End Devices Research Articles

Assessment of Communication Resource Allocation by the Transmission Control Protocol for the Target Virtual Connection under Competitive Conditions

The mathematical framework presented in this article focuses on the controlled-transmission protocol’s asynchronous process of bandwidth allocation for the target virtual connection implemented under competition for communication resources. The studied process is formalized as a two-dimensional discrete Markovian chain, taking into account the distributions of queue lengths of TCP data fragments from competing client nodes. Such a chain describes the dynamics of filling the stack of transmitted but unacknowledged data fragments of the investigated end device. Distributions of the chain states were found for various ratios of the target virtual-connection bandwidth, transmission-protocol parameters, and communication-channel characteristics. Analytical dependencies for computing the performance of the target virtual connection for different operating modes were obtained. The results of experiments conducted based on the obtained analytical constructions showed that the performance of the virtual connection with a selective repeat mode is mainly determined by the data-loss intensity, the queue size distribution in transit nodes, and the ratio between the protocol window size and the route length.

Intelligent decision making for energy efficient fog nodes selection and smart switching in the IOT: a machine learning approach.

With the emergence of Internet of Things (IoT) technology, a huge amount of data is generated, which is costly to transfer to the cloud data centers in terms of security, bandwidth, and latency. Fog computing is an efficient paradigm for locally processing and manipulating IoT-generated data. It is difficult to configure the fog nodes to provide all of the services required by the end devices because of the static configuration, poor processing, and storage capacities. To enhance fog nodes' capabilities, it is essential to reconfigure them to accommodate a broader range and variety of hosted services. In this study, we focus on the placement of fog services and their dynamic reconfiguration in response to the end-device requests. Due to its growing successes and popularity in the IoT era, the Decision Tree (DT) machine learning model is implemented to predict the occurrence of requests and events in advance. The DT model enables the fog nodes to predict requests for a specific service in advance and reconfigure the fog node accordingly. The performance of the proposed model is evaluated in terms of high throughput, minimized energy consumption, and dynamic fog node smart switching. The simulation results demonstrate a notable increase in the fog node hit ratios, scaling up to 99% for the majority of services concurrently with a substantial reduction in miss ratios. Furthermore, the energy consumption is greatly reduced by over 50% as compared to a static node.

AMSPM: Adaptive Model Selection and Partition Mechanism for Edge Intelligence-driven 5G Smart City with Dynamic Computing Resources

With the help of 5G network, edge intelligence (EI) can not only provide distributed, low-latency, and high-reliable intelligent services, but also enable intelligent maintenance and management of smart city. However, the constantly changing available computing resources of end devices and edge servers cannot continuously guarantee the performance of intelligent inference. In order to guarantee the sustainability of intelligent services in smart city, we propose the Adaptive Model Selection and Partition Mechanism (AMSPM) in 5G smart city where EI provides services, which mainly consists of Adaptive Model Selection (AMS) and Adaptive Model Partition (AMP). In AMSPM, the model selection and partition of deep neural network (DNN) are formulated as an optimization problem. Firstly, we propose a recursive-based algorithm named AMS based on the computing resources of edge devices to derive an appropriate DNN model that satisfies the latency demand of intelligent services. Then, we adaptively partition the selected DNN model according to the computing resources of edge devices. The experimental results demonstrate that, when compared with state-of-the-art model selection and partition mechanisms, AMSPM not only reduces latency but also enhances computing resource utilization.

