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We propose a four-state reference-frame-independent quantum key distribution (RFI-QKD) protocol with the heralded pair-coherent source (HPCS). We investigate the performance of the proposed protocol and simulation results show that our protocol can achieve a high key generation rate in long-distance transmission, taking source flaws and statistical fluctuations into consideration. Although fewer states are used, this protocol not only has a higher key generation rate at the same transmission distance but also a longer transmission distance with the same secure communication compared with the original six-state RFI-QKD protocol using the weak coherent source (WCS).

Considering the particularity of fragile people with reduced mobility, we design a nonintrusive platform called EVAL cane to assist and monitor the user’s walking. On the one hand, it has important walking-assistance functions, such as obstacle warning and fall detection. On the other hand, it collects the user’s long-term walking data in the background, which may be the potential physical-health-assessment data. Compared with an ordinary cane, the above two functions are implemented without any burden on the user’s life. In addition, we have considered the authentication security for that the walking data are private. To this end, we propose a novel user-identification scheme, leveraging the user walking gait data collected by EVAL cane. To the best of our knowledge, it is the first time that the gait information collected by cane is used for reinforcing monitoring system security. This scheme does not require the user to remember and enter any identity (such as a password), which is user-friendly for fragile people. In the scheme, a statistics-based rough gait feature-extraction method is put forward at first. Then, in order to improve the identification precision, we design a performance-based feature-deletion (PFD) algorithm to remove the bad features. Finally, a minimum Mahalanobis distance classifier is used. Experimental results show that the user-identification rate without the PFD algorithm can reach as high as 90.48%. In addition, the PFD algorithm further improves the performance by about 6%, reaching an excellent result of 96.43%.

In presence of a high traffic load, not all offered traffic can be satisfied at all time. Luckily, several kinds of services (e.g. video, file transfer) are elastic because they can be degraded and accepted with a lower bit-rate, corresponding to a lower quality of service (QoS) level. In this paper, we aim at maximizing network revenue leveraging elastic service provisioning. To tackle this problem, we propose an integer linear programming model as well as a decomposition method, which selects a QoS level and provisions a lightpath for elastic request. Meanwhile, in order to exploit the spectrum resources of elastic services under a progressive network load, we propose an auto-degrading provisioning scheme to trigger the network reconfiguration for existing lightpaths in an effort to increase the total network revenue. Simulation results validate the revenue improvement by supporting elastic service provisioning scheme in the scenarios of static and progressive network load.

In the network planning of software-defined optical networks (SDONs), the control plane design is of great importance because it directly affects the performance and reliability of network control and management (NC&M). In this article, we consider the planned physical-layer attacks from a rational attacker, which can analyze the control plane of an SDON and target its attacks to the most vulnerable part. To address such attacks, we model the control plane design as a bilevel optimization, where the upper-level optimization is for the network planner to design the control plane whose vulnerability to planned attacks is minimized, while the lower-level optimization is for the attacker to plan its attacks such that the control plane can be disturbed as severely as possible. We first develop two approaches to solve the bilevel model exactly. Specifically, we first leverage the cutting plane method to solve it directly, and then transform it into a single-level mixed integer linear programming (MILP) model with the Bellman method for problem solving. To improve the time efficiency for large-scale problems, we also propose a polynomial-time approximation algorithm based on linear programming (LP) relaxation and randomized rounding. Extensive simulations with various physical topologies verify the effectiveness of our proposals.

The fast development of cloud computing and Big Data applications has promoted virtualization technologies such as network function virtualization (NFV), which in turn dramatically increased the amount of sensitive data being transmitted over the optical networks for datacenter interconnections (DCIs). To ensure the physical-layer security in DCIs, people have developed optical transport network (OTN) encryption technologies, i.e., leveraging high-speed encryption cards (ECs) to encrypt OTN payload frames. Although experimental studies have confirmed the benefits of ECs in terms of line-speed processing, low latency, and small encryption overhead, the problem of how to utilize them to build a secure packet-over-optical network with high cost-effectiveness has not been explored yet. In this paper, we study how to realize cost-effective and security-aware multilayer planning in a packet-over-optical network that covers both trusted and untrusted zones, in consideration of OTN encryption. We first formulate an integer linear programming (ILP) model to minimize the total capital expenditure (CAPEX) of the multilayer planning, which includes the costs of OTN linecards (LCs), ECs, and bandwidth resources, and solve the optimization exactly. Then, we prove the $\mathcal {NP}$ -hardness of the multilayer planning, and to reduce the time complexity, we propose a column generation (CG) model and design a more time-efficient approximation algorithm based on it. Our simulation results confirm the performance and advantages of our CG-based proposal, i.e., it is much more time-efficient than solving the ILP directly, and outperform the existing heuristic in terms of total CAPEX and costs of used LCs and ECs.

In a digital communication system, if the channel has a random carrier frequency shift or two nodes that are in motion with each other, the communication channel must have a frequency shift. Therefore, the received signal can only be non-coherent demodulation at the receiver. Double-Differential coding is a commonly used non-coherent demodulation method. The unknown frequency and phase changes can be eliminated through three consecutive signal samples. Since the Double-Differential coding take advantage of three consecutive signal samples, it is only suitable for slow fading transmission channels. During the observation time of the three signal samples, the channel characteristics change slowly and linearly. The combination of the Double-Differential coding and the Direct Sequence Spread Spectrum technology can effectively improve the ability of the Direct Sequence Spread Spectrum to resist Doppler frequency offset. However, due to the limitation of the DSSS system, the ability of the Double-Differential coding to cancel the Doppler frequency offset in the DSSS system is limited. Its ability to resist Doppler is related to the duration of spread spectrum chips.

