototype of STCChain using Xuperchain, and the results show that STCChain can effectively prevent attackers from stealing data as well as ensuring the security of the encryption key.

Speech emotion recognition (SER) is becoming the main human–computer interaction logic for autonomous vehicles in the next generation of intelligent transportation systems (ITSs). It can improve not only the safety of autonomous vehicles but also the personalized in-vehicle experience. However, current vehicle-mounted SER systems still suffer from two major shortcomings. One is the insufficient service capacity of the vehicle communication network, which is unable to meet the SER needs of autonomous vehicles in next-generation ITSs in terms of the data transmission rate, power consumption, and latency. Second, the accuracy of SER is poor, and it cannot provide sufficient interactivity and personalization between users and vehicles. To address these issues, we propose an SER-enhanced traffic efficiency solution for autonomous vehicles in a 5G-enabled space–air–ground integrated network (SAGIN)-based ITS. First, we convert the vehicle speech information data into spectrograms and input them into an AlexNet network model to obtain the high-level features of the vehicle speech acoustic model. At the same time, we convert the vehicle speech information data into text information and input it into the Bidirectional Encoder Representations from Transformers (BERT) model to obtain the high-level features of the corresponding text model. Finally, these two sets of high-level features are cascaded together to obtain fused features, which are sent to a softmax classifier for emotion matching and classification. Experiments show that the proposed solution can improve not only the SAGIN’s service capabilities, resulting in a large capacity, high bandwidth, ultralow latency, and high reliability, but also the accuracy of vehicle SER as well as the performance, practicality, and user experience of the ITS

To achieve user identities protection in transaction, the stealth address is introduced. However, double-key stealth address protocol (DKSAP) requires the receiver to continuously calculate and determine whether it is the real receiver of the transaction until it detected a transaction that matches. In this process, the receiver needs to perform many time-consuming elliptic curve scalar multiplication operations, which limits the application of DKSAP in low-performance devices, such as mobile terminals, IoT devices, etc. This paper proposes an efficient double-key stealth address protocol based on bilinear mapping, EDKSAP. Senders calculate the transaction temporary output address and receivers perform the verification calculation using bilinear mapping, which achieves the purpose of improving computing performance. Finally, we verify the feasibility and applicability of the protocol through experiments. In addition, the experimental results show that the computational performance of EDKSAP is significantly improved compared to DKSAP.

Blockchain is a mainstream technology in which many untrustworthy nodes work together to maintain a distributed ledger with advantages such as decentralization, traceability, and tamper-proof. The network layer communication mechanism in its architecture is the core of the networking method, message propagation, and data verification among blockchain nodes, which is the basis to ensure blockchain’s performance and key features. When blocks are propagated in peer-to-peer (P2P) networks with gossip protocol, the high propagation delay of the protocol itself reduces the propagation speed of the blocks, which is prone to the chain forking phenomenon and causes double payment attacks. To accelerate the propagation speed and reduce the fork probability, this paper proposes a blockchain network propagation mechanism based on proactive network provider participation for P2P (P4P) architecture. This mechanism first obtains the information of network topology and link status in a region based on the internet service provider (ISP), then it calculates the shortest path and link overhead of peer nodes using P4P technology, prioritizes the nodes with good local bandwidth conditions for transmission, realizes the optimization of node connections, improves the quality of service (QoS) and quality of experience (QoE) of blockchain networks, and enables blockchain nodes to exchange blocks and transactions through the secure propagation path. Simulation experiments show that the proposed propagation mechanism outperforms the original propagation mechanism of the blockchain network in terms of system overhead, rate of data success transmission, routing hops, and propagation delay.

The industrial Internet of Things (IIoT) supports recent developments in data management and information services, as well as services for smart factories. Nowadays, many mature IIoT cloud platforms are available to serve smart factories. However, due to the semicredibility nature of the IIoT cloud platforms, how to achieve secure storage, access control, information update and deletion for smart factory data, as well as the tracking and revocation of malicious users has become an urgent problem. To solve these problems, in this article, a blockchain-enhanced security access control scheme that supports traceability and revocability has been proposed in IIoT for smart factories. The blockchain first performs unified identity authentication, and stores all public keys, user attribute sets, and revocation list. The system administrator then generates system parameters and issues private keys to users. The domain administrator is responsible for formulating domain security and privacy-protection policies, and performing encryption operations. If the attributes meet the access policies and the user's ID is not in the revocation list, they can obtain the intermediate decryption parameters from the edge/cloud servers. Malicious users can be tracked and revoked during all stages if needed, which ensures the system security under the Decisional Bilinear Diffie-Hellman (DBDH) assumption and can resist multiple attacks. The evaluation has shown that the size of the public/private keys is smaller compared to other schemes, and the overhead time is less for public key generation, data encryption, and data decryption stages.

