Mobile crowdsensing is an emerging paradigm that selects users to complete sensing tasks. Recently, mobile vehicles are adopted to perform sensing data collection tasks in the urban city due to their ...ubiquity and mobility. In this article, we study how mobile vehicles can be optimally selected in order to collect maximum data from the urban environment in a future period of tens of minutes. We formulate the recruitment of vehicles as a maximum data limited budget problem. The application scenario is generalized to a realistic online setting where vehicles are continuously moving in real-time and the data center decides to recruit a set of vehicles immediately. A deep learning-based scheme through mobile vehicles (DLMV) is proposed to collect sensing data in the urban environment. We first propose a deep learning-based offline algorithm to predict vehicle mobility in a future time period. Furthermore, we propose a greedy online algorithm to recruit a subset of vehicles with a limited budget for the NP-Complete problem. Extensive experimental evaluations are conducted on the real mobility dataset in Rome. The results have not only verified the efficiency of our proposed solution but also validated that DLMV can improve the quantity of collected sensing data compared with other algorithms.
Wireless sensor networks (WSNs) play an important role in the industrial Internet of Things (IIoT) and have been widely used in many industrial fields to gather data of monitoring area. However, due ...to the open nature of wireless channel and resource-constrained feature of sensor nodes, how to guarantee that the sensitive sensor data can only be accessed by a valid user becomes a key challenge in IIoT environment. Some user authentication protocols for WSNs have been proposed to address this issue. However, previous works more or less have their own weaknesses, such as not providing user anonymity and other ideal functions or being vulnerable to some attacks. To provide secure communication for IIoT, a user authentication protocol scheme with privacy protection for IIoT has been proposed. The security of the proposed scheme is proved under a random oracle model, and other security discussions show that the proposed protocol is robust to various attacks. Furthermore, the comparison results with other related protocols and the simulation by NS-3 show that the proposed protocol is secure and efficient for IIoT.
The mobile crowdsensing (MCS) technology with a large number of Internet of Things (IoT) devices provides an economic and efficient solution to participation in coordinated large-scale sensing tasks. ...Edge computing powers MCS to form the mobile edge crowdsensing (MECS) framework. Privacy disclosure of sensing data in multiple stages is a significant challenge in the MECS. To tackle this issue, combining machine learning with game theory, in this article, we propose an artificial intelligence (AI)-enabled three-party game (ATG) framework for guaranteed data privacy in the MECS of IoT. Specifically, based on the random forest classifier and the k-anonymity algorithm, we propose a classification-anonymity model that effectively guarantees the privacy of sensitive data. Moreover, we construct a three-party game model for analyzing the data privacy leakage in different phases in the MECS. Finally, we conduct numerical and theoretical analyses and ample simulations. The results indicate that the ATG framework is effective and efficient, and better suited to the MECS of IoT.
The Internet of Things (IoT)-Cloud combines the IoT and cloud computing, which not only enhances the IoT's capability but also expands the scope of its applications. However, it exhibits significant ...security and efficiency problems that must be solved. Internal attacks account for a large fraction of the associated security problems, however, traditional security strategies are not capable of addressing these attacks effectively. Moreover, as repeated/similar service requirements become greater in number, the efficiency of IoT-Cloud services is seriously affected. In this paper, a novel architecture that integrates a trust evaluation mechanism and service template with a balance dynamics based on cloud and edge computing is proposed to overcome these problems. In this architecture, the edge network and the edge platform are designed in such a way as to reduce resource consumption and ensure the extensibility of trust evaluation mechanism, respectively. To improve the efficiency of IoT-Cloud services, the service parameter template is established in the cloud and the service parsing template is established in the edge platform. Moreover, the edge network can assist the edge platform in establishing service parsing templates based on the trust evaluation mechanism and meet special service requirements. The experimental results illustrate that this edge-based architecture can improve both the security and efficiency of IoT-Cloud systems.
Nowadays, the applications related to Internet of connected vehicles (IoCV) have been greatly promoted by the roadside units (RSUs). To improve the transmission efficiency by the RSUs, 5G is ...introduced to the IoCV scenario for offering sufficient communication bandwidth. Generally, the traditional offloading destinations of the computing tasks in IoCV are the distant cloud servers, which consequently increases the response time of the tasks. Edge servers, placed together with macro base stations (MABSs) in 5G and RSUs, offer alternatives to host the tasks. However, the complicated locations of MABSs and RSUs make it difficult to distinguish the offloading destinations of the computing tasks in IoCV. In view of this, an adaptive computation offloading method, named ACOM, is devised for edge computing in 5G-envisioned IoCV to optimize the task offloading delay and resource utilization of the edge system. More specifically, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is fully leveraged to generate the available solutions. Then, the optimal offloading solution is obtained by utility evaluation. Eventually, the experimental results demonstrate the effectiveness of ACOM.
