IoT represents the fourth-generation technology that facilitates the connection and transformation of products into smart, intelligent and communicative entities. IoT has already established its footprint in various business verticals such as medical, heath care, automobile, and industrial applications. IoT empowers the collection, analysis, and transmission of information across various networks, encompassing both server and edge devices. This information can then undergo further processing and distribution to multiple inter-connected devices through cloud connectivity.
IoT Application in Oil & Gas Industry:
IoT is used in the Oil and Gas Industry for two basic reasons: First - low power design, a fundamental requirement for intrinsically safe products, Second - two-way wireless communication. These two advantages are a boon for the products used in Oil and Gas industries. The only challenge is for the product design to meet the hazardous location certification.
An intrinsic safe certification is mandatory for any device placed in hazardous locations. The certification code depends on the type of protection, zone, and the region where the product shall be installed.
In the North American and Canadian markets, the area classification is done in three classes:
Class I: Location where flammable gases and vapors are present.
Class II: Location where combustible dust is present.
Class III: Location where flying is present.
The hazardous area is further divided into two divisions, based upon the probability that a dangerous fuel to air mixture will occur or not.
Dvision-1: Location is where there is a high probability (by underwriting standards) that an explosive concentration of gas or vapor is present during normal operation of the plant.
Division-2: Location is where there is a very low probability that the flammable material is present in the explosive concentration during normal operation of the plant; so, an explosive concentration is expected only in case of a failure of the plant containment system.
The GROUP is also one of the meaningful nomenclatures of the hazardous area terms.
The four gas groups were created so that electrical equipment intended to be used in hazardous (classified) locations could be rated for families of gases and vapors and tested with a designated worst-case gas/air mixture to cover the entire group.
The temperature class definitions are used to designate the maximum operating temperatures on the surface of the equipment, which should not exceed the ignition temperature of the surrounding atmosphere.
Areas classified per NEC Article 505 are divided into three zones based on the probability of an ignitable concentration being present, rather than into two divisions as per NEC article 501. Areas that would be classified division 1 are further divided into zone 0 and zone 1. A zone 0 area is more likely to contain an ignitable atmosphere than zone 1 area. Division 2 and zone 2 areas are essentially equivalent.
Zone-0: Presence of ignitable concentration of combustible gases and vapors continuously, or for long periods of time.
Zone-1: Intermittent hazard may be present.
Zone-2: Hazard will be present under abnormal conditions.
IoT-based products can be designed for various applications, a few of them are listed below:
- Temperature Sensor
- Pressure Monitoring
- Gas Monitoring
- Flow Monitoring
A typical block diagram of the IoT application is shown below:
Figure 1: IOT Block Diagram
An IoT product might consist of a battery as a power source or can be powered externally from either 9V ~ 36V DC supply available in the process control applications or 110/230Vac input.
The microcontroller can be selected based on the applications, power consumption, and the peripheral requirements. The microcontroller converts the analog signal to digital and based on the configuration can send the data on wired/wireless to the remote station. Analog signal conditioning stands as a pivotal component of the product, bridging the connection between the sensor and facilitating the conversion of analog signals for compatibility with the microcontroller. The Bluetooth interface suggested in the example is due to its wide acceptance and low power consumption. The wireless interface depends on the end-application of the product.
Electronics Design Consideration
The electronics design of an IoT product for a hazardous location is very complex and needs a careful selection of the architecture and base components as compared to the IoT developed for commercial applications. In case the IoT is for a hazardous location, the product must be intrinsically safe and should not cause an explosion under fault conditions. The product architecture should be designed considering various mechanical, and electronics requirements as defined in the IEC 60079 standards, certification requirements and the functional specifications.
Power Source: This is one of the main elements in an IoT-based product. Battery selection should meet the overall power budget of the product, followed by the battery lifetime. In case of intrinsic safety, special consideration is required for where the battery in charged. IEC 60079-11 clause 7.4 provide details for the type of battery and its construction details. Separation distance from the battery and electrical interface should be done as per Table-5 of IEC 60079-11. If the battery is used in the compartment, sufficient ventilation must be provided to ensure that no dangerous gas accumulation occurs during discharge or inactivity periods. In scenarios where IoT operates on DC power sources such as 9~36Vdc (nominal 24Vdc), the selection of power supply barrier protection becomes a critical consideration, particularly when catering to intrinsic safety norms. This necessitates a thorough analysis of the product’s prerequisites and the mandatory certifications. Adding to the complexity is the existence of IoT devices functioning on 230Vac, demands intrinsic safe calculations and certifications aligned with Um = 250V.
Microcontroller: Its central processing unit of the IoT product. The architecture of the microcontroller, power, and clock frequency processing must be carefully selected for a particular application. The Analog to Digital Conversion (ADC) part of the microcontroller should be selected based on the required accuracy, update rate, and resolution. Microcontroller should have enough sleep modes so that the power is optimally utilized for IoT applications and should have sufficient memory/peripheral interface to meet the product specifications.
Analog Signal Conditioning: The front-end block should meet the intrinsic safe requirements as per the IEC 60079 standards and should also protect the product from EMI-EMC testing. Barrier circuit should provide enough isolation for meeting the spark-gap ignition requirements and impedance requirement of the transducer. Also, along with the safety requirements, the designer should ensure that extracted sensor signal is not degraded from the excessive noise present in outside environment. All the sensors used for collecting data from the process parameters to the signal conditioning block must be certified for the particular zone.
Wireless Communications: There are various wireless options available for sending data from the IoT product to the sensor such as (6LOWPAN, ZigBEE, ZWave, Bluetooth, Wi-Fi, Wireless HART). Selection of a particular wireless interface requires knowledge of end application, RF-power, antenna, and protocol. Selection of the interface for a particular IoT application should be done keeping these basic things in mind:
- The amount of data to be shared to the server.
- RF power.
- Power consumed for each bit of data transferred.
- Update rate of the data and distance of communication.
- Security of data.
In case of intrinsic safe applications, it’s important to note that the use of certified modules does not directly confer suitability for deployment in hazardous locations. The product must undergo fresh testing within an intrinsic safe lab to assess both quantifiable and non-quantifiable ffaults, along with spark testing. or the countable and non-countable faults and spark testing. The RF power transmitted from the devices should be limited as per Table-1x of IEC 60079-0.
When building IoT solutions for hazardous locations, special conditions relating to creepage and clearance, encapsulation, and separation distance must be carefully considered. Also, when battery and RF signals are used, it’s expected the designer should be aware of the applicable standards and limitation of these standards for such products.
With more than 25 years of experience in designing mission-critical and consumer-grade embedded hardware designs, eInfochips is well poised to make products which are smaller, faster, reliable, efficient, intelligent and economical. We have worked on developing complex embedded control systems for avionics and industrial solutions. At the same time, we have also developed portable and power efficient systems for wearables, medical devices, home automation and surveillance solutions.
eInfochips, as an Arrow company, has a strong ecosystem of manufacturing partners who can help right from electronic prototype design, manufacturing, production, and certification. eInfochips works closely with the contract manufacturers to make sure that the designs are optimized for testing (DFT) and manufacturing (DFM) to reduce design alterations on production transfer. To know more about this contact us.
