Multi-band elastic optical networks (MB-EONs) transmit information in multiple bands to increase the available capacity. However, they suffer from quality of transmission (QoT) degradation caused by the inter-channel stimulated Raman scattering effect, which requires addressing through tailored resource assignment. Additionally, dynamically arriving and departing optical service requests generate spectrum fragmentation (SF), where spectrum resources become scattered into non-continuous chunks and aggravate service blocking ratio (SBR) even when the total available bandwidth is sufficient. To jointly address these challenges, we propose an SF- and QoT-aware algorithm for routing, band, modulation format, and spectrum assignment (RBMSA), along with proactive spectrum defragmentation (SD), referred to asSFQA-defrag. The algorithm considers SF metrics and QoT levels of available channels across multiple candidate paths to ensure that the QoT requirements are met while minimizing the SF. The SD process proactively reorganizes spectrum allocation to reduce fragmentation by consolidating the spectrum gaps, which leads to lower blocking of future requests. TheSFQA-defragalgorithm is evaluated against benchmark algorithms that independently consider either QoT or SF in three reference backbone topologies. The results demonstrate thatSFQA-defragsignificantly reduces the SBR and SF compared to benchmarks, albeit with a slight increase in the average path length.

Reinforcement Learning (RL) has emerged as a powerful tool for automating complex network management tasks, yet its lack of transparency and black-box nature hinder trust and adoption in operational environments. In this work, we focus on explaining the behavior of an $\mathbf{R L}$ agent applied to the problem of network slice admission control. We present a framework that integrates three key components: a Deep Reinforcement Learning (DRL) agent for admission control, an Integer Linear Programming (ILP) model for network slice embedding, and an explanation module for interpreting the DRL agent’s policies, namely Shapley Value Explainable Reinforcement Learning (SVERL). Our analysis aims gives particular attention to cases where the RL agent rejects admitting a network slice request despite sufficient network capacity to provision it, and investigates whether explanations can be used to verify and validate the agent’s behavior prior to deployment approval. Experimental results reveal that the agent’s decisions are primarily influenced by substrate network conditions such as congestion, rather than by the intrinsic characteristics of slice requests. While this conservative policy prevents overload, it also leads to overly cautious rejections. Importantly, the proposed explanation framework provides operators with actionable insights to scrutinize, validate, and refine RL-driven policies before operational deployment.

Integrated access and backhaul (IAB) is one of the promising techniques for 5G networks and beyond (6G), in which the same node/hardware is used to provide both backhaul and cellular services in a multi-hop architecture. Due to the sensitivity of the backhaul links with high rate/reliability demands, proper network planning is needed to ensure the IAB network performs with the desired performance levels. In this paper, we study the effect of infrastructure planning and optimization on the coverage of IAB networks. We concentrate on the cases where the fiber connectivity to the nodes is constrained due to cost. Thereby, we study the performance gains and energy efficiency in the presence of free-space optical (FSO) communication links. Our results indicate hybrid fiber/FSO deployments offer substantial cost savings compared to fully fibered networks, suggesting a beneficial trade-off for strategic link deployment while improving the service coverage probability. As we show, with proper network planning, the service coverage, energy efficiency, and cost efficiency can be improved.

As artificial intelligence and machine learning (AI/ML) models become integral to network operations, their lack of transparency poses a significant barrier to operator trust. Existing explainable artificial intelligence (XAI) techniques often fail to bridge this gap for non-specialists, producing technical outputs that are difficult to translate into actionable insights. This paper presents a framework specifically designed to address this shortcoming. It leverages a moderately sized large language model (LLM) and extends beyond the standard use of SHapley Additive exPlanations (SHAP) feature influence values. The framework employs a structured prompt enriched with mutual feature interaction data to generate human-understandable natural language explanations. To validate our framework, we performed an empirical evaluation on an optical quality of transmission (QoT) estimation use case with human evaluators. We collected independent performance evaluations from specialists, which showed a high inter-evaluator agreement. Compared to a state-of-the-art baseline that uses only SHAP feature influence values in a straightforward prompt, our approach improves the explanation usefulness and scope by$\mathbf{1 2. 2 \%}$and$\mathbf{6. 2 \%}$, while achieving 97.5% correctness.

