Search

A novel paradigm named Wireless Powered Mobile Edge Computing (WP-MEC) emerges recently, which integrates Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) technologies. It enables mobile clients to both extend their computing capacities by task offloading, and charge from edge servers via energy transmission. Existing studies generally focus on the centralized design of task scheduling and energy charging in WP-MEC networks. To meet the decentralization requirement of the near-coming 6G network, we propose an online learning algorithm for computation offloading in WP-MEC networks with a distributed execution manner. Specifically, we first define the delay minimization problem by considering task deadline and energy constraints. Then, we transform it into a primal-dual optimization problem based on the Bellman equation. After that, we design a novel neural model that learns both offloading and time division decisions in each time slot to solve the formulated optimization problem. To train and execute the designed algorithm distributivity, we form multiple learning models decentralized on edge servers and they work coordinately to achieve parameter synchronization. At last, both theoretical and performance analyses show that the designed algorithm has significant advantages in comparison with other representative schemes. [ABSTRACT FROM AUTHOR]

The explosive growth of mobile devices promotes the prosperity of novel mobile applications, which can be realized by service offloading with the assistance of edge computing servers. However, due to limited computation and storage capabilities of a single server, long service latency hinders the continuous development of service offloading in mobile networks. By supporting multi-server cooperation, Pervasive Edge Computing (PEC) is promising to enable service migration in highly dynamic mobile networks. With the objective of maximizing the system utility, we formulate the optimization problem by jointly considering the constraints of server storage capability and service execution latency. To enable dynamic service placement, we first utilize Lyapunov optimization method to decompose the long-term optimization problem into a series of instant optimization problems. Then, a sample average approximation-based stochastic algorithm is proposed to approximate the future expected system utility. Afterwards, a distributed Markov approximation algorithm is utilized to determine the service placement configurations. Through theoretical analysis, the time complexity of our proposed algorithm is linear to the number of users, and the backlog queue of PEC servers is stable. Performance evaluations are conducted based on both synthetic and real trace-driven scenarios, with numerical results demonstrating the effectiveness of our proposed algorithm from various aspects. [ABSTRACT FROM AUTHOR]

The rapid development of IoMT facilitates pervasive healthcare networks. However, numerous MUs lead to deficient wireless channel and computation resources. Edge computing based healthcare system is appreciated as a satisfactory paradigm to solve this obstacle. Based on the physical bound of WBANs, the IoMT is separated into two sub-networks, that is, intra- WBANs and beyond-WBANs. Considering the characteristics of healthcare systems, the cost of MUs relies on medical urgency, AoI and energy dissipation. In intra-WBANs, the wireless channel resource allocation problem is modeled by a cooperative game. The Nash bargaining solution is utilized to obtain the unique Pareto optimal point. In beyond-WBANs, MUs are considered to be rational and potentially selfish. Thus, a non-cooperative game is formulated to minimize the system-wide cost. To demonstrate the effectiveness of our proposed solution, performance evaluations are conducted with respect to the system-wide cost and the number of MUs benefiting from edge computing. Finally, several research challenges and open issues are discussed for further work. [ABSTRACT FROM AUTHOR]

The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system. [ABSTRACT FROM AUTHOR]

Although modern transportation systems facilitate the daily life of citizens, the ever-increasing energy consumption and air pollution challenge the establishment of green cities. Current studies on green IoV generally concentrate on energy management of either battery-enabled RSUs or electric vehicles. However, computing tasks and load balancing among RSUs have not been fully investigated. In order to satisfy heterogeneous requirements of communication, computation and storage in IoVs, this article constructs an energy-efficient scheduling framework for MEC-enabled IoVs to minimize the energy consumption of RSUs under task latency constraints. Specifically, a heuristic algorithm is put forward by jointly considering task scheduling among MEC servers and downlink energy consumption of RSUs. To the best of our knowledge, this is a prior work to focus on the energy consumption control issues of MEC-enabled RSUs. Performance evaluations demonstrate the effectiveness of our framework in terms of energy consumption, latency and task blocking possibility. Finally, this article elaborates some major challenges and open issues toward energy-efficient scheduling in IoVs. [ABSTRACT FROM AUTHOR]

