End Devices Research Articles

Super Low Profile 5G mmWave Highly Isolated MIMO Antenna with 360∘ Pattern Diversity for Smart City IoT and Vehicular Communication

This study introduces a planar folded super low profile 5G mmWave monopole antenna for small IoT devices with superior efficiency and gain. Low-loss Rogers RT-5880 substrate material has been utilized to achieve higher efficiency, gain, and diversity performance in a relatively compact size. The unit element planar monopole antenna has been designed within 6.15×5.13×0.8mm3 with a partial ground. Four unit element antenna was orthogonally arranged on alternating sides of the substrate plane with narrow inter-component spacing in the multiple input multiple output (MIMO) system. The integration of the double negative (DNG) metamaterial array on the antenna led to a cumulative improvement in the overall performance of the antenna, which includes gain, efficiency, and isolation. The minimum isolation of the antenna increased by 11 dB with substrate-loaded metamaterial. The proposed MIMO antenna model with substrate-loaded metamaterial demonstrates 8 GHz instantaneous bandwidth (33.1 GHz - 41.1 GHz), 0.001 envelope correlation coefficient (ECC), diversity gain (DG) greater than 9.99 dB, channel capacity loss (CCL) less than 0.2bits/s/Hz, and a total active reflection coefficient (TARC) of 25 dB, which suggests that the diversity characteristics of the MIMO antenna system is impressive. Moreover, a densely packed 12-element antenna model has been introduced in this study with 360∘ pattern diversity, capable of receiving signals from any direction. Three 4-element MIMO systems have been arranged in an architecture to develop the 12-element MIMO system, and the size of the 12-element MIMO architecture is 15×15×15mm3. Despite having this compact size and dense packing, the 12-element MIMO antenna model shows similar diversity performance as the 4-element antenna model. Analog equivalent circuit models or resistor-inductor-capacitor (RLC) equivalent systems for the antenna models were created. A concept of a 128-element massive MIMO antenna has been presented to support the end devices, creating an application scenario of an IoT-interconnected smart city.

Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN

Long-Range Wide-Area Network (LoRaWAN) technology offers efficient connectivity for numerous end devices over a wide coverage area in the Internet of Things (IoT) network, enabling the exchange of data over the Internet between even the most minor Internet-connected devices and systems. One of LoRaWAN’s hallmark features is the Adaptive Data Rate (ADR) algorithm. ADR is a resource allocation function which dynamically adjusts the network’s data rate, airtime, and energy dissipation to optimise its performance. The allocation of spreading factors plays a critical function in defining the throughput of the end device and its robustness to interference. However, in practical deployments, LoRaWAN networks experience considerable interference, severely affecting the packet delivery ratio, energy utilisation, and general network performance. To address this, we present a novel ADR framework, SSFIR-ADR, which utilises randomised spreading factor allocation to minimise energy consumption and packet collisions while maintaining optimal network performance. We implement a LoRa network composed of a single gateway that connects loads of end nodes to a network server. In terms of energy use, packet delivery rate, and interference rate (IR), our simulation implementation does better than LoRaWAN’s legacy ADR scheme for a range of application data intervals.

Edge-device collaborative computing for multi-view classification

Motivated by the proliferation of Internet-of-Thing (IoT) devices and the rapid advances in the field of deep learning, there is a growing interest in pushing deep learning computations, conventionally handled by the cloud, to the edge of the network to deliver faster responses to end users, reduce bandwidth consumption to the cloud, and address privacy concerns. However, to fully realize deep learning at the edge, two main challenges still need to be addressed: (i) how to meet the high resource requirements of deep learning on resource-constrained devices, and (ii) how to leverage the availability of multiple streams of spatially correlated data, to increase the effectiveness of deep learning and improve application-level performance. To address the above challenges, we explore collaborative inference at the edge, in which edge nodes and end devices share correlated data and the inference computational burden by leveraging different ways to split computation and fuse data. Besides traditional centralized and distributed schemes for edge-end device collaborative inference, we introduce selective schemes that decrease bandwidth resource consumption by effectively reducing data redundancy. As a reference scenario, we focus on multi-view classification in a networked system in which sensing nodes can capture overlapping fields of view. The proposed schemes are compared in terms of accuracy, computational expenditure at the nodes, communication overhead, inference latency, robustness, and noise sensitivity. Experimental results highlight that selective collaborative schemes can achieve different trade-offs between the above performance metrics, with some of them bringing substantial communication savings (from 18% to 74% of the transmitted data with respect to centralized inference) while still keeping the inference accuracy well above 90%.

