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"Remote computing"
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Fight Fire with Fire: Detecting Forest Fires with Embedded Machine Learning Models Dealing with Audio and Images on Low Power IoT Devices
Forest fires are the main cause of desertification, and they have a disastrous impact on agricultural and forest ecosystems. Modern fire detection and warning systems rely on several techniques: satellite monitoring, sensor networks, image processing, data fusion, etc. Recently, Artificial Intelligence (AI) algorithms have been applied to fire recognition systems, enhancing their efficiency and reliability. However, these devices usually need constant data transmission along with a proper amount of computing power, entailing high costs and energy consumption. This paper presents the prototype of a Video Surveillance Unit (VSU) for recognising and signalling the presence of forest fires by exploiting two embedded Machine Learning (ML) algorithms running on a low power device. The ML models take audio samples and images as their respective inputs, allowing for timely fire detection. The main result is that while the performances of the two models are comparable when they work independently, their joint usage according to the proposed methodology provides a higher accuracy, precision, recall and F1 score (96.15%, 92.30%, 100.00%, and 96.00%, respectively). Eventually, each event is remotely signalled by making use of the Long Range Wide Area Network (LoRaWAN) protocol to ensure that the personnel in charge are able to operate promptly.
Journal Article
Reinforcement learning for intelligent online computation offloading in wireless powered edge networks
The method of charging mobile devices with wireless power transfer (WPT) from the base station (BS) integrated with mobile edge computing (MEC) increases the potential of MEC. The increasing demand for intelligent computation offloading requires effective decisions among local or remote computation specifically in wireless fading channels of the dynamic environment. Our main aim is to generate an effective offloading decision between local and remote computation in a real-time environment for each wireless channel while preserving optimal computation rate. In this article, we consider a wireless powered MEC system that governs a binary offloading decision to execute the task locally at the edge devices or the remote server. We propose a reinforcement learning based intelligent online offloading (RLIO) framework that adopts an optimal offloading action based on reinforcement methods. This framework acquires a worthy decision among local or remote computation for the time varying wireless channel conditions in dense networks. Numerical results show that the proposed framework can achieve optimal performance while preserving the computation time compared with existing optimization methods. Second, the average execution cost of RLIO is less than 0.4 ms per channel, which enables real-time and optimal offloading in dynamic and large-scale networks.
Journal Article
A Survey of Security Architectures for Edge Computing-Based IoT
The Internet of Things (IoT) is an innovative scheme providing massive applications that have become part of our daily lives. The number of IoT and connected devices are growing rapidly. However, transferring the corresponding huge, generated data from these IoT devices to the cloud produces challenges in terms of latency, bandwidth and network resources, data transmission costs, long transmission times leading to higher power consumption of IoT devices, service availability, as well as security and privacy issues. Edge computing (EC) is a promising strategy to overcome these challenges by bringing data processing and storage close to end users and IoT devices. In this paper, we first provide a comprehensive definition of edge computing and similar computing paradigms, including their similarities and differences. Then, we extensively discuss the major security and privacy attacks and threats in the context of EC-based IoT and provide possible countermeasures and solutions. Next, we propose a secure EC-based architecture for IoT applications. Furthermore, an application scenario of edge computing in IoT is introduced, and the advantages/disadvantages of the scenario based on edge computing and cloud computing are discussed. Finally, we discuss the most prominent security and privacy issues that can occur in EC-based IoT scenarios.
Journal Article
HAGP: A Heuristic Algorithm Based on Greedy Policy for Task Offloading with Reliability of MDs in MEC of the Industrial Internet
In the Industrial Internet, computing- and power-limited mobile devices (MDs) in the production process can hardly support the computation-intensive or time-sensitive applications. As a new computing paradigm, mobile edge computing (MEC) can almost meet the requirements of latency and calculation by handling tasks approximately close to MDs. However, the limited battery capacity of MDs causes unreliable task offloading in MEC, which will increase the system overhead and reduce the economic efficiency of manufacturing in actual production. To make the offloading scheme adaptive to that uncertain mobile environment, this paper considers the reliability of MDs, which is defined as residual energy after completing a computation task. In more detail, we first investigate the task offloading in MEC and also consider reliability as an important criterion. To optimize the system overhead caused by task offloading, we then construct the mathematical models for two different computing modes, namely, local computing and remote computing, and formulate task offloading as a mixed integer non-linear programming (MINLP) problem. To effectively solve the optimization problem, we further propose a heuristic algorithm based on greedy policy (HAGP). The algorithm achieves the optimal CPU cycle frequency for local computing and the optimal transmission power for remote computing by alternating optimization (AP) methods. It then makes the optimal offloading decision for each MD with a minimal system overhead in both of these two modes by the greedy policy under the limited wireless channels constraint. Finally, multiple experiments are simulated to verify the advantages of HAGP, and the results strongly confirm that the considered task offloading reliability of MDs can reduce the system overhead and further save energy consumption to prolong the life of the battery and support more computation tasks.