- "IEC 60079–0" in Explosive Atmospheres - Part 0: General Requirements, Geneva. Switzerland.
- "IEC 60079–11 Part 11" in Equipment Protection by Intrinsic Safety “i”, Geneva, Switzerland.
- "UL 2225" in Standard for Safety; Cables and Cable Fittings for Use In Hazardous (Classified) Locations, Northbrook. IL:UL.
- "CSA C22.1–18 Rule 18–092" in Canadian Electrical Code Part I, Toronto, Canada:CSA Group.
- "NFPA 70" in National Electrical Code, Quincy, MA: National Fire Protection Association.
- "CAN/CSA C22.2 No.60079–0" in Explosive Atmospheres - Part 0: General Requirements, Toronto, Canada:CSA Group.
Kartik Gandhi, currently serving in the capacity of Senior Director of Engineering, possesses a distinguished career spanning over two decades, marked by a profound expertise in fields including Business Analysis, Presales, and Embedded Systems. Throughout his professional journey, Mr. kartik has demonstrated his proficiency across diverse platforms, notably Qualcomm and NXP, and has contributed his talents to several esteemed product-based organizations.
Dr. Suraj Pardeshi has more than 20 years of experience in Research & Development, Product Design & Development, and testing. He has worked on various IoT-enabled platforms for Industrial applications. He has more than 15 publications in various National and International journals. He holds two Indian patents, Gold Medalist and Ph.D (Electrical) from M.S University, Vadodara.
MQTT (Message Queuing Telemetry Transport, Message Queuing Telemetry Transport) is a lightweight messaging protocol based on publish/subscribe method under the ISO standard, which is usually used for communication between devices and applications such as the Internet of Things and smart homes.
The MQTT protocol consists of two parts: publisher/subscriber and message broker. As shown in Figure 1, the publisher is responsible for pushing the message to the broker, and the broker pushes the message to the matching subscriber.
Publisher: The device sends messages to subscribers through topics.
Subscriber: As a terminal device, the subscriber receives messages from the publisher through the topic.
Message broker (Broker): The server acts as a central hub and is responsible for organizational-level communication between publishers and subscribers.
There are two main versions of MQTT: v3 and v5.The principle of these two versions is basically the same, but there are some key differences between them. The following will introduce the differences between them from the following aspects.
MQTT v5 has added a Property field, which allows MQTT v5 to support more new features.In MQTT v3, MQTT has nothing to expand, which limits the possibility of MQTT expanding its functions.
Subject is the core concept in MQTT, which is used to identify the content and intent of the message.In MQTT v3, the subject is just a simple string, and its structure is composed of a series of words separated by slashes.
For example, an MQTT v3 theme can be sensors/temperature/room1, where sensors is the top-level theme, temperature is its sub-theme, and room1 is a specific device under the sub-theme.
However, in MQTT v5, the structure of the theme has been expanded and some more advanced features have been added.Specifically, MQTT v5 introduces a new concept called topic aliasing, which allows clients to map topic strings to pre-defined topic IDs, thereby reducing network traffic and message size.
Subject aliases are maintained by the client and the server, and the life cycle and scope of scope are limited to the current connection.
For a topic, set an alias when it is first published, and then you can use the topic alias to publish.This allows the client to send only the subject ID when sending a message, rather than having to send the complete subject string every time.This is very useful for IoT devices and environments with limited network bandwidth.
MQTT v5 introduces a new subscription type called shared subscription.Other flags and filtering functions can be used to achieve more flexible subscriptions.As shown in the figure below, shared subscriptions allow multiple clients to share a subscription and allocate it according to certain rules.This subscription type is very useful for subscribing to high-load topics because it can balance subscription requests and reduce the load pressure on a single client.
In addition, MQTT v5 adds the concept of subscription options. You can specify subscription options, such as QoS level, Retain As Publish, Retain Handling, message life cycle, etc., to control subscription behavior more finely.
Will message is the ability that MQTT provides for devices that may be accidentally disconnected to gracefully send the will to a third party.In the payload of the CONNECT message, some fields have changed, among which Will Message (will Message) becomes Will Payload (will Load).
Will Properties (WILL Properties) is a new field in MQTT v5. Different types of packets have different attributes. For example, CONNECT packets have attributes such as maximum packet length and session expiration interval, and SUBSCRIBE packets have attributes such as subscription identifier.Moreover, compared with v3, MQTT v5 makes the content of messages more flexible, and can contain any topic and any message content.
MQTT v5 supports a more detailed error handling mechanism, which can locate and solve problems through error codes and error causes.At the same time, MQTT v5 also introduces a new control message-Disconnect message, which can help clients and servers better handle error conditions.
MQTT v5 introduces some new mechanisms for flow control on the basis of the v3 version, in order to better control the transmission and processing of messages, and avoid network congestion and excessive load caused by excessive message transmission speed.
Maximum Packet Size limit (Maximum Packet Size): MQTT v5 allows the client and the server to negotiate the maximum packet size when shaking hands.As shown in the figure below, this maximum packet size limit can be used to control the maximum message size transmitted between the client and the server to prevent network congestion and excessive load due to excessive transmission of messages.
Message Queue: When the message sent by the server exceeds the speed of the client's processing, the server can store the message in the message queue and wait for the client to process it.MQTT v5 defines the queue size and timeout time of the message queue to control the size and life cycle of the message queue.
Compared with MQTT v3, MQTT v5 can better handle large-scale data transmission and improve the efficiency and performance of communication.For example, MQTT v5 supports functions such as Batch Publish and Message Prefetch, which can greatly reduce the overhead of MQTT communication.
In short, compared with MQTT v3, MQTT v5 has more new features and security.However, it should be noted that MQTT v5 has added many new functions and concepts. Therefore, when using MQTT v5, it is necessary to have an in-depth understanding of the new features of the MQTT protocol so that this new protocol can be better used to build reliable applications.
Chengdu Ebyte Electronic Technology Co., Ltd. specializes in the research and development and production of various frequency bands and various functional wireless data transmission modules. The products have been widely used in the Internet of Things, consumer electronics, industrial control, medical care, security alarm, field collection, smart home, highway, property management, water and electricity meter reading, power monitoring, environmental monitoring and other application scenarios.
Adaptive Systems and Models at Runtime (ASMR) refers to a field of study and a set of techniques that enable software systems to dynamically adapt their behavior and structure in response to changing conditions or requirements at runtime. ASMR focuses on building systems that can monitor their own execution, assess their performance, and make appropriate adjustments to improve their behavior or meet desired objectives.
