Low-latency Data Research Articles

Blockchian Based Hybrid Consensus Algorithm for Data Security in Edge Computing

Abstract: Edge computing has emerged as a paradigm to process data closer to its source, enabling low-latency and real-time applications. However, the distributed and heterogeneous nature of edge environments poses significant challenges for ensuring data security and integrity. Blockchain technology offers a promising solution by providing a decentralized and immutable ledger for recording transactions. This abstract proposes a consensus algorithm tailored for securing data in edge computing environments using blockchain technology. The proposed consensus algorithm leverages a hybrid approach combining Proof of Authority (PoA) and Practical Byzantine Fault Tolerance (PBFT). PoA ensures that only trusted nodes within the edge network are eligible to participate in the consensus process, mitigating the risk of malicious attacks. PBFT enhances the efficiency and scalability of the consensus algorithm by allowing fast decision-making among a subset of nodes, thereby reducing latency in data validation and verification. Furthermore, smart contracts are employed to enforce data access control and execute predefined rules for data processing and sharing. These smart contracts facilitate secure and transparent interactions among edge devices, ensuring that only authorized parties can access and manipulate sensitive data. The integration of blockchain technology with edge computing offers several benefits, including enhanced data security, traceability, and accountability. By employing a tailored consensus algorithm, edge computing environments can effectively mitigate security threats while maintaining low-latency data processing capabilities. Future research directions may explore optimization techniques to further improve the performance and scalability of the proposed solution in large-scale edge networks.

Fog-based deep learning framework for real-time pandemic screening in smart cities from multi-site tomographies

The quick proliferation of pandemic diseases has been imposing many concerns on the international health infrastructure. To combat pandemic diseases in smart cities, Artificial Intelligence of Things (AIoT) technology, based on the integration of artificial intelligence (AI) with the Internet of Things (IoT), is commonly used to promote efficient control and diagnosis during the outbreak, thereby minimizing possible losses. However, the presence of multi-source institutional data remains one of the major challenges hindering the practical usage of AIoT solutions for pandemic disease diagnosis. This paper presents a novel framework that utilizes multi-site data fusion to boost the accurateness of pandemic disease diagnosis. In particular, we focus on a case study of COVID-19 lesion segmentation, a crucial task for understanding disease progression and optimizing treatment strategies. In this study, we propose a novel multi-decoder segmentation network for efficient segmentation of infections from cross-domain CT scans in smart cities. The multi-decoder segmentation network leverages data from heterogeneous domains and utilizes strong learning representations to accurately segment infections. Performance evaluation of the multi-decoder segmentation network was conducted on three publicly accessible datasets, demonstrating robust results with an average dice score of 89.9% and an average surface dice of 86.87%. To address scalability and latency issues associated with centralized cloud systems, fog computing (FC) emerges as a viable solution. FC brings resources closer to the operator, offering low latency and energy-efficient data management and processing. In this context, we propose a unique FC technique called PANDFOG to deploy the multi-decoder segmentation network on edge nodes for practical and clinical applications of automated COVID-19 pneumonia analysis. The results of this study highlight the efficacy of the multi-decoder segmentation network in accurately segmenting infections from cross-domain CT scans. Moreover, the proposed PANDFOG system demonstrates the practical deployment of the multi-decoder segmentation network on edge nodes, providing real-time access to COVID-19 segmentation findings for improved patient monitoring and clinical decision-making.

BRAND: a platform for closed-loop experiments with deep network models.

Objective.Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g. Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g. C and C++).Approach.To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termednodes, which communicate with each other in agraphvia streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes.Main results.In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1 ms chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 ms of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial (ClinicalTrials.gov Identifier: NCT00912041) performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems.Significance.By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments.

