Search

Ultraviolet (UV) networks, owing to their advantages of low eavesdropping rates, low background noise, strong anti-interference abilities, and non–line-of-sight (NLOS) communication, have high prospects for application in the complex electromagnetic environment. In this study, UV network coverage was systematically evaluated based on the NLOS channel. Combined with a designed node model and a specific communication mode, a two-dimensional network coverage model was established and expressions of network coverage area and connectivity were derived mathematically. Subsequently, the effects of the transceiver elevation combination, number of nodes, data rate, and transmission power on the network coverage performance were analyzed, and the communication parameters were optimized to match different network scenarios. The results show that the bigger the transceiver elevation combination, the more stringent the communication parameters required by the UV network coverage. Finally, a coverage evaluation experiment with four-node network was conducted. This study provides theoretical reference information for actual network deployment.

The ratio of torque to copper loss can be increased by injecting third-harmonic current into five-phase permanent-magnetic synchronous motor (PMSM). Accurate flux linkage information is indispensable in the process of calculating third-harmonic current reference corresponding to the maximum torque/copper loss ratio. In harsh working environments, the flux linkage will deviate from the initial measured value, resulting in the deviation between the third-harmonic current and the maximum torque/copper working point. Therefore, a third-harmonic current injection control strategy of five-phase PMSM based on third-harmonic current reference online identification is proposed in this paper. The key of this strategy is to transform the steady-state voltage equations into an equation containing only voltages and currents. The transformed equation has a characteristic that it is equal to 0 when the motor operates at the maximum torque/copper loss ratio working point. Meanwhile, an observer is designed to estimate the third-harmonic current reference by using the characteristic. Finally, the observer is applied in third-harmonic current injection control of five-phase PMSM. Compared with previous methods, the proposed strategy is simple, no need of flux linkage information and not affected by the change of flux linkage. The experimental results demonstrate the robustness of the proposed strategy.

Ultraviolet (UV) networks are important approaches to modern wireless communications owing to their unique characteristics, such as non-line-of-sight communication, strong anti-interference ability, and all-weather working mode. Given the lack of media access control (MAC) protocols suitable for UV networks at present, a novel UV lossless contention MAC (UVLLC-MAC) protocol is proposed herein based on UV physical properties. A fully connected finite-population multinode UV model is adopted, and its network performance is systematically derived and mathematically analyzed under the UVLLC-MAC protocol. Compared with the time-slot ALOHA protocol, the UVLLC-MAC protocol achieves 36.7% better throughputs, with 13.2% drop in packet loss rates, and the excellent performance of the protocol is obtained and fully verified. The UVLLC-MAC protocol can provide guidance for multinode and multiservice UV networking.

Technological evolution has brought great changes to traditional industries. Nowadays farmers obtain great benefits such as intelligent production and convenient management from intelligent agriculture. Nevertheless, there still exists many flaws in current solutions. Deficiencies in energy management, data transmission and privacy preservation are supposed to be solved. In this paper, the privacy preserved cyber-physical searching framework is proposed to tackle these problems. This four-layer framework can provide a convenient way for farmers to manage production by simply searching for information they interested in. The data source layer contains many sensors for data collection. The data aggregation layer consists of data aggregation nodes deployed in the farms. The data transfer layer employs drones for retrieving data from data aggregation nodes. The cloud control layer is responsible for searching requests processing and privacy preservation. To realize better data collection and searching. The information centric network is introduced into this framework. Moreover, some methods are employed for privacy preservation. The simulation results show the feasibility of this framework.

This manuscript studies on the k-connectivity (k = 1, 2, 3) of ultraviolet (UV) non-line-of-sight (NLOS) communication network in the arbitrary polygon area where the nodes move according to random waypoint (RWP) mobility. In the paper, based on the relationship between the path loss and the transceiver elevation angles, the change of the communication radii of the UV nodes can be analyzed. Besides, a series of parameters that affect the UV network connectivity probability are numerically researched like node density, transmission power and data rate. The results illustrate that performance of communication mode with small elevation angle is better than that of mode with large elevation angle in the arbitrary polygon area. Furthermore, the 2-connectivity with better robustness in a square area is numerically compared with that in a circular area, and then the network parameters that meet the actual applications are clearly given.