By using virtualization technology, Network Function Virtualization (NFV) decouples the traditional Network Functions (NFs) from dedicated hardware, which allows the software to progress separately from the hardware. One of the major challenges for NFV deployment is to map Service Function Chains (SFCs), which are chains of sequenced Virtual Network Functions (VNFs), onto the physical network components. Meanwhile, network availability faces the threats of various natural disasters, one of which makes all network devices in the Disaster Zone (DZ) fail if it occurs. Thus, it is critical to establish an efficient disaster protection scheme for NFV deployment.In this paper, we introduce a novel disaster protection scheme for SFC embedding using multi-path routing. The major advantage of this scheme is to cut at least half of the reserved bandwidth on the backup path by balancing the SFC traffic load on multiple simultaneous DZ-disjoint working paths. The studied problem involves VNF entity placement, SFCs routing, content splitting and protection mechanisms. The objective is to minimize the network resource consumption, including bandwidth consumption for requests routing and computing resource for VNF execution. As we treat an optimization problem of multiple dimensions (i.e., NF placement, routing and protection), it is a challenging work to obtain the optimal solution. To this end, we propose a novel flow-based integer linear program (ILP) to model the SFC protection leveraging multi-path routing and the concept of layered graph. Numerical results demonstrate that our proposed multi-path based SFC protection strategy outperforms the traditional dedicated protection in terms of bandwidth and processing resources, saving up to 21.4% total network cost.

Natural disasters have challenged the survivability of Elastic Optical Inter-DataCenter Networks (EO-DCNs), and it is urgent to establish efficient disaster protection schemes. In this paper, we investigate the disaster-resilient service provisioning problem leveraging cooperative storage system (CSS). Instead of mirrored content backup on a single DC, our proposed CSS partitions a required content into no less than three fragments if possible, each of which is then stored on a DC located in different disaster zones. Accordingly, multi-path routing with the adaptive number of working paths to distinct DCs is employed to serve each request, while a protection path is computed to protect against a disaster failure. Our main objective is to jointly minimize the spectrum usage and maximal occupied frequency slot index (MOFI) subject to disaster resilience. Besides, we also expect to cut the content storage space. To this end, we propose for the first time a CSS-based dedicated end-to-content path protection (CDP), which allows service provisioning through multiple paths with the adaptive number of paths rather than a single path. This consequently reduces at least half of the reserved spectrum on the protection path. To find the optimal CDP strategy, we formulate the studied problem as an integer linear program (ILP) and then propose a fast heuristic algorithm. Observing the trade-off between the spectrum usage and content storage space, we further design a maximum-CDP (M-CDP), which generates the maximum number of working paths to reduce the content storage space. Simulations are conducted to compare the proposed schemes with the traditional protection strategy using mirrored storage and single-path routing. Numerical results demonstrate that the proposed CSS-based protection schemes enable to cut up to 21.6% of the spectrum usage and 15% of the content storage space.

Unmanned Aerial Ad-hoc Networks (UAANETs) pushed up UAVs’ cooperative tasks. For efficient cooperation, a fitting mobility pattern must be adopted, ensuring simple, flexible, and easy-manageable coordination. Indeed, UAANET faces an inescapable challenge due to the limited energy constraint, which significantly affects the tasks’ productivity and efficiency. Different from the literature, which only studied the impact of two classical factors affecting energy consumption, namely, communication protocols and computation, we highlight the impact of the temporal and spatial correlation involved in mobility models. Indeed, we consider a transitive relationship taking place between temporal and spatial correlation and energy consumption. Those correlation forms do affect the data loss ratio that, in turn, augments energy consumption due to the increased rate of data re-transmission, route re-discovery and maintenance, etc. On this basis, we assume the temporal and spatial correlation impact on energy consumption, which has been demonstrated and analyzed through numerical simulations.

In this paper, we propose a quality of service (QoS)-oriented adaptive virtual user association scheme for non-orthogonal multiple access (NOMA)- visible light communication (VLC) systems. Our schemes enables to surmount the throughput degradation caused by traditional synchronous NOMA multiplexing and the communication interruption due to low received QoS. Radio frequency configuration is proposed as a backup for VLC to ensure normal communication of users disconnecting from VLC, and the number of users participating in real-time association is adaptively updated. Orthogonal multiple access (OMA) is also incorporated with NOMA to further enhance the system performance. Numerical results demonstrate that compared with traditional NOMA and OMA methods, the proposed scheme could provide better network throughput and cumulative distribution function of user rate performance under perfect and imperfect successive interference cancellation in NOMA-VLC systems.

In this paper, we propose a novel network selection method oriented to users’ quality-of-experience (QoE) for indoor visible light communication (VLC) heterogeneous networks. The proposed method considers the difference between user requirements for different parameters and the actual performance of each candidate network in these reference indicators. To improve QoE, a new indicator named “benefit-cost-ratio (BCR)” is defined to represent the user demand under different businesses and assist in sorting alternatives to select the optimal network for access. Simulation results show that through the proposed network selection scheme, the candidate networks could be effectively ranked and adjusted according to user requirements.

