Network Function Virtualization (NFV) provides a flexible mechanism for deploying Virtual Network Functions (VNFs) within Service Function Chains (SFCs), thereby streamlining data transfers between end-users and edge/cloud resources. The distinct requirements for forward and backward traffic—each carrying different types of content—give rise to Hybrid SFCs (HSFCs), which must be carefully designed to address unique deployment and performance concerns. However, achieving robust disaster resilience in HSFC-based NFV environments poses significant challenges, as natural or hardware-induced disruptions within Disaster Zones (DZs) can degrade service quality or even cause outages. This paper describes the Resilient Hybrid Service Function Chain Resource Optimization (R-HSFC-RO) approach to ensure both efficient resource utilization and sustained service delivery under disaster conditions. Our model considers bandwidth consumption, computational resource allocation for VNF execution, VNF instantiation overheads, and end-to-end latency requirements. For resolution, we propose a Mixed-Integer Linear Programming (MILP) model and Constraint Programming (CP), thereby enabling optimal solutions. Simulation results demonstrate that R-HSFC-RO reduces total costs by up to 50%, enhances disaster resilience, and maintains high operational efficiency.

Network Function Virtualization (NFV) is a new technology that deploys network services and functions as software components in data centers and cloud environments. One of its key applications is Service Function Chain (SFC), which chains a set of Virtual Network Functions (VNFs) in a specific order to deliver a desired service. However, deploying NFV and SFC networks faces challenges, particularly in terms of disaster resiliency. This encompasses natural disasters and hardware failures, which can disrupt network operations and lead to service interruption or degradation across an entire disaster zone (DZ). Therefore, designing NFV and SFC networks that can withstand disasters while providing high levels of service availability and reliability is important. This paper presents a new method for protecting SFCs using adaptive multi-path routing. The proposed Multi-path Protection (MP) method has the advantage of reducing the amount of reserved bandwidth on backup paths by distributing SFC traffic over multiple DZ-disjoint working paths. The problem being addressed involves VNFs placement, routing SFCs, and implementing protection mechanisms. The objective is to minimize network resource consumption, including both the bandwidth used by request routing paths and the computing resources for VNF execution. To solve this multi-dimensional optimization problem, a path-adaptive and flow-based integer linear program (ILP) is proposed to provide the optimal solution in sall-size network settings. We also propose a heuristic approach that offers the near-optimal solution in a time-efficient way. Comprehensive simulation results show that the proposed MP strategy outperforms traditional Dedicated Protection (DP) in terms of bandwidth and processing resource consumption, resulting in a significant gain up to 20%.

Multi-band transmission is a promising technical direction for spectrum and capacity expansion of existing optical networks. Due to the increase in the number of usable wavelengths in multi-band optical networks, the complexity of resource allocation problems becomes a major concern. Moreover, the transmission performance, spectrum width, and cost constraint across optical bands may be heterogeneous. Assuming a worst-case transmission margin in U, L, and C-bands, this paper investigates the problem of throughput maximization in multi-band optical networks, including the optimization of route, wavelength, and band assignment. We propose a low-complexity decomposition approach based on Column Generation (CG) to address the scalability issue faced by traditional methodologies. We numerically compare the results obtained by our CG-based approach to an integer linear programming model, confirming the near-optimal network throughput. Our results also demonstrate the scalability of the CG-based approach when the number of wavelengths increases, with the computation time in the magnitude order of 10 s for cases varying from 75 to 1200 wavelength channels per link in a 14-node network. Code of this publication is available at github.com/cchen000/CG-Multi-Band.

Network Function Virtualization (NFV) is a technology that deploys network services and functions as software components in data centers and cloud environments. One of its key applications is Service Function Chain (SFC), which chains Virtual Network Functions (VNFs) in a specific order to deliver a desired service. However, disaster resiliency is a critical challenge when deploying NFV and SFC, as natural disasters and hardware failures can disrupt network operations and lead to service interruption or degradation across an entire disaster zone (DZ). This paper presents a new method for protecting SFCs using multi-path routing, which enables to split an SFC on multiple DZ-disjoint working paths and leverage a shared backup path for protection. The proposed Multi-path Protection (MP) minimizes network resource consumption, including both the bandwidth for request routing and the computing resources for VNF execution. We propose a heuristic approach that offers a near-optimal SFC MP solution in a time-efficient way. Numerical results show that the proposed MP strategy outperforms traditional Dedicated Protection (DP) in terms of resource consumption, resulting in significant gain up to 20%.