Traditional software systems are typically designed and implemented based on a predefined set of assumptions and requirements. However, in many real-world scenarios, these assumptions may not hold true at all times. System behavior can be affected by various factors such as changes in user needs, environmental conditions, resource availability, or even the emergence of new system components or services. ASMR aims to address these challenges by providing mechanisms for systems to continuously monitor and analyze their runtime context and adapt accordingly.
ASMR involves the use of models that capture the system's behavior, performance, and relevant contextual information. These models can be used to reason about the system's current state, predict future states, and guide decision-making processes. By leveraging these models, adaptive systems can autonomously adjust their configuration, allocate resources, select alternative strategies, or reconfigure their structure to optimize performance, maintain stability, or achieve desired goals.
The adaptation mechanisms employed in ASMR can vary depending on the specific system and its requirements. Some common techniques used in ASMR include dynamic reconfiguration, runtime verification and monitoring, machine learning, control theory, and feedback loops. These techniques enable systems to monitor their own behavior, detect anomalies or deviations from desired properties, and take corrective actions to maintain or improve system performance.
The application domains of ASMR are broad and can range from embedded systems and robotics to cloud computing and self-adaptive software. ASMR techniques have been employed in areas such as autonomic computing, cyber-physical systems, intelligent transportation systems, and software-defined networking, among others.
In the context of manufacturing, ASMR can play a significant role in improving operational efficiency, productivity, and responsiveness. ASMR techniques can be applied to various aspects of manufacturing systems, including production processes, supply chain management, quality control, and equipment maintenance. Here are a few examples of how ASMR can be utilized in manufacturing:
Production Process Optimization: ASMR can enable manufacturing systems to dynamically adjust their production processes based on real-time data and feedback. By monitoring factors such as machine performance, energy consumption, product quality, and resource availability, adaptive models can optimize process parameters, sequence operations, and allocate resources to maximize productivity and minimize waste.
Supply Chain Adaptation: Manufacturing systems are often part of complex supply chains that involve multiple stakeholders and dependencies. ASMR can help in dynamically adapting supply chain operations based on changing conditions such as material availability, demand fluctuations, and transportation disruptions. By continuously monitoring the supply chain status and utilizing predictive models, adaptive systems can make informed decisions regarding inventory management, order fulfillment, and distribution strategies.
Quality Control and Defect Detection: ASMR techniques can be applied to real-time quality control in manufacturing processes. Adaptive models can learn from historical data and identify patterns related to product defects or deviations from quality standards. By analyzing sensor data, machine learning algorithms can detect anomalies, trigger alerts, and even adjust process parameters to prevent or minimize defects during production.
Equipment Maintenance and Predictive Maintenance: Adaptive systems can continuously monitor the health and performance of manufacturing equipment. By collecting sensor data, analyzing historical patterns, and utilizing machine learning algorithms, ASMR can enable predictive maintenance strategies. Equipment condition monitoring, failure prediction, and proactive maintenance scheduling can help minimize unplanned downtime, reduce maintenance costs, and optimize equipment utilization.
Agile Manufacturing and Customization: ASMR can support agile manufacturing approaches by enabling rapid reconfiguration of production systems. Adaptive models can facilitate flexible scheduling, resource allocation, and process customization to quickly respond to changing customer demands or market trends. By dynamically adapting manufacturing systems, companies can achieve faster product introductions, shorter lead times, and improved customer satisfaction.
By enabling systems to monitor and adapt themselves, ASMR techniques contribute to the development of more flexible, robust, and self-aware software systems with many positive applications in manufacturing.
Shared massage chairs are not a rare thing anymore. We often see them when we go shopping. Do you know why it can start working immediately after scanning the QR code for payment? What principle is this based on? Let'sl take a look at the "story behind" the shared massage chair.
In addition to the basic massage function, the shared massage chair also integrates a wireless module for data transmission and control. On this large-scale shared device, due to the number of access and real-time reasons, 4G and GPRS are generally used. But let's also take a look at using NB-IoT modules and look into which of these is more suitable for use on shared massagers.
Shared products need to be promoted and distributed in large quantities to cultivate users' usage and consumption habits. Therefore, it is necessary to choose a communication solution with relatively cheap tariffs, chips, and modules.
Among 4G, GPRS, and NB-IoT modules, 4G has the highest cost, but it has a high transmission rate and a large infrastructure coverage. Relatively speaking, the Cat1 module is relatively cost-effective. Secondly, the price of GPRS is moderate, but GPRS faces the risk of withdrawing from the network; the last is The NB-IoT module has the lowest cost, but the transmission rate is small, but it is enough to be used on a shared massage chair.
Remote monitoring and sharing of product data, visual presentation of product energy consumption, location, battery, operating data, etc. This is why wireless radio frequency modules such as LoRa, ZigBee, and Sub-G are not applicable, and NB-IoT modules are relatively more suitable.
Cellular data conforms to the usage habits of users and has a wide coverage area. It can be covered as long as there is an operator's network. At the same time, it can provide products with a standby time of more than several years. By the end of 2020, NB has covered major cities and towns. Covered, you can also apply for coverage if necessary.
Through analysis, we found that the NB-IoT module is really more suitable for shared massage chairs!
Ebyte's NB-IoT modules are mainly represented by the EA01 series, especially the EA01-SG, which integrates a high-precision, high-performance positioning chip, which is more convenient for sharing devices. Let's take a look at the application of EA01-SG in shared massage chairs.
Recent enhancements in Azure IoT integration have enabled the development of more flexible and robust solutions. The process of connecting various devices for a common objective has been simplified through the implementation of intelligent infrastructure. Automation has been personified through the utilization of artificial intelligence (AI) and machine learning algorithms, empowering us to leverage the potential of these technologies. An excellent illustration of this is Azure IoT integration, which facilitates the seamless integration of devices like industrial scale load switches, sensors, and digital switches into our existing business workflows.
NXP i.MX93 is designed for integration into embedded systems, including automotive infotainment systems, industrial control systems, and consumer electronics devices. The i.MX 9x processors deliver exceptional performance, while maintaining low power consumption, and offer a diverse range of peripheral interfaces, making them highly adaptable to various applications. Key features of the i.MX 9x processors include support for multiple display interfaces, video processing capabilities, and advanced power management features. Additionally, they come equipped with a comprehensive set of peripherals such as USB, Ethernet, Wi-Fi, Bluetooth, and more.
There are numerous benefits to integrating i.MX933 with Azure IoT, which are explained below:
- Camera Interface and Image Processing
The NXP i.MX93 is a processor that can be seamlessly integrated with Azure IoT for camera interfaces and image processing. It enables capturing and processing images from cameras and transmitting the processed data to the Azure IoT platform for advanced analysis and storage. This integration proves valuable for applications like security systems, industrial automation, and self-driving cars. The processor boasts extensive support for various camera interfaces, including MIPI CSI-2 and parallel, along with advanced image processing algorithms.