During the transmission of the thermal radiation in the atmosphere, the radiant energy is attenuated due to physical processes such as refraction, absorption, and scattering, which causes a change in the detection capabilities of the thermal imaging system. As a key performance of thermal imaging system, the inspection of the operating range is of great significance to the theoretical research and practical application of thermal imaging systems. Actually, the environmental condition that does not match the specified test condition when testing the operating range which are not encountered. It's complex to judge whether the thermal imaging system meets the index requirements by the test result directly. By modeling the target, analyzing and comparing the spectral transmittance under different atmospheric conditions, the paper introduces a method for judging the operating range of the infrared thermal imaging system when inspecting environmental deviates from the specified environment proposed.

The secondary structure of protein has three-state and eight-state classification standards, and there exist certain correspondences and commonalities between the two. In order to make full use of the consistent relationship to improve protein secondary structure prediction, an ensemble method named TSAES is introduced, which enables the three-classification and eight-classification to correct each other to attain more reasonable. Based on the same considerations, the final Multi-Output Simultaneous Prediction deep model shorted for MOSP is further proposed. It first mines the feature scale of the protein sequence, and then leverages a multi-layer temporal convolution networks to effectively extract the short-range dependence and long-term information of the sequence. High-level feature representation is finally used to optimize the outputs of three-state and eight-state classifications simultaneously. Experimental results demonstrate that 38relation-aware methods proposed here averagely enhanced 0.73%, 0.48% for Q3 and Q8 accuracy upon 38relation-agnostic baseline model. Though our work, the effectiveness of relation utilization had been verified, hence a novel valuable thinking angle except model innovation when making accurate prediction have been provided. Moreover, the proposed methods could be easily transplanted to other models, exhibiting strong feasibility and practicality.

We study how to achieve cost-effective and security-aware multilayer planning for an optical transport network (OTN) that covers both trusted and untrusted zones and has the option to choose encryption solution deployment (ESD) architectures based on traffic condition. We first formulate an integer linear programming (ILP) model to solve the optimization exactly, and then propose a novel heuristic based on collapsed auxiliary graphs (CAGs) to have improved time-efficiency.

It is known that the design of the control plane (CP) is vital in the network planning for software-defined networking (SDN), while to improve throughput and enlarge geographical coverage, optical networks are commonly used as the physical infrastructure of SDN. However, physical-layer attacks can disrupt the operation of an optical network and thus complicate the CP design. In this work, we consider planned physical-layer attacks when designing the CP of an SDN that uses an optical network as its physical infrastructure. We first show that the CP design problem should be modeled as a bilevel optimization, where the network planner designs the CP with the minimum vulnerability to physical-layer attacks (i.e., the upper-level optimization), while the attacker plans and launches attacks to disrupt the designed CP (i.e., the lower-level optimization). Then, the bilevel mode is transformed into a mixed integer linear programming (MILP) model, which can solve the CP design problem exactly. We also propose a heuristic to tackle the problem time-efficiently.

International audience; The Visible Light Indoor Positioning System (VL-IPS) has been a popular research area recently. In VL-IPS, many localization methods have been proposed by leveraging the Received Signal Strength (RSS) based trilateration. However, the traditional RSS based trilateration localization (RSS-TL) method is very sensitive to the lighting environment and would results in a big localization error due to the presence of non-line-of-sight (NLOS) light signal. In light of this, we propose a novel NLOS-aware localization algorithm, namely Enhanced Fingerprinting-aided RSS-TL (EFP-RSS-TL). It permits to improve the localization accuracy for the corner regions of a room by eliminating the NLOS impact while keeping the same high accuracy for the room center. This is achieved by leveraging a RSS fingerprint database which records the line-of-sight (LOS) light power ratios beforehand. For validation purpose, we built a real VL-IPS platform and implemented the proposed algorithm. Experimental results show that the proposed NLOS-aware EFP-RSS-TL algorithm enables to reduce significantly the average positioning error (by up to 79%) compared to its counterparts. Besides, our proposal cuts the database size by 50% and is more robust to environment changes. In a room of 4.7 m x 2.7 m, the achieved average positioning error is around 6 cm when it is vacant and it is no more than 14.5 cm when it is occupied by several people.

International audience; In recent years, green networking has attracted a lot of attention from device manufacturers and Internet Service Providers (ISP) to reduce energy consumption. In the literature, energy-aware traffic engineering problem is proposed to minimize the total energy consumption by switching off unused network devices (routers and links) while guaranteeing full network connectiv-ity. In this work, we are interested in the problem of energy-aware Traffic Engineering while using multi-path routing (ETE-MPR) to minimize link capacity utilization in ISP backbone networks. To this end, we propose a bi-level optimization model where the upper level represents the energy management function , and the lower level refers to the deployed multi-path routing protocol. Then, we reformulate it as a one-level MILP replacing the second level problem by different sets of optimality conditions. We further use these formulations to solve the problem with classical branch-and-bound, cutting plane, and branch-and-cut algorithms. The computational experiments are performed on real instances to compare the proposed algorithms and to evaluate the efficiency of our model against the existing single-path and multi-objective approaches.

Deploying a radio frequency (RF) access point (AP) to the visible light communication (VLC) system is a promising strategy to overcome the VLC’s limitations, such as limited coverage, strictly line-of-sight transmission, and mobility robustness, etc. In this paper, we focus on the energy-aware design of network selection and resource allocation for a heterogeneous network combining with RF and VLC APs. For adapting to different timescale network states and stochastic data arrival, we propose an on-line two-timescale adaptive network resource optimization (ANRO) framework by employing the Lyapunov optimization technique. At the large timescale, we first develop a closed-form solution for the subproblem of network selection for user equipment. Second, we design a cost-effective and easy-to-realize algorithm for VLC’s joint transmission scheduling and power control subproblem, which is a nonconvex optimization. While at the small timescale, we obtain the optimal solution for RF’s joint resource block and power allocation subproblem, which is proven a mixed integer nonlinear optimization. Simulation results demonstrate that the ANRO can achieve a tradeoff between network power consumption and delay. Furthermore, it not only can stabilize the network but also can significantly reduce the energy consumption compared with other existing schemes.