Journal Article
A wireless and battery-less implant for multimodal closed-loop neuromodulation in small animals
Fully implantable wireless systems for the recording and modulation of neural circuits that do not require physical tethers or batteries allow for studies that demand the use of unconstrained and freely behaving animals in isolation or in social groups. Moreover, feedback-control algorithms that can be executed within such devices without the need for remote computing eliminate virtual tethers and any associated latencies. Here we report a wireless and battery-less technology of this type, implanted subdermally along the back of freely moving small animals, for the autonomous recording of electroencephalograms, electromyograms and body temperature, and for closed-loop neuromodulation via optogenetics and pharmacology. The device incorporates a system-on-a-chip with Bluetooth Low Energy for data transmission and a compressed deep-learning module for autonomous operation, that offers neurorecording capabilities matching those of gold-standard wired systems. We also show the use of the implant in studies of sleep–wake regulation and for the programmable closed-loop pharmacological suppression of epileptic seizures via feedback from electroencephalography. The technology can support a broader range of applications in neuroscience and in biomedical research with small animals.
A tether-less and battery-less implant allows for the recording of electroencephalograms, electromyograms and body temperature in freely moving small animals, and for closed-loop neuromodulation via optogenetics and pharmacology.
Journal Article
Exploring Smartphone-Based Edge AI Inferences Using Real Testbeds
The increasing availability of lightweight pre-trained models and AI execution frameworks is causing edge AI to become ubiquitous. Particularly, deep learning (DL) models are being used in computer vision (CV) for performing object recognition and image classification tasks in various application domains requiring prompt inferences. Regarding edge AI task execution platforms, some approaches show a strong dependency on cloud resources to complement the computing power offered by local nodes. Other approaches distribute workload horizontally, i.e., by harnessing the power of nearby edge nodes. Many of these efforts experiment with real settings comprising SBC (Single-Board Computer)-like edge nodes only, but few of these consider nomadic hardware such as smartphones. Given the huge popularity of smartphones worldwide and the unlimited scenarios where smartphone clusters could be exploited for providing computing power, this paper sheds some light in answering the following question: Is smartphone-based edge AI a competitive approach for real-time CV inferences? To empirically answer this, we use three pre-trained DL models and eight heterogeneous edge nodes including five low/mid-end smartphones and three SBCs, and compare the performance achieved using workloads from three image stream processing scenarios. Experiments were run with the help of a toolset designed for reproducing battery-driven edge computing tests. We compared latency and energy efficiency achieved by using either several smartphone clusters testbeds or SBCs only. Additionally, for battery-driven settings, we include metrics to measure how workload execution impacts smartphone battery levels. As per the computing capability shown in our experiments, we conclude that edge AI based on smartphone clusters can help in providing valuable resources to contribute to the expansion of edge AI in application scenarios requiring real-time performance.
Journal Article
Edge Intelligence: A Review of Deep Neural Network Inference in Resource-Limited Environments
Deploying deep neural networks (DNNs) in resource-limited environments—such as smartwatches, IoT nodes, and intelligent sensors—poses significant challenges due to constraints in memory, computing power, and energy budgets. This paper presents a comprehensive review of recent advances in accelerating DNN inference on edge platforms, with a focus on model compression, compiler optimizations, and hardware–software co-design. We analyze the trade-offs between latency, energy, and accuracy across various techniques, highlighting practical deployment strategies on real-world devices. In particular, we categorize existing frameworks based on their architectural targets and adaptation mechanisms and discuss open challenges such as runtime adaptability and hardware-aware scheduling. This review aims to guide the development of efficient and scalable edge intelligence solutions.
Journal Article
Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm for Mobile Edge Computing Networks (EHRL)
Mobile Edge Computing (MEC) is a computational paradigm that brings resources closer to the network edge to provide fast and efficient computing services for Mobile Devices (MDs). However, MDs are often constrained by limited energy and computational resources, which are insufficient to handle the high number of tasks. The problems of limited energy resources and the low computing capability of wireless nodes have led to the emergence of Wireless Power Transfer (WPT) and Energy Harvesting (EH) as a potential solution where electrical energy is transmitted wirelessly and then harvested by MDs and converted into power. This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. Simultaneously, the DNN is trained using the Nadam optimizer (Nesterov-accelerated Adaptive Moment Estimation), which combines the benefits of Adam and Nesterov momentum, offering improved convergence speed and training stability. The proposed algorithm addresses the dual challenges of limited energy availability in MDs and the need for efficient task offloading to minimize latency and maximize computational performance. Numerical results validate the superiority of the proposed approach, demonstrating significant gains in computation performance and time efficiency compared to conventional techniques, making real-time and optimal offloading design truly viable even in a fast-fading environment.
Journal Article
Arithmetic Framework to Optimize Packet Forwarding among End Devices in Generic Edge Computing Environments
Multi-access edge computing implementations are ever increasing in both the number of deployments and the areas of application. In this context, the easiness in the operations of packet forwarding between two end devices being part of a particular edge computing infrastructure may allow for a more efficient performance. In this paper, an arithmetic framework based in a layered approach has been proposed in order to optimize the packet forwarding actions, such as routing and switching, in generic edge computing environments by taking advantage of the properties of integer division and modular arithmetic, thus simplifying the search of the proper next hop to reach the desired destination into simple arithmetic operations, as opposed to having to look into the routing or switching tables. In this sense, the different type of communications within a generic edge computing environment are first studied, and afterwards, three diverse case scenarios have been described according to the arithmetic framework proposed, where all of them have been further verified by using arithmetic means with the help of applying theorems, as well as algebraic means, with the help of searching for behavioral equivalences.
Journal Article