In Long Range wide Area Network (LoRaWAN) 1.1 networks, end devices must be activated via Over-The-Air Activation (OTAA) to securely send and receive data. Activation involves a two-step process: identification using a unique identifier (DevEUI) and authentication through a Message Integrity Code computed with a pre-shared key. Both DevEUI and pre-shared keys must be provisioned beforehand. We propose a scalable blockchain-based decentralized model to improve the identification of untrusted end devices during LoRaWAN's OTAA without altering the protocol, thus allowing immediate deployment in existing networks. Our approach accommodates corrupted devices and stores data in a distributed, permanent, and publicly auditable manner while facilitating rapid detection and identification of untrusted devices. Adapting blockchain technology to LoRaWAN resource-constrained environment, we design a specific data block structure combined with a “Proof of Identification” (PoI), while maintaining distributed consensus. This approach ensures a robust and scalable LoRaWAN join procedure while enhancing device identification and traceability. Through simulation models that combine our blockchain proposal with LoRaWAN 1.1 OTAA specification, we show improved performance for various metrics, e.g., for 10,000 devices, identification is two times faster using the blockchain.

Network function virtualization (NFV) in Elastic Optical Inter-DataCenter Networks (EO-DCNs) enables a flexible, adaptive, effective, and economic network services deployment and upgrade. However, it is facing critical threats from large-scale network failures due to natural disasters. This is driving the need for efficient network protection schemes of service function chain (SFC) provisioning. In this paper, we investigate the disaster-resilient SFC provisioning problem leveraging power-efficient path protection with distance-adaptive modulation format (MF) assignment, concerning virtual network function (VNF) placement, SFC mapping, path protection, constrained recovery delay, and spectrum allocation simultaneously. An integer linear program (ILP) model is formulated to jointly minimize power consumption and spectrum usage, subject to disaster resilience. A heuristic algorithm is further developed for the sake of scalability. Numerical simulation results demonstrate that the proposed disaster protection schemes enable saving up to 32.05% power consumption.

Flexible optical networks (FONs) are being adopted to accommodate the increasingly heterogeneous traffic in today’s Internet. However, in presence of high traffic load, not all offered traffic can be satisfied at all time. As carried traffic load brings revenues to operators, traffic blocking due to limited spectrum resource leads to revenue losses. In this study, given a set of traffic requests to be provisioned, we consider the problem of maximizing operator’s revenue, subject to limited spectrum resource and physical layer impairments (PLIs), namely amplified spontaneous emission noise (ASE), self-channel interference (SCI), cross-channel interference (XCI), and node crosstalk. In FONs, adaptive modulation, multiple FEC, and the tuning of power spectrum density (PSD) can be effectively employed to mitigate the impact of PLIs. Hence, in our study, we propose a universal bandwidth-related impairment evaluation model based on channel bandwidth, which allows a performance analysis for different PSD, FEC and modulations. Leveraging this PLI model and a piecewise linear fitting function, we succeed to formulate the revenue maximization problem as a mixed integer linear program. Then, to solve the problem on larger network instances, a fast two-phase heuristic algorithm is also proposed, which is shown to be near-optimal for revenue maximization. Through simulations, we demonstrate that using adaptive modulation enables to significantly increase revenues in the scenario of high signal-to-noise ratio (SNR), where the revenue can even be doubled for high traffic load, while using multiple FECs is more profitable for scenarios with low SNR.

Network function virtualization (NFV) in Elastic Optical Inter-DataCenter Networks (EO-DCNs) enables a flex-ible, adaptive, effective, and economic network services de-ployment and upgrade. However, it is facing critical threats from large-scale network failures, due to natural disasters. This is driving the need for efficient network protection schemes of service function chain (SFC) provisioning. In this paper, we investigate the disaster-resilient SFC provisioning problem leveraging power-efficient path protection with distance-adaptive modulation format (MF) assignment, concerning virtual network function (VNF) placement, SFC mapping, path protection, and spectrum allocation simultaneously. An integer linear program (ILP) model is formulated to jointly minimize power consumption and spectrum usage, subject to disaster resilience. A heuristic algorithm is also developed for the sake of scalability. Numerical Simulation results demonstrate that the proposed disaster pro-tection schemes enable saving up to 32.05 % power consumption.