A multi‐dimensional incentive mechanism based on age of update in hierarchical federated learning

AbstractFederated learning represents a decentralized approach to machine learning, enabling numerous devices to collaboratively contribute to model training while ensuring the privacy of individual data. However, the existing incentive mechanism of hierarchical federated learning (HFL) only considers the data contribution of a single round, which needs to be revised. For non‐IID data sets, the continuous selection of any end devices will cause the weights to diverge in a specific direction. Therefore, a new metric is needed to avoid continuously selecting a certain end device to ensure the overall effectiveness. We introduce a metric to describe the importance of updates: age of update (AoU), which can help select end devices not selected in the previous round to promote a faster model convergence. We put forward an incentive mechanism based on AoU, reputation, and data quantity in HFL (ARDHFL). We have derived the optimal equilibrium solution for the three‐stage Stackelberg game. Based on this solution, we can ensure maximum edge‐cloud utility while incentivizing end devices to engage actively in HFL tasks and providing superior data to train the HFL model. Finally, we conducted extensive experiments to prove that ARDHFL can effectively improve the performance. Compared with the fixed scheme, random scheme, FMore and InFEDge, the testing accuracy of ARDHFL in the MNIST dataset has been improved by 29.7%, 9.3%, 6.8% and 6.1%, respectively. In the CIFAR‐10 dataset, it has been improved by 40.2%, 33.1%, 16.4% and 14.2%, respectively, and demands fewer communication iterations to achieve the same testing accuracy.

A privacy-preserving Self-Supervised Learning-based intrusion detection system for 5G-V2X networks

In light of the ongoing transformation in the automotive industry, driven by the adoption of 5G and the proliferation of connected vehicles, network security has emerged as a critical concern. This is particularly true for the implementation of cutting-edge 5G services such as Network Slicing (NS), Software Defined Networking (SDN), and Multi-access Edge Computing (MEC). As these advanced services become more prevalent, they introduce new vulnerabilities that can be exploited by cyber attackers. Consequently, Network Intrusion Detection Systems (NIDSs) are pivotal in safeguarding vehicular networks against cyber threats. Still, their efficacy hinges on extensive data, which often contains sensitive and confidential information such as vehicle positions and owner’s behaviors, raising privacy concerns. To address this issue, we propose a Privacy-Preserving Self-Supervised Learning (SSL) based Intrusion Detection System for 5G-V2X networks. The majority of works in the literature relying on Federated Learning (FL) and often overlook data labeling on the end devices. Our methodology leverages SSL to pre-train NIDSs using unlabeled data. Post-training is then performed with a minimal amount of labeled data, which can be carefully crafted by an expert. This novel technique allows the training of NIDSs with huge datasets without compromising privacy, consequently enhancing the efficacy of cyber-attack protection. Our innovative SSL pre-training methodology has yielded remarkable results, demonstrating a substantial improvement of up to 9% in accuracy across a diverse range of training dataset sizes, including scenarios with as few as 200 data samples. Our approach highlights the potential to enhance automotive network security significantly, showcasing groundbreaking achievements that set a new standard in the field of automotive cybersecurity.

