Waste collection is one of the targets of smart cities. It is a daily task in urban areas and it entails the planning of waste truck routes, taking into account environmental, economic and social factors. In this work, an optimal path planning algorithm has been developed together with a practical software platform for smart and sustainable cities that enables computing the optimal waste collection routes, minimizing the impact, both environmental (CO₂ emissions and acoustic damage) and socioeconomic (number of trucks to be used and fuel consumption). The algorithm is executed in Net2Plan, an open-source planning tool, typically used for modeling and planning communication networks. Net2Plan facilitates the introduction of the city layout input information to the algorithm, automatically importing it from geographical information system (GIS) databases using the so-called Net2Plan-GIS library, which can also include positions of smart bins. The algorithm, Net2Plan tool and its extension are open-source, available in a public repository. A practical case in the city of Cartagena (Spain) is presented, where the optimal path planning for plastic waste collection is addressed. This work contributes to the urban mobility plans of smart cities and could be extended to other smart cities scenarios with requests of optimal path planning.

Optical metro networks evolution driven by 5G requirements face enormous challenges. Network functions virtualized in the data centers spread to the metro nodes, IP, and optical technologies must cooperate to meet the metro traffic aggregation role. Multiple technological options exist, and carriers confront the need to economically assess them, benchmarked in realistic deployments. This paper gives relevant insights to this aim. We first construct a set of metro network benchmarks. A strategic and distinctive effort is made to incorporate metro WDM topologies, traffic profiles and daily variation patterns, fault-tolerance requisites, and network operational choices, that faithfully reflect the expected 5G metro progression for a national carrier. Then, we use these networks to assess two technological choices. On one hand, the cost-effectiveness limits in terms of CAPEX reductions and energy efficiency brought from the possibility of having an agile control plane in the metro, capable of on-demand instantiation of IT and network resources. On the other hand, we investigate the benefits of replacing ROADMs by more cost-effective filterless technologies, but just limiting this replacement to degree-1 and degree-2 optical nodes, that are prevalent (e.g. >50%) in regional metro topologies. A novel capacity planning algorithm has been developed for IT, IP and optical resources allocation and dimensioning, providing fault-tolerant designs for the realistic scenarios defined. Simulation results have been obtained using the Net2Plan NIW (NFV over IP over WDM) open-source framework. Developed algorithms and part of the testing scenarios are available for inspection in public repositories of the EU METRO-HAUL project, the umbrella for our work. Our results show CAPEX benefits in the order of 10% and energy savings in the order of 20-30% stemming from the on-demand resource allocation in the metro. In addition, degree 1 and degree 2 optical nodes have shown to be a sweet spot for applying filterless switching, with mitigated impact of the associated spectrum waste.

Transparent optical networks are the enabling infrastructure for converged multi-granular networks in the future Internet. The cross-layer planning of these networks considers physical impairments in the network layer design. This is complicated by the diversity of modulation formats, transmission rates, amplification and compensation equipments, or deployed fiber links. Thereby, the concept of quality of transmission (QoT) attempts to embrace the effects of the physical layer impairments, to introduce them in a multi-criterium optimization and planning process. This paper contributes in this field by the proposal and comparative evaluation of two novel offline impairment aware planning algorithms for transparent optical networks, which share a common QoT evaluation function. The first algorithm is based on an iterative global search driven by a set of binary integer linear programming formulations. Heuristic techniques are included to limit the binary programming complexity. The second algorithm performs different pre-orderings of the lightpath demand, followed by a sequential processing of the lightpath demands. The performance and the scalability of both approaches are investigated. Results reveal great scalability properties of the global search algorithm, and a performance similar to or better than the sequential schemes.

In translucent optical networks, the physical layer impairments degrading the optical signal are considered in the network planning. In this paper, we investigate the offline problem of routing and wavelength assignment (RWA) and regenerator placement (RP) in translucent networks, minimizing the lightpath blocking and regenerator equipment cost. We address two variants of the problem, which correspond to two different types of quality of transmission (QoT) estimators, called linear and nonlinear. In a nonlinear QoT, nonlinear impairments like crosstalk or cross-phase modulation, which account for the interferences from neighboring lightpaths in the network are explicitly computed. Then, the QoT estimated for a lightpath depends on the routes of other lightpaths in the network. In the linear QoT, the effects of the nonlinear impairments are overestimated and accumulated to the rest of the impairments in the QoT calculation. As a result, the QoT estimation of a lightpath solely depends on its route. For the linear case, we formulate an optimal integer linear programming model of the problem, to the best of the authors' knowledge, for the first time in the literature. Its simplicity allows us to test it for small- and medium-size networks. Also, we propose two heuristic methods, namely, lightpath segmentation and three-step, and a tight lower bound for the regenerator equipment cost. For the nonlinear QoT case, we propose a new heuristic called iterative RP (IRP). Both the IRP and three-step algorithms are designed to guarantee that no lightpath blocking is produced by signal degradation. This is a relevant difference with respect to earlier proposals. The performance and the scalability of our proposals are then investigated by carrying out extensive tests. Results reveal that the solutions obtained by the heuristic algorithms are optimal or close to optimal, and outperform the earlier proposals in the literature.

5G technologies promise to bring new network and service capacities and are expected to introduce significant architectural and service deployment transformations. The Cloud-Radio Access Networks (C-RAN) architecture, enabled by the combination of Software Defined Networking (SDN), Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) technologies, play a key role in the development of 5G. In this context, this paper addresses the problems of Virtual Network Functions (VNF) provisioning (VNF-placement and service chain allocation) in a 5G network. In order to solve that problem, we propose a genetic algorithm that, considering both computing resources and optical network capacity, minimizes both the service blocking rate and CPU usage. In addition, we present an algorithm extension that adds a learning stage and evaluate the algorithm performance benefits in those scenarios where VNF allocations can be reconfigured. Results reveal and quantify the advantages of reconfiguring the VNF mapping depending on the current demands. Our methods outperform previous proposals in the literature, reducing the service blocking ratio while saving energy by reducing the number of active core CPUs.