Narrowband Internet Of Things Research Articles

Enhancing 5G communication in business networks with an innovative secured narrowband IoT framework

Abstract These days, 5G mobile technology highly influences the wireless communication process, which is more responsive than the other generation. However, the 5G communication process handles the speed and latency issues while transmitting data over the network. Especially in rural areas, most people face this speed and latency problem while connecting to the network. Therefore, the network uses massive amounts of data to analyse this problem by integrating 5G with the Internet of things (IoT). The IoT-based data transmission and collection helps manage the data capacity, connection, and transmission rate. Therefore, this study uses the novel secured narrowband IoT framework for 5G communication and operational goals based on their position in the market. The 5G network uses business connectivity and access information to manage the data transmission. The network uses cellular bandwidth and IoT devices to connect more during this process. According to the connection, the information is transmitted with the minimum delay. Thus, experimental results have proposed these parameters: an accuracy ratio of 92.5%, a security ratio of 94.4%, a reliability ratio of 93.5%, a latency ratio of 91.8%, a cost-effective ratio of 20.5%, and a throughput ratio of 25.4% compared to other methods.

Saffron corm sorting and rot treatment strategy for productivity enhancement for precision agriculture

One of the most economical and precious spices ever known is saffron. It has innumerable pharmaceutical applications, and its cultivation is limited to certain topographic regions throughout the world. Although efforts are continuously being made to increase productivity and increase the area under cultivation, there are multiple factors leading to its low production. Corm size is one of the major factors affecting the quality and production of saffron. The more the corm size for a plantation, the better the yield of the crop. This can pose a serious threat to saffron cultivators who are not able to get good quality corms and sort quality corms from infected ones. Additional factors affecting saffron cultivation include attack by fungus leading to corm rots and sclerotineal disease. In this paper, IoT (Internet of Things) has been used to provide an energy-efficient, ready-to-use, reliable, and user-friendly solution for corm sorting and treatment before sowing to promote quality, better yield and minimized wastage due to corm rots. After sorting the corms, they are treated with 0.2% of tiabendazole fungicide solution and dried, before sowing and for long-term storage. Firstly, sorting and treatment system for the saffron corms processing as per weight is proposed. First two-layer cleaning mechanism to remove foreign substances, then three-step treatment has been applied to remove microorganisms. An automated conveyor system equipped with IoT devices was used for sorting and treating saffron corms. Different energy efficient LPWA (Low Power Wide Area) and LTE (Long Term Evolution) technologies used in the framework, for communication are NB-IoT (Narrow Band IoT) and LoRa (Long Range Radio).

Optimizing Energy-Efficient Communication Protocols for IoT Devices in Smart Cities Using Narrowband IoT and LTE-M Technology

In the context of optimizing energy-efficient communication protocols for IoT devices in smart cities, this research addresses the pressing need for sustainable and reliable solutions. The research provides a comprehensive understanding of the strategies that contribute to minimizing energy consumption in smart city IoT deployments, with a specific focus on Narrowband IoT (NB-IoT) technology. Employing a structured framework and an integrated system architecture, incorporating LTE-M technology and an application server, the research leverages various optimization strategies. These include Adaptive Power Control, Duty Cycling, Data Aggregation, Protocol Optimization, and Network Topology Optimization. Methodologically, the study adopts a quantitative approach, utilizing metrics such as duty cycle percentages and energy consumption reductions. Data is collected through simulations and experiments, and the analysis involves assessing the impact of each optimization strategy on energy efficiency. Key findings demonstrate significant reductions in energy consumption, ranging from 15% to 25%, depending on the specific strategy employed. Adaptive Power Control and Network Topology Optimization emerge as particularly effective approaches. The implications of these findings underscore the importance of adopting tailored optimization strategies in smart city IoT deployments to achieve both energy efficiency and reliability. The study discusses the broader significance of these results and acknowledges potential limitations, paving the way for future research endeavors in the realm of sustainable and resource-conserving smart cities.

Enhancing energy‐efficient sleep scheduling for Narrowband Internet of Things devices in coordinated 5G networks within smart environments

SummaryThis research project takes on a crucial role in the quickly changing field of integrated 5G networks inside smart environments by concentrating on the creation of an extremely effective sleep scheduling system designed especially for Narrowband Internet of Things (NB‐IoT) devices. This work introduces a unique method for precisely controlling the nodes' sleep schedules through the use of convolutional neural network (CNN) architecture, which optimizes both energy usage and operating patterns. The principal goal still stands to guarantee the extended lifetime of operation and dependability of NB‐IoT devices in the larger framework of intelligent ecosystems driven by synchronized 5G networks. To achieve its objectives, the research explores a number of complex domains and employs cutting‐edge technologies and techniques, such as CNN‐based pattern recognition. This method's real‐time component is essential since it allows for prompt modifications to sleep schedules in order to optimize energy savings. Further boosting the devices' effectiveness and flexibility is continuous contact with a central server, which guarantees that the devices are updated with the most recent data and instructions. Essentially, the main objective of this study is to greatly increase the energy economy and operational lifetime of NB‐IoT devices, which will allow for stable and long‐lasting IoT deployments in the context of 5G networks in intelligent settings. This advancement is not only a boon for businesses and industries leveraging IoT technology but also a substantial step toward building smarter, more energy‐efficient, and resilient smart ecosystems that benefit society as a whole.