The operational security of latency-sensitive networked applications is increasingly threatened by evolving malicious attacks that compromise operational integrity and network performance. Human-to-machine (H2M) applications, which rely on seamless bidirectional control signals and haptic feedback transmission, exemplify such latency-sensitive use cases. Existing learning-based malicious attack detection frameworks suffer from their reliance on pre-trained datasets, making machine learning models within them ineffective against previously unseen attack patterns. As attack profiles dynamically evolve, static models become obsolete, necessitating adaptive mechanisms to maintain detection accuracy. In this context, concept drift adaptation will serve as a critical tool for enabling models to continuously adjust to changing traffic distributions and emerging attack patterns. However, real-world H2M applications lack access to accurately labeled malicious traffic data, making real-time adaptation of defense mechanisms infeasible. To address these challenges, we propose a Concept Drift Adaptation-facilitated malicious attack Defense framework (CDAD). Firstly, CDAD employs Adaptive Random Forest as an incremental learning approach, integrating an error-rate-based concept drift detection mechanism to dynamically identify evolving attack patterns and trigger adaptive model updates. Secondly, a haptic behavior classifier is introduced to classify expected human operator interactions and compare them with real-time haptic feedback from remote machines. This enables automated traffic relabeling, allowing CDAD to adapt to previously unseen attacks without relying on pre-labeled datasets. The superior performance of CDAD over existing state-of-the-art methods is demonstrated across various malicious attack scenarios through extensive simulations. Results show that with CDAD, the attack success rate can be limited to 3%, while maintaining an inference time below 1ms, thereby ensuring effective and efficient malicious attack defense in latency-sensitive H2M applications.

The extension of elastic optical network (EON) technologies to multi-band transmission (MB-EON) promises enhanced spectral efficiency, greater throughput, and long-term cost benefits for telecom operators. However, designing such networks presents challenges, particularly in optimizing physical parameters like optical power and quality of transmission (QoT) across different frequency bands. This paper introduces a methodology for optimal span-by-span power allocation using two hyper-accelerated power optimization (HPO) modes: flat launch power (FLP) and flat received power (FRP). This methodology significantly accelerate network power optimization while ensuring service stability in scenarios such as changes in network parameters, QoT degradation due to aging, and network re-optimization or upgrading. Through a comprehensive comparison, we find that FRP notably improves signal flatness and GSNR/OSNR, particularly in the S-band, contributing to a network-wide throughput increase in the order of 12% to 75%. Additionally, we demonstrate that HPO applied to global power optimization is simpler and more cost-effective than when applied to local methods for large-scale networks.

It is essential to be able to identify hidden anomalies in order to fully automate optical networks. This requires specific features from the application programming interfaces (APIs) used by the control plane and network monitoring solution. One of the solutions, Transport API (TAPI), utilizes advanced techniques in telemetry streaming. The update policy in TAPI enables key performance indicators (KPIs) to be transmitted only when changes are detected. In this paper, we explore how the update policy configuration of TAPI and the use of unsupervised learning (UL) interact in detecting previously unseen anomalies. Results reveal various trade-offs that network operators need to consider, including compute and time overhead, as well as the overall accuracy of UL.