As the increasing traffic patterns show asymmetric feature, directed pre-configured-cycle (p-cycle) has indicated the ability of better protection in elastic optical networks (EONs). In this paper, we investigate three different integer linear program (ILP) models of directed p-cycle without candidate cycle enumeration leveraging flow conservation. Three directed p-cycle designs are based on the same directed p-cycle strategy but differ from each other in how the flows can construct the directed p-cycles, namely individual link flow (ILF) directed p-cycle, aggregated link flows (ALF) directed p-cycle, and loop-eliminating flow (LEF) directed p-cycle, respectively. These ILPs aim to jointly minimize power consumption and spectrum usage of all directed p-cycles configured in EONs. The problem formulation involves directed p-cycle generation, modulation format (MF) selection, power consumption optimization, and spectrum allocation. Furthermore, the proposed directed p-cycle strategy is designed with a compact and novel MF adaptation relying on accurate protection path lengths. Simulations are conducted to compare the proposed ILPs with the conventional method which uses a rough upper bound on MF adaptation. Numerical results demonstrate that all of the three proposed ILPs have better performances on the joint objective, in which the improvement is up to 24.31%. Although the proposed ILPs are with the same performance on the objective due to the same directed p-cycle strategy, the LEF directed p-cycle shows the best efficiency.

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.

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 theNP-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.

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.

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.

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) and multipath routing. The studied problem involves data center (DC) assignment, content partition and placement, working/protection paths computation, as well as spectrum allocation. Our main objective is to jointly minimize the spectrum usage and maximal frequency slot index. Besides, we also expect to cut the content storage space. To this end, we first formulate the studied CSS-based protection problem as an integer linear program (ILP), and then propose a fast heuristic algorithm to improve the network scalability in large instances. Numerical simulations are conducted to compare the proposed schemes with the traditional protection strategy using entire content replication and single path routing. Simulation results demonstrate that the CSS-based protection scheme enables to cut up to 17.8% of the spectrum usage and half of the content storage space.

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.

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.

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.

The Visible Light Indoor Positioning System (VLIPS) 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 nonline-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 EFPRSS-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 mx2.7m, 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.

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.

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.

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.

Network virtualization provides a promising solution to overcome the ossification of current networks, allowing multiple Virtual Network Requests (VNRs) 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 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 investigated the path and cycle embedding problems. For path embedding, we utilize Multiple Knapsack Problem (MKP) and Multi-Dimensional Knapsack Problem (MDKP), we proposed an efficient and effective MKPMDKP-based algorithm. For cycle embedding, we proposed a Weighted Directed Auxiliary Graph (WDAG) to develop a polynomial-time algorithm to determine the least-resourceconsuming embedding. Numerical results showed our customized algorithms can boost the acceptance ratio and revenue compared to generic embedding algorithms in the literature.

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.

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.

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%.

Car-sharing system with electric cars is a very convenient service for urban transportation: it allows users to pick up a vehicle at a station and rent it during a short time. To manage this kind of system in the best way, it is necessary to solve the critical problem of vehicle stock imbalance across the stations. Several decision levels must be considered to balance the car distribution by taking into account the quality of service and the system operation cost. To this end, a linear programming model is proposed to formalize the problem in a mathematical framework, which allows the computation of optimal vehicle distribution strategies. To make our solution time efficient and usable for solving large problems, a greedy algorithm and a tabu search algorithm are proposed. These two algorithms are applied to the Auto Bleue network in Nice and its surrounding (France) using extensive simulations. Besides, an integrated mapping method is provided within the Geographical Information System QGIS to estimate flows and their locations. Numerical results demonstrate that the tabu search algorithm is able to find near-optimal solutions and good compromises between client satisfaction, number of staff agents and vehicles used, and computing time.

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.

This article describes a method to provide adapted visit tours in art museums according to the preferences expressed by the visitor and exhibits prestige. It is based on a dual approach with, on the one hand an automatic textual analysis of the official information available online (labels of exhibits) that allows to rank the exhibit attractiveness for a standard museum visitor. On the other hand, individual preferences are also taken into account to adapt the visit according to the personal cultural awareness of the visitor. We use operations research to solve a routing optimization problem, aiming at finding a visit tour with time constraints and maximization of the visitor satisfaction. Depending on the instance size and the problem scale, an integer linear programming (ILP) model and a greedy algorithm are proposed to recommend personalized visit tours and applied on two museums: ‘Musée de l’Orangerie’ in Paris and ‘National Gallery’ in London. The obtained results show that it is possible to recommend a good tour to visitors of an art museum by taking into account the common prestige of the exhibits and the individual interests, joining automatic text summarization and routing optimization in a limited geographical space.