Efficient information dissemination is the pinnacle of Vehicular Ad-hoc Networks (VANETs). In case of delay-sensitive information such as safety-related messages, it is imperative to minimize the transmission delay and increase the message reliability in VANETs. In a previous work, we proposed a Time-Limited Reliable Broadcast Incremental Power (TRBIP) algorithm, which is a centralized heuristic to reduce transmission interferences for safety message dissemination in VANETs. By reducing the total transmission energy and the number of hops, TRBIP is able to reduce the transmission interference and thus-forth maximizes the reliability. Furthermore, in TRBIP, VANETs’ features such as vehicle mobility and frequent network fragmentation are also taken into account through store-carry-and-forward and the periodic multicast tree reconstruction strategies. However, multicast tree management is hard to maintain in urban environments due to the high density of nodes in VANETs (large trees), and is affected by the city streets organizations (intersections, roundabout, ... etc.). To address the afore-mentioned issues, in this paper we propose a Distributed version of Time-Limited Reliable Broadcast Incremental Power (DTRBIP) based on a road segmentation technique. Our proposal is extended to handle both delay-sensitive and delay-tolerant applications, and also both low and high dynamic VANETs scenarios respectively urban and highway environments. The results of the simulation conducted using NS-2 simulator advocate for the efficiency of our proposed method in term of reducing the total emission energy (at least by 150 dBm), reducing the average delay by 52%, increasing the packet delivery ratio by more than to 5%, and reducing the protocol overhead up to 3%. Obtained results show a clear enhancement compared to the previous solutions (i.e, RGRP-SA, RBIP, and TRBIP).

Channel assignment remains an important issue in cellular networks due to the limited frequency resource. In this paper, we study the Minimum Span Channel Assignment Problem (MS-CAP) in hexagonal cellular networks with k-band buffering restriction, where channel interference does not extend beyond k cells. The objective of MS- CAP is to minimize the max- imum frequency index used for accepting all the demands while respecting the frequency separation constraint. We start from deriving the optimal channel assignment strategies for distance- 2 clique cellular networks with homogeneous call demands, where cells are within a distance of two cells from each other. By partitioning the network into distance-2 cliques, we then further propose a time efficient algorithm GPCAA to solve the MS- CAP in large cellular networks with heterogeneous demands. Our method has been evaluated on the well-known benchmark instance Philadelphia cellular network. Numerical results show that the GPCAA algorithm outperforms its counterparts in the literature in terms of solution optimality and computing time.

Mixed-line-rate (MLR) optical networks (e.g., 10/40/100 Gb/s) offer a new paradigm in backbone networks to meet the increasing traffic demands. They are considered as a transitional solution for future flexible and dynamic optical networks. In order to provision multicast in MLR optical networks, we need to address multicast routing, line rate selection, and wavelength assignment subproblems, while still respecting the maximum transmission reach. In this paper, we investigate the multicast provisioning problem, which aims at minimizing the total cost on transponder, wavelength channel, and wavelength usage. First, a lightpath-based joint integer linear programming (ILP) model is formulated to minimize the total cost. To overcome the scalability of this joint ILP model, a decomposition approach based on column generation is proposed, which achieves comparable results within an extremely short period of time. Extensive simulations are performed to study the impact on the total cost of multicast provisioning with the MLR regarding the number of multicast sessions, destinations, traffic amount, and network topologies. It is observed that the multicast provisioning with the MLR has significant total cost savings over the single line rate, especially in the scenarios of a large number of multicast sessions and in optical networks with small average nodal degree.

The last decade has seen a growing interest in the use of Unmanned Aerial Vehicles (UAVs) for various applications and services. UAVs, or drones as referred to, have shown to be efficient in completing complex tasks when organized as ad hoc connected groups, thus forming a Flying Ad hoc Network (FANET). Although similar to Mobile Ad hoc Network (MANET) and Vehicular Ad hoc Network (VANET), FANETs have their own characteristics. One of the main difference is the fact that UAVs in general, but particularly when organized are FANETs, are mission-based, and their mobility models are often dictated by the purpose of their mission and the nature of the task they plan to accomplish. Therefore, routing protocols for FANETs should take into consideration the nature of the applications and services that the UAVs are deployed for, and factor in the mobility models. However, designing routing protocols for FANETs is not an easy task given the highly dynamic topology of FANETs and the flying constraints they are subjected to. Compared to topology-based routing, position-based routing demonstrated high efficiency and resilience to handle the high mobility of FANET nodes. To this end, in this paper, we propose a comprehensive survey of position-based routing protocols for FANETs with their various categories. We propose a classification and a taxonomy of these protocols, including a detailed description of the routing schemes used in each category. We propose a comparative study based on various criteria, and discuss the advantages and weaknesses of each protocol. Furthermore, new challenges for future research are presented, which introduce a new kind of coordination between UAVs and existing VANETs on the ground. The originality of this survey is that it complements the existing surveys on the same theme by providing more details on some aspects that have been addressed only ostensibly by other surveys in the literature.