WiNoC has become a promising on-chip interconnect architecture. Due to the integration and manufacturing limits of wireless interconnects in nanotechnology, WiNoC systems are more susceptible to high failure rates. In this paper, we propose a novel fault-tolerant routing algorithm based on regional fault-aware techniques for link failures in WiNoC. We trade off performance and overhead by adopting 2-hop awareness for wireless nodes and 1-hop awareness for wired nodes. And the node status is not defined merely based on its own faulty condition but with a “double sensing” mechanism. Besides, since congestion is prone to occur around faulty nodes, we consider congestion mitigation in fault-tolerant algorithm design. Simulation results demonstrate that compared with several counterparts, the performance benefits clearly overweigh the overhead with acceptable area increase. The newly proposed routing algorithm significantly enhances the robustness of the system.

Traditional methods of multi-label text classification, particularly deep learning, have achieved remarkable results. However, most of these methods use word2vec technology to represent sequential text information, while ignoring the logic and internal hierarchy of the text itself. Although these approaches can learn the hypothetical hierarchy and logic of the text, it is unexplained. In addition, the traditional approach treats labels as independent individuals and ignores the relationships between them, which not only does not reflect reality but also causes significant loss of semantic information. In this paper, we propose a novel Hierarchical Graph Transformer based deep learning model for large-scale multi-label text classification. We first model the text into a graph structure that can embody the different semantics of the text and the connections between them. We then use a multi-layer transformer structure with a multi-head attention mechanism at the word, sentence, and graph levels to fully capture the features of the text and observe the importance of the separate parts. Finally, we use the hierarchical relationship of the labels to generate the representation of the labels, and design a weighted loss function based on the semantic distances of the labels. Extensive experiments conducted on three benchmark datasets demonstrated that the proposed model can realistically capture the hierarchy and logic of text and improve performance compared with the state-of-the-art methods.

With the rapid development of automatic vehicles (AVs), vehicles have become important intelligent objects in Smart City. Vehicles bring huge amounts of data for Intelligent Transportation System (ITS), and at the same time, they also put forward new application requirements. However, it is difficult to obtain and analyze massive data and provide accurate application services for AVs. In today's society of traffic explosion, how to plan the route of vehicles has become a hot issue. In order to solve this problem, we introduced content-data-friendly information-center networking (ICN) architecture into the Internet of Vehicles (IoV), and achieved efficient route planning for AVs through the Big Data acquisition and analysis architecture in ICN. We use the analytical capabilities of the network to achieve active cognitive access to traffic data. At the same time, we use game theory to achieve the incentive mechanism for task distribution and information sharing. Finally, the simulation results show that the method is effective.

The diverse applications and high-quality services in smart cities have led to the geographical unbalance of computation requirements. Traditional centralized cloud computing services and the massive migration of computing tasks result in the increase of network delay and the aggravation of network congestion. Deploying fog nodes at the network edge has become an effective way to improve the quality of service. However, the dynamic requirements and application in various scenarios still challenge the network, resulting in the geographical unbalance of computing resource demands. Nowadays, the computing resources of on-board computers and devices in the Internet of Vehicles are abundant enough to mitigate the geographical unbalance in computing power demand. The efficient usage of the natural mobility of constantly moving vehicles to solve the above-mentioned problems remains an urgent need. In this paper, a vehicle mobility-based geographical migration model of the vehicular computing resource is established for the fog computing-enabled smart cities. The vehicle as a service framework takes the full advantage of the unbalance and randomness of vehicular computing resource and improves the flexibility of traditional cloud computing architecture. An incentive scheme that affects the vehicle path selection through resource pricing is proposed to balance the resource requirements and to geographically allocate computing resources. The simulation results indicate that the advantages and efficiency of the proposed scheme are significant.