As an application of “smart transport” for Internet of Things, Internet of Vehicle (IoV) has emerged as a new research field based on vehicular ad hoc networks (VANETs). With the development of smart vehicles and the integration of sensors, applications of traffic management and road safety in large-scale IoV systems have drawn great attentions. By sensing events occurred on roads, vehicles can broadcast messages to inform others about traffic jams or accidents. However, the store-carry-and-forward transmission pattern may cause a large transmission delay, making the implementation of large-scale traffic management difficult. In this paper, we put forward a feasible solution to minimize the response time for traffic management service, by enabling real-time content dissemination based on heterogeneous network access in IoV systems. We first design a crowdsensing-based system model for large-scale IoV systems. Then, a cluster-based optimization framework is investigated to provide timely responses for traffic management. Specifically, we estimate the message transmission delay by stochastic theory, which can provide a guideline for the next-hop relay selection in our delay-sensitive routing scheme. Furthermore, network performances are evaluated based on two city-road maps, and performance metrics, containing average delivery delay, average delivery ratio, average communication cost, and access ratio, demonstrate the superiority of our system. Finally, we conclude our work and discuss the further work. [ABSTRACT FROM AUTHOR]

The proliferation of IoTs beside the emergence of various cloud services push the horizon of edge computing. By offering cloud capabilities at the network edge closer to mobile devices, edge computing is a promising paradigm to resolve several vital challenges in IoTs, such as bandwidth saturation, energy constraints, low latency transmission, and data security and privacy. To provide a comprehensive understanding of edge computing supported by the integration of IoTs and cloud computing, that is, CoTs, this article first discusses some distinct research directions in CoTs with respect to edge computing. Given the significance of energy efficiency and sustainability of edge deployment in CoTs, we put forward a green and sustainable virtual network embedding framework for cooperative edge computing in wireless-optical broadband access networks. Specifically, we leverage a reliability function to confirm the number of backup edge devices, and embed virtual networks onto the suitable edge devices in CoTs. Finally, several research challenges and open issues are discussed. [ABSTRACT FROM AUTHOR]

The automated diagnosis helps us better understand the complex landscape of diseases, leading to more effective, early and reliable medical diagnosis and therapy. The human–machine interactions in healthcare delivery relying on automated cyber-physical systems (ACPSs) play an important role in the automated diagnosis. Currently, the multicore accelerator used for ACPS has utilized the network-on-chip (NoC) for personalized healthcare. However, the discrete cores based on NoC are affected by limited computation speed, since the data have to pass through an electrical interconnect. In this paper, we propose a novel optical NoC (ONoC) solution of designing discrete cores to quickly understand biomarkers for early detecting abnormal pathophysiology, such as the deviation from the protein’s native state. We analyze the performance of our ONoC-based ACPS accelerator for personalized healthcare by virtue of the tested proteins widely adopted in the lattice protein model. Our mathematical analysis and simulation results demonstrate that: 1) the chip area becomes smaller than a traditional design, which makes the personalized healthcare product more convenient; 2) the computation speed is promoted, resulting in the rapid understanding of biomarkers; and 3) we improve the data transmission reliability through accurately capturing the photonic effect so that desirable human–machine interactions can be guaranteed. Note to Practitioners—We design an on-chip hardware accelerator for automated diagnosis and personalized healthcare by predicting biological protein folding. The simulation results based on the lattice protein model can well guide the practitioners to design a more convenient and reliable product quickly detecting the biomarker, such as the deviation from the protein’s native state. [ABSTRACT FROM AUTHOR]