Network function virtualization (NFV) is a promising technology to decouple the network functions from dedicated hardware elements, leading to the significant cost reduction in network service provisioning. As more and more users are trying to access their services wherever and whenever, we expect the NFV-related service function chains (SFCs) to be dynamic and adaptive, i.e., they can be readjusted to adapt to the service requests’ dynamics for better user experience. In this paper, we study how to optimize SFC deployment and readjustment in the dynamic situation. Specifically, we try to jointly optimize the deployment of new users’ SFCs and the readjustment of in-service users’ SFCs while considering the trade-off between resource consumption and operational overhead. We first formulate an integer linear programming (ILP) model to solve the problem exactly. Then, to reduce the time complexity, we design a column generation (CG) model for the optimization. Simulation results show that the proposed CG-based algorithm can approximate the performance of the ILP and outperform an existing benchmark in terms of the profit from service provisioning.

In elastic optical networks, two lightpaths sharing common fiber links might have to be isolated in the spectrum domain with a proper guard-band to prevent crosstalk and/or reduce physical-layer security threats. Meanwhile, the actual requirements on guard-band sizes can vary for different lightpath pairs, because of various reasons. Therefore, in this paper, we consider the situation in which the actual guard-band requirements for different lightpath pairs are different, and formulate the distance spectrum assignment (DSA) problem to investigate how to assign the spectrum resources efficiently in such a situation. We first define the DSA problem formally and prove its $\mathcal {NP}$ -hardness and inapproximability. Then, we analyze and provide the upper and lower bounds for the optimal solution of DSA, and prove that they are tight. In order to solve the DSA problem time-efficiently, we develop a two-phase algorithm. In its first phase, we obtain an initial solution and then the second phase improves the quality of the initial solution with random optimization. We prove that the proposed two-phase algorithm can get the optimal solution in bipartite DSA conflict graphs and can ensure an approximate ratio of $\mathcal {O}(\log (|V|))$ in complete DSA conflict graphs, where $|V|$ is the number of vertices in the conflict graph, i.e. , the number of lightpaths to be considered. Numerical results demonstrate our proposed algorithm can find near-optimal solutions for DSA in various conflict graphs.

The process of routing in Vehicular Ad hoc Networks (VANET) is a challenging task in city environments. Finding the shortest end-to-end connected path satisfying delay restriction and minimal overhead is confronted with many constraints and difficulties. Such difficulties are due to the high mobility of vehicles, the frequent path failures, and the various obstructions, which may affect the reliability of the data transmission and routing. Commercial Unmanned Aerial Vehicles (UAVs) or what are commonly referred to as drones can come in handy in dealing with these constraints. In this paper, we study how UAVs operating in ad hoc mode can cooperate with VANET on the ground so as to assist in the routing process and improve the reliability of the data delivery by bridging the communication gap whenever it is possible. In a previous work, we have proposed UVAR a UAV-Assisted VANETs routing protocol, which improves data routing and connectivity of the vehicles on the ground through the use of UAVs. However, UVAR does not fully exploit UAVs in the sky for data forwarding because it uses UAVs only when the network is poorly dense. In this paper, we propose an extension of this protocol by supporting two different ways of routing data: (i) delivering data packets exclusively on the ground using UVAR-G; and (ii) transmitting data packets in the sky using a reactive routing based on UVAR-S. Simulation results demonstrate that the hybrid communication between vehicles and UAVs is ideally suited for VANETs compared to traditional vehicle-to-vehicle (V2V) communications.

In this letter, a new protection technique, namely Spectrum Shared pre-configured cycles (SS- $p$ -cycles), is proposed for survivable elastic optical networks. SS- $p$ -cycles permit to cut spectrum usage and spectrum fragmentation ratio (SFR) by leveraging special spectrum sharing among multiple $p$ -cycles that have common link(s). To compute SS- $p$ -cycles, we first formulate an integer linear programming model to obtain the optimal solution in small instance, and then develop a time-efficient heuristic algorithm to solve large-scale instances. Simulation results have demonstrated that the proposed SS- $p$ -cycles outperform conventional $p$ -cycles without spectrum sharing in terms of spectrum usage and SFR.

Radar transmitter identification technology based on pulse descriptor word (PDW) is broadly used in military and civilian applications. However, as the complexity of the electromagnetic environment has increased, radar identification has been challenging. Radio frequency fingerprint (RFF) is an intrinsic hardware characteristic and has been widely employed for device identification. In this paper, we propose a robust RFF identification scheme for linear frequency modulation (LFM) pulse radars. The scheme includes a proposed piecewise curve fitting based denoising (PCFD) algorithm and a hybrid RFF identification algorithm. The PCFD algorithm can reduce the noise of LFM pulses without undermining RFF features. The hybrid RFF identification algorithm extracts both transient-based and modulation-based RFF features. Experimental results demonstrate that the proposed radar identification scheme can achieve a 100% identification accuracy when the SNR is about 0 dB.

Large-scale failures are critical issues for the survivability of elastic optical inter-data center networks (EO-DCNs). Nowadays, cloud services can be served by an alternative data center (DC) with replicated content once the original connection fails. In this paper, we investigate the disaster-resilient cloud services provisioning problem involving content placement, routing, protection of path and content, and spectrum allocation. Both dedicated end-to-content backup path protection (DEBPP) and shared end-to-content backup path protection (SEBPP) are studied. To minimize the spectrum usage, a column generation (CG) based decomposition approach is proposed. Numerical simulations are performed to study the spectrum usage of DEBPP and SEBPP with respect to the traffic amount and the number of DCs and content replicas. Results demonstrate that a reasonable number of DCs with efficient content replicas in network design enables to achieve efficient spectrum usage for disaster-resilient cloud services provisioning.

