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.

The Industry 4.0 (I4.0) adoption comprises the change of traditional factories into smart using the ICTs. The goal is to monitor processes, objects, machinery, and workers in order to have real‐time knowledge about what is going on in the factory and for achieving an efficient data collection, management, and decision‐making that help improve the businesses in terms of product quality, productivity, and efficiency. Internet of Things (IoT) will have an important role in the I4.0 adoption because future smart factories are expected to rely on IoT infrastructures composed of constellations of hundreds or thousands of sensor devices spread all over the industrial facilities. However, some problems could arise in the massive IoT deployment in a medium‐high factory: thousands of IoT devices to cope from different technologies and vendors could mean dozens of vendor tools and user interfaces to manage them. Moreover, the heterogeneity of IoT devices could entail different communication protocols, languages, and data formats, which can result in lack of interoperability. On the other hand, conventional IT networks and industrial machinery are expected to be managed together with the IoT infrastructure, maybe using a tool or a set of tools, for orchestrating the whole smart factory. This work meets these challenges presenting an open‐source software architecture solution based on OpenDaylight (ODL), a Software Defined Network (SDN) controller, for orchestrating an industrial IoT scenario. This work is addressed by shedding light on critical aspects from the SDN controller architectural choices, to specific IoT interfaces and the difficulties for covering the wide range of communication protocols, popular in industrial contexts. Such a global view of the process gives light to practical difficulties appearing in introducing SDN in industrial contexts, providing an open‐source architecture solution that guarantees devices and networks interoperability and scalability, breaking the vendor lock‐in barriers and providing a vendor‐agnostic solution for orchestrating all actor of an I4.0 smart factory.

The Industry 4.0 (I4.0) aims to develop a framework where the new technologies interoperate with each other and with employees, creating a smart and efficient environment. Although there are many public and private initiatives focused on boosting the deployment of I4.0 in all sectors worldwide, the adoption is slower than expected. One of the main reasons is the lack of training in those technologies involved in I4.0, the so-called key-enabling technologies (KET). In this article, the current status of I4.0 adoption from the industry, employees, and training point of view is analyzed. The lack of I4.0 competences in the curricula of vocational education training (VET) and higher education (HE) is also highlighted. Finally, the European innovative training action IN4WOOD is presented as a successful open and free training tool developed to offer students, employees, and managers an easy way to learn, use, and deploy KET of I4.0. Although the main target users of the training action are those in the furniture and woodworking sector, it has been designed to be useful also for users in other business sectors. The training tool is composed of more than 300 video learning pills, practical use cases, gamification, and evaluation test for measuring the level of knowledge acquired. The training tool has been tested in a pilot launched in four European countries. The results from the pilot prove that the IN4WOOD training helps to fill the skill gaps identified in the current VET/HE students and improves the competitiveness of employees, managers, and enterprises.

Metropolitan networks are undergoing a major technological breakthrough leveraging the capabilities of software-defined networking (SDN) and network function virtualization (NFV). NFV permits the deployment of virtualized network functions (VNFs) on commodity hardware appliances which can be combined with SDN flexibility and programmability of the network infrastructure. SDN/NFV-enabled networks require decision-making in two time scales: short-term online resource allocation and mid-to-long term offline planning. In this paper, we first tackle the dimensioning of SDN/NFV-enabled metropolitan networks paying special attention to the role that latency plays in the capacity planning. We focus on a specific use-case: the metropolitan network that covers the Murcia - Alicante Spanish regions. Then, we propose a latency-aware multilayer service-chain allocation (LA-ML-SCA) algorithm to explore a range of maximum latency requirements and their impact on the IT resources for dimensioning the metropolitan network. We observe that design costs increase for low latency requirements as more data center facilities need to be spread to get closer to the network edge, reducing the economies of scale on the IT infrastructure. Subsequently, we review our recent joint computation of multi-site VNF placement and multilayer resource allocation in the deployment of a network service in a metro network. Specifically, a set of subroutines contained in LA-ML-SCA are experimentally validated in a network optimization-as-a-service architecture that assists an Open-Source MANO instance, virtual infrastructure managers and WAN controllers in a metro network test-bed.