- Industry 4.0
Industry 4.0, also referred to as the fourth industrial revolution, encompasses the automation and digitization of manufacturing processes. A crucial element of Industry 4.0 is the incorporation of IoT technology, enabling the gathering and analysis of data from industrial equipment to enhance efficiency, minimize downtime, and facilitate informed decision-making. The i.MX 9X family of processors, notably the i.MX 9X3, is ideally suited for Industry 4.0 applications, including IoT integration. With its high performance and low power consumption, the i.MX 9X3 is designed for embedded applications, such as industrial automation and control systems, medical devices, and more.
With Azure IoT, you can leverage the capabilities of the i.MX 9X3 to establish connections between industrial equipment and the cloud, facilitating real-time data collection and analysis. For instance, the i.MX 9X3 can gather sensor data from industrial machinery and transmit it to Azure IoT Hub for processing and analysis. Azure IoT Edge allows you to deploy machine learning models and cloud-based services directly on the i.MX 9X3, enabling advanced data analysis and predictions regarding equipment performance. Azure Stream Analytics and Azure Machine Learning, both accessible through Azure IoT Edge, enable real-time data stream processing and the creation and deployment of machine learning models on the i.MX 9X3. Ultimately, the combination of the i.MX 9X3 and Azure IoT present a robust solution for Industry 4.0 and IoT integration in industrial automation and control systems.
You can utilize the i.MX 9X3 processor to gather data from industrial equipment and transmit it to Azure IoT for processing and analysis. Additionally, Azure IoT Edge enables the deployment of machine learning models and other cloud-based services directly on the i.MX 9X3, facilitating more sophisticated data analysis and predictions regarding future equipment performance.
- Security with Azure Sphere: -
Long after the initial deployment, maintaining the security of an edge device can be challenging and requires continuous trusted management services. Azure Sphere offers not only secured hardware but also the protected Azure Sphere OS, the cloud-based Azure Sphere Security Service, and regular OS updates and security enhancements for over 10 years. The i.MX 93 family of products incorporates Microsoft Pluton enabled on Edge Lock secure enclave, serving as the protected root of trust integrated into the silicon itself. This critical step enables the complete Azure Sphere security stack for various IoT and industrial applications. Specifically, the i.MX 93-CS model within the i.MX 9 series processors will have Azure Sphere chip-to-cloud security enabled, expanding the range of processor options available to developers.
A reference design platform based on i.MX93 is being developed by eInfochips to accelerate product development and simplify design complexities. This platform is well-suited for various applications such as smart cities, smart homes, smart factories, and smart buildings, offering efficient and affordable machine learning acceleration.
eInfochips' foundation is built on NXP technologies, including application processors, low-power processors, microcontrollers, and S32 automotive platforms. With over 20 years of experience, eInfochips excels in engineering services, covering areas such as hardware design, firmware and system software development, application software, and cloud enablement. Clients have greatly benefited from eInfochips' successful products and services, contributing to their numerous success stories.
Author Bio – Rohit Biradar
Rohit Biradar works as an IoT Solutions Trend Analyst at eInfochips. His areas of interest include AI, IoT, and Automation. In his free time, he loves playing video games, traveling solo, and playing cricket and volleyball.
Cloud-based motor monitoring as a service is revolutionizing the way industries manage and maintain their critical assets. By leveraging the power of the cloud, organizations can remotely monitor motors, analyze performance data, and predict potential failures. However, as this technology continues to evolve, several challenges emerge that need to be addressed for successful implementation and operation. In this blog post, we will explore the top challenges faced in cloud-based motor monitoring as a service in 2023.
Data Security and Privacy:
One of the primary concerns in cloud-based motor monitoring is ensuring the security and privacy of sensitive data. As motor data is transmitted and stored in the cloud, there is a need for robust encryption, authentication, and access control mechanisms. In 2023, organizations will face the challenge of implementing comprehensive data security measures to protect against unauthorized access, data breaches, and potential cyber threats. Compliance with data privacy regulations, such as GDPR or CCPA, adds an additional layer of complexity to this challenge.
Connectivity and Network Reliability:
For effective motor monitoring, a reliable and secure network connection is crucial. In remote or industrial environments, ensuring continuous connectivity can be challenging. Factors such as signal strength, network coverage, and bandwidth limitations need to be addressed to enable real-time data transmission and analysis. Organizations in 2023 will need to deploy robust networking infrastructure, explore alternative connectivity options like satellite or cellular networks, and implement redundancy measures to mitigate the risk of network disruptions.
Scalability and Data Management:
Cloud-based motor monitoring generates vast amounts of data that need to be efficiently processed, stored, and analyzed. In 2023, as the number of monitored motors increases, organizations will face challenges in scaling their data management infrastructure. They will need to ensure that their cloud-based systems can handle the growing volume of data, implement efficient data storage and retrieval mechanisms, and utilize advanced analytics and machine learning techniques to extract meaningful insights from the data.
Integration with Existing Systems:
Integrating cloud-based motor monitoring systems with existing infrastructure and software can pose significant challenges. In 2023, organizations will need to ensure seamless integration with their existing enterprise resource planning (ERP), maintenance management, and asset management systems. This includes establishing data pipelines, defining standardized protocols, and implementing interoperability between different systems. Compatibility with various motor types, brands, and communication protocols also adds complexity to the integration process.
Cost and Return on Investment:
While cloud-based motor monitoring offers numerous benefits, organizations must carefully evaluate the cost implications and expected return on investment (ROI). Implementing and maintaining the necessary hardware, software, and cloud infrastructure can incur significant expenses. Organizations in 2023 will face the challenge of assessing the financial viability of cloud-based motor monitoring, considering factors such as deployment costs, ongoing operational expenses, and the potential savings achieved through improved motor performance, reduced downtime, and optimized maintenance schedules.
Connectivity and Reliability:
Cloud-based motor monitoring relies heavily on stable and reliable internet connectivity. However, in certain remote locations or industrial settings, maintaining a consistent connection can be challenging. The availability of high-speed internet, network outages, or intermittent connections may impact real-time monitoring and timely data transmission. Service providers will need to address connectivity issues to ensure uninterrupted monitoring and minimize potential disruptions.
Scalability and Performance:
As the number of monitored motors increases, scalability and performance become critical challenges. Service providers must design their cloud infrastructure to handle the growing volume of data generated by motor sensors. Ensuring real-time data processing, analytics, and insights at scale will be vital to meet the demands of large-scale motor monitoring deployments. Continuous optimization and proactive capacity planning will be necessary to maintain optimal performance levels.
Integration with Legacy Systems:
Integrating cloud-based motor monitoring with existing legacy systems can be a complex undertaking. Many organizations have legacy equipment or infrastructure that may not be inherently compatible with cloud-based solutions. The challenge lies in seamlessly integrating these disparate systems to enable data exchange and unified monitoring. Service providers need to offer flexible integration options, standardized protocols, and compatibility with a wide range of motor types and manufacturers.
Data Analytics and Actionable Insights:
Collecting data from motor sensors is only the first step. The real value lies in extracting actionable insights from this data to enable predictive maintenance, identify performance trends, and optimize motor operations. Service providers must develop advanced analytics capabilities that can process large volumes of motor data and provide meaningful insights in a user-friendly format. The challenge is to offer intuitive dashboards, anomaly detection, and predictive analytics that empower users to make data-driven decisions effectively.