We propose a first Asynchronous Federated Split Learning (AFSL), to add the flexibility of asynchronous computing to the combination of federated and split learning. This amounts to designing a harmonious combination of different paradigms in order to benefit from the advantages of each of them and to reduce the impacts of their shortcomings.This way, AFSL answers to the increasingly rising interest for distributed algorithms with the advent of edge computing in order to support new market segments, such as cloud gaming, immersive eXtended Reality (XR), indoor positioning, and mission critical IoT networks, with stringent requirements on latency and reliability.Computational experiments are conducted on IID and non-IID datasets to investigate the added value of the asynchronous feature. Results indicate that AFSL can accelerate model learning by up to 86% without sacrificing the model’s convergence and accuracy. Indeed, not only average training times are reduced, but clients use fewer resources, a critical characteristic for devices with limited computing capabilities, e.g., in edge devices. Performance degradation can be mitigated by a careful selection of the aggregation principle. Other advantages are with AFSL training in dynamic scenarios as it provides robustness with a short recovery time by leveraging asynchronous client training.

A large language model (LLM) chatbot is integrated within TeraFlowSDN for intent manipulation. The resulting chatbot is capable of understanding the context and is able to carry out three actions: create, find, and explain intents using natural language while being flexible regarding the language used.

We present P5; an event-driven policy framework that allows network operators to realize end-to-end policies on top of P4-based data planes in an intuitive and effective manner. We demonstrate how P5 adheres to a service-level agreement (SLA) by applying P4-based traffic engineering with latency constraints.

Service providers can leverage shared resources to reduce the overall amount of required resources while keeping acceptable Quality of Service (QoS) levels. Kubernetes (K8s) provides a Horizontal Pod Autoscaling (HPA) mechanism that allows to automatically adjust the number of Pods to closely follow the user demand variations over time. To properly leverage shared resources with HPA, service providers need to limit the use of dedicated resources and overprovisioning. However, in the case of traffic spikes, there may not be enough resources to satisfy the demand. The HPA, which relies on resource usage to drive the scaling, is unaware of how many requests could not be served with the required QoS. This might result in an underestimation of the number of required Pods to be added, leading to additional QoS degradation. This demonstration showcases the effectiveness of a new Pod autoscaling mechanism (i.e., Service Aware Pod Autoscaling (SAPA)) that relies on user request measurements from the service load balancer to better estimate the number of required Pods. SAPA allows selecting the amount of Pod resources (dedicated and shared) in a simple way. We demonstrate the benefits of SAPA by comparing it to a K8s cluster based on the traditional HPA in terms of resource usage and service latency.

Exponential growth of bandwidth demand, spurred by emerging network services with diverse characteristics and stringent performance requirements, drives the need for dynamic operation of optical networks, efficient use of spectral resources, and automation. One of the main challenges of dynamic, resource-efficient Elastic Optical Networks (EONs) is spectrum fragmentation. Fragmented, stranded spectrum slots lead to poor resource utilization and increase the blocking probability of in-coming service requests. Conventional approaches for Spectrum Defragmentation (SD) apply various criteria to decide when, and which portion of the spectrum to defragment. However, these polices often address only a subset of tasks related to defragmentation, are not adaptable, and have limited automation potential. To address these issues, we propose DeepDefrag, a novel framework based on reinforcement learning that addresses the main aspects of the SD process: determining when to perform de-fragmentation, which connections to reconfigure, and which part of the spectrum to reallocate them to. DeepDefrag outperforms the well-known Older-First First-Fit (OF-FF) defragmentation heuristic, achieving lower blocking probability under smaller defragmentation overhead.

ANIARA (https://www.celticnext.eu/project-ai-net) attempts to enhance edge architectures for smart manufacturing and cities. AI automation, orchestrated lightweight containers, and efficient power usage are key components of this three-year project. Edge infrastructure, virtualization, and containerization in future telecom systems enable new and more demanding use cases for telecom operators and industrial verticals. Increased service flexibility adds complexity that must be addressed with novel management and orchestration systems. To address this, ANIARA will provide en-ablers and solutions for services in the domains of smart cities and manufacturing deployed and operated at the network edge(s).