With high spectrum efficiency, flexible optical networks are preferred to accommodate the increasing heterogeneous traffic in present-day Internet. However, due to resource crunch issue, not all services can be satisfied at the same time. As each satisfied service is supposed to bring a revenue for the network, the resource crunch will lead to network revenue loss. Given a set of service requests to be provisioned in a flexible optical network, we consider the problem of maximizing the network revenue subject to the physical layer constraint and limited spectrum resource. To mitigate the impact of non-linear interference and assure the quality of transmission, adaptive modulation and FEC are employed. Thus, we formulate a joint optimization problem consisting of routing, modulation, FEC and spectrum assignment. To solve it efficiently, a fast two-phase heuristic algorithm is proposed, which is shown to be near-optimal for revenue maximization. By simulations, we demonstrate that both adaptive modulation and multiple FEC enable to improve significantly the network revenue: Adaptive modulation is preferred in networks with a high signal-to-noise ratio (SNR); while using multiple FEC is more profitable for scenarios with a low SNR.

Elastic Optical Network (EON) has been considered as a promising optical networking technology to architect the next-generation backbone networks. The spectrum management in EONs is directly determined by the Routing and Spectrum Assignment (RSA). Generally, the RSA is solved by routing the requests with lightpaths first and then assigning spectrum resources to the lightpaths to optimize the spectrum usage. Thus, the spectrum assignment explicitly determines the spectrum usage. Besides, the network topology, traffic distribution and routing scheme implicitly impact the spectrum usage. However, few related work involves this implicit impact. In this paper, we aim to provide a thoroughly theoretical analysis on the impact of the three key factors on the spectrum usage. To this end, two theoretical chains are proposed: (1) The optimal spectrum usage can be measured by the chromatic number of the conflict graph, which is positively correlated to the intersecting probability, i.e., the smaller the intersecting probability, the smaller the optimal spectrum usage; (2) The intersecting probability is determined by the network topology, traffic distribution and routing scheme via a quadratic programming parameterized with a matrix of conflict coefficients. The effectiveness of our theoretical analysis has been validated by extensive numerical results.

Radio Frequency Fingerprinting (RFF) is one of the promising passive authentication approaches for improving the security of the Internet of Things (IoT). However, with the proliferation of low-power IoT devices, it becomes imperative to improve the identification accuracy at low SNR scenarios. To address this problem, this paper proposes a general Denoising AutoEncoder (DAE)-based model for deep learning RFF techniques. Besides, a partially stacking method is designed to appropriately combine the semi-steady and steady-state RFFs of ZigBee devices. The proposed Partially Stacking-based Convolutional DAE (PSC-DAE) aims at reconstructing a high-SNR signal as well as device identification. Experimental results demonstrate that compared to Convolutional Neural Network (CNN), PSCDAE can improve the identification accuracy by 14% to 23.5% at low SNRs (from −10 dB to 5 dB) under Additive White Gaussian Noise (AWGN) corrupted channels. Even at SNR = 10 dB, the identification accuracy is as high as 97.5%.

International audience; We study the fixed charge network design problem with shortest path constraints which is modeled as a bi-level program. We first review three one-level formulations obtained by applying the complementarity slackness theorem, Bellman's optimality conditions and cycle elimination constraints. We propose two new binary integer programming (BILP) formulations inspired by path and cycle inequalities. The two formulations have exponential numbers of constraints. We incorporate the path and the cycle based formulations in a branch-and-cut algorithm and in another cutting-plane based method. Numerical experiments are performed on real instances, and random data sets generated with different criteria to examine the difficulty of the instances. The results show that the proposed cutting plane algorithms can solve up to 19% more instances than the classic branch-and-bound algorithms.

Network virtualization provides a promising solution to overcome the ossification of current networks, allowing multiple Virtual Network Requests (VNRs) to be embedded on a common infrastructure. The major challenge in network virtualization is the Virtual Network Embedding (VNE) problem, which is to embed VNRs onto a shared substrate network and known to be $\mathcal {NP}$ -hard. The topological heterogeneity of VNRs is one important factor hampering the performance of the VNE. However, in many specialized applications and infrastructures, VNRs are of some common structural features, e.g., paths and cycles. To achieve better outcomes, it is thus critical to design dedicated algorithms for these applications and infrastructures by taking into accounting topological characteristics. Besides, paths and cycles are two of the most fundamental topologies that all network structures consist of. Exploiting the characteristics of path and cycle embeddings is vital to tackle the general VNE problem. In this paper, we investigate the path and cycle embedding problems. For path embedding, we prove its $\mathcal {NP}$ -hardness and inapproximability. Then, by utilizing Multiple Knapsack Problem (MKP) and Multi-Dimensional Knapsack Problem (MDKP), we propose an efficient and effective MKP-MDKP-based algorithm. For cycle embedding, we propose a Weighted Directed Auxiliary Graph (WDAG) to develop a polynomial-time algorithm to determine the least-resource-consuming embedding. Numerical results show our customized algorithms can boost the acceptance ratio and revenue compared to generic embedding algorithms in the literature.

Quantum communication plays an important role in quantum information science due to its unconditional security. In practical implementations, the users of each communication vary with the transmitted information, and hence not all users are required to participate in each communication round. Therefore, improving the flexibility and efficiency of the actual communication process is highly demanded. Here, we propose a theoretical quantum communication scheme that realizes secret key distribution for both the two-party quantum key distribution (QKD) and multi-party quantum secret sharing (QSS) modes. The sender, Alice, can freely select one or more users to share keys among all users, and nonactive users will not participate in the process of secret key sharing. Numerical simulations show the superiority of the proposed scheme in transmission distance and secure key rate. Consequently, the proposed scheme is valuable for secure quantum communication network scenarios.