Enabling pandemic‐resilient healthcare: Narrowband Internet of Things and edge intelligence for real‐time monitoring

AbstractThe Internet of Things (IoT) in deploying robotic sprayers for pandemic‐associated disinfection and monitoring has garnered significant attention in recent research. The authors introduce a novel architectural framework designed to interconnect smart monitoring robotic devices within healthcare facilities using narrowband Internet of Things (NB‐IoT) technology. The core objective is establishing a seamless data transmission pipeline that bridges these devices with the nearest base station. The associated data is routed directly from the edge computing infrastructure to the Cloud storage. The proposed architecture increases data security and optimises system performance by avoiding dependencies on central data centres. The critical components of this system allow the edge node to transmit data to the cloud, where primary data processing and analysis occur. The selection of NB‐IoT as the underlying protocol facilitates real‐time data collection and monitoring in healthcare with boosted capacity and coverage, extended battery life, and cost efficiency.

Narrowband Internet of Things via Low Earth Orbit Satellite Networks: An Efficient Coverage Enhancement Mechanism Based on Stochastic Geometry Approach.

With the development of IoT technology and 5G massive machine-type communication, the 3GPP standardization body considered as viable the integration of Narrowband Internet of Things (NB-IoT) in low Earth orbit (LEO) satellite-based architectures. However, the presence of the LEO satellite channel comes up with new challenges for the NB-IoT random access procedures and coverage enhancement mechanism. In this paper, an Adaptive Coverage Enhancement (ACE) method is proposed to meet the requirement of random access parameter configurations for diverse applications. Based on stochastic geometry theory, an expression of random access channel (RACH) success probability is derived for LEO satellite-based NB-IoT networks. On the basis of a power consumption model of the NB-IoT terminal, a multi-objective optimization problem is formulated to trade-off RACH success probability and power consumption. To solve this multi-objective optimization problem, we employ the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) method to obtain the Pareto-front solution set. According to different application requirements, we also design a random access parameter configuration method to minimize the power consumption under the constraints of RACH success probability requirements. Simulation results show that the maximum number of repetitions and back-off window size have a great influence on the system performance and their value ranges should be set within [4, 18] and [0, 2048]. The power consumption of coverage enhancement with ACE is about 58% lower than that of the 3GPP proposed model. All this research together provides good reference for the scale deployment of NB-IoT in LEO satellite networks.

Quantum-Secure Signalling Model for L1/L2 Next-Gen Interconnect and Roaming Networks Over IPX for NB-IoT Traffic: A Review

Signalling security is critical for next-generation mobile (NGN) networks to ensure integrity, privacy, and confidentiality of communication protocols. However, recent research on existing 5G standards has uncovered vulnerabilities that could be exploited by emerging surveillance threats such as HiddenArt and Harvest Now Decrypt Later (HNDTL), as well as the prospect of quantum computing threatening to break classical cryptographic techniques. This systematic literature review (SLR) investigates quantum-based signalling solutions to mitigate these threats in interconnect and roaming 5G networks (IRN) over IP eXchange for Narrowband Internet of Things (NB-IoT) traffic. A database search was conducted, studies were selected and compared based on methods, approaches, techniques, and limitations. The SLR found that quantum key distribution (QKD) combined with quantum teleportation (QT) can protect and mitigate threats and attacks on signalling networks when protocols interact and interoperate in carrier networks. QKD techniques can be used for L1 protocols, while secure direct quantum communication can be used for L2 protocols. This study concludes that further studies are needed to integrate different techniques and protocols for L1/L2 signalling networks to create a robust quantum-secure signalling model for global interconnect and roaming NGN.

5G NB-IoT System Integrated with High-Performance Fiber Sensor Inspired by Cirrus and Spider Structures.

Real-time telemedicine detection can solve the problem of the shortage of public medical resources caused by the coming aging society. However, the development of such an integrated monitoring system is hampered by the need for high-performance sensors and the strict-requirement of long-distance signal transmission and reproduction. Here, a bionic crack-spring fiber sensor (CSFS) inspired by spider leg and cirrus whiskers for stretchable and weavable electronics is reported. Trans-scale conductive percolation networks of multilayer graphene around the surface of outer spring-like Polyethylene terephthalate (PET) fibers and printing Ag enable a high sensitivity of 28475.6 and broad sensing range over 250%. The electromechanical changes in different stretching stages are simulated by Comsol to explain the response mechanism. The CSFS is incorporated into the fabric and realized the human-machine interactions (HMIs) for robot control. Furthermore, the 5G Narrowband Internet of Things (NB-IoT) system is developed for human healthcare data collection, transmission, and reproduction together with the integration of the CSFS, illustrating the huge potential of the approach in human-machine communication interfaces and intelligent telemedicine rehabilitation and diagnosis monitoring.