Slicing allows 5G networks to accommodate services with different needs over a unified physical network infrastructure. Particularly, in radio access networks (RANs), the baseband processing functions of different slices are treated as virtual applications running on shared compute nodes. Managing this cloud-native network in a cost-effective way requires tightly cou-pled control of connectivity and processing resources. This paper proposes an optimization problem to minimize the overall cost while guaranteeing distinct latency and reliability requirements of different network slices deployed over the infrastructure. This is achieved by choosing for each service, the functional split and transmission parameters that best adapt to the connectivity and processing resources available in the infrastructure. Results demonstrate that the proposed flexible resource allocation scheme provides considerable cost saving (up to 2.4 times) and reduced request blocking (up to 2 times) compared to conventional fixed deployment techniques. The reliability requirements can be guaranteed by packet duplication and/or virtualized forward error correction at the expense of consuming connectivity and/or processing resources, respectively. The flexible assurance of the reliability requirements in the proposed scheme contributes considerably to the achieved improvements on cost saving and request blocking.

The operation and management of software-based communication systems and services is a big challenge for infrastructure and service providers. The challenge is mainly associated with the more significant number of configurable elements and the higher dynamicity in the software-based systems than the classical ones. On the other hand, the modularity and programmability in software-based networks enabled by technologies like Software-Defined Networking (SDN) and Network Function Virtualization (NFV) provide new opportunities for operators to realize advanced network and service management strategies beyond the classical techniques. In our work, we elaborate on these new opportunities and propose a novel strategy for the management of survivable cloud services. In particular, we leverage the flexibility of SDN and NFV to combine proactive protection and reactive restoration mechanisms, and we put forward a novel strategy for enhancing the survivability of cloud services. Through comprehensive evaluations, we demonstrate that the proposed strategy offers significant benefits in terms of availability and restorability of services while reducing, at the same time, the overhead caused by the relocation of cloud services in case of failures.

Being able to account for the value of latency introduced by the transport infrastructure is considered a crucial feature for any 5G service provisioning strategy. This paper proposes a methodology for the automatic monitoring of the link latency in networks with a distributed SDN-based control plane architecture. The proposed link monitoring solution does not introduce any overhead in the OpenFlow channel and in the data plane. Experimental tests demonstrate the accuracy of the proposed methodology and its ability to scale to large network instances.

In this paper, we propose a new restoration algorithm for disaster recovery in Elastic Optical Networks (EONs). The algorithm considers the different requirements on restoration delay and bandwidth degradation while restoring over multiple routes. Furthermore, the algorithm is able to adapt the connection recover process to the post-disaster network conditions and, consequently, increase the number of restored connections. Results indicate that the proposed strategy is able to improve connections restorability and decrease bandwidth blocking probability when compared to restoration approach based on bandwidth degradation and multipath recovery.

Supporting end-to-end network slices and services across operators has become an important use case of study for 5G networks as can be seen by 5G use cases published in 3GPP, ETSI as well as NGMN. This paper presents the in- depth architecture, implementation and experiment on a multi-domain orchestration framework that is ab le to deploy such multi-operator service as well as monitor the service for SLA compliance. Our implemented architecture allows operators to abstract their sensitive details while exposing the relevant amount of information to support inter-operator slice creation. Our experiment shows that the implemented framework is capable of creating services across operators while fulfilling the respective service requirements.

The use of an optical fiber infrastructure to accommodate cloud services is gaining a lot of momentum. This is mainly driven by the bandwidth and latency performance that optical transmission can guarantee. On the other hand failures in the optical infrastructure may result in the concurrent loss of a possibly high number of cloud services. For this reason, being able to offer cloud service resiliency at a contained cost is of the utmost importance for operators. This paper proposes a heuristic for the restoration of optical cloud services in the presence of a single fiber link failure. The heuristic leverages on two parameters specified in the service class (i.e., restoration delay and bandwidth degradation) in order to make the best use of the available optical resources during the recovery process. The numerical results presented in the paper show that the proposed restoration algorithm is able to improve cloud service restorability without a negative impact on the cloud service blocking probability.