Applications for mobile ad-hoc networks are heavily dependent on information about the nodes positions. Since the network may include malicious nodes providing bogus data (e.g., fake positions), the reliability of this information is critical. Although this problem has been addressed by some predictive models, challenges still exist regarding (a) the accuracy/security of such models, (b) the potential advantages of combining different prediction models, (c) the power consumption resulting from the simultaneous application of multiple models, and (d) the lack of strategic approaches in the analysis of information aiming to find the most advantageous data and balance divergent results. To address these issues, we analyze in this paper the performance of two prediction methods: linear regression and Grey model. We have evaluated their corresponding energy costs for reliably identifying each node position. The results, obtained via simulations, are added to independent vectors and statistical indicators are gathered to create a game theory payoff matrix. The proposed model allows the evaluation of the predictive methods either individually or collectively, facilitating the identification of the best parameters for a target energy saving profile.

We study the problem of maximizing the network lifetime of a data-gathering tree in a wireless sensor network (WSN). Both data routing and data aggregation are considered at the same time to improve the energy efficiency for data collection in WSNs. With different data-aggregation methods, three aggregation modes are studied: full aggregation, non-aggregation, and a hybrid partial-aggregation using compressive sensing. The full aggregation means that an intermediate sensor node will aggregate all the received data from its children and its own generated data into one data unit and then send it to its parent. If no aggregation is used, an intermediate sensor node will forward all the received data units plus its own data separately. For the hybrid partial-aggregation, once the number of incoming data units is bigger than a compression threshold, they will be compressed into a constant size for transmission. For each mode, an exact solution based on mixed-integer linear programming (MIP) is proposed to find the optimal data-gathering tree with the maximum network lifetime. Although non-linear relations exist between the lifetime of a sensor node and the number of data units received or transmitted, we succeed to express it by a set of linear equations. The correctness of the MIP models is proven formally. Simulation results demonstrate that the network lifetime can be increased tenfold by changing the compression threshold from five to one, which is, however, at the expense of bigger end-to-end delay and lower data accuracy. Thus, this compression threshold should be properly chosen for real applications.

In this paper, we investigate power-efficient directed preconfigured cycles ( $p$ -Cycles) for asymmetric traffic protection in elastic optical networks (EONs) against single link failure. Owing to the advantage of distinguishing traffic amount in two directions, directed $p$ -cycles consume low power by allocating different spectrum slots and modulation formats for each direction. A mixed integer linear programming (MILP) model is formulated to minimize total power consumption, which takes into account directed cycle generation, spectrum allocation, modulation adaptation, and protection capacity. To increase the scalability, the MILP model is decomposed, and a two-step approach is proposed: improved cycle enumeration and a simplified integer linear programming model. Extensive simulations are performed to study the power consumption of $p$ -cycles under different traffic patterns in terms of traffic asymmetry ( $TASY$ ), anycast ratio (AR), and the number of data centers (DCs). The results strongly demonstrate that directed $p$ -cycles obtain significant power savings for protecting asymmetric traffic in EONs. The power savings rise up to $46.91\%$ and $36.38\%$ compared with undirected $p$ -cycles as the $TASY$ and AR increase, respectively. Moreover, the directed $p$ -cycles achieve valuable power savings (up to $46.1\%$ ) with the introduction of DCs while the amount of power savings does not depend on the number of DCs.

The source flaw associated with the basis vector in the reference-frame-independent measurement-device-independent quantum key distribution (RFI-MDI-QKD) has not been systematically studied. As a result, it is often assumed that bit error is equal to phase error, which is not theoretically rigorous. Here, we propose a postprocessing method to estimate the phase error rate from the discarded mismatched-basis statistics, where the qubit source does not need to be characterized in detail. The source flaw in the basis vector of the RFI-MDI-QKD protocol can thus be corrected using this method. The numerical simulation results clearly demonstrate that the RFI-MDI-QKD protocol with uncharacterized sources is also insensitive to the misalignment of the reference frame.

Starting with the high concurrent, massive data requirements in the web system, the analysis of the system structure of the cache technology, module processing technology, thread technology and the appropriate application of indexing technology to solve the large number of concurrent data real-time processing problems. To meet the high load, high concurrency, large amount of data to improve the overall system performance requirements for the effective solution to large quantities of concurrent data processing provides a convenience.

In this article, a novel strategy for dual three-phase converter operating in the over-modulation region is proposed. The strategy enables the magnitude of inverter output voltage a linear output with modulation index varying in both fundamental and harmonic components. In the proposed algorithm, the functional relationship between modulation index and the fundamental or harmonic voltage magnitude can be described by equations. It is a single-mode over-modulation strategy avoiding storing massive correlation data between modulation index and hold angle, which indicates the proposed algorithm is suitable for digital implementation. Furthermore, this strategy extends the range of operation of the power converter from linear-modulation range to 12-step operation with a smooth and linear transition characteristic, while stator harmonic current is suppressed based on a vector decomposition technique.

