The Internet of Things (IoT) is used for various domains such as monitoring the environment, health care, and smart cities. Monitoring and measuring energy consumption of these systems is a crucial step in making them energy efficient. External Hardware-based power monitoring is not always available for IoT edge nodes. An alternative is to create an accurate power model that relates easy-to-monitor parameters (e.g., instructions count, cache misses, node temperature, etc) to externally monitored power. This relationship helps to estimate the power drawn by the nodes. IoT edge nodes have several power optimization leverages like Dynamic Voltage and Frequency Scaling (DVFS). When models calibration does not consider these leverages, the gap between power estimation and actual power usage increases. In related works, several power models and corresponding Software-defined power meters do not consider CPU frequency on IoT edge nodes. These Software-defined power meters provide regression-based power models for IoT edge nodes. This work compares predictions made by these state of the art power models to accurate external power monitoring. We show that not considering CPU frequency can result in incorrect estimations. We investigate and compare several methodologies for building power models, considering the CPU frequency, power, and energy leverage. Different performance metrics and regression methods are explored to estimate power usage. We demonstrate that linear and polynomial regression-based models are able to account for various CPU frequencies on IoT edge nodes. Using these models, we can predict the power consumed by IoT edge nodes running a specific workload, with a MAPE of 2% compared to accurate Hardware-based power meters.

Disseminating data in a wireless distributed system under limited resources, where nodes have constrained energy and communications capabilities, is challenging. In such contexts, common data dissemination techniques cannot be used. Instead, simple device-to-device communication policies allow mitigating the impact of communications on nodes energy consumption. However, depending on nodes configuration (up-times duration, wireless technology capabilities and energy consumption), choosing a suitable communication policy is challenging.In this paper, we propose and study two approaches based on classification algorithms that aim at predicting the most suitable communication policy to use, for a given node configuration, to match a given coverage and energy consumption target. The first approach called in situ learning, trains the classification models during deployment. The second approach called offline learning, uses existing data that are collected from previous deployments or simulations. Results show that, for a resource constrained environment, common classification models can take several months to converge with in situ learning. Depending on the policy, in situ learning can have a significant energy consumption overhead. Results underline offline learning as an interesting alternative to in situ learning, but requires to collect data from previous deployments or simulations.

A Cyber-Physical System (CPS) deployed in remote and resource-constrained environments faces multiple challenges. It has, no or limited: network coverage, possibility of energy replenishment, physical access by humans. Cyber-physical nodes deployed to observe and interact with the Arctic tundra face these challenges. They are subject to environmental factors such as avalanches, low temperatures, snow, ice, water and wild animals. Without energy supply infrastructures and humans available, nodes must achieve long operational lifetime from a single battery charge. They must be extremely energy-efficient. To reduce energy costs and increase their energy efficiency, cyber-physical nodes sleep most of the time, and avoid to communicate when they are unreachable. But, a CPS needs to disseminate data between the nodes for multiple purposes including data reporting to a back-end service, resilient operations, safe-keeping of observational data, and propagating nodes updates. Loosely-coupled data dissemination policies offer this possibility [1] . Although, investigations should be made on their applicability to large-scale CPS. In this paper, we evaluate and discuss the efficiency in energy, time and number of successful delivery of four data dissemination policies proposed in [1] . This evaluation is based on flow-level simulations. We study small and large-scale CPS, and evaluate the effects of the number of nodes and the size of the disseminated data on the nodes energy consumption and the dissemination's delivery success. To mitigate negative effects raised on large-scale CPS and large disseminated data sizes, different strategies are proposed and evaluated. We show that energy saving strategies do not always imply energy efficiency, and better data dissemination often comes at a cost. This last result highlights the importance of simulation prior to real CPS deployments in constrained environments. • CPS deployed in remote and resource-constrained environments faces challenges. • Energy efficient and scalable approaches for data dissemination in CPS are needed. • Simulation of CPS provides trade-offs in energy and data dissemination performance.