In sharing economy, people offer idle social resources to others in a sharing manner. Through community-based online platforms, the people offering services can earn commission while others can enjoy a better life via renting social resources. Consequently, the value-in-use of services is expectedly strengthened within the unit time, although the total amount of social resources remains constant. Influenced by sharing economy, some famous companies have developed intelligent systems to analyze the most appropriate coincidence between citizens’ idle supply and renting demand from numerous data sets. However, the big data analysis of the optimal service-demand matching usually runs on the traditional multiprocessors equipped in intelligent systems, so-called “system-on-chip.” In this paper, we design a novel computer architecture—the accelerator based on optical network-on-chip (ONoC)—to further speed up the matching between citizens’ offer and demand in sharing economy. Our ONoC-based accelerator is able to quickly calculate the optimal service-demand matching by processing computation tasks on parallel cores, i.e., task-core mapping. In addition, to improve the accelerator reliability, the assorted task-core mapping algorithm is also designed. The extensive simulation results based on real trace file demonstrate the effectiveness of our system and algorithm. Note to Practitioners—Sharing economy is of great importance for realizing green consumption and sustainable development in our human society. Sharing economy enterprise calls for intelligent system design for service-demand matching in the current big data era. In this paper, we design the accelerator based on ONoC to further speed up the matching between citizens’ offer and demand in sharing economy. By processing computation tasks on parallel cores using our algorithm, the task-core mapping can be performed with high speed and reliability. The simulation results—based on the trace file of Amazon Mechanical Turk—can well guide the practitioners to design a more clever and reliable product by quickly calculating the optimal service-demand matching. [ABSTRACT FROM AUTHOR]

As an emerging platform based on ITS, SIoV is promising for applications of traffic management and road safety in smart cities. However, the endto- end delay is large in store-carry-and-forwardbased vehicular networks, which has become the main obstacle for the implementation of large-scale SIoV. With the extensive applications of mobile devices, crowdsensing is promising to enable realtime content dissemination in a city-wide traffic management system. This article first provides an overview of several promising research areas for traffic management in SIoV. Given the significance of traffic management in urban areas, we investigate a crowdsensing-based framework to provide timely response for traffic management in heterogeneous SIoV. The participant vehicles based on D2D communications integrate trajectory and topology information to dynamically regulate their social behaviors according to network conditions. A real-world taxi trajectory analysis-based performance evaluation is provided to demonstrate the effectiveness of the designed framework. Furthermore, we discuss several future [ABSTRACT FROM AUTHOR]

How to put fifth-generation (5G) into the commercial service by 2020 is still challenging. Since physical-layer network coding (PNC) does not bring unnecessary redundancy and offers satisfactory end-to-end services, it is promising to provide network services in 5G networks. Existing works on PNC mainly focus on the case that two packets are simultaneously sent by a node pair for information exchange, and the transmitted signals are superposed and encoded at a relay. To further exploit the potential performance improvement of PNC, this study aims at encoding multiple packets through jointly processing multiple superposed signals sent by diverse node pairs. Particularly, we formulate the problem of joint-encoding method design as a binary integer programming model and propose a low-complexity solution based on the idea of filling a Latin rectangle. We find that network throughput enhancement does not accompany the increasing number of coded node pairs, and some node pairs cannot be encoded together since they fail to decode the resulting encoded packet. Therefore, the decodability-based node pair grouping scheme is further investigated. Simulation results in various scenarios show that the proposed low-complexity scheme performs neck to neck to the optimum one with high computational complexity. [ABSTRACT FROM PUBLISHER]

The ever-increasing transmission requirements of quality of service (QoS)-sensitive applications, especially real-time multimedia applications, can hardly be met by the single path routing protocols. Multipath transmission mechanism is a feasible approach to provide QoS for various applications. On the basis of the general framework of multipath transport system based on application-level relay, we present a relay path allocation scheme, whose goal is to select suitable relay paths, while balancing the overlay traffic among the different domains and relayers. With the application-layer traffic optimization service under the standardization within the Internet Engineering Task Force (IETF), the controller has the topology-aware ability to allocate relay paths with excellent routing performance. To further develop the universality of our method, the controller perceives transmission performance of relay overlay network through relayers' performance detection processes and, thus, has the application-aware ability to allocate relay paths with excellent transmission performance for different applications by consulting application-specific transmission metrics. Simulation results demonstrate that the proposed relay path allocation algorithm performs well in allocating superior relay paths and can balance the distribution of overlay traffic across domains in different network situations. [ABSTRACT FROM AUTHOR]

Vehicular transportation is an essential part of modern cities. However, the ever increasing number of road accidents, traffic congestion, and other such issues become obstacles for the realization of smart cities. As the integration of the Internet of Vehicles and social networks, vehicular social networks (VSNs) are promising to solve the above-mentioned problems by enabling smart mobility in modern cities, which are likely to pave the way for sustainable development by promoting transportation efficiency. In this article, the definition of and a brief introduction to VSNs are presented first. Existing supporting communication technologies are then summarized. Furthermore, we introduce an application scenario on trajectory data-analysis-based traffic anomaly detection for VSNs. Finally, several research challenges and open issues are highlighted and discussed. [ABSTRACT FROM PUBLISHER]

