Considering the complexity in the design of future satellites and the need for compliance with ECSS standards, the METASAT project proposes a novel design methodology based on model-based engineering and supported by open architecture hardware. The initiative highlights the potential of software vir-tualisation layers, like hypervisors, to meet standards compliance on high-performance computing platforms. Our focus is on the development of a toolchain tailored for these advanced hard-ware/software layers. In our view, without such innovation, the satellite industry may face high and unsustainable development costs and timelines, which could affect its competitiveness and reliability. This paper provides an overview of the model-based engineering toolchain, the workflow, and the digital twin concept proposed for the METASAT project. We present the purpose of each component and how they work together in the broader METASAT vision, with the objective of showing how this approach can make the development process more efficient and enhance dependability in the sector.

We consider several Software as a Service (SaaS) providers that offer services using the Cloud resources provided by an Infrastructure as a Service (IaaS) provider which adopts a pay-per-use scheme similar to the Amazon EC2 service, comprising flat, on demand, and spot virtual machine instances. For this scenario, we study the virtual machine provisioning and spot pricing strategies. We consider a two-stage provisioning scheme. In the first stage, the SaaS providers determine the optimal number of required flat and on demand instances. Then, in the second stage, the IaaS provider sells its unused capacity as spot instances for which the SaaS providers compete by submitting a bid. We study two different IaaS provider pricing strategies: the first assumes the IaaS provider sets a unique price; in the second, instead, the IaaS provider can set different prices for different customers. We model the resulting problem as a Stackelberg game. For each pricing scheme, we show the existence of the game equilibrium and provide the solution algorithms. Through numerical evaluation we compare the provisioning and spot price under the two different pricing strategies as function of the system parameters.

Routing solutions for multi-hop underwater wireless sensor networks suffer significant performance degradation as they fail to adapt to the overwhelming dynamics of underwater environments. To respond to this challenge, we propose a new data forwarding scheme where relay selection swiftly adapts to the varying conditions of the underwater channel. Our protocol, termed CARMA for Channel-aware Reinforcement learning-based Multi-path Adaptive routing, adaptively switches between single-path and multi-path routing guided by a distributed reinforcement learning framework that jointly optimizes route-long energy consumption and packet delivery ratio. We compare the performance of CARMA with that of three other routing solutions, namely, CARP, QELAR and EFlood, through SUNSET-based simulations and experiments at sea. Our results show that CARMA obtains a packet delivery ratio that is up to 40% higher than that of all other protocols. CARMA also delivers packets significantly faster than CARP, QELAR and EFlood, while keeping network energy consumption at bay.

We demonstrate the feasibility of long lasting underwater networking by proposing the smart exploitation of the energy harvesting capabilities of underwater sensor nodes. We define a data routing framework that allows senders to select the best forwarding relay taking into account both residual energy and foreseeable harvestable energy. Our forwarding method, named HyDRO, for Harvesting-aware Data ROuting, is also configured to consider channel conditions and route-wide residual energy, performing network wide optimization via local information sharing. The performance of our protocol is evaluated via simulations in scenarios modeled to include realistic underwater settings as well as energy harvesting based on recorded traces. HyDRO is compared to state-of-the-art forwarding protocols for underwater networks. Our results show that jointly considering residual and predicted energy availability is key to achieve lower energy consumption and latency, while obtaining much higher packet delivery ratio.

Superior performance in wireless sensor networks is obtained by taking key protocol decisions based on the outcome of local learning-based computations, informing nodes on past and expected availability of resources. This paper investigates the impact on protocol performance of local computational requirements of learning techniques. We consider a recent routing solution, named WHARP, which makes decentralized and proactive decisions based on a Markov Decision Process (MDP) that takes into account key parameters of wireless green networks, including energy harvesting capabilities, and wakeup radio technology. We show that in these scenarios solving the MDP incurs energy expenditures by far superior to that required by wireless communication, even at very high data traffic. In order to maintain the performance advantages of the learning-based protocol machinery, we propose a heuristic solution that closely approximates the MDP trading off optimality for considerably lighter computational requirements. We compare the performance of the heuristic-based WHARP (called W-HEU) to that of the MDP-based WHARP that uses the standard Backward Value Iteration (W-BVI) through GreenCastalia-based simulations with real computational energy measurements. Our results show that W-HEU outperforms W-BVI on key metrics such as energy consumption and packet delivery ratio, making up for the lost optimality of BVI through the remarkable energy savings of its lighter computational requirements.