Accurate and fast congestion feedback in MEC-enabled RDMA datacenters

Mobile edge computing (MEC) is a novel computing paradigm that pushes computation and storage resources to the edge of the network. The interconnection of edge servers forms small-scale data centers, enabling MEC to provide low-latency network services for mobile users. Nowadays, Remote Direct Memory Access (RDMA) has been widely deployed in such data centers to reduce CPU overhead and network latency. Plenty of congestion control mechanisms have been proposed for RDMA data centers, aiming to provide low-latency data delivery and high throughput network services. However, our fine-grained experimental analysis reveals that existing congestion control mechanisms still have performance limitations due to inappropriate congestion notifications and the long congestion feedback cycle. In this paper, we propose Mercury, which is an accurate and fast congestion feedback mechanism. Mercury comprises two key components: (1) the state-driven congestion detection and (2) the window-based congestion notification. Specifically, the state-driven congestion detection monitors the queue length and the number of packets received at the switch egress port when the PFC is triggered. It determines the states of egress ports and identifies flows that really contribute to congestion. Then, window-based congestion notification calculates the window sizes for congested flows and rapidly returns Congestion Notification Packets (CNPs) with the window information to the sender. It facilitates the rate adjustment of congested flows. Mercury is compatible with existing RDMA CC mechanisms and can be easily implemented in switches. We employ real-world data sets and conduct both micro-benchmark and large-scale simulations to evaluate the performance of Mercury. The results indicate that, thanks to the accurate and fast congestion feedback, Mercury achieves a reduction in the 99th tail flow completion time by up to 45.1%, 41.8%, 38.7%, 30.9%, and 37.9% compared with Timely, DCQCN, DCQCN+TCD, PACC, and HPCC, respectively.

Low-latency equal optical path difference sampling for multi-field VLWIR interference signals

The spaceborne Fourier Transform Spectrometer has been utilized for vertical atmospheric soundings with a HgCdTe infrared focal plane arrays (FPAs) detector. However, the VLWIR FPA Fourier spectrometer system faces the difficulty of poor sensitivity due to the major contribution of the system sampling error. In this work, low-latency data acquisition of multi-field VLWIR interference signals based on VLWIR FPA Fourier spectrometer system is realized with an optimized field programmable gate array (FPGA) timing driver scheme and layout algorithm. This scheme can effectively reduce routing delay and constrain critical timing paths, and thus leads to a significant decrease in the deviation of equal optical path difference sampling time from 10.27μs to 0.10μs. Interferograms and spectrograms are efficiently obtained. Furthermore, the average value of noise equivalent spectral radiance (NESR) in VLWIR FPA Fourier spectrometer system, is reduced by 1.75% by timing optimization. In particular, the decay of the NESR can be as high as 15% for some of the pixels. These results illustrate that the proposed scheme will acquire hyperspectral data accurately with VLWIR FPA and meet the needs of rapid soundings development of the Geostationary Interferometric Infrared Sounder (GIIRS) availing a rapid prediction of the appearance of an extreme weather event.

Automatic edge app placement for personalized heart attack predictions

Edge computing brings data processing, analytics and storage closer to the source, but the major limitation of edge devices is that they have limited processing power and storage. Some have argued that cloud can be a solution to overcome the edge computing limitations. However, performing all tasks on the cloud introduces latency issues. Therefore, we need a solution that can use edge and cloud computing intelligently and interchangeable such that the merits of both can be utilized based on the specific scenario. Such a model has not been discussed in the literature so far which poses a very important limitation. We proposed a novel architecture that intelligently switches data processing between the cloud and edge just-in-time based on specific conditions, i.e., if a healthcare scenario demands low latency data are processed at the edge, otherwise data are automatically processed on the cloud. We illustrate this by creating an Automatic edge application to monitor high risk cardiovascular disease patients who are at the risk of heart attacks after a post-operative surgery. Low latency is very important while monitoring such patients and the application is built to automatically detect all such cases and performs heart attack predictions on the edge while other patients data is processing on the cloud. The experimental results have demonstrated that our application can automatically detect high risk cardiovascular disease patients and place their workloads on the edge which is a new and unique invention in the area of automated edge computing. We have also demonstrated that the data retrieval from the edge is 55% faster than the cloud thereby ensuring low latency with edge.