Socially aware networking (SAN) is a new communication paradigm, in which the social characteristics of mobile nodes are exploited to improve the performance of data distribution. In SAN, mobile carriers may exhibit selfish behaviors and refuse to relay messages for others for various reasons, such as limited resources (e.g., buffer, energy, and bandwidth) or social relationships. Several incentive schemes have recently been investigated to stimulate selfish users for cooperation in data forwarding. However, a majority of the existing methods have not fully studied nodes' social relationships in their selfish behaviors. In this paper, we propose a copy adjustable incentive scheme (CAIS), which adopts the virtual credit concept to stimulate selfish nodes to cooperate in data forwarding. In CAIS, we consider a network in which the nodes are divided into certain communities based on their social relationships. Then, we apply two types of credits, i.e., social credit and nonsocial credit, to reward the nodes when they relay data for other nodes inside their community or outsiders, respectively. Based on our mechanism, the number of messages a node can replicate to other nodes is adjusted according to its cooperation level and earned credits. To further improve the performance of CAIS, a single-copy data replication policy is employed, which manages the credit distribution of each node according to its available resources. The results of our extensive experiments using both synthetic and trace-driven simulations illustrate that CAIS copes well with node selfishness in community-based networks and outperforms other benchmark protocols with high data delivery ratio, low communication overhead, and short data delivery latency. [ABSTRACT FROM PUBLISHER]

It is a fact that energy consumption is a key issue in wireless sensor networks. In order to improve the energy efficiency of network utilization, distributed power control methods are advocated. However, the sensors are deployed randomly, which results in the channel gains that are difficult to be obtained; thus, how to estimate channel gains accurately to increase node communication efficiency and reduce energy consumption is the motivation of our work. In this paper, a cooperative and distributed algorithm based on diffusion least mean square (LMS) is firstly proposed to estimate channel gain by utilizing the equality and collaboration of sensor nodes. Its objective is to enhance the accuracy and convergence of the estimation process. Because the presented globe-based scheme is unrealistic for practical utilization, two distributed estimation algorithms are investigated for different network scenarios, namely, exchange diffusion combine LMS and exchange combine diffusion LMS. Simulation results demonstrate the effectiveness of our methods. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Load-Balancing and Coding-Aware Multicast (LCM) protocol for mobile ad hoc networks. In LCM protocol, a new route metric named Expected Transmission Time with Coding and Load Balancing (ETTCL) is presented at first, aiming at effectively selecting the path not only that has the possible coding opportunity but also where overflow due to network overload can be prevented. Then, we describe the route discovery phase by constructing the node-disjoint multicast tree on the basis of ETTCL and employ network coding to encode the data flows for route maintenance. The effectiveness of LCM protocol is simulated and analyzed by NS-2, which shows that this protocol has good performance in reducing average end-to-end delay and control overhead and can improve packet delivery ratio compared with the existing protocol. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper focuses on the spectrum sensing mechanisms, which could improve network throughput through the sensing strategy optimization and cooperative spectrum sensing methods. In order to guarantee an integrated and effective research, we take the whole channel scenarios into consideration, i.e., Single Secondary user with Single and Multiple Channels (SSSC and SSMC), Multiple Secondary users with Single and Multiple Channels (MSSC and MSMC). Moreover, according to the specific feature of each scenario, different sensing methods are adopted, i.e., optimal sensing period to maximize network throughput for SSSC, a novel sensing method to minimize searching time for SSMC, partial cooperative spectrum sensing mechanism for MSMC, and setting a spectrum pool in the fusion center to record the channel states for MSMC. Simulation results demonstrate that our methods can improve spectrum efficiency, network throughput and channel utilization, especially when the spectrum is utilized inadequately. [ABSTRACT FROM AUTHOR]

Highlights: [•] Our work has considered the QoS factors and constructed a reward function to model the QoS properties. [•] Our work has considered MT’s velocity, network access cost and switching cost for vertical handoff decision. [•] We have applied G1 and entropy methods to calculate the weight of each QoS factor in the reward function. [•] By taking the MT’s velocity into consideration, our work can avoid unnecessary handoffs. [ABSTRACT FROM AUTHOR]

