Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but now they work to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of sustainable agriculture today, and there is increasing consumer interest in humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitats. There are many reasons, including societal and economic ones, to explore how new computing technologies can be used to ensure the welfare of animals in these settings. The goal of this review is to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm, and wild animal welfare. We use a broad notion of animal welfare to refer to an assessment of whether animals are healthy, free of pain and suffering, and positively stimulated in their environment. Smart computing and sensing is also used in broad terms, to refer to systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange, and analysis. The findings of this review are expected to motivate future research in computer science and engineering, as well as contribute to data, information, and communication management for animal welfare.

The specific demands of supply chains built upon large and complex IoT systems, make it a must to design a coordinated framework for cyber resilience provisioning, intended to guarantee trusted supply chains of ICT systems, built upon distributed, dynamic, potentially insecure, and heterogeneous ICT infrastructures. As such, the solution proposed in this paper is envisioned to deal with the whole supply chain system components, from the IoT ecosystem to the infrastructure connecting them, addressing security and privacy functionalities related to risks and vulnerabilities management, accountability, and mitigation strategies, as well as security metrics and evidence-based security assurance. In this paper, we present FISHY as a preliminary architecture that is designed to orchestrate existing and beyond state-of-the-art security appliances in composed ICT scenarios. To this end, the FISHY architecture leverages the capabilities of programmable networks and IT infrastructure through seamless orchestration and instantiation of novel security services, both in real-time and proactively. The paper also includes a thorough business analysis to go far beyond the technical benefits of a potential FISHY adoption, as well as three real-world use cases highlighting the envisioned benefits of a potential FISHY adoption.

Elastic optical networks are prone to spectrum fragmentation, resulting in poor resource utilization and often higher blocking probability. To overcome the spectrum fragmentation, a defragmentation (DF) of the spectrum can be applied by reconfiguring some or all active connections. However, reconfiguration is generally not desirable, as it can interrupt the services of the existing connections. In this paper, we propose two novel connection reconfiguration schemes to efficiently address spectrum DF: (i) a reactive–disruptive scheme and (ii) a proactive–non-disruptive scheme. Both schemes utilize the information of holding times of the existing connections in order to reduce fragmentation and thus improve resource utilization and minimize connection blocking. The reactive–disruptive reconfiguration scheme tries to allocate an incoming connection, which could not be accepted otherwise, by reconfiguring some existing connections. The proactive–non-disruptive reconfiguration method, on the other hand, allows us to reconfigure only such connections, which can be shifted without crossing over the spectrum of other connections. This paves the way for a non-disruptive DF when setting up a new connection, which is a desirable feature. Simulation results show that the reconfiguration schemes with holding-time awareness can be effectively utilized to reduce the spectrum fragmentation and, hence, result in better resource utilization and overall lower connection blocking probability.

The wide adoption of the recently coined fog and edge computing paradigms alongside conventional cloud computing creates a novel scenario, known as the cloud continuum, where services may benefit from the overall set of resources to optimize their execution. To operate successfully, such a cloud continuum scenario demands for novel management strategies, enabling a coordinated and efficient management of the entire set of resources, from the edge up to the cloud, designed in particular to address key edge characteristics, such as mobility, heterogeneity and volatility. The design of such a management framework poses many research challenges and has already promoted many initiatives worldwide at different levels. In this paper we present the results of one of these experiences driven by an EU H2020 project, focusing on the lessons learnt from a real deployment of the proposed management solution in three different industrial scenarios. We think that such a description may help understand the benefits brought in by a holistic cloud continuum management and also may help other initiatives in their design and development processes.

In recent years, edge computing has attracted significant attention because it can effectively support many delay-sensitive applications. Despite such a salient feature, edge computing also faces many challenges, especially for efficiency and security, because edge devices are usually heterogeneous and may be untrustworthy. To address these challenges, we propose a unified framework to provide efficiency and confidentiality by coded distributed computing. Within the proposed framework, we use matrix multiplication, a fundamental building block of many distributed machine learning algorithms, as the representative computation task. To minimize resource consumption while achieving information-theoretic security, we investigate two highly-coupled problems, (1) task allocation that assigns data blocks in a computing task to edge devices, and (2) linear code design that generates data blocks by encoding the original data with random information. Specifically, we first theoretically analyze the necessary conditions for the optimal solution. Based on the theoretical analysis, we develop an efficient task allocation algorithm. Using the task allocation, we then design secure coded computing schemes, for two cases, (1) with redundant computation and (2) without redundant computation. Moreover, we also theoretically analyze the optimization of the proposed scheme. Finally, we conduct extensive simulation experiments to demonstrate the effectiveness of the proposed schemes.

This paper considers service provider network design with a view of meeting application availability. Our primary goal is to design a service provider network using disparate network components and low-availability Virtual Network Functions (VNFs) while achieving high-availability Service Function Chains (SFCs). To this end, we attempt to answer the questions as to how much redundancy, and what type of redundancy should be incorporated in the network. Initially, we model a state-of-the-art provider architecture that is spread across the access, metro-core and backbone networks with associated discrete components such as switches, routers, optical equipment and data-centers. Our network design model leads to computing the amount of over-the-top (OTT) services that can be provisioned over a given network while achieving a particular availability. To this end, we formulate a constrained optimization model whose objective is profit maximization subject to availability measures that OTT services demand. We provide constraints of robustness that facilitate traffic churn. Four heuristics are also proposed with objectives that consider the breadth of the three key impacting parameters: VNF licensing cost, server utilization and delay optimization and the case for dynamic traffic. A simulation model presents comparative data for efficiency, latency and server utilization as well as validates our optimization model. The results stress the importance of an efficient optimization model in planning the network, as well as planning VNF placement ahead in time.