Distributed Mobility Management Support for Low-Latency Data Delivery in Named Data Networking for UAVs

Named Data Networking (NDN) has emerged as a promising architecture to overcome the limitations of the conventional Internet Protocol (IP) architecture, particularly in terms of mobility, security, and data availability. However, despite the advantages it offers, producer mobility management remains a significant challenge for NDN, especially for moving vehicles and emerging technologies such as Unmanned Aerial Vehicles (UAVs), known for their high-speed and unpredictable movements, which makes it difficult for NDN to maintain seamless communication. To solve this mobility problem, we propose a Distributed Mobility Management Scheme (DMMS) to support UAV mobility and ensure low-latency content delivery in NDN architecture. DMMS utilizes decentralized Anchors to forward proactively the consumer’s Interest packets toward the producer’s predicted location when handoff occurs. Moreover, it introduces a new forwarding approach that combines the standard and location-based forwarding strategy to improve forwarding efficiency under producer mobility without changing the network structure. Using a realistic scenario, DMMS is evaluated and compared against two well-known solutions, namely MAP-ME and Kite, using the ndnSIM simulations. We demonstrate that DMMS achieves better results compared to Kite and MAP-ME solutions in terms of network cost and consumer quality-of-service metrics.

Use of ZDR columns for early detection of severe convection within the operational radar network of the United Kingdom

AbstractDifferential reflectivity () columns were observed using a Met Office three‐dimensional radar composite. An algorithm for automatic detection of columns was developed, based on dB and dBZ. Across three case days, detected columns were found to precede severe convection in tracked convective cells with a range of lead times from 0 to 20 min depending on the case day. Requiring maxima above 1.4 dB and 30 dBZ of and respectively was an appropriate second condition for all three cases although the skill in the early detection of severe convection varied across case days. Despite the high probability of detections, the high false alarm rate accompanied by low critical success index and data latency limit performance based on the three cases considered in this study. Nevertheless, the ability to detect columns in operational radar data with a useful lead time prior to severe convection in certain conditions is a promising development towards advancing nowcasting of severe convection in the United Kingdom.

GpDB: A Graph-partition Based Storage Strategy for DAG-Blockchain in Edge-cloud IIoT

The industrial Internet-of-things (IIoT) has attracted extensive attention due to its real-time and automation characteristics. Edge computing and blockchain technologies facilitate the IIoT in terms of low latency services and data security respectively. However, with the continuous expansion of industrial data and the growth of industrial nodes, traditional blockchain technology has some critical limitations on low transaction throughput and high data storage costs. Directed Acyclic Graph (DAG)-blockchain adopts a graph structure of a single transaction as the basic unit, and it has the characteristics of asynchronous consensus. Some existing studies use DAGblockchain to replace the traditional blockchain to alleviate its low throughput problems like IOTA. However, with the rapid data generation in the IIoT environment, the topology scale of DAG-blockchain will increase sharply, which will aggravate the data storage cost of blockchain nodes. In this article, to reduce the data storage cost of edge servers, we design a Graphpartition based storage strategy for DAG-Blockchain (GpDB), equipped with a graph partition algorithm based on transaction freshness, which can partition DAG-blockchain topology in edge servers into two parts, which will be retained and removed respectively. Simulation shows that, in terms of storage cost, GpDB outperforms LDV and Layerchain by 62% and 74% respectively, and with the increasing number of transactions, GpDB has good scalability in reducing the storage cost, and better transaction throughput than IOTA.

High-Frequency Trading Strategies and their Market Impact in Kenya's Capital Market Authority

This study explores the impact of high-frequency trading (HFT) on financial markets in Kenya compared to established economies like the United States and Europe. Through a comparative analysis, the research highlights significant differences in market liquidity, regulatory frameworks, technological infrastructure, investor behavior, and price volatility. The findings indicate that while HFT generally enhances liquidity in developed markets by narrowing bid-ask spreads, its introduction in Kenya may not yield similar benefits due to historically lower trading volumes and limited participation. The regulatory environment in Kenya is underdeveloped, posing challenges for effective oversight and risk management, in contrast to the comprehensive frameworks in established markets. Technological disparities further hinder local firms from competing effectively in HFT, as essential infrastructure such as low-latency data feeds and co-location services is lacking. Moreover, the impact of HFT on market behavior in Kenya is less pronounced, with insufficient competitive pressure for traditional investors to adopt algorithmic strategies. Concerns about increased price volatility in Kenya are heightened by inadequate regulatory safeguards, potentially leading to market instability. The study emphasizes the need for tailored regulatory approaches and technological investments to harness HFT's potential benefits while mitigating associated risks in emerging markets.