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 guardband 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 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 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.

Abstract 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.

We study the problem of maximizing the lifetime of data-gathering tree for wireless sensor networks (WSNs). Both data routing and 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 partialaggregation using Compressive Sensing. For each mode, an exact solution based on Mixed-integer linear programming (MIP) is proposed to find the optimal data-gathering tree. Although nonlinear relation exists between the sensor node lifetime and the number of data units that receives or transmits in each time slot, we succeed to express it by a set of linear equations. Performance results demonstrate that the lifetime of data-gathering tree can be increased tenfold with efficient data aggregation methods.

This paper develops and evaluates a new protection solution for pre-configured-cycle (p-cycle) design in Mixed-Line-Rate (MLR) optical networks. Conventional p-cycle approaches require enumerating candidate cycles in advance and screening p-cycles using heuristic algorithms. Our method generates p-cycles directly in one-step using an Integer Linear Programming (ILP) model. Cost-effective transponders and distance-adaptive line rates are provisioned for every p-cycle to minimize joint cost of transponders and spare capacity. The design problem is solved together with spectral clustering based graph partitioning, which permits to compute the optimal solution in independent sub-graphs concurrently. The results show that our protection method is cost-efficient for p-cycle design with mixed line rates and scalable for large optical networks.

For many applications based on Mobile Ad Hoc Networks (MANETs), the position of the nodes is generally hard to be determined. In sensor networks, for instance, such information may be critical for the MANETs. Additionally, one problem to be faced in this scenario is the fake parameters broadcasted by misbehaving/malicious nodes, which can either compromise results about positioning, or deplete power resources of mobile devices. Therefore, in this paper we propose a model for (1) identifying the fake parameters broadcasted in the network, and for (2) detecting the malicious/misbehaving nodes. The Linear Regression and Variance Analysis (LRVA) are both the basis for the multi-step-ahead predictions in this paper. Through NS-2 and Avrora, we simulated the movement and energy consumption of nodes in a MANET, analyzing the time series of beacon-packets exchanged in the network. As a result of the LRVA employment, the fake parameters broadcasted in the network were detected, with the malicious/misbehaving nodes identified. The simulations presented in this paper show low power consumption, which allows the jointly employment of LRVA with other security techniques in the MANETs.

In this paper, we study the problem of efficient safety message dissemination in Vehicular Ad-Hoc Networks (VANETs). The objective is to reduce vehicular communication interferences for safety message delivery while guaranteeing the timeliness and the reliability of messages to avoid accidents. Therefore, we propose a heuristic algorithm called Time-Limited Reliable Broadcast Incremental Power (TRBIP) to construct a safety message delivery tree. Extensive simulation results show that the proposed algorithm outperforms its counterparts in terms of message timeliness, reliability and interference reduction.

Distributed denial of service attacks aim at making a given computational resource unavailable to users. A substantial portion of commercial Intrusion Detection Systems operates only with detection techniques based on rules for the recognition of pre-established behavioral patterns (called signatures) that can be used to identify these types of attacks. However, the characteristics of these attacks are adaptable, compromising thus the efficiency of IDS mechanisms. Thus, the goal of this paper is to evaluate the feasibility of using the Hidden Markov Model based on Viterbi algorithm to detect distributed denial of service attacks in data communication networks. Two main contributions of this work can be described: the ability to identify anomalous behavior patterns in the data traffic with the Viterbi algorithm, as well as, to obtain feasible levels of accuracy in the detection of distributed denial of service attacks.

The delivery of live video channels for services such as twitch.tv leverages the so-called Telco-CDN-Content Delivery Network (CDN) deployed within the Internet Service Provider (ISP) domain. A Telco-CDN can be regarded as an intra-domain overlay network with tight resources and critical deployment constraints. This paper addresses two problems in this context: (1) the construction of the overlays used to deliver the video channels from the entrypoints of the Telco-CDN to the appropriate edge servers; and (2) the allocation of the required resources to these overlays. Since bandwidth is critical for entrypoints and edge servers, our ultimate goal is to deliver as many video channels as possible while minimizing the total bandwidth consumption. To achieve this goal, we propose two approaches: a two-step optimization where the optimal overlays are firstly computed, then an optimal resource allocation based on these pre-computed overlays is performed; and a joint optimization where both optimization problems are simultaneously solved. We also devise fast heuristic algorithms for each of these approaches. The conducted evaluations of these two approaches and algorithms provide useful insights into the management of critical Telco-CDN infrastructures.