It is expected that a mixture of autonomous and manual vehicles will persist as a part of the intelligent transportation system (ITS) for many decades. Thus, addressing the safety issues arising from this mix of autonomous and manual vehicles before autonomous vehicles are entirely popularized is crucial. As the ITS system has increased in complexity, autonomous vehicles exhibit problems such as a low intention recognition rate and poor real-time performance when predicting the driving direction; these problems seriously affect the safety and comfort of mixed traffic systems. Therefore, the ability of autonomous vehicles to predict the driving direction in real time according to the surrounding traffic environment must be improved and researchers must work to create a more mature ITS. In this paper, we propose a deep learning-based traffic safety solution for a mixture of autonomous and manual vehicles in a 5G-enabled ITS. In this scheme, a driving trajectory dataset and a natural-driving dataset are employed as the network inputs to long-term memory networks in the 5G-enabled ITS: the probability matrix of each intention is calculated by the softmax function. Then, the final intention probability is obtained by fusing the mean rule in the decision layer. Experimental results show that the proposed scheme achieves intention recognition rates of 91.58% and 90.88% for left and right lane changes, respectively, effectively improving both accuracy and real-time intention recognition and improving the lane change problem in a mixed traffic environment.

Green Internet of things (GIoT) generally refers to a new generation of Internet of things design concept. It can save energy and reduce emissions, reduce environmental pollution, waste of resources, and harm to human body and environment, in which green smart device (GSD) is a basic unit of GIoT for saving energy. With the access of a large number of heterogeneous bottom-layer GSDs in GIoT, user access and control of GSDs have become more and more complicated. Since there is no unified GSD management system, users need to operate different GIoT applications and access different GIoT cloud platforms when accessing and controlling these heterogeneous GSDs. This fragmented GSD management model not only increases the complexity of user access and control for heterogeneous GSDs, but also reduces the scalability of GSDs applications. To address this issue, this article presents a blockchain-empowered general GSD access control framework, which provides users with a unified GSD management platform. First, based on the World Wide Web Consortium (W3C) decentralized identifiers (DIDs) standard, users and GSD are issued visual identity ( VID ). Then, we extended the GSD-DIDs protocol to authenticate devices and users. Finally, based on the characteristics of decentralization and non-tampering of blockchain, a unified access control system for GSD was designed, including the registration, granting, and revoking of access rights. We implement and test on the Raspberry Pi device and the FISCO-BCOS alliance chain. The experimental results prove that the framework provides a unified and feasible way for users to achieve decentralized, lightweight, and fine-grained access control of GSDs. The solution reduces the complexity of accessing and controlling GSDs, enhances the scalability of GSD applications, as well as guarantees the credibility and immutability of permission data and identity data during access.

Crowdsourcing refers to a distributed problem-solving mechanism that solves complex problems which are difficult for a single individual to solve by integrating the unknown free and voluntary masses on the Internet. In the existing crowdsourcing systems, requesters and workers are usually required to use the crowdsourcing platform as the trust center, and the payment of the fee depends on the third-party central payment institutions. This centralized service mechanism has a large security risk. Once these centers are attacked, or the centers are doing evil, it will bring greater losses to the crowdsourcing parties. Based on the blockchain technology, we propose a new and decentralized trusted service mechanism for crowdsourcing system. The crowdsourcing service process is divided into nine stages, and the smart contract controls the execution of each step in each stage. In addition, the payment is completed by transferring within the blockchain. Finally, we develop smart contracts to conduct experiments based on Ethereum, and conduct comparison experiments. The experimental results show that the effectiveness and applicability of the service of the crowdsourcing system service mechanism without the central institutions.