Cloud-based motor monitoring as a service offers tremendous potential for organizations seeking to optimize motor performance and maintenance. However, in 2023, several challenges need to be addressed to ensure its successful implementation. From data security and connectivity issues to scalability, integration, and advanced analytics, service providers must actively tackle these challenges to unlock the full benefits of cloud-based motor monitoring. By doing so, organizations can enhance operational efficiency, extend motor lifespan, and reduce costly downtime in the ever-evolving landscape of motor-driven industries.
We all know how IoT has revolutionized the way we interact with the world. IoT devices are now ubiquitous, from smart homes to industrial applications. A significant portion of these devices are Wireless Sensor Networks (WSNs), which are a key component of IoT systems. However, designing and implementing WSNs presents several challenges for embedded engineers. In this article, we discuss some of the significant challenges that embedded engineers face when working with WSNs.
WSNs are a network of small, low-cost, low-power, and wirelessly connected sensor nodes that can sense, process, and transmit data. These networks can be used in a wide range of applications such as environmental monitoring, healthcare, industrial automation, and smart cities. WSNs are typically composed of a large number of nodes, which communicate with each other to gather and exchange data. The nodes are equipped with sensors, microprocessors, transceivers, and power sources. The nodes can also be stationary or mobile, depending on the application.
One of the significant challenges of designing WSNs is the limited resources of the nodes. WSNs are designed to be low-cost, low-power, and small, which means that the nodes have limited processing power, memory, and energy. This constraint limits the functionality and performance of the nodes. Embedded engineers must design WSNs that can operate efficiently with limited resources. The nodes should be able to perform their tasks while consuming minimal power to maximize their lifetime.
Another challenge of WSNs is the limited communication range. The nodes communicate with each other using wireless radio signals. However, the range of the radio signals is limited, especially in indoor environments where the signals are attenuated by walls and other obstacles. The communication range also depends on the transmission power of the nodes, which is limited to conserve energy. Therefore, embedded engineers must design WSNs that can operate reliably in environments with limited communication range.
WSNs also present a significant challenge for embedded engineers in terms of data management. WSNs generate large volumes of data that need to be collected, processed, and stored. However, the nodes have limited storage capacity, and transferring data to a centralized location may not be practical due to the limited communication range. Therefore, embedded engineers must design WSNs that can perform distributed data processing and storage. The nodes should be able to process and store data locally and transmit only the relevant information to a centralized location.
Security is another significant challenge for WSNs. The nodes in WSNs are typically deployed in open and unprotected environments, making them vulnerable to physical and cyber-attacks. The nodes may also contain sensitive data, making them an attractive target for attackers. Embedded engineers must design WSNs with robust security features that can protect the nodes and the data they contain from unauthorized access.
The deployment and maintenance of WSNs present challenges for embedded engineers. WSNs are often deployed in harsh and remote environments, making it difficult to access and maintain the nodes. The nodes may also need to be replaced periodically due to the limited lifetime of the power sources. Therefore, embedded engineers must design WSNs that are easy to deploy, maintain, and replace. The nodes should be designed for easy installation and removal, and the network should be self-healing to recover from node failures automatically.
Final thought; WSNs present significant challenges for embedded engineers, including limited resources, communication range, data management, security, and deployment and maintenance. Addressing these challenges requires innovative design approaches that can maximize the performance and efficiency of WSNs while minimizing their cost and complexity. Embedded engineers must design WSNs that can operate efficiently with limited resources, perform distributed data processing and storage, provide robust security features, and be easy to deploy
Some say that if World War III breaks out, it will be fought in cyberspace. As IoT systems gather more and more under the “umbrella” of the network, security has never been more important to various user groups. From the traffic lights that play an important role in our urban traffic to the power system that provides energy for them, to the management and monitoring systems that keep cars running well, security in the use of networks and devices has become the basis and basis of modern communication devices and systems. necessary condition. Providing solid security in the online world is no easy task. Security is one of the very few scientific and technological means that must be confronted with external forces to achieve overdue results. What is more complicated is that these external forces can break through the defense line time after time through traditional and innovative means. Because of the many potential attack methods, information and network security has become an attractive and challenging topic, which is closely related to enterprises, industries and life.
For decades, the information technology (IT) environment has been very active and the hardest hit area for attacks and threats, which has also allowed IT to grow rapidly. In contrast, the operational technology (OT) environment is relatively traditional and closed, and the connection methods and channels between devices and the network are very limited. Therefore, compared with IT, OT records relatively fewer attack events, but its learning opportunities Countermeasures are also relatively scarce. But security in the OT world tends to have a broader security scope than IT. For example, in OT, security is almost equivalent to safety. In fact, the connected security standards of IIoT also incorporate the safety of equipment and people. This installment will focus on common challenges facing OT security.
The erosion problem of network architecture. The main issues facing the protection of industrial environments are initial design and ongoing maintenance. The original design concept stems from a premise that the network itself is safe, because it is isolated on the physical level of the enterprise, with little or no connection with the external environment, and the attacker lacks sufficient correlation knowledge to perform security attacks. In the vast majority of cases, the initial network design is sound, even good practice and relative standards. But in fact, from the point of view of security design, it is better to cope with the growing demand than to conceal the lack of communication and improve the response. It is relatively common that, over time, an otherwise hidden problem may be exposed by temporary updates and cracks to the hardware, allowing the problem to go unchecked and spread across the entire device family leading to a complete network and system crash Case.
Pervasive system legacy issues. In an industrial environment, the span of new and old equipment is large, the equipment life cycle is long, and the operating system of the equipment is not uniform enough, which makes the maintenance of the equipment extremely troublesome, and also exposes security issues such as system vulnerabilities. For example, in the context of urban power systems, it is not uncommon for older mechanical equipment to intersect with modern smart electronics. For the legacy components, because the old equipment cannot be connected to the network, the equipment is encouraged to run, but the entire system is integrated into the network, and some conditions cannot be monitored. From a security point of view, this situation is a potential threat, because many devices are likely to be unpatched or have vulnerabilities due to legacy issues, and it is more likely that the corresponding solutions for devices that are aging due to the passage of time cannot be applied. Therefore, we should strengthen the management of patches and devices, generate corresponding tools, and protect the vulnerabilities that may be exploited as much as possible.
Unsafe operating protocol. Among industrial control protocols, especially those based on serial ports, they are only considered for communication at the beginning of design, and there is no relative requirement for security. This has become the weakness and inherent loophole of the current network transmission protocol. In addition, the security considerations in the embedded operating system are relatively lacking. In old industrial protocols, data protocols such as monitoring and data acquisition often have coexisting security issues. Including the lack of communication authentication, static and dynamic data cannot establish effective protection, which makes the data in transmission often public. Although the data may not be so important, the risk of data tampering must be prevented.