Determining Exact Quantiles with Randomized Summaries

Quantiles are fundamental statistics in various data science tasks, but costly to compute, e.g., by loading the entire data in memory for ranking. With limited memory space, prevalent in end devices or databases with heavy loads, it needs to scan the data in multiple passes. The idea is to gradually shrink the range of the queried quantile till it is small enough to fit in memory for ranking the result. Existing methods use deterministic sketches to determine the exact range of quantile, known as deterministic filter, which could be inefficient in range shrinking. In this study, we propose to shrink the ranges more aggressively, using randomized summaries such as KLL sketch. That is, with a high probability the quantile lies in a smaller range, namely probabilistic filter, determined by the randomized sketch. Specifically, we estimate the expected passes for determining the exact quantiles with probabilistic filters, and select a proper probability that can minimize the expected passes. Analyses show that our exact quantile determination method can terminate in P passes with 1-δ confidence, storing O(N 1/P logP-1/2P (1/δ)) items, close to the lower bound Ømega(N1/P) for a fixed δ. The approach has been deployed as a function in an LSM-tree based time-series database Apache IoTDB. Remarkably, the randomized sketches can be pre-computed for the immutable SSTables in LSM-tree. Moreover, multiple quantile queries could share the data passes for probabilistic filters in range estimation. Extensive experiments on real and synthetic datasets demonstrate the superiority of our proposal compared to the existing methods with deterministic filters. On average, our method takes 0.48 fewer passes and 18% of the time compared with the state-of-the-art deterministic sketch (GK sketch).

Enhanced power supply circuitry with long duration and high‐efficiency charging for indoor photovoltaic energy harvesting internet of things end device

AbstractLight is a popular choice as an indoor energy source for Internet of Things (IoT) end devices. However, indoor light sources are intermittent, which can disrupt the operation of IoT end devices, potentially leading to safety concerns or inaccurate data. Therefore, there is a growing need to develop a long‐duration power supply for IoT end devices. The energy from ambient light is harnessed to charge a supercapacitor through an energy manager chip. This supercapacitor serves as the power source for IoT nodes when the ambient light is unavailable. Nevertheless, as the voltage of the supercapacitor drops below the operating threshold, the IoT end node will eventually shut down. This paper proposes a circuit that utilizes a Joule Thief circuit, booster converter, and capacitor stack‐up circuit to extract the remaining energy from the supercapacitor and boost the voltage, thereby extending the operational lifespan of IoT end nodes. Additionally, capacitor stack‐up circuits significantly enhance charging efficiency. PSpice design and simulations confirm circuit feasibility. High‐efficiency charging and long‐duration IoT nodes suggest replacing traditional batteries with supercapacitors, reducing environmental impact.

Security-enhanced firmware management scheme for smart home IoT devices using distributed ledger technologies

With the increase of IoT devices generating large amounts of user-sensitive data, improper firmware harms users’ security and privacy. Latest home appliances are integrated with features to assure compatibility with smart home IoT. However, applying complex security mechanisms to IoT is limited by device hardware capabilities, making them vulnerable to attacks. Such attacks have recently become frequent. To address this issue, we developed a secure verification mechanism for firmware released by the device’s manufacturer. We proposed an IoT gateway for secure firmware verification and updating for smart home IoT devices utilizing the IOTA MAM (Masked Authenticated Messaging) protocol and a distributed file system with IPFS (Inter-Planetary File System) protocol. These two communication protocols ensure decentralized communication and firmware file distribution between the IoT device vendor and the IoT end device. The proposed scheme securely shares latest firmware content over IOTA and IPFS networks, performs a secure firmware update on IoT end devices and ensures authenticity and integrity of the firmware. Two types of validation methods were proposed for firmware updating and validation. We implemented the proposed scheme using three entities, Vendor, IoT gateway, and IoT end device. Our system yielded promising results in performing secure automated firmware updates on IoT end devices with very low computational power. The system’s functionality was implemented using IOTA’s MAM run on Raspberry Pi as an IoT gateway along with an ESP8266 Wi-Fi microcontroller, demonstrating the effectiveness of our approach. Our proposed methodology can be used for secure firmware distribution on home IoT applications.

Levelized costs and potential production of green hydrogen with wind and solar power in different provinces of mainland China