Beacon-Based Uplink Transmission for LoRaWAN Direct to LEO Satellite Internet of Things

Direct-to-satellite IoT (DtS-IoT) communication structure is a promising solution to provide connectivity and extend the coverage of traditional low-power and long-range technologies, especially for isolated and remote areas where deploying traditional infrastructure is impracticable. Despite their bounded visibility, the Low Earth Orbit (LEO) satellites complement the terrestrial networks, offering broader gateway coverage and terrestrial network traffic offloading. However, the dynamics of LEO and the nature of such integration come with several challenges affecting the efficacy of the network. Therefore, this paper proposes Beacon-based Uplink LoRaWAN (BU-LoRaWAN) to enhance satellite-terrestrial communication efficiency. The proposed scheme exploits the LoRaWAN class B synchronization mechanism to provide efficient uplink transmission from LoRaWAN devices placed on the ground to satellite gateways. BULoRaWAN proposes an uplink transmission slot approach to synchronize ground devices’ uplink traffic with LEO-based orbiting gateways. It also uses a queue data structure to buffer end devices’ ready-to-send packets until the appropriate moment. BU-LoRaWAN avoids possible transmission collision by optimizing a random transmission slot for an end device within the beacon window. The proposed system is implemented and evaluated using OMNeT++ network simulator and FLoRaSat framework. The result demonstrates the feasibility of the proposed system. BU-LoRaWAN achieves better performance compared to the standard LoRaWAN, which manages to deliver almost double the traffic delivered by the standard one.

Load-balanced offloading of multiple task types for mobile edge computing in IoT

The continuous development of mobile networks poses new challenges for end devices with limited computing power. Mobile or multi-access edge computing (MEC) has been proposed for providing the computing resources close to the end devices that need them. However, in real network environments, MEC servers also have limited computing resources that need to be shared among many devices and efficient resource allocation is critical to ensure that the limited resources are optimally used. In view of this, we propose the Balanced Offload for Multi-type Tasks (BOMT) algorithm. The tasks to be offloaded are first prioritized according to their type, size and maximum tolerable delay; then different offloading algorithms are executed for different priority tasks according to the level of the priority and the current load on the MEC server. Following which, the optimal offloading policy is determined iteratively. Simulation results show that BOMT can effectively reduce system delay, increase user coverage and offload task completion rates.

PDAC-SL: A PUF-enabled drone access control technique for smart logistics

The conventional logistics’ paradigm is being replaced with smarter ways of supply chain management to readily serve the customers with quick modes of delivery in affordable operational costs. The weak infrastructure in rural terrain and congested roads in urban cities act as a big impediment in the successful accomplishment of last mile delivery down the lane across the complete chain of logistics. The traditional logistics has been evolved and improved in many respects, the last mile delivery is still plagued by security and privacy risks in logistics. The use of drones for package deliveries may offer promising gains in terms of efficiency. Nonetheless, drones undergo security and privacy risks besides physical threats for being deployed in public places communicating on open channels. Thus, more efforts are needed to secure the communication of inducted drones in delivery assignments. We propose a novel PUF-enabled drone access control mechanism PDAC-SL for smart logistics. By the careful employment of symmetric crypto-primitives, the contributed protocol not only supports low end devices but stands resistant to physical drone and other known threats besides preserving anonymity and untraceability features. The RoR model-based formal security analysis and performance analysis demonstrate distinct security properties and applicability of the PDAC-SL.

Energy Performance of LR-FHSS: Analysis and Evaluation.

Long-range frequency hopping spread spectrum (LR-FHSS) is a pivotal advancement in the LoRaWAN protocol that is designed to enhance the network's capacity and robustness, particularly in densely populated environments. Although energy consumption is paramount in LoRaWAN-based end devices, this is the first study in the literature, to our knowledge, that models the impact of this novel mechanism on energy consumption. In this article, we provide a comprehensive energy consumption analytical model of LR-FHSS, focusing on three critical metrics: average current consumption, battery lifetime, and energy efficiency of data transmission. The model is based on measurements performed on real hardware in a fully operational LR-FHSS network. While in our evaluation, LR-FHSS can show worse consumption figures than LoRa, we find that with optimal configuration, the battery lifetime of LR-FHSS end devices can reach 2.5 years for a 50 min notification period. For the most energy-efficient payload size, this lifespan can be extended to a theoretical maximum of up to 16 years with a one-day notification interval using a cell-coin battery.