Routing in cooperative vehicular networks is a challenging task because of high mobility of vehicles and difficulty of localization. In this paper, we study the geocast routing problem in Vehicular Ad-hoc NETworks VANETs, which aims at delivering data to a specific group of mobile vehicles identified by their geographical location. Although many geocast routing protocols have been proposed, only partial inherent constraints of VANETs such as mobility, internal network fragmentation problem, external network fragmentation problem, and overload are taken into account. Therefore, we propose two novel and robust geocast routing protocols: the first one, called Robust Geocast Routing Protocol for Safety Applications RGRP-SA, is dedicated to road safety applications, while the second, namely, Robust Geocast Routing Protocol for Comfort Applications RGRP-CA, is designed for comfort applications. Simulations conducted in NS-2 demonstrate that our safety-oriented RGRP-SA protocol outperforms Inter-Vehicle Geocast protocol and Mobicast Routing Protocol in VANETs by sending up to 25% more packets, cutting the end-to-end delay in half, and solving the internal network fragmentation problem. Besides, it is also shown that our comfort-oriented RGRP-CA protocol serves well comfort applications with only light overhead by solving internal and external network fragmentation problems and providing more reliable data delivery with a 25% higher packet delivery ratio and higher network throughput utilization in comparison with Mobicast with Carry-and-Forward protocol. Copyright © 2015 John Wiley & Sons, Ltd.

Setting guard band is an important way to mitigate the physical layer impairments like channel interference and cross-talk interference in Elastic Optical Networks (EONs). The goal of this paper is to study the optimal guard band size against non-linear noise caused by cross channel interferences. Through a classical routing and spectrum assignment algorithm, we investigate the total blocking probability as a function of guard band size. Simulation results demonstrate the inverse impact on Quality of Transmission (QoT) blocking and resource blocking. We also study the optimal guard band under different non-linear interference and available spectrum resource. The results show that, the optimal guard band should be larger for strong non-linear optical networks, and be unchanged for wide available spectrum. The observation can provide an important guidance for the planning of EONs so as to minimize the total blocking probability.

In this paper, we propose a dynamic resource optimization scheme for a heterogeneous wireless network combining with a radio frequency (RF) access point (AP) and multiple visible light communication (VLC) APs. For adapting to the stochastic media content arrival rates and dynamic wireless channel conditions, a two-timescale stochastic network resource optimization problem is formulated by employing the Lyapunov optimization technique. To this end, we propose an online two-timescale adaptive network resource optimization (ANRO) framework. At the ANRO, the network selections for user equipments (UE) are operated at a large timescale, while the resource allocations for both RF and VLC APs are operated at a small timescale. Simulation results exhibit that the ANRO not only stabilizes the network but also significantly reduces the energy consumption than other existing schemes.

International audience; Vehicular ad hoc networks (VANETs) are characterized by frequent path failures due to the high mobility caused by the sudden changes of vehicles direction. The routing paths between two different vehicles should be established with this challenge in mind. It must be stable and well connected in order to guarantee a reliable and safe delivery of packets. The aim of this work is to present a new reactive routing technique providing effective and well-regulated communication paths. These discovered paths are created based on a robust flooding discovery process involving UAVs (Un-manned Aerial Vehicles) to ensure the connectivity when the network is sparsely connected. The evaluation of this technique is performed using NS-2 simulator and its performances are compared with on-demand protocols dedicated for VANET. Simulation results show clearly that our approach gives interesting outcomes ensuring a high delivery ratio with a minimum delay. This hybrid communication between the vehicles and UAVs is attractive to initiate more smart connected nodes in the near future.

This paper studies spectrum allocation of Spectrum Shared pre-configured Cycle (SS-p-cycle) design in Elastic Optical Networks (EONs) with and without spectrum conversion capabilities. SS-p-Cycle design enhances spectrum sharing among multiple p-cycles that have common link(s). We develop Integer Linear Programming (ILP) models to minimize both spare capacity and the maximum number of spectrum usage for each of the two spectrum conversion cases. Simulation results indicate that the SS-p-cycle design requires the same maximum number of spectrum usage in the two cases while SS-p-cycle with spectrum conversion earns higher spectrum efficiency. Moreover, compared with traditional spectrum no-shared p-cycle design, SS-p-cycle design acquires more efficient spectrum allocation in both cases.

Routing and Spectrum Assignment (RSA) is the fundamental problem in Elastic Optical Networks (EONs), which is an \(\mathcal{N}\mathcal{P}\)-complete problem. In this paper, we compare the performances of two typical RSA algorithms namely Spectrum-First and Route-First, and put forward a connection between the performances and the request distributions. The numerical results demonstrated that even though the Spectrum-First RSA algorithm outperforms significantly the Route-First RSA algorithm under concentrated distribution, the latter performs pretty better under uniform distribution, which is however somewhat different from that demonstrated in the literature.

In a variety of visual programming environment, if some features not provided directly to the user, use the Windows system to provide a large number of API functions tend to play a multiplier effect. This paper describes what API functions and the core of the Windows API DLL, followed by a brief description of the classification API function, and then focuses on the visualization platform how to call Windows system API function, the application API function advanced programming capabilities, and finally and analysis tips and cautions users to call the API function.

A new algorithm DSMFP-Miner was proposed. When the data stream reach continuously, a maximal frequent pattern tree called MFP-Tree is adopted to maintain the transactions in data screams dynamically. Transactions in the same Transaction-sensitive sliding window are set to own the same “importance”. Besides, the support of the transactions in old window is decayed to reduce their influence to mining results, and infrequent patterns and overdue patterns are deleted periodically. In the mining process, the algorithm put an enumeration tree with each node of MFP-Tree as root as the search space, and use the "depth-first" search strategy to mining the maximal frequent patterns with this node as a suffix.