This paper proposes a new method to provide personalized tour recommendation for museum visits.It combines an optimization of preference criteria of visitors with an automatic extraction of artwork importance from museum information based on Natural Language Processing using textual energy.This project includes researchers from computer and social sciences.Some results are obtained with numerical experiments.They show that our model clearly improves the satisfaction of the visitor who follows the proposed tour.This work foreshadows some interesting outcomes and applications about on-demand personalized visit of museums in a very near future.

Nodes positioning is an essential issue for diverse applications in Mobile Ad Hoc Networks (MANETs). However, besides misbehaving nodes that could cause power depletion, MANETs are also susceptible to cyber-attacks, which can make the network unstable and/or unavailable. Therefore, considering the gaps aforementioned, the goal of this paper is to propose a model for identifying malicious/misbehaving nodes by: (1) the use of two forecasting methods (Grey Model and Polynomial Regression); (2) variability analysis; and (3) simulation of fake node positions. The obtained results allow concluding our model has high rate of accuracy for detecting malicious/misbehaving nodes.

A Telco-CDN can be regarded as an intra-domain overlay network with tight resources and critical deployment constraints. This paper addresses two problems in this context: (1) the construction of the overlays used to deliver the video channels from the entrypoints of the Telco-CDN to the appropriate edge servers; and (2) the allocation of the required resources to these overlays. Our ultimate goal is to maximize the number of delivered channels while preserving network resources. Two classes of heuristic algorithms, namely two-step optimization and joint-optimization, are proposed to solve these problems. The conducted evaluations confirm the efficiency of the joint-optimization approach.

Road safety can be improved a lot with the help of Cooperative Collision Warning System, in which vehicles cooperate with each other for relaying safety messages to avoid accidents. Once a vehicle is in an abnormal status, which may lead to potential accidents, an emergency safety message will be generated and sent out to warn surrounding vehicles. In this paper, we address the routing problem of reliable dissemination of emergency safety messages in Vehicle Ad-Hoc Networks (VANETs). We aim to minimize the total transmission energy and reduce the transmission interference for delivering emergency safety messages to endangered vehicles while guaranteeing timeliness of safety messages and transmission reliability of inter-vehicle communications. The emergency safety message dissemination problem is formulated as a spanning tree optimization problem with both hop-count constraint and reliability-height constraint. A mixed integer programming model (MILP) is proposed for solving the studied problem optimally, which can be used as a benchmark for evaluating future fast heuristic algorithms. Simulations are conducted to validate our proposed MILP model.

We propose a peer-to-peer system for streaming user-generated live video. Peers are arranged in levels so that video is delivered at about the same time to all peers in the same level, and peers in a higher level watch the video before those in a lower level. We encode the video bit stream with rate less codes and use trees to transmit the encoded symbols. Trees are constructed to minimize the transmission rate for the source while maximizing the number of served peers and guaranteeing on-time delivery and reliability at the peers. We formulate this objective as a height bounded spanning forest problem with nodal capacity constraint and compute a solution using a heuristic polynomial-time algorithm. We conduct ns-2 simulations to study the trade-off between used bandwidth and video quality for various packet loss rates and link latencies.

Large-scale live streaming systems can experience bottlenecks within the infrastructure of the underlying Content Delivery Network. In particular, the "equipment bottleneck" occurs when the fan-out of a machine does not enable the concurrent transmission of a stream to multiple other equipments. In this paper, we aim to deliver a live stream to a set of destination nodes with minimum throughput at the source and limited increase of the streaming delay. We leverage on rateless codes and cooperation among destination nodes. With rateless codes, a node is able to decode a video block of k information symbols after receiving slightly more than k encoded symbols. To deliver the encoded symbols, we use multiple trees where inner nodes forward all received symbols. Our goal is to build a diffusion forest that minimizes the transmission rate at the source while guaranteeing on-time delivery and reliability at the nodes. When the network is assumed to be lossless and the constraint on delivery delay is relaxed, we give an algorithm that computes a diffusion forest resulting in the minimum source transmission rate. We also propose an effective heuristic algorithm for the general case where packet loss occurs and the delivery delay is bounded. Simulation results for realistic settings show that with our solution the source requires only slightly more than the video bit rate to reliably feed all nodes.