Green hydrogen produced from renewable sources such as wind and photovoltaic (PV) power is expected to be pivotal in China's carbon neutrality target by 2060. This study assessed the potential production, levelized costs of hydrogen (LCOH), and the cost structure in diverse mainland Chinese provinces from 2020 to 2060. It considered various combinations of electrolysis technologies, specifically alkaline electrolysis (AE) and proton exchange membrane (PEM), in conjunction with green electricity sources. The analysis considers the technological learning effects of wind power, PV power, AE, and PEM. This study's primary conclusions and policy recommendations are as follows: (1) PV power would be the predominant energy for green hydrogen production in nearly all of mainland China, providing a potential 2.25–28 642.19 kt/yr hydrogen production in different provinces. (2) AE exhibits cost (with LCOH around 3.18–8.74 USD/kg) competitiveness than PEM (with LCOH around 3.33–10.24 USD/kg) for hydrogen production. Thus, policymakers are advised to focus on the PV power combined with the AE pathway for large-scale hydrogen production. PEM is suggested to be mainly used in cases with high power fluctuations and end devices. (3) The provinces (especially Inner Mongolia, Xinjiang, and Gansu Province) in the Northwest of China show the greatest potential (about 74.35%) and have the lowest LCOH (with around 3.18–4.78 USD/kg). However, these provinces are quite distant from existing energy demand hubs. Thus, decision-makers are advised to focus on developing long-distance transmission/transportation infrastructure for either green electricity or green hydrogen.

A Lightweight and Chip-Level Reconfigurable Architecture for Next-Generation IoT End Devices

A Lightweight and Chip-Level Reconfigurable Architecture for Next-Generation IoT End Devices

Advancing Performance in LoRaWAN Networks: The Circular Region Grouped Bit-Slot LoRa MAC Protocol

LoRaWAN is an emerging Low-Power Wide-Area Network (LPWAN) technology, widely adopted in various Internet of Things (IoT) applications due to its long transmission range, low power consumption, and robust anti-interference capabilities. However, using the ALOHA medium access control (MAC) protocol in LoRaWAN significantly reduces the packet delivery rate, particularly in high-density networks where end devices (EDs) access the network randomly. It seriously affects the overall network performance. This paper introduces the Circular Region Grouped Bit-Slot CGBS-LoRa MAC protocol to address this challenge. The protocol takes a proactive approach by allocating transmission parameters for end devices, executing regional segmentation based on the distance between EDs and the gateway using different spreading factors. Concurrently, improving the ALOHA access method ensures the efficient communication of EDs in the region. Experimental results demonstrate that our proposed protocol markedly improves the scalability of LoRa networks and minimizes device collisions compared to three other MAC protocols. Even as the LoRaWAN network expands, the proposed protocol maintains a high packet delivery rate and low latency.

A Review on Intrusion Detection in IoT Networks: Existing Challenges

Advancements in computer networks has led to the inter-connectivity of different types of smart devices over the internet. Such a diversified connected network is often termed as internet of things or IoT. Off late, an ancillary of the IoT framework called the fogging or fog computing has gained tremendous prominence. . Fog computing decentralizes the infrastructure without depending on centralizing it, such as with cloud computing. Fog computing is a paradigm proposed that integrates the IoT and the cloud concept to support user mobility, low latency, and location awareness. Due to the de- centralized nature of the Fog architecture, the sharing of data among different smart devices is susceptible to security threats. In this paper, a comprehensive review on fog computing and the allied performance metrics such as coverage, error rate and throughput have been discussed. Moreover, a channel load sensing techniques utilizing the channel state information (CSI) has also been proposed with the aim to enhance the throughput and error rate of the system. Keywords: Internet of Things (IoT), Fog Computing, End Device, Error Rate, Throughput, Channel State Information.

Predictive mobility and cost-aware flow placement in SDN-based IoT networks: a Q-learning approach

Software-Defined Networking (SDN) has emerged as an innovative networking method that offers effective management and remarkable flexibility. However, current SDN-based solutions primarily focus on static networks or concentrate on backbone networks, where network dynamics have minimal impact. The existing methods for placing flow entries in Software-Defined Networking (SDN)-based Internet of Things (IoT) systems exhibit shortcomings in accurately predicting outcomes and efficiently reducing table misses and associated performance metrics. This research introduces a new approach, specifically the mobility-aware adaptive flow entry placement scheme for SDN-based Internet of Things (IoT) environments, to address the mobility aspect of networks. The proposed scheme utilizes the Q-learning algorithm to predict the next possible location of end devices, while the cost-sensitive AdaBoost algorithm is employed to select heavy and active flows. As a result, efficient flow rules for incoming flows can be dynamically implemented without controller intervention. Extensive computer simulations demonstrate that this approach significantly enhances match probability and prediction accuracy while concurrently reducing the number of table misses and resource expenditure compared to existing schemes.