Base station (BS) densification is increasingly adopted by mobile operators in order to support increasing traffic demand. However, a large number of BSs requires many backhaul connections, which may be very expensive. For this reason, provisioning high speed backhaul connections to BSs in a cost-effective way is challenging, and it is important to efficiently utilize an existing fixed network infrastructure if possible. This paper proposes two optimized infrastructure-aware planning strategies for small cells and fiber backhaul. The first strategy is referred to as joint design (JD) and is based on the joint cost minimization of small cells and fiber backhaul. The JD strategy is compared to a traditional design (TD) solution based on a two-step optimization approach. In the latter a cost-optimal small cells placement is identified first, then the corresponding minimum cost fiber backhaul deployment is determined accordingly. A comparison between these two approaches in dense 5G urban scenarios shows that the relative performance of JD and TD largely depends on the expected traffic demand and on the existing infrastructure. In dense urban areas with the average traffic levels expected for the year 2020 and beyond, JD returns up to 50% lower deployment cost in a greenfield scenario, and up to 70% lower deployment cost in a brownfield scenario. Only in areas with extremely high traffic demand (e.g., open-air festivals and stadiums) JD returns deployment costs very similar to TD.

Providing high capacity to the end users is one of the main challenges for the fifth generation (5G) of mobile networks. The users' habit to consume online contents indoor makes the outdoor-to-indoor capacity provisioning impractical, especially when the high-frequency bands proposed for 5G are employed. The centralized radio architecture (CRA) is an in-building solution, which relies on the centralization of baseband processing functions, fully or partly allowing for centralized cell management while providing signals directly inside the buildings. On the other hand, the massive deployment of CRAs in urban areas may yield to unacceptably high installation costs, due to the radio network equipment to be activated. To make CRAs appealing to mobile operators, we propose different deployment strategies to minimize the CRA deployment cost. We define the remote radio unit placement (RRUP) problem and formulate it as an integer linear program, obtaining optimal deployment solutions in small urban residential scenarios. We prove the RRUP problem to be NP-hard, requiring heuristic approaches to solve large problem instances. To this end, we propose an effective and scalable heuristic for minimizing the amount of radio equipment required to deploy CRAs in large urban areas.

Each mobile network architecture able to meet the traffic requirements of future 5G services comes with its own set of benefits vs. requirements for the radio and the transport segments. This paper focuses on the energy performance of four mobile network architectures, each one with different splitting options for the baseband processing functions. The radio segment under exam is based on a new radio access technology referred to as 5G-NX, while the transport segment is based on wavelength division multiplexing (WDM). The energy consumption of each mobile network architecture is weighted against (i) the benefits for the radio segment as a function of the level of centralization of the baseband processing resources and (ii) the required transport capacity and consequently the power consumption levels needed to accommodate the backhaul, midhaul, and/or fronthaul traffic generated at each base station. Our results show that a fully centralized radio access network (C-RAN) with centralization of all the baseband functions is not a practical approach for 5G mobile networks, while a partially centralized C-RAN architecture in which the physical layer baseband processing is performed at the base station site represents a promising solution.

Mobile networks are the largest contributor to the carbon footprint of the telecom sector and their contribution is expected to rapidly increase in the future due to the foreseen traffic growth. Therefore, there is an increasing urgency in the definition of green mobile network deployment strategies. This paper proposes a four-step design and power assessment methodology for mobile networks, taking into consideration both radio and transport segments. A number of mobile network deployment architectures for urban residential areas based on different radio (i.e., macro base station, distributed indoor radio, femto cell) and transport (i.e., microwave, copper, optical fiber) technologies are proposed and evaluated to identify the most energy efficient solution. The results show that with low traffic the conventional macro base station deployment with microwave based backhaul is the best option. However, with higher traffic values heterogeneous networks with macro base stations and indoor small cells are more energy efficient. The best small cell solution highly depends on the transport network architecture. In particular, our results show that a femto cell based deployment with optical fiber backhaul is the most energy efficient, even if a distributed indoor radio architecture (DRA) deployment with fiber fronthaul is also a competitive approach.