This paper concerns the smart exploitation of multimodal communication capabilities of underwater nodes to enable reliable and swift underwater networking. To contrast adverse and highly varying channel conditions we define a smart framework enabling nodes to acquire knowledge on the quality of the communication to neighboring nodes over time. Following a model-based reinforcement learning approach, our framework allows senders to select the best forwarding relay for its data jointly with the best communication device to reach that relay. We name the resulting forwarding method MARLIN, for MultimodAl Reinforcement Learning-based RoutINg. Applications can choose whether to seek reliable routes to the destination, or whether faster packet delivery is more desirable. We evaluate the performance of MARLIN in varying networking scenarios where nodes communicate through two acoustic modems with widely different characteristics. MARLIN is compared to state-of-the-art forwarding protocols, including a channel-aware solution, a machine learning-based solution and to a flooding protocol extended to use multiple modems. Our results show that a smartly learned selection of relay and modem is key to obtain a packet delivery ratio that is twice as much that of other protocols, while maintaining low latencies and energy consumption.

Green wireless networks are characterized by devices that are pervasively deployed and that harvest energy from the surrounding environment. Devices are also endowed with low-power triggering techniques (e.g., wake-up radios) to obviate costly idle communication times. In this paper, we present a novel data forwarding strategy for green wireless networks that fully exploits the self-powered wake-up radio capabilities of the network nodes. The proposed strategy, named WHARP for Wake-up and HARvesting-based energy-Predictive forwarding, sends data to their destination by making decentralized and proactive decisions based on forecast energy and expected traffic. The performance of WHARP has been compared to that of the Energy Harvesting Wastage-Aware (EHWA) strategy through GreenCastalia-based simulations. Results show that our approach delivers up to 72% more packets, 1.6 times faster, and consuming 58% less energy than EHWA. This is obtained through a learned selection of forwarder relays allowing WHARP nodes to be operational 98% of the time: A 30% improvement over EHWA.

In this paper we present GREENROUTES, an energy-aware routing protocol for Energy Harvesting-based ("green") Wireless Sensor Networks that leverages self-powered technologies for eliminating the need of energy storage device replacement. GREENROUTES combines energy harvesting and wake-up radios with semantic addressing. Semantic addressing capabilities are effectively used to enhance communication by allowing nodes to selectively wake-up a suitable subset of neighboring nodes. This subset is determined by the distance of nodes from the sink, and, greedily, by the residual energy along routes to the sink. The performance of GREENROUTES has been compared to that of the Energy Harvest Wastage-Aware (EHWA) routing solution in scenarios where all nodes harvest energy from the same source, either sun or wind. Results show that GREENROUTES achieves a packet delivery ratio significantly higher (up to 40%) than EHWA, while delivering packets faster and for less power.

In this paper, we consider an application provider that executes simultaneously periodic long running jobs and needs to ensure a minimum throughput to guarantee QoS to its users; the application provider uses virtual machine (VM) resources offered by an IaaS provider. Aim of the periodic jobs is to compute measures on data collected over a specific time frame. We assume that the IaaS provider offers a pay for only what you use scheme similar to the Amazon EC2 service, comprising on demand and spot VM instances. The former are sold at a fixed price, while the latter are assigned on the basis of an auction. We focus on the bidding decision process by the application provider and model the bidding problem as a Q-Learning problem, taking into account the workloads, the maximum completion times since jobs start, the last checkpoint, and the past spot prices observed. In Q-Learning, a form of model-free Reinforcement Learning, the player is repeatedly faced with a choice among N different actions, which will determine immediate rewards or costs and will influence future evolutions. Through numerical experiments, we analyze the resulting bidding strategy under different scenarios. Our results show the application provider ability to refine its behavior and to determine the best action so to minimize the average cost per job, also taking into account checkpointing issues and QoS constraints.

In this paper we consider several Software as a Service (SaaS) providers, that offer a set of applications using the Cloud facilities provided by an Infrastructure as a Service (IaaS) provider. We assume that the IaaS provider offers a pay only what you use scheme similar to the Amazon EC2 service, comprising flat, on demand, and spot virtual machine instances. We propose a two stage provisioning scheme. In the first stage, the SaaS providers determine the number of required flat and on demand instances by means of standard optimization techniques. In the second stage the SaaS providers compete, by bidding for the spot instances which are instantiated using the unused IaaS capacity. We assume that the SaaS providers want to maximize a suitable utility function which accounts for both the QoS delivered to their users and the associated cost. The IaaS provider, on the other hand, wants to maximize his revenue by determining the spot prices given the SaaS bids. We model the second stage as a Stackelberg game, and we compute its equilibrium price and allocation strategy by solving a Mathematical Program with Equilibrium Constraints (MPEC) problem. Through numerical evaluation we study the equilibrium solutions as function of the system parameters.