Abstract: Physical-layer network coding (PNC) is a promising approach for wireless networks: it allows nodes to transmit simultaneously and encode packets by superposing signals at the physical layer, which can improve throughput. Because of the difficulty in scheduling simultaneous transmissions, we propose an opportunistic scheduling algorithm in the media access control (MAC) layer and analyze the achievable throughput of the Denoise-and-Forward (DF) PNC scheme for both symmetric and asymmetric channel statuses, where random errors may occur for both BPSK and QPSK modulation techniques. Simulation results show that our algorithm cannot only improve throughput but can also adapt to varying channel conditions. It is further revealed that for the same throughput, our algorithm is more power-efficient with an asymmetric channel status than with a symmetric channel status. [Copyright &y& Elsevier]

Abstract: Wireless mesh network (WMN) is a promising solution for last mile broadband internet access. Mesh nodes or mesh routers are connected via wireless links to form a multi-hop backbone infrastructure and improving throughput is the primary goal. While previous works mainly focused on either link level protocol design or complex mathematical model, in this paper, we investigate the performance gains from jointly optimizing scheduling and routing in a multi-radio, multi-channel and multi-hop wireless mesh network. Then, two optimization objectives are addressed by considering wireless media contention and spatial multiplexing. The first objective is to maximize throughput by exploiting spatial reuse while the second one is to ensure fairness among different links. We design a cross-layer algorithm by considering both MAC layer and network layer. Simulation results show that our joint optimization algorithm can significantly increase throughput as well as fairness. [Copyright &y& Elsevier]

Vehicular social networks (VSNs) have attracted the research community due to its diverse applications ranging from safety to entertainment. Social vehicles standing for private cars and floating cars standing for taxis are two important components of VSN. However, the lack of social vehicles data causes some factors are neglected including social aspects and macroscopic features, which blocks researching social attributes of vehicles in VSN. Generating a realistic mobility dataset for VSN validation has been a great challenge. In this paper, we present the detailed procedure to generate social vehicular mobility dataset from the view of floating car data, which has the advantage of wide universality. First, through the deep analysis and modeling of the dataset of floating cars and combining with the official data, we predict the origin–destination (OD) matrix of social vehicles with the gravity model, and then calibrate the OD matrix with the average growth factor method. Second, we construct network description after editing the road network. Third, we use simulation of urban mobility to reproduce the scenario in view of microsimulation by generating the mobility dataset of social vehicles based on floating car data and urban functional areas. At last, we prove the effectiveness of our method by comparing with real traffic situation in Beijing. The generated mobility model may not accurately represent the mobility of social vehicles in few spots, such as train station or airport, however, exploiting figures and facts of transportation in the city have been considered in the study to calibrate the model up to maximum possible realization. [ABSTRACT FROM PUBLISHER]

In this paper, we focus on physical-layer network coding (PNC) used in multiple-input multiple-output (MIMO) Y channels, where K users simultaneously exchange messages with each other via a shared relay. For MIMO-PNC, signal space alignment for network coding (SSA-NC) is a promising precoding method and can achieve K( K-1) degrees of freedom if certain conditions can be satisfied. An important one is that the number of relay antennas is less than twice of the user antennas, that is, the dimensionality constraint of SSA-NC. In this paper, we focus on a particular scenario where the dimensionality constraint is not satisfied. In order to resolve this problem, we develop a scheme of jointly selecting relay antennas and precoding vectors. In particular, a novel precoding and detection method is presented to alleviate the interference caused by simultaneous transmissions. Through analyzing the symbol error rate performance of MIMO-PNC, we establish the relationship between the achievable flow diversity gain and the optimal number of the antennas, and a precoding selection method is designed under a given relay antenna configuration. Finally, we propose a joint scheme to select the optimal relay antenna configuration and precoding vectors. Numerical results demonstrate that the presented scheme can effectively support MIMO-PNC in various scenarios. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Mobile social networks include a large number of social members who forward messages cooperatively. However, spammers post links to viruses and advertisements, or follow a large number of users, which produces many misleading messages in mobile social networks. In this paper, we propose an adaptive social spammer detection (ASSD) model. We build a spammer classifier by using a small number of labeled patterns and some unlabeled patterns. The prediction accuracy is high compared with some conventional supervised learning methods. Moreover, the time and energy required to label the identity of social members are reduced by applying ASSD. Because social spammers frequently change their behavior to deceive the spammer detection model, an incremental learning method is designed to update the spammer detection model adaptively, without retraining. We evaluate ASSD by comparing it with other supervised and semi-supervised machine learning methods using the Social Honeypot Dataset. Experimental results show that the proposed model outperforms the baseline methods in terms of recall and precision. Additionally, ASSD maintains a high detection accuracy by adaptively updating the model with newly generated social media data. [ABSTRACT FROM AUTHOR]