A Cyber-Physical System (CPS) deployed to a resource-constrained environment can face multiple challenges like (i) no or limited network coverage, (ii) no or limited possibility of energy replenishment, (iii) no or limited physical access by humans, (iv) nodes must cope with environmental factors including avalanches, low temperatures, snow, ice, water and wild animals. Devices being part of such a CPS must be battery powered and be energy efficient to achieve long life-time. However, the CPS still has to disseminate data to increase resiliency, safely keep results or update nodes. A trade-off between energy spent and data dissemination needs to be found, ideally by minimizing the energy usage and maximizing the relevant data dissemination performance metrics. In this paper, we evaluate and discuss the efficiency (in energy, time and number of successful distributions) of multiple data distribution policies by mean of flow-level simulations. We report on the trade-off between (i) successful data dissemination and (ii) energy and uptime overheads resulting from the usage of loosely coupled policies. To fully explore the scope of possibilities, we simulate a wide range of scenarios extracted from real measurements and previous deployments. Characteristics of CPS devices developed by the Distributed Arctic Observatory (DAO) are used as simulation platforms. Results show that an efficient policy in a given scenario can perform worse in another scenario. We also show that simple policies, especially when combined, can help in minimizing the energy consumed by most of the devices composing the CPS and maximizing the relevant dissemination performance metrics.

To do in-situ observations of the arctic tundra, many small sensor nodes are used. Reporting the data to remote back-ends is hard because backhaul networks are scarce, and a node cannot expect to see one. Also, there are typically no humans physically close to the nodes to fetch the data and to replace batteries. Consequently, the nodes need a highly available, energy-efficient long range network. While LoRaWAN provides energy-efficient communication for nodes, it does so at the cost of needing an always-on gateway with a high energy consumption. The gateway is also a single point of failure. Instead we propose LoRaLitE for Energy-Limited environments where the gateway enters sleep phases to reduce energy consumption. The sleep phases of the nodes are coordinated accordingly. For high availability, any end-node with a backhaul-network can be elected to become the gateway. We conducted a series of simulation experiments to document the performance behavior of both LoRaWanand LoRaLitE. The results show that the LoRaLitE gateway spends from 10 to 10,000 times less energy than a LoRaWAN gateway. A LoRaLitE node spends from 13% to 42% more energy than a LoRaWAN node. The achievable bandwidth for LoRaLitE is only insignificantly smaller than for LoRaWAN. A LoRaWangateway needs a relatively large, heavy battery. If the gateway fails, assuming a new gateway can be elected, several nodes must have similar large batteries to be able to take over as the gateway. This is impractical to achieve for more than a few nodes because the nodes become more expensive, harder to camouflage, and impractical to deploy. A LoRaLitE gateway on the other hand only needs a battery like any other node. Even if all nodes need larger batteries than for the LoRaWancase, the increase is in practical terms insignificant. We conclude that LoRaLitE makes it realistic to use LoRa for nodes on the arctic tundra, providing for insignificant increased energy usage at the nodes, insignificant reduced bandwidth, but with significant increase in gateway availability. LoRaLitE also provides for an easier deployment in practice because all nodes can be kept small and light,

Wi-Fi networks are extensively used to provide Internet access to end-users and to deploy applications at the edge. By playing a major role in modern networking, Wi-Fi networks are getting bigger and denser. However, studying their performance at large-scale and in a reproducible manner remains a challenging task. Current solutions include real experiments and simulations. While the size of experiments is limited by their financial cost and potential disturbance of commercial networks, the simulations also lack scalability due to their models' granularity and computational runtime. In this paper, we introduce a new Wi-Fi model for large-scale simulations. This model, based on flow-level simulation, requires fewer computations than state-of-the-art models to estimate bandwidth sharing over a wireless medium, leading to better scalability. Comparing our model to the already existing Wi-Fi implementation of ns-3, we show that our approach yields to close performance evaluations while improving the runtime of simulations by several orders of magnitude. Using this kind of model could allow researchers to obtain reproducible results for networks composed of thousands of nodes much faster than previously.

Information and Communication Technology takes a growing part in the worldwide energy consumption. One of the root causes of this increase lies in the multiplication of connected devices. Each object of the Internet-of-Things often does not consume much energy by itself. Yet, their number and the infrastructures they require to properly work have leverage. In this paper, we combine simulations and real measurements to study the energy impact of IoT devices. In particular, we analyze the energy consumption of Cloud and telecommunication infrastructures induced by the utilization of connected devices, and we propose an end-to-end energy consumption model for these devices.