Routing Task in Dynamic Fog Computing Network

Relevance. In the context of traffic growth, transition to IMT-2030 networks and Telepresence services, the tasks of efficient management of network and computing resources occupy a special place. Fog computing as the next stage of decomposition of the architecture of multi access edge cloud computing is designed to radically change the models and methods of distributing computing tasks, influencing, among other things, the user-operator interaction models. At the moment, there is a whole layer of scientific problems for revealing the possibilities of fog computing. They can be divided into a number of areas, such as: study of models and methods for implementing services of ultra-reliable and ultra-low latency communications, defined in IMT-2020 networks; study of models and methods for ensuring quality of service, including quality of experience; study of methods for live migration of microservices, as well as groups of typical microservices; study of models and methods for distributing resources of dynamic fog computing while ensuring the stability of fog computing forms (clusters, nebulae); one of the potentially effective areas is research in the field of combining federated learning with dynamic fog computing. This paper solves a routing problem that can be attributed to the direction of infrastructure research in dynamic fog computing.Problem statement: research and develop the effective methods for routes determination in a dynamic fog computing network, including tasks of migrating microservices of telepresence services. Goal of the work: research and development of an effective method for ways determination to migrate microservices in communication networks using fog computing technologies, which could take into account not only the characteristics of connections (edges of the network graph), but also the computing capabilities and limitations of fog computing devices, as well as their features - the dynamics of computing devices. Methods: in order to test the proposed method, the program model was developed in the NS-3 modeling environment. Result. Analysis of the results showed the effectiveness of the proposed method within the framework of the task and various application scenarios. Novelty. A microservice migration method has been developed as a new routing protocol in a dynamic fog computing environment, which differs from the known ones in that this method ensures the interaction of fog computing devices for migrating microservices, while achieving a reduction in energy consumption by fog computing devices by 41% and reducing the share of lost packages on average up to 34%. Practical significance: The developed method can be used to implement fog computing in conditions of mobility of end devices in order to achieve the requirements of promising services of IMT-2030 networks.

Machine learning-based DDOS attack detection and mitigation in SDNs for IoT environments

Software-defined network (SDN) platforms play a key role in providing security against today's Internet attacks. SDNs decouple the control plane from the data plane to maximize network performance. A DDOS attack is one of several in cloud-based networks. SDNs play a crucial role in controlling DDoS attacks and protecting end nodes like IoT nodes, as well as other computing devices, in large-scale cloud networks. This paper provides an efficient approach to DDoS attack detection and prevention using machine learning algorithms. The paper analyses the performance of SDNs in IoT systems, incorporating a huge set of computing devices that use multi-controllers. It also proposes an effective method to handle DDoS attacks. DDoS attacks are generated from IoT end devices in the infrastructure layer, which targets resources via an SDN-controlled testbed. The proposed ML method outperforms existing methods in terms of accurately and effectively detecting and mitigating flooding DDoS attacks with 99.99% accuracy. The proposed work's results are also compared to the results of other articles to prove the effectiveness of the results.