This paper targets the lifetime-aware management of a set of optical line amplifiers (OLAs) in an optical network exploiting sleep mode (SM) in order to save energy. We first present a simple model to predict the OLA lifetime. We then provide different mixed-integer linear programming (MILP) formulations, which jointly consider energy saving and lifetime. The proposed MILP formulations are then solved on different realistic scenarios, by taking into account the spatial and temporal variations of traffic demands. Results show that our lifetime-aware approach outperforms classical energy saving ILP formulations, which instead tend to notably decrease the OLA lifetime. More important, the proposed approaches can achieve a good lifetime performance without consuming significantly more energy than purely energy-aware strategies.

Long-reach passive optical networks (LR-PONs) are able to effectively support the growing demand of traffic originating from residential and business customers. Failures of metro/core (M/C) nodes serving the traffic to/from the access networks covered by LR-PONs, may potentially affect hundreds or thousands of customers. One way of guaranteeing 100% survivability from single-node failures is to apply dual-homing, where each LR-PON is connected to two M/C nodes, and combine it with node-disjoint dedicated-path protection (DPP). In this paper, we present a new approach to provide network survivability against single M/C node failures. Instead of applying dedicated path protection (DPP) strategy, which can require huge amount of extra resources, we combine an unprotected network design with a dynamic multilayer restoration algorithm. Our aim is to determine a suitable amount of resource overbuild (in terms of extra transponders) needed to provide average connection availability close to that guaranteed by DPP. Preliminary results show that dimensioning for the worst-case scenario among a set of predefined M/C node failures, i.e., the one disrupting the highest number of connections, yields to a cost-effective strategy requiring up to 35% less transponders than DPP, while offering the same average connection availability.

Service relocation represents a promising strategy to provide flexible and resource efficient resiliency from link failures in the optical cloud environment. However, when a failure affects a node hosting a datacenter (DC), service relocation from the affected DC is not possible. One alternative to protect against DC failures relies on using design strategies that duplicate the IT (i.e., storage and processing) resources in a backup DC at the expense of increasing resource overbuild (i.e., cost) of the network. This work proposes a dimensioning strategy based on the shared-path shared-computing (SPSC) concept able to protect against any single link, server, or DC failure scenario with minimal resource overbuild for the network and IT infrastructures. SPSC is based on the intuition that only storage units need complete replication in backup DC, while processing units can be instantiated only after the occurrence of a failure, leaving the design strategy some leeway to minimize their number. As result, the proposed SPSC design shows a considerable reduction in the amount of backup resources when compared to the dedicated protection strategies.

Mobile operators are facing an exponential traffic growth due to the proliferation of portable devices that require a high-capacity connectivity. This, in turn, leads to a tremendous increase of the energy consumption of wireless access networks. A promising solution to this problem is the concept of heterogeneous networks, which is based on the dense deployment of low-cost and low-power base stations, in addition to the traditional macro cells. However, in such a scenario the energy consumed by the backhaul, which aggregates the traffic from each base station towards the metro/core segment, becomes significant and may limit the advantages of heterogeneous network deployments. This paper aims at assessing the impact of backhaul on the energy consumption of wireless access networks, taking into consideration different data traffic requirements (i.e., from todays to 2020 traffic levels). Three backhaul architectures combining different technologies (i.e., copper, fiber, and microwave) are considered. Results show that backhaul can amount to up to 50% of the power consumption of a wireless access network. On the other hand, hybrid backhaul architectures that combines fiber and microwave performs relatively well in scenarios where the wireless network is characterized by a high small-base-stations penetration rate.

