Performance Evaluation of C/C++, MicroPython, Rust and TinyGo Programming Languages on ESP32 Microcontroller

The rapid growth of the Internet of Things (IoT) and its applications requires high computational efficiency, low-cost, and low-power solutions for various IoT devices. These include a wide range of microcontrollers that are used to collect, process, and transmit IoT data. ESP32 is a microcontroller with built-in wireless connectivity, suitable for various IoT applications. The ESP32 chip is gaining more popularity, both in academia and in the developer community, supported by a number of software libraries and programming languages. While low- and middle-level languages, such as C/C++ and Rust, are believed to be the most efficient, TinyGo and MicroPython are more developer-friendly low-complexity languages, suitable for beginners and allowing more rapid coding. This paper evaluates the efficiency of the available ESP32 programming languages, namely C/C++, MicroPython, Rust, and TinyGo, by comparing their execution performance. Several popular data and signal processing algorithms were implemented in these languages, and their execution times were compared: Fast Fourier Transform (FFT), Cyclic Redundancy Check (CRC), Secure Hash Algorithm (SHA), Infinite Impulse Response (IIR), and Finite Impulse Response (FIR) filters. The results show that the C/C++ implementations were fastest in most cases, closely followed by TinyGo and Rust, while MicroPython programs were many times slower than implementations in other programming languages. Therefore, the C/C++, TinyGo, and Rust languages are more suitable when execution and response time are the key factors, while Python can be used for less strict system requirements, enabling a faster and less complicated development process

Distributed Agent-Based Orchestrator Model for Fog Computing

Fog computing is an extension of cloud computing that provides computing services closer to user end-devices at the network edge. One of the challenging topics in fog networks is the placement of tasks on fog nodes to obtain the best performance and resource usage. The process of mapping tasks for resource-constrained devices is known as the service or fog application placement problem (SPP, FAPP). The highly dynamic fog infrastructures with mobile user end-devices and constantly changing fog nodes resources (e.g., battery life, security level) require distributed/decentralized service placement (orchestration) algorithms to ensure better resilience, scalability, and optimal real-time performance. However, recently proposed service placement algorithms rarely support user end-device mobility, constantly changing the resource availability of fog nodes and the ability to recover from fog node failures at the same time. In this article, we propose a distributed agent-based orchestrator model capable of flexible service provisioning in a dynamic fog computing environment by considering the constraints on the central processing unit (CPU), memory, battery level, and security level of fog nodes. Distributing the decision-making to multiple orchestrator fog nodes instead of relying on the mapping of a single central entity helps to spread the load and increase scalability and, most importantly, resilience. The prototype system based on the proposed orchestrator model was implemented and tested with real hardware. The results show that the proposed model is efficient in terms of response latency and computational overhead, which are minimal compared to the placement algorithm itself. The research confirms that the proposed orchestrator approach is suitable for various fog network applications when scalability, mobility, and fault tolerance must be guaranteed

Framing Network Flow for Anomaly Detection Using Image Recognition and Federated Learning

The intrusion detection system (IDS) must be able to handle the increase in attack volume, increasing Internet traffic, and accelerating detection speeds. Network flow feature (NTF) records are the input of flow-based IDSs that are used to determine whether network traffic is normal or malicious in order to avoid IDS from difficult and time-consuming packet content inspection processing since only flow records are examined. To reduce computational power and training time, this paper proposes a novel pre-processing method merging a specific amount of NTF records into frames, and frame transformation into images. Federated learning (FL) enables multiple users to share the learned models while maintaining the privacy of their training data. This research suggests federated transfer learning and federated learning methods for NIDS employing deep learning for image classification and conducting tests on the BOUN DDoS dataset to address the issue of training data privacy. Our experimental results indicate that the proposed Federated transfer learning (FTL) and FL methods for training do not require data centralization and preserve participant data privacy while achieving acceptable accuracy in DDoS attack identification: FTL (92.99%) and FL (88.42%) in comparison with Traditional transfer learning (93.95%)

Older adults–potential users of technologies

Background and objective: The successful adoption of technology is becoming increasingly important to functional independence and successful ageing in place. A better understanding of technology usage amongst older people may help to direct future interventions aimed at improving their healthcare. We aimed to obtain the first data regarding technology use, including gerontechnologies, represented by fall detectors, from older adults in Lithuania. Material and methods: The research was carried out in the framework of the project Smart Gerontechnology for Healthy Ageing, which involved assessing the use of technologies and the readiness to use gerontechnologies, as represented by fall detectors. A total of 375 individuals that were more than 60 years of age were enrolled in the study. The self-reporting questionnaires were completed by geriatric in-patients, hospitalized in the geriatric department, and also by community-dwelling older adults. Results: Geriatric in-patients’ use of computers and the internet was associated with age (every year of age decreased the probability of computer and internet use by 0.9-times) and a positive attitude towards new technologies—this predictor increased the use of a computer by six-times in comparison with people who did not have such an attitude. Sex and education had no influence on computer use for geriatric in-patients. For community-dwelling older adults, the use of computers and internet was associated with age, education (a university education increased the use of computers and the internet by four times), and a positive attitude towards technologies. Conclusions: Lithuanian older women in the study used computers, the internet, and cell phones equally with men. Increasing age was a strong negative predictor of technology use. A positive attitude to new technologies was a strong positive predictor of technology use. [...]