Network Time Protocol based Black Box Timing Accuracy Analysis for Internet of Things Devices

With large-scale Cyber-Physical Systems (Internet of Things) deployments, the low-cost networked devices are becoming ubiquitous. Time stamping of events is a basic requirement and it is crucial for distributed/isolated systems/devices to work in unison. The system clocks of these devices are not very stable and their behaviour is affected by ageing and environmental conditions. A clock’s timing behaviour is generally analysed independently by comparison to a highly stable standard reference clock using specialized instruments. Network Time Protocol (NTP) is widely used for time synchronization over computer networks. As the NTP server clock is more stable in comparison to a client’s system clock, the variation in the end device’s clock offset values with respect to the NTP server clock over a long time can be considered for analysing the behaviour of the end device’s clock. This paper presents a method for analysing clock behaviour based on NTP messages. A computer programme is developed, which utilizes an open-source NTP client library to exchange NTP messages and records end device’s system clock’s offset variations over time for a given periodicity to analyse the behaviour. This black box analysis approach does not require any direct access to the end device’s clock circuit. The solution may be useful for analysing clock behaviour for long-term stability under actual environmental conditions, for deciding time synchronization periodicity to optimize the number of synchronization packet exchanges, and for pre-deployment timing stability assessment of devices for deployment scenarios having intermittent/non-availability of periodic time synchronization.

Artificial Intelligence Workload Allocation Method for Vehicular Edge Computing

Real-time applications such as smart transportation systems require minimum response time to increase performance. Incorporating edge computing, processing units near end devices, achieving fast response time. The collaboration between edge servers and cloud servers is beneficial in achieving the lowest response time by using edge servers and high computational resources by using cloud servers. The workload allocation between edge–cloud servers is challenging, especially in a highly dynamic system with multiple factors varying over time. In this paper, the workload allocation decisions among the edge servers and cloud are considered for autonomous vehicle systems. The autonomous vehicle system generates multiple tasks belonging to different AI applications running on the vehicles. The proposed method considers allocating the tasks to edge or cloud servers. The cloud servers can be reached through a cellular network or a wireless network. The proposed method is based on designing a neural network model and using a high number of features that contribute to the decision-making process. A huge dataset has also been generated for the implementation. The EdgeCloudSim is used as a simulator for implementation. The competitor's methods considered for the comparison are random, simple moving average (SMA) based, multi-armed bandit (MAB) theory-based, game theory-based, and machine learning-based workload allocation methods. The result shows an improvement in the average Quality of Experience (QoE), ranging from 8.33% to 28.57%, while the average failure rate achieved enhancement up to 50%.

Size shrinkage and compressive behaviour of Al2O3 honeycomb structures fabricated via Digital Light Processing technology

Al2O3 ceramic have excellent chemical inertness, superior heat resistance, and excellent corrosion resistance, so they are considered ideal materials for manufacturing high-performance components such as thermal end devices in aerospace, heat dissipation panels in electronic communication, and exhaust filters in the automotive industry. Digital Light Processing (DLP) is a useful technology to fabricate complex Al2O3 ceramic components. However, the size shrinkage effect of DLP-fabricated components need to be investigated deeply before its further application. In this work, we utilized DLP technology to fabricate Al2O3 ceramic honeycomb structures and systematically investigated their microstructure, size shrinkage, and mechanical properties. Our findings revealed that the Al2O3 ceramic grains exhibit flake-like structures with an average diameter of 1–2 μm, and the ceramic particles are tightly bonded. The shrinkage rates in the X and Y axes were 7.54 % and 7.42 %, respectively, while the Z-axis exhibited a more significant shrinkage of 13.04 %. This anisotropic shrinkage in different direction was attributed to accumulation of the interlayer bonding under gravity action during debinding and sintering process. The compressive stress-strain curve of the honeycomb structures demonstrated a typical brittle compressive behavior, including elastic, stress plateau, and brittle fracture stages. The honeycomb structures exhibited an elastic modulus of 54.49 ± 1.22 MPa and a compressive strength of 10.22 ± 0.42 MPa. The fracture analysis indicated that the fractures region of honeycomb structures initiated at the corners of the honeycomb structures, indicating their easy-broken compared to the walls. These findings provide valuable guidance for the high-precision and high-performance manufacturing of complex Al2O3 ceramic components.