The device is not secure. In addition to the defects of the communication protocol, the control equipment and the communication components themselves also have loopholes and defects. Before 2010, the world paid little attention to the security of industrial design, which also led to the fact that industrial design did not undergo the fire-zero test like IT, which led to frequent occurrence of vulnerability-related problems in the industry. This also reminds the OT industry to pay attention to the safety of the equipment itself.
IoT security issues are often more than that, including supplier dependence, security knowledge presentation and demonstration issues, etc. All these aspects remind the importance of safety all the time.
In recent days, neural networks have become a topic for discussion. But the question still needs to be solved- How can it affect our world today and tomorrow?
The global neural network market's compound annual growth rate (CAGR) is expected to be 26.7% from 2021 to 2030. This means that new areas of application for them might appear soon. The Internet of Things that is IoT, is today's most fascinating and required technological solution for business. Around 61% of companies utilize IoT platforms, and we can anticipate the integration of neural networks into enterprise IoT solutions. This anticipation raises many questions, like what gets such collaboration and how to prepare it. Can we optimize the IoT ecosystem using neural networks, and who will approach such solutions?
What do you understand by a neural network, and how is it beneficial for enterprise IoT?
An artificial neural network that is ANN is a network of artificial neurons striving to simulate the analytical mechanisms taken by the human brain. This type of artificial intelligence includes a range of algorithms that can "learn" from their own experience and improve themselves, which is very different from classical algorithms that are programmed to resolve only specific tasks. Thus, with time, the neural network will remain pertinent and keep on improving.
With the proper implementation, enterprise internet of things (EIoT) and ANN can offer the business the most valuable things: precise analytics and forecasts. In general, it is not possible to compare both. Enterprise IoT is a system that needs software for data analysis, whereas ANN is a component that needs a large amount of data to be operational. Their team naturally controls the analytical tasks; therefore, high-level business tasks are performed most effectively, reducing costs, automating processes, finding new revenue sources, etc.
In the Internet of Things ecosystem, neural networks help in two areas above all:
- Data acquisition via ANN-based machine vision
- Advanced-data analysis
If it needs significant investments to execute ANN in big data analytics solutions, neural network image processing can decrease the cost of the IoT solution. Thus, neural networks improve enterprise IoT solutions, enhance their value, and speed up global adoption.
Which solutions within enterprise IoT can be enhanced using neural networks?
IoT-based visual control
The IoT ecosystem begins with data collection. Data quality impacts the accuracy of the ultimate prediction. If you implement visual control in your production processes, neural networks can boost the quality of products by superseding outdated algorithms. Besides this, they will optimize the EIoT solution. Conventional machine vision systems are pricey as they require the highest resolution cameras to catch minor defects in a product. They come with complex specific software that fails to respond to immediate changes.
Neural networks within machine vision systems can:
- Diminish camera requirements
- Self-learn on your data
- Automate high-speed operations
Indeed, industrial cameras use large-format global shutter sensors having high sensitivity and resolution to develop the highest quality images. Nevertheless, a well-trained ANN starts to identify images with time. It allows them to reduce the technical needs for the camera and ultimately cuts the final cost of the enterprise IoT implementation. You cannot compromise the quality of images to detect small components like parts in circuit boards; however, it is manageable for printing production, completeness checking, or food packaging.
After training, neural networks use massive amounts of data to identify objects from the images. It enables you to customize the EIoT solution and train the ANN to operate specifically with your product by processing your images.
For example, convolutional neural networks are utilized actively in the healthcare industry to detect X-rays and CT scans. The outcome offered by such custom systems is more precise than conventional ones. The capability to process information at high speeds permits the automation of production processes. When the problem or defect is caught, neural networks promptly report it to the operator or launch an intelligent reaction, like automating sorting. Hence, it allows real-time detection and rejection of defective production.
An exclusive example of how ANN is utilized for edge and fog computing. As per PSA, a neural network executed in a machine vision system permits lowering the number of defects by 90% in half a year, whereas production costs are decreased by 30%. Prospective areas for ANN in IoT visual control are quality assurance, sorting, production, collecting, marking, traffic control, and ADAS.
Big data advanced analytics for enterprise IoT:
Today, neural networks allow businesses to grab advantages like predictive maintenance, new revenue flows, asset management, etc. It is possible via deep neural networks (DNN) and the deep Learning (DL) method involving multiple layers for data processing. They detect hidden data trends and valuable information from a significant dataset by employing classification, clustering, and regression. It results in effective business solutions and the facilitation of business applications.
In comparison to traditional models, DL manages with the attributes that are expected for IoT data:
- Assess the time of taking measurements
- Resist the high noise of the enterprise IoT data
- Conduct accurate real-time analysis
- Determine heterogeneous and discordant data
- Process a large amount of data
In practice, this implies that you don't require middle solutions to deliver and sort the data in the cloud or to analyze them in real-time. For example, full-cycle metallurgical enterprises can execute one solution to analyze the variable and unstructured data from metal mining, smelting, and final manufacturing products. Airplanes generate about 800TB of data per hour, making it impossible to process it all ideally using conventional analytical systems.
Today, DNN models are successful in the following enterprise IoT applications.
Today, it has become easy to predict disease using AI-based IoT systems, and this technology is developing for further improvements. For instance, the latest invention based on the neural network can detect the risk of heart attacks by up to 94.8%. DNN is also helpful in disease detection: the spectrogram of a person's voice received using IoT devices can identify voice pathologies after DNN processing. In general, ANN-based IoT health monitoring systems' accuracy is estimated to be above 85%.
DL systems in the enterprise Internet of Things have provided results in power demand prediction based on power price forecasting, consumption data, anomaly, power theft detection, and leak detection. Smart meter data analysis permits you to calculate consumption, determine the unusual usage of electricity, and predict with an accuracy of more than 95%, which will help you to adjust energy consumption.
Neural networks help to use the most demanded IoT service among manufacturers properly- predictive equipment maintenance. It was ascertained to be a workable practice for mechanical and electrical systems. This network provides accurate real-time status monitoring and predicts proper life rest. Another best example is the recognition of employee activity by taking readings and following in-depth analysis.
Transportation & Logistics:
Deep Learning has made smart transportation systems possible. It offers better traffic congestion management by processing travel time, speed, weather, and occupational parking forecasting. Analytical reports based on vehicle data help to discover dangerous driving and possible issues before the failure happens.
As we know, the previous industries generate heterogeneous data. Therefore, the potential of ANN analytics within EIoT will be unlocked for multiple complicated systems.
When to consider ANN for enterprise IoT:
Till now, research in the field of ANNs been very active, and we cannot foretell all the advantages or pitfalls these solutions will convey. No doubt, neural networks find out correlations, models, and trends better than other algorithms. The IoT ecosystem's data will become more extensive, complex, and diverse with time. So, the development of neural networks is the future of IoT.
For now, we can look into the following features of neural networks for enterprise IoT:
- They suit the IoT ecosystem architecture, substituting alternative solutions with significant advantages.