One of the most attractive features of Massively MuItiplayer Online Games (MMOGs) is the possibility for users to interact with a large number of other users in a variety of collaborative and competitive situations. Garners within an MMOG typically become members of active communities with mutual interests, shared adventures, and common objectives. This demonstration presents a peer-to-peer live video system that enables MMOG players to stream screen-captured video of their game. Players can use the system to show their skills, share experience with friends, or coordinate missions in strategy games.

All-optical multicast routing (AOMR) is implemented by the concept of light-tree in WDM networks. The cost-optimal multicast light-tree is NP-hard to compute, especially when taking sparse splitting into account. Thus many heuristic algorithms have been proposed. In this paper, the approximation ratios of two classical heuristic AOMR algorithms for sparse splitting WDM network are studied. Let K be the number of destinations in a multicast session, it is proved that Reroute-to-Source (R2S) algorithm achieves a tight approximation ratio equal to K in the non-equally-weighted WDM network while Member-Only (MO) algorithm approaches the optimal solution with a ratio inferior to (K2+3K)/4 for any WDM network. It is also found that if the WDM network G is unweighted, both the approximation ratios of R2S and MO are no bigger than the diameter of the network Diam(G). Simulation results illustrate that both R2S and MO obtain good performances in candidate WDM backbone NSF network, which are far from the worst cases.

Based on the false assumption that multicast incapable (MI) nodes could not be traversed twice on the same wavelength, the light-tree structure was always thought to be optimal for multicast routing in sparse splitting Wavelength Division Multiplexing (WDM) networks. In fact, for establishing a multicast session, an MI node could be crosswise visited more than once to switch a light signal towards several destinations with only one wavelength through different input and output pairs. This is called Cross Pair Switching (CPS). Thus, a new multicast routing structure light-hierarchy is proposed for alloptical multicast routing, which permits the cycles introduced by the CPS capability of MI nodes. We proved that the optimal structure for minimizing the cost of multicast routing is a set of light-hierarchies rather than the light-trees in sparse splitting WDM networks. Integer linear programming (ILP) formulations are developed to search the optimal light-hierarchies. Numerical results verified that the light-hierarchy structure could save more cost than the light-tree structure.

To minimize the number of wavelengths required by a multicast session in sparse light splitting wavelength division multiplexing (WDM) networks, a light-hierarchy structure, which occupies the same wavelength on all links, is proposed to span as many destinations as possible. Different from a light-tree, a light-hierarchy accepts cycles, which are used to traverse crosswise a 4-degree (or above) multicast incapable (MI) node twice (or above) and switch two light signals on the same wavelengths to two destinations in the same multicast session. In this paper, firstly, a graph renewal and distance priority light-tree algorithm (GRDP-LT) is introduced to improve the quality of light-trees built for a multicast request. Then, it is extended to compute light-hierarchies. Obtained numerical results demonstrate the GRDP-LT light-trees can achieve a much lower links stress, better wavelength channel cost, and smaller average end-to-end delay as well as diameter than the currently most efficient algorithm. Furthermore, compared to light-trees, the performance in terms of link stress and network throughput is greatly improved again by employing the light-hierarchy, while consuming the same amount of wavelength channel cost.

The routing and wavelength assignment (RWA) problem for multipoint-to-point communications in all-optical wavelength division multiplexing (WDM) networks is investigated in this paper. Two efficient algorithms, namely Reverse Shortest Path Tree routing (RSPT) and k-Bounded Edge Disjoint Path routing (EDPR), are proposed. The problem of minimizing the total cost while establishing a multipoint-to-point session can be solved by RSPT algorithm in polynomial time. Nevertheless, EDPR algorithm produces a significant reduction in the maximum number of wavelengths required per link (i.e., the link stress) by a multipoint-to-point session. Simulations demonstrate the efficiencies of these two algorithms in supporting multipoint-to-point communications in WDM networks.

Although many multicast routing algorithms have been proposed in order to reduce the total cost in WDM optical networks, the link stress and delay are two parameters which are not always taken into consideration. This paper proposes a novel wavelength routing algorithm, which tries to avoid the multicast incapable branching nodes (MIB, branching nodes without splitting capability) to diminish the link stress for the shortest path based multicast tree and maintains good parts of the shortest path tree to reduce the end-to-end delay. Firstly a DijkstraPro algorithm with priority assignment and node adoption is introduced to produce a shortest path tree with up to 38% fewer MIB nodes, and then critical articulation and deepest branch heuristics are used to process the MIB nodes. Finally distance based reconnection algorithm is proposed to create the multicast tree or forest.