Analysis of Networking Tools Using Cisco Packet Tracer (CPT)

This study aims to evaluate the performance and effectiveness of network devices using Cisco Packet Tracer (CPT) as a simulation tool. CPT is a widely utilized network simulation software, commonly employed for designing and testing network configurations prior to real-world implementation. This research investigates various network topologies to assess the performance of devices such as routers, switches, and end devices. The research methodology encompasses the design of network scenarios, device configuration, and testing of various network protocols. The analysis reveals that CPT is capable of accurately replicating most functions and behaviors of network devices, providing a clear depiction of network performance under different conditions. The findings suggest that CPT serves as an effective and efficient tool for both educational purposes and network planning, despite certain limitations in features and realism when compared to actual hardware. This study is expected to offer valuable insights for professionals and academics in understanding and optimizing network design before deployment in real-world environments.

Affordable generic digital twins for existing building environment management and an onsite deployment

The energy consumption during the operation and maintenance phase of buildings is huge. As the built-up area in China increases, the demand for energy conservation in existing buildings has become a key focus of its dual carbon policy. Intelligent operation and maintenance based on digital twins is an emerging means to reduce carbon emissions from buildings, but it faces some problems in the process of promotion. Complete digital and intelligent transformation requires significant investment and has certain requirements for project parties and operation and maintenance teams. Small businesses or individual households have relatively simple requirements for intelligent operation and maintenance scenarios and do not require complete digital twins. To address the above issues, this article uses an affordable universal digital twin framework to provide a digital solution for intelligent operation and maintenance of existing buildings. This solution allows networking communication between devices and uses IoT modules to monitor and control the environment. This digital twinning model can reduce the measurement and control of energy-consuming end devices without on-site transformation and has rich scalability. This article uses the solution to deploy an office at a university in Henan Province and specifically measures the power consumption of displays, indoor environment, and air conditioning. According to the needs, it expands the space occupation, fans, air handlers, lights, and other end devices of the digital twin. The digital twin accurately presents the energy consumption of the office during extreme weather conditions, which has an auxiliary role in promoting digital twins in the region and optimizing energy consumption in existing buildings.

The effect of task processing management on energy consumption at the edge of Internet of things network with using reinforcement learning method

In this paper, the task processing approach at the edge of the Internet of Things (IoT) network in improving energy consumption was investigated. First, the subject of maximizing the use of energy is defined as the main problem and then based on the state of assignment of task processing from end devices at the edge of the network to smart gateways and local servers at the edge using possible low bandwidth and low battery consumption, the problem is divided into two subject which are obtained independently. After modeling by recombining these two sub-problems, the subject of maximizing the utility gain and battery life of the end devices is solved despite the limitations of processing time and energy resources.Considering that the environment of the problem and how the terminal devices are connected with the edge devices are unknown, a repetitive reinforcement learning algorithm has been used to create an optimal solution to maximize the operational benefit of the energy consumed in edge processing. The results achieved from the simulation show the increase in network load and processing overhead with the increase in the number of active devices. The suggested method, while increasing the edge processing speed, decreases the delay and maximizes the performance gain of the IOT edge system compared to the central cloud system. It should be noted that when the number of active end devices dramatically increases, the energy consumption and bandwidth at the network edge improves, and energy consumption at the network edge decreases lonely by 12.5%.

Lightweight Food Recognition via Aggregation Block and Feature Encoding

Food image recognition has recently been given considerable attention in the multimedia field in light of its possible implications on health. The characteristics of the dispersed distribution of ingredients in food images put forward higher requirements on the long-range information extraction ability of neural networks, leading to more complex and deeper models. Nevertheless, the lightweight version of food image recognition is essential for improved implementation on end devices and sustained server-side expansion. To address this issue, we present Aggregation Feature Net(AFNet), a lightweight network that is capable of effectively capturing both global and local features from food images. In AFNet, we develop a novel convolution based on a residual model by encoding global features through row-wise and column-wise information integration. Merging aggregation block with classic local convolution yields a framework that works as the backbone of the network. Based on the efficient use of parameters by the aggregation block, we constructed a lightweight food image recognition network with fewer layers and a smaller scale, assisted by a new type of activation function. Experimental results on four popular food recognition datasets demonstrate that our approach achieves state-of-the-art performance with higher accuracy and fewer FLOPs and parameters. For example, in comparison to the current state-of-the-art model of MobileViTv2, AFNet achieved 88.4% accuracy of the top-1 level on the ETHZ Food-101 dataset, with similar parameters and FLOPs but 1.4% more accuracy. The source code will be provided in supplementary materials.