Energy consumption of communication networks is growing very fast due to the rapidly increasing traffic demand. Consequently, design of green communication networks gained a lot of attention. In this paper we focus on optical Wavelength Division Multiplexing (WDM) networks, able to support this growing traffic demand. Several energy-aware routing and wavelength assignment (EA-RWA) techniques have been proposed for WDM networks in order to minimize their operational cost. These techniques aim at minimizing the number of active links by packing the traffic as much as possible, thus avoiding the use of lightly loaded links. As a result, EA-RWA techniques may lead to longer routes and to a high utilization on some specific links. This has a detrimental effect on the signal quality of the optical connections, i.e., lightpaths. In this study we quantify the impact of power consumption minimization on the optical signal quality. and address this problem by proposing a combined impairment and energy-aware RWA (IEA-RWA) approach. Towards this goal we developed a complete mathematical model that incorporates both linear and non-linear physical impairments together with an energy efficiency objective. The IEA-RWA problem is formulized as a Mixed Integer Linear Programming (MILP) model where both energy efficiency and signal quality considerations are jointly optimized. By comparing the proposed IEA-RWA approach with existing RWA (IA-RWA and EA-RWA) schemes, we demonstrate that our solution allows for a reduction of energy consumption close to the one obtained by EA-RWA approaches, while still guaranteeing a sufficient level of the optical signal quality.

Energy efficiency in cellular mobile radio networks has recently gained great interest in the research community. The development of more energy efficient hardware and software components aside, effect of different deployment strategies on energy efficiency are also studied in the literature. The latter mainly consist of optimizing the number and the location of different types of base stations in order to minimize the total power consumption. Usually, in the literature, the total network power consumption is restricted to the sum of the power consumption of all base stations. However, the choice of a specific deployment also affects the exact implementation of the backhaul network, and consequently its power consumption, which should therefore be taken into account when devising energy efficient deployment. In this paper, we propose a new power consumption model for a mobile radio network considering backhaul. We then handle a case study and perform a comparison of the power consumption of three different heterogeneous network deployments, and show how backhaul has a non-negligible impact on total power consumption, which differs for different deployments. An energy efficiency analysis is also carried out for different area throughput targets.

Energy-efficient optical networks are gaining momentum as environmental-friendly solutions with reduced operational costs. Energy-efficiency can be achieved by using devices in sleep mode, i.e., a low-power, inactive state in which devices can be suddenly waken-up upon occurrence of triggering events. This paper advocates a sleep mode option for the optical devices (e.g., amplifiers, optical switches) installed for protection purposes only. These devices can be put in sleep mode to reduce the network power consumption, but they can be promptly waken up (if necessary) upon a failure occurrence. This principle is proposed and applied in Wavelength Division Multiplexing (WDM) networks with dedicated-path protection to ensure survivability against single-link failures. The main contribution of the paper is the definition of the energy-efficient network planning problem for resilient WDM networks where optical devices can be configured in sleep mode. Optimal results of the integer linear programming (ILP) problem show savings of up to 25% in the overall power consumption.

The growing demand for online media content delivery and multi-player gaming is expected to increase the amount of multicast service requests in both public and private networks. Careful traffic engineering of multicast service requests is becoming increasingly essential, as establishing the lowest cost tree, e.g., shortest path tree, in the network for every individual multicast request does not always ensure a global optimization. In this paper, the authors investigate the use of alternate tree routing, according to which multiple sub-optimal tree candidates are computed for each multicast request. When performing global routing optimization, each request is then assigned one of the tree candidates, in a way that yields the desired result, e.g., to minimize the number of (active) links that are required in the network to bear the entire set of requests. A key component of the investigated approach is the timely construction of the set of alternate trees for every given root and destination set. An algorithm is proposed to compute multiple sub-optimal tree candidates with a guaranteed upper bound on the maximum end-to-end transmission latency. The algorithm builds on the widely used K shortest path algorithm, generalizing the technique of computing K candidate shortest paths to obtain a number of candidate sub-optimal trees with desirable properties. Simulation experiments obtained for a multi-protocol label switching (MPLS) traffic engineering network are discussed to investigate the effectiveness, performance, and scalability of the proposed algorithm.