Cost-Effective and Low-Latency Data Placement in Edge Environment Based on PageRank-Inspired Regional Value

Cost-Effective and Low-Latency Data Placement in Edge Environment Based on PageRank-Inspired Regional Value

Re-routable and Low-latency All Optical Switching Algorithm for Next Generation DCN

Digital infrastructure is now indispensable to the function of society and quality of life of the citizen. All over the world there is leveraging of digital infrastructure that comprises use of data, computerized devices, methods and systems. During COVID-19 pandemic it comes more prominently in front. India being the most popular and populated country in the world, comprises nearly half billion internet users expected to transform the digital ecosystem. With the increasing demand of smartphone application, online activities gigantic amount of data has been generated, so evolution and development of data centers be the high importance not only for India but as well as for the world. Thus, it is the need to promote and create robust data center network infrastructure that supports emerging technologies such as 5G, IoT, AI, machine learning, adaptive manufacturing, smart agriculture, and automation and so on. In this paper we propose an all Optical re-routable, low latency Data Center Network (DCN) architecture using Passive Optical Datacenter Switch (PODS). The PODS consists of an Arrayed Waveguide Grating Router (AWGR) in association with a controller unit. An efficient wavelength assignment algorithm is integrated in PODS, to reroute the packet for congestion avoidance and packet loss.. This feature makes the DCN architecture more scalable, proficient with high throughput, low latency, power efficient under high traffic and bursty traffic conditions. The performance of the proposed PODS-based DCN is compared with existing passive optical DCN architectures PODCA and finds improved characteristics in terms of delay, throughput and blocking probability. Simulation of the framework is done in Python platform and executed on google COLAB in the Windows environment and the result shows 46.3% improvement in latency and throughput in terms of 90% network load compared with PODCA architecture.

StarFront: Cooperatively Constructing Pervasive and Low-Latency CDNs Upon Emerging LEO Satellites and Clouds

Internet content providers (ICPs) typically exploit content distribution networks (CDNs) to provide wide-area data access with high availability and low latency. However, our analysis on a large-scale trace collected from seven major CDN operators has revealed that: from a global perspective, there are still a large portion of users suffering from high user-perceived latency due to the insufficient deployment of terrestrial cloud infrastructures, especially in remote or rural areas where even the closest available cache server is too far away. This paper presents, a cost-effective content distribution framework to optimize global CDNs and enable low content access latency anywhere. collaboratively builds CDNs upon emerging low earth orbit (LEO) constellations and existing cloud platforms to satisfy the low latency requirements while minimizing the operational cost. Specifically, exploits a key insight that emerging mega-constellations will consist of thousands of LEO satellites which can be equipped with high-speed data links and storage, and thus can potentially work as “cache in space” to enable pervasive and low-latency data access. judiciously places replicas on either LEO satellite caches or terrestrial cloud caches, and dynamically assigns user requests to proper cache servers based on different constellation parameters, cloud/user distributions and pricing policies. We have implemented a prototype in our testbed, and extensive trace-driven evaluations covering multiple geo-distributed vantage points have demonstrated that can effectively reduce the global content access latency with acceptable operational cost under representative CDN traffic.

Hybrid photonic integrated circuits for neuromorphic computing [Invited

The burgeoning of artificial intelligence has brought great convenience to people’s lives as large-scale computational models have emerged. Artificial intelligence-related applications, such as autonomous driving, medical diagnosis, and speech recognition, have experienced remarkable progress in recent years; however, such systems require vast amounts of data for accurate inference and reliable performance, presenting challenges in both speed and power consumption. Neuromorphic computing based on photonic integrated circuits (PICs) is currently a subject of interest to achieve high-speed, energy-efficient, and low-latency data processing to alleviate some of these challenges. Herein, we present an overview of the current photonic platforms available, the materials which have the potential to be integrated with PICs to achieve further performance, and recent progress in hybrid devices for neuromorphic computing.

