An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

The great amounts of energy consumed by large-scale computing and network systems, such as data centers and supercomputers, have been a major source of concern in a society increasingly reliant on information technology. Trying to tackle this issue, the research community and industry have proposed myriad techniques to curb the energy consumed by IT systems. This article surveys techniques and solutions that aim to improve the energy efficiency of computing and network resources. It discusses methods to evaluate and model the energy consumed by these resources, and describes techniques that operate at a distributed system level, trying to improve aspects such as resource allocation, scheduling, and network traffic management. This work aims to review the state of the art on energy efficiency and to foster research on schemes to make network and computing resources more efficient.

https://hal.inria.fr/hal-00767582/document

A survey on techniques for improving the energy efficiency of large-scale distributed systems

Wireless Sensor Networks (WSNs) are crucial in supporting continuous environmental monitoring, where sensor nodes are deployed and must remain operational to collect and transfer data from the environment to a base-station. However, sensor nodes have limited energy in their primary power storage unit, and this energy may be quickly drained if the sensor node remains operational over long periods of time. Therefore, the idea of harvesting ambient energy from the immediate surroundings of the deployed sensors, to recharge the batteries and to directly power the sensor nodes, has recently been proposed. The deployment of energy harvesting in environmental field systems eliminates the dependency of sensor nodes on battery power, drastically reducing the maintenance costs required to replace batteries. In this article, we review the state-of-the-art in energy-harvesting WSNs for environmental monitoring applications, including Animal Tracking, Air Quality Monitoring, Water Quality Monitoring, and Disaster Monitoring to improve the ecosystem and human life. In addition to presenting the technologies for harvesting energy from ambient sources and the protocols that can take advantage of the harvested energy, we present challenges that must be addressed to further advance energy-harvesting-based WSNs, along with some future work directions to address these challenges.

https://dl.acm.org/doi/pdf/10.1145/3183338

Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review

Due to various challenging issues such as, computational complexity and more delay in cloud computing, edge computing has overtaken the conventional process by efficiently and fairly allocating the resources i.e., power and battery lifetime in Internet of things (IoT)-based industrial applications. In the meantime, intelligent and accurate resource management by artificial intelligence (AI) has become the center of attention especially in industrial applications. With the coordination of AI at the edge will remarkably enhance the range and computational speed of IoT-based devices in industries. But the challenging issue in these power hungry, short battery lifetime, and delay-intolerant portable devices is inappropriate and inefficient classical trends of fair resource allotment. Also, it is interpreted through extensive industrial datasets that dynamic wireless channel could not be supported by the typical power saving and battery lifetime techniques, for example, predictive transmission power control (TPC) and baseline. Thus, this paper proposes 1) a forward central dynamic and available approach (FCDAA) by adapting the running time of sensing and transmission processes in IoT-based portable devices; 2) a system-level battery model by evaluating the energy dissipation in IoT devices; and 3) a data reliability model for edge AI-based IoT devices over hybrid TPC and duty-cycle network. Two important cases, for instance, static (i.e., product processing) and dynamic (i.e., vibration and fault diagnosis) are introduced for proper monitoring of industrial platform. Experimental testbed reveals that the proposed FCDAA enhances energy efficiency and battery lifetime at acceptable reliability (∼0.95) by appropriately tuning duty cycle and TPC unlike conventional methods.

Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications

Wireless sensor networks (WSNs) provide noteworthy benefits over traditional approaches for several applications, including smart homes, healthcare, environmental monitoring, and homeland security. WSNs are integrated with the Internet Protocol (IP) to develop the Internet of Things (IoT) for connecting everyday life objects to the internet. Hence, major challenges of WSNs include: (i) how to efficiently utilize small size and low-power nodes to implement security during data transmission among several sensor nodes; (ii) how to resolve security issues associated with the harsh and complex environmental conditions during data transmission over a long coverage range. In this study, a secure IoT-based smart home automation system was developed. To facilitate energy-efficient data encryption, a method namely Triangle Based Security Algorithm (TBSA) based on efficient key generation mechanism was proposed. The proposed TBSA in integration of the low power Wi-Fi were included in WSNs with the Internet to develop a novel IoT-based smart home which could provide secure data transmission among several associated sensor nodes in the network over a long converge range. The developed IoT based system has outstanding performance by fulfilling all the necessary security requirements. The experimental results showed that the proposed TBSA algorithm consumed less energy in comparison with some existing methods.

A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network.