Highlights: [•] We consider a multi-hop network situation instead of traditional two-hop topologies in OFDMA network. [•] We adopt Tabu searching algorithm to reduce the possibility of choosing low-quality subcarriers. [•] Our methods can combine the link groups with subcarrier groups in a global optimization method. [•] Our proposed algorithms can not only increase resource utilization but also realize dynamic selection of each link. [ABSTRACT FROM AUTHOR]

Sixth generation (6G) wireless communication networks are expected to provide ultra-low latency and energy-efficient services for global users. The paradigm of wireless powered mobile edge computing (WP-MEC) provides a feasible framework to real-ize the above purposes by leveraging the advantage of both wireless power transfer and MEC. Howev-er, massive network traffic and control information overburden the centralized server. In this article, we integrate federated learning and deep reinforcement learning with WP-MEC to jointly optimize computing and communication resources. Specif-ically, we design an online learning framework to schedule computation tasks of mobile clients, where the learning model can be distributed over edge servers, and parameter synchronization happens on the cloud. The superiority of the designed frame-work is evaluated and compared with benchmarks in terms of average task completion delay and task completion ratio. Finally, we discuss some research challenges and opportunities for future WP-MEC. [ABSTRACT FROM AUTHOR]

Design flaws and unseen vulnerabilities in the photonic network-on-chip (PNoC) have made it a focal point of potential security attacks. Previous works well investigate remote attacks launched outside from electrical NoCs. However, it is still a challenge to find possible threats and efficient countermeasures for PNoCs. In PNoCs, for example, micro resonators can be maliciously controlled by a remote attacker to miss expected tuning function, thus leading to data loss and information disclosure. In this article, we first summarize remote denial of service attacks caused by hardware Trojan insertion and timing channel in NoCs. We then show that these two kinds of remote attacks are also effective threat models in PNoCs. We design, for the first time, countermeasures protecting against threat models in the hybrid wavelength and mode division multiplexing PNoC. Finally, we propose the challenging issues of enhancing the safety of the 3D PNoC under new potential threats. [ABSTRACT FROM AUTHOR]

Automation in smart manufacturing depends on many parties in the supply chain and distribution networks. A smart manufacturer needs to track the location and state of materials from its suppliers and products to its customers/distributors to manage the corresponding production schedule. The need for such information can be fulfilled by the use of Industrial Internet of Things (IIoT) devices installed at each respective party. However, even though all parties may trust one another, they have confidential data produced by the IIoT devices that are not allowed to be shared with other parties, or only allowed to be exchanged with certain parties. Even though the data are not confidential, they may still be required to share partial data based on the needs of each party. On the other hand, all parties have their independent systems that differ from each other, which leads to complex integration. Regarding those situations, a private-blockchain-based IIoT is promising to bridge the need for product and material tracking information exchange between parties while maintaining the respective confidentiality. [ABSTRACT FROM AUTHOR]