Optimal Energy-efficient Resource Allocation and Fault Tolerance scheme for task offloading in IoT-FoG Computing Networks

Due to technology development in the recent past, it is observed that an exponential growth in the usage of high speed intelligent devices which includes smart objects, smart home, smart vehicles etc. Therefore, for the efficient transmission between different smart objects/devices situated across different regions, an effective communication is required and the corresponding technology is called as ‘Internet of Things (IoT)’. This leads to an issues in various aspects like, increase in complexity, low data rate & latency and security may reduce the performance of the existing technologies. Therefore, there is a huge requirement of advanced technology which may support ultra-low latency transmission for the task received from the devices. Fog computing (FC) is one of an emerging technology that can improve network performance and provide resource-constrained applications for the IoT devices. In this paper, the Optimal Energy-efficient Resource Allocation (OEeRA) algorithm is proposed based on Minimal Cost Resource Allocation (MCRA) and Fault Identification and Rectification (FIR) algorithms for effective task offloading of IoT-FoG computing networks. The MCRA algorithm is proposed to assign at least one FN and Resource Block (RB) for each device, and also it ensures that each FN is connected with one or more RBs and devices. The leftover RBs are collected and stored in the buffer to replace the faulty RBs, as proposed in the FIR algorithm, which achieves better processing and response time with higher fault detection accuracy. The energy efficiency (EE) of the proposed OEeRA algorithm is computed through MCRA and FIR algorithms by varying FN, RB, and IoT devices. The performance analysis shows that the proposed algorithm achieved the maximum EE of 6.12 × 109 bit/J, 5.69 × 1010 bit/J, and 3.019 × 1010 (bit/J) for varying RBs, IoTs, and FNs, respectively.

Ellipsoidal Trajectory Optimization for Minimizing Latency and Data Transmission Energy in UAV-Assisted MEC Using Deep Reinforcement Learning

Due to their flexible deployment and movement capability, unmanned aerial vehicles (UAVs) are being utilized as flying mobile edge computing (MEC) platforms, offering real-time computational resources and low-latency data processing for a wide range of applications. This article aims to explore a UAV-assisted MEC system where multiple UAVs provide MEC services to mobile devices (MDs) using an ellipsoidal trajectory. Depending on the position, size, and orientation of the ellipsoidal trajectories, the coverage area of the UAV, the energy consumption, and the task transmission latency of MDs change. This has rarely been investigated in the existing works. Furthermore, unlike other studies, we consider that each MD has varying task offloading rates, which, together with varying user densities, makes the problem more challenging. Therefore, we formulate an optimization problem that finds the center position, major radius, minor radius, and rotation angle of the ellipsoidal trajectory of UAV-assisted MEC servers, to minimize the total transmission latency and energy consumption of mobile devices while taking into account the required data transmission rate, task transmission time, and energy consumption constraints. Then, we transform this optimization problem into a Markov decision process and propose a deep Q-learning-based ellipsoidal trajectory optimization (DETO) algorithm, to resolve it. The results from our simulations demonstrate that DETO efficiently computes the optimal position and trajectory for each UAV, and can achieve better performance compared to other baselines, leading to the reduced data transmission latency and energy consumption of mobile devices across a range of simulation scenarios.

The future service scenarios of 6G telecommunications technology

In the field of mobile communication, 6G technology has enhanced features, such as ultrahigh data rates and sharing, bandwidth connectivity, and low data latency, compared to 5G technology. As new services become possible due to these characteristics, attempts to present promising future services based on 6G technology are occurring in academia and industry. However, because 6G technology has a very wide range of applications as a fundamental technology, 6G service scenario research is also being conducted for each sector. To predict 6G technology use in the future, it is necessary to understand comprehensively the scenarios being studied sporadically by sector. Therefore, we aim to derive major service scenarios by analyzing in-depth public data for 6G service scenarios. We collected research papers, corporate reports, and news articles on 6G service scenarios and performed topic modeling to derive four major domains and 16 application areas. Afterward, the derived service's feasibility and promising scenario were evaluated by utilizing the future-context canvas and business model canvas that analyze the service's value from the user's and supplier's perspectives, respectively. The research results are expected to support technology development and business planning in the field by enhancing understanding of 6G service scenarios.