Nowadays, critical information services such as emergency management and in-situation navigation rely heavily on the assumption of a reliable networking infrastructure and stable cloud processing, which is ineffective in infrastructure-less environments where disrupted or even no telecommunication connectivity is the norm. On the other hand, fog computing is an extension of cloud computing that is at the physical proximity of end-users to enable local storage, computing, and various forms of communication between devices and users. To this end, we design a fog system spanning hardware, software and networking, thus meeting the requirements of various stakeholders in difficult surroundings. As a complement to a cloud-centred solution, this system is geo-distributed, self-powered, self-managed, location-aware, highly efficient and able to provide situational information services without Internet connectivity. The proposed system has been implemented in national parklands of Australia to achieve a personalized information service, emergency management and in-park navigation for all types of parkland users.

Multi-tenant software-defined networks have emerged as a promising service platform to manage and share virtual network services between tenants and end-users. However, when using a service level agreement (SLA) in a multi-tenant circumstance, the resource competition among tenants incurred in many challenges on the quality of services (QoS) provisioning. First, as the existing QoS provisioning was to translate the high-level SLA contact into a group of corresponding low-level QoS rules, this traditional top-down translation mechanism was too ossified to support the dynamic SLA negotiation. Second, since an end-user usually requested multiple services and different services had different QoS requirements, the QoS provisioning required tenants to monitor and control the network status though OpenFlow protocol in a fine-grained way. To address these issues, we propose an SLA-aware fine-grained QoS provisioning (SFQP) scheme for MTSDN. The SFQP scheme extracts the eigen characteristics of each packet by applicationaware technology automatically. Considering tenants are more willing to serve for users which have popular SLA contact to earn more revenues, we predict the SLA popularity by K-nearest neighbor. Moreover, we present an SLA-aware dynamic bandwidth allocation algorithm to implement the proposed SFQP scheme. The feasibility of the proposed scheme is verified by simulations of the SLA taxonomy, bandwidth utilization, and bandwidth fluctuation.

Internet of Energy (IoE) is a novel decentralized energy supplying paradigm, which integrated highly scalable and distributed energy resources to satisfy the various demands in future green applications. The existing works focus on the monitor and control of the state of networked energy storage devices. However, optimizing the security of demand response (DR) management with given energy states under IoE circumstance is rarely studied. Due to the connection to the Internet, the DR management in IoE faces a number of unique cyber-physical security challenges. First, as distributed energy resources have a large number of stakeholders and any illegal skip-level energy access may cause disastrous results, it requires fog computing paradigm to enforce a more secure DR management. Second, in the localized energy networks of IoE, a corrupt DR participator can maliciously read and write DR strategies by using collusion attacks (e.g., reputation-based cheating and unfair competing). To address these issues, we propose a fog computing-enabled secure demand response (FSDR) scheme for IoE against collusion attacks using consensus and access control encryption. In FSDR, the fog node was reconstructed as a sanitizer to randomly transfer encrypted energy states and DR strategies with homomorphic operations. Moreover, a simulated annealing-based consensus algorithm was presented to examine the validity of the energy states and DR strategies. In addition, we establish the mathematical models of collusion attacks and attack defense approaches. The performance evaluation validated its efficiency.

As the proportion of information content in the power grid increased, information-centric smart grid (iCenS) has drawn much attention in recent years. By introducing features of information-centric networking on decentralization, multi-path transmission, and in-network caching to distributed power systems, the iCenS enabled more efficient demand response, disruption tolerant, and mobility support. However, due to the strict quality of services (QoSs) requirement of power grid and a large number of resource-limited infrastructures existed in iCenS, it still faces the following challenges. First, multi-path addressing will cause redundant packets and the transmission of duplicate packet consumes the node energy which may lead to link failure; the delayed transaction of key component information like tele-protection will induce electricity accidents. Second, it is hard to model the traffic of iCenS without introducing extra computing burden because of its interest-driven feature and constantly updated forwarding information base. In this paper, we propose a QoS-predicted energy efficient routing (QPER) scheme for iCenS, which leverage on the network calculus. Upon collecting the real-time state information of the iCenS nodes, the QPER rapidly predicts the QoS boundary and energy consumption of each link for upcoming traffic flows by establishing the service curve and arrival curve. The simulation results validate that our method is superior to traditional multicast routing algorithm in terms of delay, energy consumption, packet loss, and others. Simultaneously, providing various routing methods for different service information can ensure the stability and minimize the security risks.