Abstract With the increasing conflict between population growth and limited environmental resources, building intelligent and efficient data transmission networks is crucial to cleaner production in smart and sustainable cities. More and more people are eager to exchange all kinds of information in urban life through an efficient network. Therefore, designing a smart data distribution and transmission mechanism can not only improve people's quality of life but also pave the way for reducing environmental pollution. Optical networks have been widely used in the core network due to high-capacity characteristics. However, in inter-data-center (Inter-DC) optical networks, the coexisted heterogeneous optical devices and multi-granularity network resources bring new challenges to network management and traffic scheduling. Recently, software-defined network (SDN) and network function virtualization (NFV) have been introduced to enable the unified control for improving network agility and automation. Thus for software-defined multi-granularity Inter-DC optical networks, we propose a list of online service provisioning strategies. Moreover, in terms of the network controller, the novel routing applications are also developed by extended OpenFlow protocol. Finally, we experimentally demonstrate the overall feasibility of proposed solutions in a semi-practical platform. Numerical simulations conducted to quantitatively evaluate the algorithm efficiency also verify the superiorities of improving spectrum efficiency and reducing service blocking probability. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet, the world has come into a new era of network interaction. Internet development in China makes various kinds of software available to users, by which relatives, close friends, and partners can achieve real-time interactions. It is acknowledged that the recommended issues and persons are the most important parts for user experience. This study concentrates on how to provide a recommendation for users with a common interest in Microblog. We first illustrate some recommended algorithms based on data mining, and the implementation process of friend-recommended system is then stated. After that, by integrating the characteristics of the user information, the forwarding and comments on information to associated users, we present an improved Apriori-based algorithm to rank the recommended information with confidence interval. Simulation results demonstrate the superiority and efficiency of our method. [ABSTRACT FROM AUTHOR]

This paper presents a bibliographic analysis of Nature articles based on altmetrics. We assess the concern degree of social users on the Nature articles through the coverage analysis of Twitter and Facebook by publication year and discipline. The social media impact of a Nature article is examined by evaluating the mention rates on Twitter and on Facebook. Moreover, the correlation between tweets and citations is analyzed by publication year, discipline and Twitter user type to explore factors affecting the correlation. The results show that Twitter users have a higher concern degree on Nature articles than Facebook users, and Nature articles have higher and faster-growing impact on Twitter than on Facebook. The results also show that tweets and citations are somewhat related, and they mostly measure different types of impact. In addition, the correlation between tweets and citations highly depends on publication year, discipline and Twitter user type. [ABSTRACT FROM AUTHOR]

In Intensity-Modulation Direct-Detection (IM/DD) Orthogonal Frequency Division Multiplexing Passive Optical Networks (OFDM-PONs), aside from Subcarrier-to-Subcarrier Intermixing Interferences (SSII) induced by square-law detection, the same laser frequency for data sending from Optical Network Units (ONUs) results in ONU-to-ONU Beating Interferences (OOBI) at the receiver. To mitigate those interferences, we design a Low-Density Parity Check (LDPC)-coded and spectrum-efficient upstream transmission system. A theoretical channel model is also derived, in order to analyze the detrimental factors influencing system performances. Simulation results demonstrate that the receiver sensitivity is improved 3.4 dB and 2.5 dB under QPSK and 8QAM, respectively, after 100 km Standard Single-Mode Fiber (SSMF) transmission. Furthermore, the spectrum efficiency can be improved by about 50%. [ABSTRACT FROM AUTHOR]

Evaluating the impact of a scholarly article is of great significance and has attracted great attentions. Although citation-based evaluation approaches have been widely used, these approaches face limitations e.g. in identifying anomalous citations patterns. This negligence would inevitably cause unfairness and inaccuracy to the article impact evaluation. In this study, in order to discover the anomalous citations and ensure the fairness and accuracy of research outcome evaluation, we investigate the citation relationships between articles using the following factors: collaboration times, the time span of collaboration, citing times and the time span of citing to weaken the relationship of Conflict of Interest (COI) in the citation network. Meanwhile, we study a special kind of COI, namely suspected COI relationship. Based on the COI relationship, we further bring forward the COIRank algorithm, an innovative scheme for accurately assessing the impact of an article. Our method distinguishes the citation strength, and utilizes PageRank and HITS algorithms to rank scholarly articles comprehensively. The experiments are conducted on the American Physical Society (APS) dataset. We find that about 80.88% articles contain contributed citations by co-authors in 26,366 articles and 75.55% articles among these articles are cited by the authors belonging to the same affiliation, indicating COI and suspected COI should not be ignored for evaluating impact of scientific papers objectively. Moreover, our experimental results demonstrate COIRank algorithm significantly outperforms the state-of-art solutions. The validity of our approach is verified by using the probability of Recommendation Intensity. [ABSTRACT FROM AUTHOR]