Multi-Compression Scale DNN Inference Acceleration based on Cloud-Edge-End Collaboration

Edge intelligence has emerged as a promising paradigm to accelerate DNN inference by model partitioning, which is particularly useful for intelligent scenarios that demand high accuracy and low latency. However, the dynamic nature of the edge environment and the diversity of end devices pose a significant challenge for DNN model partitioning strategies. Meanwhile, limited resources of the edge server make it difficult to manage resource allocation efficiently among multiple devices. In addition, most of the existing studies disregard the different service requirements of the DNN inference tasks, such as its high accuracy-sensitive or high latency-sensitive. To address these challenges, we propose a Multi-Compression Scale DNN Inference Acceleration (MCIA) based on cloud-edge-end collaboration. We model this problem as a mixed-integer multi-dimensional optimization problem, jointly optimizing the DNN model version choice, the partitioning choice, and the allocation of computational and bandwidth resources to maximize the tradeoff between inference accuracy and latency depending on the property of the tasks. Initially, we train multiple versions of DNN inference models with different compression scales in the cloud, and deploy them to end devices and edge server. Next, a deep reinforcement learning-based algorithm is developed for joint decision making of adaptive collaborative inference and resource allocation based on the current multi-compression scale models and the task property. Experimental results show that MCIA can adapt to heterogeneous devices and dynamic networks, and has superior performance compared with other methods.

Brave New GES World: A Systematic Literature Review of Gestures and Referents in Gesture Elicitation Studies

How do we determine highly effective and intuitive gesture sets for interactive systems tailored to end users’ preferences? A substantial body of knowledge is available on this topic, among which gesture elicitation studies stand out distinctively. In these studies, end users are invited to propose gestures for specific referents, which are the functions to control for an interactive system. The vast majority of gesture elicitation studies conclude with a consensus gesture set identified following a process of consensus or agreement analysis. However, the information about specific gesture sets determined for specific applications is scattered across a wide landscape of disconnected scientific publications, which poses challenges to researchers and practitioners to effectively harness this body of knowledge. To address this challenge, we conducted a systematic literature review and examined a corpus of N = 267 studies encompassing a total of 187,265 gestures elicited from 6,659 participants for 4,106 referents. To understand similarities in users’ gesture preferences within this extensive dataset, we analyzed a sample of 2,304 gestures extracted from the studies identified in our literature review. Our approach consisted of (i) identifying the context of use represented by end users, devices, platforms, and gesture sensing technology; (ii) categorizing the referents; (iii) classifying the gestures elicited for those referents; and (iv) cataloging the gestures based on their representation and implementation modalities. Drawing from the findings of this review, we propose guidelines for conducting future end-user gesture elicitation studies.

Edge-to-Cloud Adaptive Offloading for Next-Generation Services

Due to the continuous growth of user traffic demands, there is a need to cope with the increased processing and storage requirements. The increased number of connected end devices causes a problem with carrying the generated loads efficiently. Edge and cloud computing and storage are real solutions to overcome the limitations of end devices on many prospective including computation capabilities, storage capabilities, and power consumption. Terminal devices offload their overflow tasks to the cloud for processing, analysis, and storage. This paper aims to improve computation offloading from edge nodes to the cloud in Internet of Things networks by making efficient decisions using an adaptive offloading algorithm. Offloading is controlled by a processing time offloading threshold value, which is determined automatically by edge nodes based on their traffic intensity and adaptively increased or decreased in loads. The proposed algorithm had been programmed and simulated; experimental evaluations show that the proposed adaptive offloading algorithm minimizes the edge mean response time by up to 58% and the cloud mean response time by up to 25% compared to the existing fixed, pre-defined offloading threshold value.