Prototype of Monitoring Transportation Pollution Spikes through the Internet of Things Edge Networks

Air pollution is a critical problem in densely populated urban areas, with traffic significantly contributing. To mitigate the adverse effects of air pollution on public health and the environment, there is a growing need for the real-time monitoring and detection of pollution spikes in transportation. This paper presents a novel approach to using Internet of Things (IoT) edge networks for the real-time detection of air pollution peaks in transportation, specifically designed for innovative city applications. The proposed system uses IoT sensors in buses, cabs, and private cars. These sensors are equipped with air quality monitoring capabilities, including the measurement of pollutants such as particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and carbon dioxide (CO2). The sensors continuously collect air quality data and transmit them to edge devices within the transportation infrastructure. The data collected by these sensors are analyzed, and alerts are generated when pollution levels exceed predefined thresholds. By deploying this system within IoT edge networks, transportation authorities can promptly respond to pollution spikes, improving air quality, public health, and environmental sustainability. This paper details the sensor technology, data analysis methods, and the practical implementation of this innovative system, shedding light on its potential for addressing the pressing issue of transportation-related pollution. The proposed IoT edge network for real-time air pollution spike detection in transportation offers significant advantages, including low-latency data processing, scalability, and cost-effectiveness. By leveraging the power of edge computing and IoT technologies, smart cities can proactively monitor and manage air pollution, leading to healthier and more sustainable urban environments.

Data integration for digital twins in the built environment based on federated data models

Improving the efficiency of operations is a major challenge in facility management given the limitations of outsourcing individual building functions to third-party companies. The status of each building function is isolated in silos that are controlled by these third-party companies. Companies provide access to aggregated information in the form of reports through web portals, emails or bureaucratic processes. Digital twins represent an emerging approach to returning awareness and control to facility managers by automating all levels of information access (from granular data to defined key performance indicators and reports) and actuation. This paper proposes a low-latency data integration method that supports actuation and decision making in facility management, including construction, operation and maintenance data, and Internet of things. The method uses federated data models and semantic web ontologies, and it is implemented within a data lake architecture with connections to siloed data to keep the delegation of responsibilities of data owners. A case study in the Alan Reece Building (Cambridge, UK) demonstrates the approach by enabling fault detection and diagnosis of the heating, ventilation and air-conditioning system for facility management.

Role of 5G Technology in Enhancing Agricultural Mechanization

Advanced mechanization strategies such as site specific crop management machinery, Unmanned Aerial Vehicles (UAVs), robotic harvesters relies greatly on the precision technologies and IoT. Present networks (3G/4G), WiFi are in limited availability and bandwidth that cause delays in data transfer (latency) and hinder effective data transfer. But high-speed communication system, reliable and secured data connection is required for precision agriculture, cyber physical management systems and use of IoT in farm operations. Loss of data connection between devices, sensors or data servers’ impacts the precision and effectiveness of technology applied. High data transfer volumes and low latency data exchange were the major advantages of 5G technology, facilitates its use in precision farming and IoT applications in a most efficient way. It also enables the autonomous and robotic machinery for various operations in the aspects of agriculture and livestock production. The major application of 5G in mechanization is control systems for robotic harvesters, drones, autonomous vehicles, surveillance etc. Remote locations such as hill farms has the inherent constraints such as remoteness, marginality, slope etc., remains underutilized, can be developed with advanced communication systems. Even though there are several constraints in adoption of 5G technology, such as shortage in electricity supply, lower range coverage, the technology can ensure raise in agricultural production through mechanization. The chapter discusses the advancements in application of 5G technology in agricultural mechanization.