A careful wavelength assignment (WA) to lambda services must be performed to reduce the total number of wavelength converters (WCs) that are required when the wavelength continuity constraint cannot be met in wavelength division multiplexing (WDM) networks. With the successful introduction of reconfigurable optical add-drop multiplexers (ROADMs), WDM networks are now growing in size, both in the number of optical nodes and the number of wavelengths supported. Fast and memory efficient WA algorithms are required to design cost effective large WDM networks. This paper presents a scalable and efficient WA heuristic algorithm aimed at reducing the total number of WCs that are required in (large) WDM networks bearing static lambda services. The WA algorithm is applied to both unprotected and (dedicated) protected lambda services. In the latter case, the wavelength continuity constraint between the working and protection path of a lambda service is taken into consideration when non-tunable optical transceivers are employed.

This paper presents an algorithm to find a simple path in the given network with multiple must-include nodes in the path. The problem of finding a path with must-include node(s) can be easily found in some special cases. However, in general, including multiple nodes in the simple path has been shown to be NP-Complete. This problem may arise in network areas such as forcing the route to go through particular nodes, which have wavelength converter (optical), have monitoring provision (telecom), have gateway functions (in OSPF) or are base stations (in MANET). In this paper, a heuristic algorithm is described that follows divide and conquer approach, by dividing the problem in two subproblems. It is shown that the algorithm does not grow exponentially in this application and initial re-ordering of the given sequence of must-include nodes can improve the result. The experimental results demonstrate that the algorithm successfully computes in reasonable time.

One class of wireless sensor networks makes use of sensor nodes that recharge their batteries by harvesting energy from the surrounding environment. Being continuously recharged, the battery does not need to be replaced regularly and the sensor node is maintenance-free. A key module in such sensor network solutions is the data link automatic repeat request (ARQ) protocol, which must be designed to reliably deliver sensor nodes data at the minimum energy cost. With this objective in mind, two ARQ protocol classes are compared. In one class, each sensor node operates individually. In the other, the concept of cooperative communications is adopted, whereby neighboring sensor nodes help each other during the retransmission process. It is shown that the use of cooperative ARQ protocols in energy harvesting sensor networks enables sensor nodes to balance their energy consumption to match their own battery recharge rate. In turn, a balanced energy consumption-to-recharge rate ratio has the potential to improve the network throughput. Both classes of ARQ protocols are analyzed and compared. Estimated throughput gains are discussed under various network scenarios.

The generic autonomous platform for sensor systems (GAP4S) is a maintenance-free wireless sensor network in which the sensor node battery does not need to be replaced. Power is delivered to the sensor node via a microwave signal that is radiated by a base-station. The base-station also acts as the entry point to a wider communication network, e.g., the Internet. The paper describes an automatic repeat request (ARQ) protocol that may be used in GAP4S to yield reliable and fair data transmission from the sensor nodes to the base-station. The protocol takes advantage of cooperative communication, whereby neighboring sensor nodes help during the retransmission process. The transmission power level is optimized at each sensor node to increase the saturation throughput of the ARQ protocol.

In reliable wavelength division multiplexed (WDM) networks, the optimal choice of routing and wavelength assignment (RWA) for the working and protection path-pair is often a complex problem to solve. To cope with such problem complexity, this paper presents the disjoint path-pair matrix (DPM) approach. With the DPM approach, the RWA problem complexity - i.e., the size of the solution space - can be significantly reduced by limiting the number of candidate path-pairs considered in the optimization. Simulation results are collected using the DPM approach to solve the online RWA problem in a network based on the shared path protection switching scheme with differentiated reliability. When compared to the conventional k-shortest paths approach, the DPM approach requires up to one order of magnitude less candidate path-pairs. In addition, the DPM approach finds solutions with reduced hop length of both the working and protection paths by up to 3% and 14%, respectively.