Over the past few decades, the intelligent transportation system (ITS) have emerged with new technologies and becomes the data-driven ITS, because the substantial amount of data is assembled from the ...multiple sources. Vehicular Ad hoc networks (VANETs), are a particular case of ad hoc networks that are used in the smart ITS. VANETs have become one of the most, encouraging, promising, and fastest-growing subsets of the mobile ad hoc networks (MANETs). They are comprised of smart vehicles and roadside units (RSUs) and on-board units (OBUs) which communicate through unreliable wireless media. Other than lacking infrastructure, delivering entities move with different increasing speeds. Thus, this delays establishing reliable end-to-end communication paths and having efficient data transfer. In this manner, VANETs have diverse system concerns and security difficulties in getting the accessibility of ubiquitous availability, secure communication, interchanges, and reputation management system. Which influence the trust in collaboration and arrangement between the portable system. By their fluctuation in nature, they are genuinely defenseless against assaults, which may result in life-jeopardizing circumstances. In this survey, we provide an extensive overview of the ITS and the evolution of ITS to VANETs. We provide the details of VANETs, discussed the privacy and security attacks in VANETs with their applications and challenges. We address the effectiveness of VANETs and cloud computing with architecture and related privacy and security issues. We also examined the communication protocols for each network layer with the relevant attacks occurred at each layer. We also discussed the potential benefits of the different proposed techniques related to VANETs, application, and challenges in details. In the end, we provide a conclusion with some open and emerging issues in VANETs.
The recent emergence of cloud computing has drastically influenced everyone’s perception of infrastructure architectures, data transmission and other aspects. With the advent of both mobile networks ...and cloud computing, the computationally-intensive services are moving to the cloud, and the end user’s mobile device is used as an interface to access these services. However, cyber threats are also becoming various and sophisticated, which will endanger the security of users’ private data. In traditional service mode, users’ data is totally stored in the cloud, they lose the right of control on their data and face cyber threats such as data loss and malicious modification. To this end, we propose a novel cloud storage scheme based on fog computing. In our scheme, user’s private data is separately stored in the cloud and fog servers. By this way, the integrity, availability and confidentiality of user’s data can be ensured because the data is retrieved from cloud as well as fog, which is safer. We implement a system prototype and design a series of mechanisms. Extensive experiments results also validate the proposed scheme and methods.
The development of the Internet of Things (IoT) and intelligent vehicles brings a comfortable environment for users. Various emerging vehicular applications using artificial intelligence (AI) ...technologies are expected to enrich users' daily life. However, how to execute computation-intensive applications on resource-constrained vehicles based on AI still faces great challenges. In this article, we consider the vehicular computation offloading problem in mobile-edge computing (MEC), in which multiple mobile vehicles select nearby MEC servers to offload their computing tasks. We propose a multiagent deep reinforcement learning (DRL)-based computation offloading scheme, in which the uncertainty of a multivehicle environment is considered so that the vehicles can make offloading decisions to achieve an optimal long-term reward. First, we formalize a formula for the computation offloading problem. The goal of this article is to determine the optimal offloading decision to the MEC server under each observed system state, so as to minimize the total task processing delay in a long-term period. Then, we use a multiagent DRL algorithm to learn an effective solution to the vehicular task offloading problem. To evaluate the performance of the proposed offloading scheme, a large number of simulations are carried out. The simulation results verify the effectiveness and superiority of the proposed scheme.
Anomaly detection for smart contracts can effectively prevent hidden security risks such as financial fraud, illegal financing, and money laundering. Ethereum is currently the largest platform for ...smart contracts, and anomaly detection is imminent. However, the data related to smart contracts is huge and contains complex objects and relationships. It is impossible to extract high-order attributes and low efficiency using traditional methods. The key to reduce fraud is extracting features from complex smart contracts and effectively identifying abnormal contracts. Therefore, this paper constructs a Heterogeneous Graph Transformer Networks (S_HGTNs) suitable for smart contract anomaly detection to detect financial fraud on the Ethereum platform. For feature representation, this paper first extracts the features to construct a Heterogeneous Information Network (HIN) for smart contract, and uses the relationship matrix obtained from the meta-path learned in the transformer network as the input of the convolution network, and finally uses the node embedding for classification tasks. The classification results show that this model performs better than the traditional model and the standard deviation is small, which proves the effectiveness and stability of the model.
•This paper defines a representational approach to smart contracts. The diagram is used to describe the flow of the smart contract.•This paper uses graph transform network to learn heterogeneous graph meta-paths, which can learn the paths between multi-hop connection nodes, that is, heterogeneous information network is represented by meta-path network. The efficiency of the model is improved because it does not need to be given a meta-path manually.
Wireless sensor networks (WSNs) are being suggested at an increasing rate for structural health monitoring (SHM). The objective is to monitor complex events (e.g., damage) in structures (e.g., an ...industrial machine and a high-rise building) that are usually carried out with wired-based SHM systems. However, monitoring events with a WSN deployed over large structures is challenging due to WSN constraints (high-resolution data transmission and energy) and the quality of monitoring. In this paper, we attempt to design a cyber-physical system (CPS) of structural event monitoring with WSNs and propose a novel model-based in-network decision making in the CPS named MODEM. We think of the idea of generic event detection (like target/object) schemes, and enable each sensor to sense and make a simplified local decision (0/1) on the complex events. We then think of the formation of engineering structures and find that a large physical structure consists of a number of substructures. We enable deployed sensors to be organized into groups in such a way that a groupwise final decision (e.g., 0/1) can be provided for each substructure independently so that the existence of an event (if there is any) in a specific substructure can be identified by WSNs. MODEM is fully distributed in nature, promises to have the monitoring quality similar to the original wired-based schemes, and consumes much less energy for transmissions and computations than existing schemes do. The effectiveness of MODEM is shown via both simulations and real experiments.
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