As a shared economy platform, Airbnb allows customers to collaborate and guides them to hosts' rooms. Based on the records and ratings, it attaches great significance to infer users' satisfaction with their rooms. Several essential problems arise when evaluating satisfaction and matching. Data confidence and prediction bias influence the inference performance of the satisfaction. When two users stay in one room, their joint satisfaction also deserves particular research because of the roommate effect. In this paper, a matching model is built based on the inferred satisfaction considering confidence and prediction uncertainties. The satisfaction with the confidence uncertainty is modeled using a normalized variance of the Beta distribution. The algorithms for inferring satisfaction with the prediction uncertainties are divided into two parts: a weighted matrix factorization-based algorithm for individuals and a preference similarity-based algorithm for pairs. Two matching algorithms are proposed with constraints. Finally, extensive experiments using real-world data show the effectiveness and accuracy of the proposed method.

Although the ways to protect vehicular location privacy have been actively studied in recent years, the locations of vehicles are frequently submitted for authentication during accessing location-based services (LBS), which makes it easier for attackers to launch attacks by threaten the location privacy of vehicles. Moreover, the rapid deployment of electric vehicles requires location privacy-preserving to be more energy-efficient. In this paper, we proposed a novel energy-efficient location privacy preserving (ELPP) scheme in vehicular networks using social intimate fogs (SIFs). ELPP is based on three novel techniques. First, ELPP deal with large bursts of LBS requests by transferring them to their SIF. The key insight is that computing efficiency for location privacy preserving can be gained at the price of some communication delay. Second, ELPP introduces a travel plan-based LBS content pre-caching mechanism, which enables quick and exible retrieval and distribution of the LBS contents. Third, to guarantee confidentiality, ELPP randomly encrypted the data by access control encryption. ELPP is deployable on existing devices, without modifying protocols in vehicular networks. We present an implementation case of ELPP and validate that it is energy-efficient, fast, and secure.

Community structure is an important mesoscale topological characteristic of complex networks, which is significant for understanding structural features and organizational functions in networks. Local expansion methods have been proved to be efficient and effective for community detection. However, it has been shown that there are inherent drawbacks for these methods to uncover overlapping communities. Most methods are sensitive to initial seeds and built-in parameters, while others are inadequate to reveal the pervasive overlaps. In this paper, we propose a new local expansion method for uncovering overlapping communities based on structural centrality. The key idea of our approach is to locate structural centers of communities with the structural centrality and then expand these structural centers with a weighted strategy and a local search procedure. Experimental results both on artificial and real-world networks demonstrate that our method is effective and promising in term of finding overlapping community structures. We also show that our local expansion strategies are efficient in uncovering cohesive clusters and producing stable clustering results.

We have demonstrated a scheme to realize the generation of frequency-sextupled signals with low phase noise (PN). The structure is based on a multimode optoelectronic oscillator (OEO) and two cascaded Mach-Zehnder modulators (MZMs). A drive source signal is modulated by the MZM1, which is biased at the minimum transmission point (MITP); the generated first-order sidebands are injected into the multimode OEO, which is contributed by the MZM2, the single-mode fiber, the photodetector, and the amplifier. The third-order sidebands are generated by the MZM2, which is also biased at the MITP. The modulated signal is then divided into reflection components and transmission components by a fiber Bragg grating (FBG). The reflection components, including the first-order sidebands, are sent to the photodetector and fed back to the MZM2 to form an optoelectronic hybrid loop. The transmission components, including the third-order sidebands, are heterodyned by another photodetector to generate a frequency-sextupled signal. Compared with the drive signal, the generated signal has a lower PN due to the selection of the oscillation mode. In the experiment, a frequency-sextupled signal from 15 to 24 GHz is generated by a drive signal, which is tuned from 2.5 to 4 GHz. The PN of the generated signal at 24 GHz is -103.6 dBc/Hz@10 kHz; 14.6 dB PN reduction is realized.