In this paper, we propose a global power management approach for energy harvesting sensor nodes. Our approach is based on a joint duty-cycle optimization and transmission power control. By simultaneously adapting both parameters, the node can maximize the number of transmitted packets while respecting the limited and time-varying amount of available energy. We obtain a high-packet delivery by using an original predictive transmission power control that can efficiently adapt the transmission power to the wireless channel conditions. To accurately model the wireless channel and the node communication hardware, a waveform-level radio frequency simulator has been developed. Simulation results show 6.5 times improvement in energy efficiency and a packet reception ratio which is 9 times more efficient than a recently published technique. A 15% increase in energy efficiency, with respect to a fixed transmission power configuration, has also been observed. Finally, the global power management strategy has been validated on a real wireless sensor networks platform. Experimental results are very similar to those obtained in simulations, and thus confirm the efficiency of our power management approach.

A Joint Duty-Cycle and Transmission Power Management for Energy Harvesting WSN

Wireless sensor networks (WSNs) require an extremely energy-efficient design. As sensor nodes have limited power sources, the problem of autonomy is crucial. Energy harvesting provides a potential solution to this problem. However, as current energy harvesters produce only a small amount of energy and their storage capacity is limited, efficient power management techniques must also be considered. In this article we address the problem of modeling and simulating energy harvesting WSN nodes with efficient power management policies. We propose furthermore a framework that permits to describe and simulate an energy harvesting sensor node by using a high level modeling approach based on power consumption and energy harvesting. The node architectural parameters as well as the on-line power management techniques will also be specified. Two new power management architectures will be introduced, taking into account energy-neutral and negative-energy conditions. Simulations results show that the throughput of a sensor node can be improved up to 50% when compared to a state of the art power management algorithm for solar harvesting WSN. The simulation framework is then used to find an efficient system sizing for a solar energy harvesting WSN node.

/pdf/a-framework-for-modeling-and-simulating-energy-harvesting-1cyxxt533x.pdf

A Framework for Modeling and Simulating Energy Harvesting WSN nodes with Efficient Power Management Policies

A lot of task scheduling algorithms and power management policies have been developed based on simplistic power models, which rarely take into account the effects of the power consumptions of the different components of a real system. Most of the models on which the study of the DVFS scheduling is based, make the assumption that the power consumption of a processor could be modelled as a E ∝ V 2 model. This hypothesis, even if partly true, is not generally applicable when considering the complete system, which consists of the processor, memories and power conversion circuits. In this paper we present a power and energy model for a DVFS enabled mobile computing platform. The platform is based on a low power SoC, which integrates both the processor core and memory, as well as other hardware accelerators. We include in our analisys the study of the power conversion components, which supply the SoC. Starting from measures, we first characterize the power consumption of the SoC and the converters, then a power and energy model for the processor is proposed. The model is able to predict the power consumption of the processor core with an average error less than 10%. This is then used to analyse two DVFS scheduling techniques based on the EDF algorithm, Cycle Conserving and Look Ahead. The results show that the CPU energy saving computed using our model, is far less than what would be expected using a model that does not take into account the effect of the static power.

Power Consumption Modeling for DVFS Exploitation

Harvesting energy from the environment has the potential to enable near-perpetual system operation. This comes at the cost of an increased complexity of both the hardware and software that respectively provide and use those features. In this paper we characterize a solar energy harvesting WSN platform using a high level and global approach. The harvesting transducer, the capacity of the battery, the platform load and the application behavior are modeled. We also propose a generic model for the prediction of the State of Charge (SoC) of the battery and we extract high level parameters that can be exploited by a power manager.

An efficient state of charge prediction model for solar harvesting WSN platforms

Bringing artificial intelligence to embedded devices has become a central research topic in many scientific domains (environment, agriculture, sociology, health…). For Human Activity Recognition, Artificial Neural Networks (ANNs) have shown their capability to provide better performance compared to other machine learning methods. However, ANNs suffer from two major limitations. First, ANNs are often trained using supervised learning requiring labelled databases, which are often difficult to build in real applications. Then, those algorithms are usually very expensive in terms of computing power. For that reason, their integration into low-power microcontrollers has been so far only evaluated to a limited extent. In this paper, we propose to evaluate quantitatively and qualitatively the embedded implementation of different neural networks for human activity recognition. First, supervised learning approaches are presented, followed by an exploratory study of unsupervised learning approaches using Self-Organizing Maps. Finally, some aspects of embedded unsupervised online learning are investigated to improve classification results using subject-specific data over a more general training. Each neural network is tested on a Human Activity Recognition dataset acquired from a smartphone using accelerometer and gyroscope sensing information (UCI HAR) and deployed on the SparkFun Edge board. This board hosts a low-power ARM Cortex-M4F-based microcontroller.

Andrea Castagnetti

Papers

A Joint Duty-Cycle and Transmission Power Management for Energy Harvesting WSN

A Framework for Modeling and Simulating Energy Harvesting WSN nodes with Efficient Power Management Policies

Power Consumption Modeling for DVFS Exploitation

An efficient state of charge prediction model for solar harvesting WSN platforms

Toward unsupervised Human Activity Recognition on Microcontroller Units