- Essential for industrial image processing.
- Progressive ANN-based data analytics gets the high-level business value of the enterprise IoT solutions – improves productivity, and exactness, boosts sales, and produces informed business decisions.
- Training the ANN requires time and expenditure but will become fully customizable.
- We cannot conclude it is an affordable solution, but the advantages are priceless if the IoT ecosystem is executed accurately.
Therefore, if you are provided with a neural network as one of the opportunities for executing your idea within the IoT ecosystem, give it a chance. You never know, this solution will become a must-have in the coming years.
Technologies like blockchain, IoT, AR, VR, and AI are playing a big role in transforming the gambling industry. They are changing the way of gambling and players all around the world are liking this innovative approach.
The Internet of things has added a lot of attraction to casinos because with the help of IoT, offering gambling according to the regulation, to ensure players' safety, secure their assets, data security, and excellent player gaming experience becomes so much easier. Not only the gambling industry but also other various industries have adopted it and collaborating with AI, Crypto, and blockchain gives a new shape to the casino world. According to the study, it is expected more than 41 billion IoT devices will be used by 2027. All digital devices such as smartphones, PCs, digital watches, cameras, and other smart gadgets are examples of IoT.
In this article, we are going to discuss how blockchain and IoT are bringing fruitful results in the gambling industry. And why the demand for blockchain development services is so high in the market.
IoT is a network of physical objects that are connected with each other by sensors, software, and other technologies to connect and exchange data in a secure and smooth way over the Internet. IoT is defining the gaming industry with positive and fruitful way.
Before diving in-depth, let’s know about blockchain and IoT.
The main objective of blockchain is to record data in the form of blocks and all blocks are linked together in a chain. That means blockchain is an immutable ledger where all records are saved but cannot be changed, deleted, or destroyed.
Basically, it is an advanced database mechanism that offers you high security and transparency. There are four types of blockchains:
- Public blockchain
- Private blockchain
- Hybrid blockchain
- Consortium Blockchain
Lets’ know the positive aspects of Blockchain and IoT in gambling business Industry.
Benefits of Blockchain in Gambling Industry
Here are the reasons for the popularity of blockchain and IoT in gambling world and most games and service providers and online casinos are using both technologies in order to create a difference.
- High Security
Blockchain offers you high-end security and when you make any transaction using cryptocurrencies then it will automatically be added to the distributed ledger and will automatically be added in the whole blocks and entry of new coins is added in the blockchain.
Blockchain in the casino is offering high security to both players and owners. With its help, there is no need to do registration at casinos and there is no need to validate yourself, and no credit card information is required there. You can do all without sharing your personal data and no one regulates you due to the absence of central authorities.
As we all know that according to a specific location, there are certain rules to regulate online casino business and as a user, you have to follow these rules. But with the help of blockchain, you can enjoy any casino all over the world and you can make payments without facing any issues and no one will know your identity. Cryptocurrency and blockchain are not regulated by any central authority and you can earn huge profits by accessing all casinos all over the world and crypto can be used for payment.
At present, privacy is everything and you can gamble and make transactions without being noticed by anyone because no one can track you here and you have no need to share your personal information.
- Instant and Cost-effective
Blockchain makes it possible to do fast and instant transactions. As we all know that crypto is based on blockchain and not regulated by a central authority so it means there are no mediators and you can make your transaction in a faster way. And cost-effectiveness is another reason that is making it more popular and it charges less than credit cards, debit cards and traditional platforms.
- Transparency, Efficiency, and Access
When you integrate blockchain in the online gambling industry, then you get transparency and no one can make fraud with you. Like you cannot trust traditional casinos, and online platforms for reliable betting services but on blockchain oriented casinos you can. Because here blockchain maintains all records that are impossible to manipulate.
You can also enjoy casino games without registration because it uses only your wallet address so it becomes quite easy to access and platform efficiency also improves.
- Smart chips
In online casinos, there is a huge amount of data and managing that is quite a tedious task and thanks to IoT that has made it quite easy and user-friendly. With the help of RFID microchips, all illegal activities have become so minimal. RFID microprocessors have made it possible to take care of all aspects of online casinos.
So, we can say that IoT has increased the security of casinos and now players can enjoy gambling services in an easy way.
This article helps you to know all about the IoT and Blockchain and how they are bringing positive changes in the online casino world and gambling industry. After reading this article, we can say that now players and bettors can enjoy online gambling in an effective way without facing any issues. Now they are paying full attention to gambling without caring about extra issues. You can also invest in the gambling business with the help of a sports betting developmet company.
With the advent of the Internet of Things, Big Data is becoming more and more important. After all, when you have devices that are constantly collecting data, you need somewhere to store it all. But the Internet of Things is not just changing the way we store data; it’s changing the way we collect and use it as well. In this blog post, we will explore how the Internet of Things is transforming Big Data. From new data sources to new ways of analyzing data, the Internet of Things is changing the Big Data landscape in a big way.
How is the Internet of Things transforming Big Data?
The Internet of Things is transforming Big Data in a number of ways. One way is by making it possible to collect more data than ever before. This is because devices that are connected to the Internet can generate a huge amount of data. This data can be used to help businesses and organizations make better decisions.
Another way the Internet of Things is transforming Big Data is by making it easier to process and analyze this data. This is because there are now many tools and technologies that can help with this. One example is machine learning, which can be used to find patterns in data.
The Internet of Things is also changing the way we think about Big Data. This is because it’s not just about collecting large amounts of data – it’s also about understanding how this data can be used to improve our lives and businesses.
The Benefits of the Internet of Things for Big Data
- The internet of things offers a number of benefits for big data.
- It allows for a greater volume of data to be collected and stored.
- Also, it provides a more diverse range of data types, which can be used to create more accurate and comprehensive models.
- It enables real-time data collection and analysis, which can help organizations make better decisions and take action more quickly.
- It can improve the accuracy of predictions by using historical data to train predictive models.
- Finally, the internet of things can help reduce the cost of storing and processing big data.
The Challenges of the Internet of Things for Big Data
The internet of things is transforming big data in a number of ways. One challenge is the sheer volume of data that is generated by devices and sensors. Another challenge is the variety of data formats, which can make it difficult to derive insights. Additionally, the real-time nature of data from the internet of things presents challenges for traditional big data infrastructure.
The Internet of Things is bringing a new level of connectivity to the world, and with it, a huge influx of data. This data is transforming how businesses operate, giving them new insights into their customers and operations.
The Internet of Things is also changing how we interact with the world around us, making our lives more convenient and efficient. With so much potential, it's no wonder that the Internet of Things is one of the most talked-about topics in the tech world today.
IoT is disrupting almost every industry sector including communications. As power consumption has become a challenge for IoT devices, cellular IoT has introduced some standards that are cutting-edge. Let’s take a look at those standards and their device categories.
Remember the days when the “E” icon on the notification bar of our phones used to make us excited?