Proactive auto-scaling technique for web applications in container-based edge computing using federated learning model

Edge computing has emerged as an attractive alternative to traditional cloud computing by utilizing processing, network, and storage resources close to end devices, such as Internet of Things (IoT) sensors. Edge computing is still in its infancy, and resource provisioning and service scheduling remain research concerns. Kubernetes is a container orchestration tool in distributed environments. Proactive auto-scaling techniques in Kubernetes improve utilization by allocating resources based on future workload prediction. However, prediction models run on central cloud nodes, necessitating data transfer between edge and cloud nodes, which increases latency and response time. We present FedAvg-BiGRU, a proactive auto-scaling method in edge computing based on FedAvg and multi-step prediction by a Bidirectional Gated Recurrent Unit (BiGRU). FedAvg is a technique for training machine learning models in a Federated Learning (FL) model. FL reduces network traffic by exchanging only model updates rather than raw data, relieving learning models of the need to store data on a centralized cloud server. In addition, a technique for determining the number of Kubernetes pods based on the Cool Down Time (CDT) concept has been developed, preventing contradictory scaling actions. To our knowledge, our work is the first to employ FL for proactive auto-scaling in cloud and edge computing. The results demonstrate that the FedAvg-BiGRU method has a slightly higher prediction error than the load centralized processing mode, although this difference is not statistically significant. At the same time, it reduces the amount of data transmission between the edge nodes and the cloud server.

Proximal Policy Optimization for Efficient D2D-Assisted Computation Offloading and Resource Allocation in Multi-Access Edge Computing

In the advanced 5G and beyond networks, multi-access edge computing (MEC) is increasingly recognized as a promising technology, offering the dual advantages of reducing energy utilization in cloud data centers while catering to the demands for reliability and real-time responsiveness in end devices. However, the inherent complexity and variability of MEC networks pose significant challenges in computational offloading decisions. To tackle this problem, we propose a proximal policy optimization (PPO)-based Device-to-Device (D2D)-assisted computation offloading and resource allocation scheme. We construct a realistic MEC network environment and develop a Markov decision process (MDP) model that minimizes time loss and energy consumption. The integration of a D2D communication-based offloading framework allows for collaborative task offloading between end devices and MEC servers, enhancing both resource utilization and computational efficiency. The MDP model is solved using the PPO algorithm in deep reinforcement learning to derive an optimal policy for offloading and resource allocation. Extensive comparative analysis with three benchmarked approaches has confirmed our scheme’s superior performance in latency, energy consumption, and algorithmic convergence, demonstrating its potential to improve MEC network operations in the context of emerging 5G and beyond technologies.

Dynamic and Distributed Intelligence over Smart Devices, Internet of Things Edges, and Cloud Computing for Human Activity Recognition Using Wearable Sensors

A wide range of applications, including sports and healthcare, use human activity recognition (HAR). The Internet of Things (IoT), using cloud systems, offers enormous resources but produces high delays and huge amounts of traffic. This study proposes a distributed intelligence and dynamic HAR architecture using smart IoT devices, edge devices, and cloud computing. These systems were used to train models, store results, and process real-time predictions. Wearable sensors and smartphones were deployed on the human body to detect activities from three positions; accelerometer and gyroscope parameters were utilized to recognize activities. A dynamic selection of models was used, depending on the availability of the data and the mobility of the users. The results showed that this system could handle different scenarios dynamically according to the available features; its prediction accuracy was 99.23% using the LightGBM algorithm during the training stage, when 18 features were used. The prediction time was around 6.4 milliseconds per prediction on the smart end device and 1.6 milliseconds on the Raspberry Pi edge, which can serve more than 30 end devices simultaneously and reduce the need for the cloud. The cloud was used for storing users’ profiles and can be used for real-time prediction in 391 milliseconds per request.

Optimized LoRaWAN Architectures: Enhancing Energy Efficiency and Long-Range Connectivity in IoT Networks for Sustainable, Low-Power Solutions and Future Integrations with Edge Computing and 5G