BFLIDS: Blockchain-Driven Federated Learning for Intrusion Detection in IoMT Networks.

The Internet of Medical Things (IoMT) has significantly advanced healthcare, but it has also brought about critical security challenges. Traditional security solutions struggle to keep pace with the dynamic and interconnected nature of IoMT systems. Machine learning (ML)-based Intrusion Detection Systems (IDS) have been increasingly adopted to counter cyberattacks, but centralized ML approaches pose privacy risks due to the single points of failure (SPoFs). Federated Learning (FL) emerges as a promising solution, enabling model updates directly on end devices without sharing private data with a central server. This study introduces the BFLIDS, a Blockchain-empowered Federated Learning-based IDS designed to enhance security and intrusion detection in IoMT networks. Our approach leverages blockchain to secure transaction records, FL to maintain data privacy by training models locally, IPFS for decentralized storage, and MongoDB for efficient data management. Ethereum smart contracts (SCs) oversee and secure all interactions and transactions within the system. We modified the FedAvg algorithm with the Kullback-Leibler divergence estimation and adaptive weight calculation to boost model accuracy and robustness against adversarial attacks. For classification, we implemented an Adaptive Max Pooling-based Convolutional Neural Network (CNN) and a modified Bidirectional Long Short-Term Memory (BiLSTM) with attention and residual connections on Edge-IIoTSet and TON-IoT datasets. We achieved accuracies of 97.43% (for CNNs and Edge-IIoTSet), 96.02% (for BiLSTM and Edge-IIoTSet), 98.21% (for CNNs and TON-IoT), and 97.42% (for BiLSTM and TON-IoT) in FL scenarios, which are competitive with centralized methods. The proposed BFLIDS effectively detects intrusions, enhancing the security and privacy of IoMT networks.

MONITORING ADMINSTRASI JARINGAN DENGAN MIKROTIK DAN TELEGRAM BOT PADA INTERNET SERVICE PROVIDER

Network management using MikroTik devices at Lajur Net still has shortcomings. Although internet access is available around the clock, the limited number of employees makes it difficult for managers to access computers to manage MikroTik. Administrators hope to manage the MikroTik network remotely through end devices. Currently, accessing MikroTik online incurs high operational costs for renting public IPs and web-based management application services. This study aims to utilize Telegram Bot for network management and monitoring that can be accessed remotely via the internet. The use of Telegram Bot for network management at Lajur Net can be successfully implemented. The implementation of Telegram Bot includes managing interfaces, hotspots, ICMP, and monitoring the resources of MikroTik RouterBoard devices in the Lajur Net network. Telegram Bot can be accessed remotely through the internet and works well on the Lajur Net network. The implementation of Telegram Bot includes interface administration, hotspots, ping, and monitoring the resources of MikroTik RouterBoard devices. The additional firewall feature allows network administrators to add and delete filter rules within the Lajur Net network security system. Moreover, Telegram Bot utilizes button features on Telegram messages to facilitate usage without having to type all commands. Thus, Telegram Bot offers an efficient and effective solution in MikroTik network management at Lajur Net, reducing the need for expensive public IPs and web-based management applications. This demonstrates that the use of chatbot technology can provide significant benefits in network administration, especially for internet service providers with limited resources. Through this research, it is hoped that the implementation of Telegram Bot can serve as a reference for developing more innovative and efficient network management in the future.