Well, if we compare that to today, technology has skyrocketed like anything. It was just a matter of time before that E icon turned to 4G LTE.
Today, there are billions of devices that run on the 4G network providing lightning-fast internet to the users. And it does not end here. The wave of 5G is ready to take on the world. Though some countries have already deployed 5G, it is yet to conquer the entire world.
Now, IoT is not a buzzword anymore. It is an awesome technology that connects various internet-enabled devices and is known to everybody. The use of IoT allows devices to share data at a faster pace. But, there is one challenge!
As these devices are connected to cellular networks like 3G and 4G LTE, they consume a lot of power. In a way, it is acceptable, but not if the devices are sending a small amount of data occasionally. So what’s the solution here? Cellular IoT!
Cellular IoT deals with some of the best IoT standards and devices that make the existing cellular technology fit for low-powered devices. If you are interested to know how; read ahead and find out!
Why are IoT LTE devices necessary?
Well, the need for IoT devices comes into the picture when we analyze applications like predictive maintenance, asset tracking, fleet management, inventory management, remote service, etc.
All these applications are backed by powerful yet sensitive devices that transmit data to ensure that all your business processes are running fine. LTE is the technology that helps them. IoT devices under LTE can be classified based on the LTE standards!
This standard covers devices that run under the bandwidth of 1.4 MHz. Most of the devices under the standard are smart meters, fleet management devices, and asset tracking devices.
The operating bandwidth of Cat-1 devices is 20 MHz which allows for devices like ATMs, POS terminals, and wearables to operate.
The devices under Cat-4 have the maximum download and upload speed, which makes them ideal for applications like autonomous vehicles, real-time video, and in-car infotainment.
The IoT LTE devices under NB-IoT have the maximum latency, which makes them crucial for applications like parking sensors, street lighting, industrial monitors, and more.
What are the various IoT LTE devices categories?
Well, if we talk about the device categories, IoT LTE devices can be classified into four categories based on cellular IoT standards. The newest of these four standards are LTE-M and NB-IoT.
Let’s read ahead and find out about the IoT LTE device categories!
1. LTE-M/ Cat-M1
Let’s begin with the LTE-M standard. The LTE-M standard is an excellent discovery that is ideal for devices that require less power and less bandwidth. Here are some key pointers related to the device categories of LTE-M!
- The devices based on the LTE-M standard have an upload speed of 1 Mbps, and the same is the download speed.
- On top of that, the latency in the case of LTE-M devices is 10-15 milliseconds. The latency is enough to ensure that the required data is transmitted at regular intervals.
- The bandwidth of the LTE-M is enough to ensure that the devices are able to function well in the prevailing 2G and 3G applications.
- The best thing about the LTE-M standard is handoff for devices. It allows seamless handoff that makes the standard ideal for applications like asset tracking and fleet management where devices are on the move.
- Cat-M1 was created as an integral part of Release 13 of the 3GPP’s LTE standards.
Apart from the above-described device categories, Cat-1 is a category that is a part of Release 8 of the 3GPP standard. Though it is a part of the old technology, it is still widely used across the globe. Here are some features of the Cat-1!
- The Cat-1 standard is made for IoT device categories that have low and medium bandwidth needs.
- The speed of the Cat-1 device is more than that of LTE-M. The upload speed of the Cat-1 devices is 5 Mbps, and the download speed is 10 Mbps.
- One of the best things about Cat-1 is that it has less latency. The latency of the signals is just 50-100 milliseconds.
- The Cat-1 standard uses a massive bandwidth of 20 Mhz in a full duplex. The full duplex capability of the devices allows for smooth handoff, making it ideal for wearables, ATMs, POS terminals, etc.
Well, the Cat-4 standard is what it takes to support applications like autonomous cars. The speed of devices in this standard is way more than Cat-1. It can provide you with 50 Mbps upload speed, and 150 Mbps download speed.
The best advantage of the Cat-4 standard is that it supports in-car infotainment, in-car hotspots, and video surveillance.
4. NB-IoT/ Cat-NB1
After the LTE-M, there is NB-IoT or Cat-NB1 standard. Just like LTE-M, there are many aspects that make it a bit different and unique. Here are some key pointers about the devices supporting the NB1 standard.
- The low-cost technology makes use of DSSS modulation technology vs. LTE spread technology to ensure connectivity.
- The cost factor of the technology is not the only USP. The devices that come under Cat-NB1 have less power consumption, offer excellent in-building coverage, and have longer battery life.
- If we talk about the upload and download speed of the NB-IoT device category, it is relatively less compared to LTE-M. The upload speed is 66 kbps, and the download speed is 26 kbps. This is in half duplex mode.
- The latency of NB-IoT is also more than the LTE-M. It oscillates between 1.6 to 10 seconds. Though it seems way more, there are advantages to it. The latency is ideal for small, intermittent data transmissions.
- NB-IoT is also part of Release 13 of the 3GPP’s LTE standard. It is an LPWAN technology that works on a licensed spectrum.
- The devices that come under this standard are smart gas, street lights, parking sensors, etc.
Other than these device and standard categories, there are two more standards:
As there is a need for low-cost devices and processes, Cat-0 lays the groundwork for that. It eliminates the need for features that require a high data rate in Cat-1. On top of all, Cat-0 is slowly doing the groundwork for Cat-M by replacing 2G.
It is a standard that does not have as much buzz as the LTE-M and NB-IoT. But, it has been tested by brands like Ericsson and Intel for supreme practicality and modularity.
Why Do We Need To Care?
Well, if you are a cellular carrier service provider, you have to care about it. There are many factors that need to be considered while choosing the IoT LTE device category. Here is a brief elaboration of some of the critical ones!
1. Power consumption:
Out of all the IoT LTE devices listed above, those who come under the Cat-4 consume the maximum power. After that come the devices under Cat-1. Cat-M1 and NB-IoT devices are the ones that have the minimum power consumption.
2. Battery life:
Battery life is the key factor if the devices are placed in remote locations like the agricultural field. If you are choosing LTE IoT devices, go for devices under standards Cat-M1 and NB-IoT.
If cost is your concern, then again, Cat-M1 and NB-IoT are the ideal picks for you. They are best for high-volume device applications. Devices under Cat-1 and Cat-4 are more pricey.
When it comes to adoption, the adoption of LTE-M and NB-IoT are quickly being adopted by carrier service providers across the globe.
Latency is the highest in NB-IoT, which makes it ideal for applications that do not need to send continuous data. LTE-M is a bit faster than NB-IoT. Cat-4 is the fastest, which makes it ideal for video applications.
So, now we are clear about what type of IoT devices are under each standard of LTE. LTE-M and NB-IoT are the standards that are being quickly adopted as they are low cost, consume less power, and have max battery life. To make an informed choice, it is necessary for you to analyze each aspect closely. As of now, carrier companies are inclined toward adopting NB-IoT and LTE-M as they can serve vast applications while being balanced in all aspects.
Note: this page contains paid content.
Please, subscribe to get an access.