The Internet of Things (IoT) has expanded rapidly, allowing for a network of sensors and gadgets to collect and share information to make people's lives easier and more convenient. As the Internet of Things (IoT) grows, however, energy efficiency becomes a major issue, especially for portable and wireless gadgets. Low-power, long-range communication capabilities are needed, and Long-Range Wide Area Network (LoRaWAN) has emerged as a viable solution to meet this need. This study provides an in-depth analysis of the LoRaWAN-based, low-power Internet of Things. The suggested network architecture is optimized for low power consumption and high connectivity for numerous Internet of Things (IoT) use cases. This low-power Internet of Things network relies on LoRaWAN gateways, end devices, and a server to function. LoRaWAN is a technology that enables the low-power, long-range transmission of data packets. The results show that the optimized case and non-optimized case have a delivery ratio of 0.85 to 0.73 from node 100 to 500. LoRaWAN significantly reduces energy usage compared to conventional IoT connectivity alternatives, making it a fantastic option for battery-powered devices in far-flung or limited-resource locations. Finally, the adoption of LoRaWAN provides a viable solution to address the energy efficiency concerns in IoT networks, hence allowing for sustainable, long-lasting IoT installations and enabling a wide variety of new applications within the IoT ecosystem. Furthermore, addresses the potential applications of this technology in the future, including upgrades and integration with other technologies like edge computing and 5G networks.

Research on software and hardware for the implementation of virtual reality technology

Virtual reality (VR) technology is rapidly entering the life space of humanity. Modern VR is not only entertainment and games. This technology is used in education, marketing, medicine, and the military industry, which gives a great impetus to its further development. With the help of this technology, it is possible to change the ways of communication between people, using all the charms of the virtual world. This article is devoted to the problem of studying various software and hardware tools for the implementation of virtual reality, in particular, as an example, the development of a VR application for the organization of personal computer maintenance training is presented. The article substantiates the relevance of the research, reviews scientific and methodical literature on the implementation of VR in various spheres of society. The existing software and hardware components of virtual reality technology, tools and frameworks are described. A comparative analysis of the created VR simulators for training in computer maintenance was conducted, their characteristics were presented, which made it possible to improve some operations in the developed application: the process of assembling computer components was detailed. The Unity game engine was used to develop this training, as it is quite simple and cross-platform. Processing of 3D models was carried out with the help of the «Blender» program. The C# programming language was used for development. The application was developed for the Oculus Quest 2 glasses, so the Oculus Integration SDK was used. After writing the project, a build was created for the «Android» platform, which is a file with the «apk» extension that can be installed on the end device. The main types of interaction are represented using a UML diagram of use cases. Currently, the following training tasks are implemented in the application: replacing the BIOS battery, RAM, applying thermal paste to the processor. In the further stages of development, it is planned to add other maintenance tasks, thereby expanding the functionality of the application. This application can be used in the educational process for students of the field of «Information Technologies» .

Methods and means of conflict-free data exchange in the group of mobile robotic platforms

When using groups of mobile robotic platforms (MRP), problems arise related to the management of individual platforms, the organization of cooperation in the group, and the management of the group as a whole. Management of the MRP group involves managing the actions of individual platforms to achieve the group's overall goal. To ensure the management of the MRP group in such a case, it is advisable to choose a hybrid method that requires solving the problem of conflict-free data exchange and control commands between the MRPs in the group. To solve this problem, it is proposed to improve the relevant methods and tools. The scientific novelty of the obtained research results is that a method of multi-channel conflict-free data exchange has been developed, which provides a real-time mode due to the coordination of the intensity of data arrival with the intensity of access. The method of controlling the movement of a group of mobile robotic platforms has been improved, which, by taking into account the changing parameters of the platforms and the changing state of the surrounding environment, provides effective management of the MRP group in real time. The practical significance of the research results is that it is proposed to use the CSMA/CA slotted mechanism for non-time-critical traffic to improve performance, and for time-critical traffic, coordinator-controlled access using guaranteed time slots. The hybrid method of management takes into account the advantages of centralized and distributed depending on specific tasks and conditions of use. It is proposed to use a multi-channel device for conflict-free exchange using the method of time allocation of RAM resources for data exchange in hybrid control. It is shown that global low-power networks LPWANs (Low-Power Wide Area Networks) can be used to transmit small blocks of data at a low speed when exchanging with MRP. It is proposed to use the slotted CSMA/CA mechanism for the transmission of non-time-critical traffic, and for time-critical traffic, coordinator-controlled access using guaranteed time slots. It is shown that the performance of the network during the conflict-free access period CFP depends on the results of the distribution of guaranteed GTS time slots among active users. LoRa technology was selected for long-distance data exchange between MRPs, which at the MAC (Media Access Control) sublayer allows for transmission planning and communication management between end devices and gateways, avoiding collisions and optimizing network performance.