An optimization NPUSCH uplink scheduling approach for NB-IoT application via the feasible combinations of link adaptation, Resource assignment and energy efficiency

Low-power wide area (LPWA) technology, specifically Narrowband Internet of Things (NB-IoT), has emerged as a pivotal sixth-generation (6G) network technology, catering to the demands of massive-type communications (m-MTC), extended battery life, wide range coverage, and high reliability. Despite its advancements, existing studies often fall short in simultaneously addressing energy efficiency, latency, link adaptation, and resource allocation in the bandwidth-constrained environments typical of NB-IoT. The complexity of finding a comprehensive optimal solution for resource allocation is compounded by its non-convex and NP-hard combinatorial nature. This research addresses these challenges by introducing a Hybrid-Optimizer approach, which integrates link adaptation, resource allocation, and scheduling algorithms. This approach aims to identify the optimal feasible combination for each device (UE), focusing on energy conservation and improved signal reception. Applied to the Narrow Physical Uplink Shared Channel (NPUSCH) uplink scheduling in NB-IoT, this method is benchmarked against the conventional Round-Robin scheduling algorithm. Simulation results demonstrate that the Hybrid-Optimizer approach significantly outperforms the Round-Robin algorithm in terms of scheduling processing time and energy efficiency. Furthermore, the proposed algorithm is capable of generating candidate combinations that enhance signal reception in UEs and meet the required subframe criteria, adhering to system modeling assumptions for scheduling optimization.

Fog Computing-Based System for Decentralized Smart Parking System by Using Firebase

The growth of vehicle number is unavoidable whilst the availability of parking is not directly proportional with this condition. Nowadays, many shopping centers do not have sufficient parking spot, causing customers to have difficulty in finding available parking spots. Research has been conducted to tackle the issue of finding available parking spots. Much of this research proposed the narrowband-Internet of things (NB-IoT) as a fog node. For communication purposes, this NB-IoT-based fog node has some shortcomings, such as security and privacy, lower data rate, higher cost in development, dependency with wireless system, and only covers one area. In this research, the fog computing was proposed to decentralize smart parking system by using Firebase to cover several areas or malls in one system and interface. Instead of using NB-IoT, this research employed decentralized local server as a fog node to deliver a fast data exchange. Firestore database (Firebase) was also used to secure, manage, and analyze the data in the cloud. Conjunctively, the Android application was created as a user interface to book and find the availability of parking spots. The Android application was built using Android Studio and implemented authentication to keep the data access secure and private. The testing scenario was done following the design unified modeling language (UML). The research results confirmed that the fog computing system successfully supported the decentralized smart parking system and was able to be implemented for covering several areas or malls in one system.

An IoT Real-Time Potable Water Quality Monitoring and Prediction Model Based on Cloud Computing Architecture.

In order to achieve the Sustainable Development Goals (SDG), it is imperative to ensure the safety of drinking water. The characteristics of each drinkable water, encompassing taste, aroma, and appearance, are unique. Inadequate water infrastructure and treatment can affect these features and may also threaten public health. This study utilizes the Internet of Things (IoT) in developing a monitoring system, particularly for water quality, to reduce the risk of contracting diseases. Water quality components data, such as water temperature, alkalinity or acidity, and contaminants, were obtained through a series of linked sensors. An Arduino microcontroller board acquired all the data and the Narrow Band-IoT (NB-IoT) transmitted them to the web server. Due to limited human resources to observe the water quality physically, the monitoring was complemented by real-time notifications alerts via a telephone text messaging application. The water quality data were monitored using Grafana in web mode, and the binary classifiers of machine learning techniques were applied to predict whether the water was drinkable or not based on the data collected, which were stored in a database. The non-decision tree, as well as the decision tree, were evaluated based on the improvements of the artificial intelligence framework. With a ratio of 60% for data training: at 20% for data validation, and 10% for data testing, the performance of the decision tree (DT) model was more prominent in comparison with the Gradient Boosting (GB), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) modeling approaches. Through the monitoring and prediction of results, the authorities can sample the water sources every two weeks.

A Digital Twin (DT) approach to Narrow-Band Internet of Things (NB-IoT) wireless communication optimization in an industrial scenario

The pervasive realization of virtual replication of physical entities termed Digital Twin (DT) has been utilized in this paper to optimize the wireless communication of the Narrowband Internet of Things (NB-IoT) in an industrial scenario. This optimization is exclusively achieved through DT approach. NB-IoT is a Low-Powered Wide Area Network (LPWAN) standardized by 3GPP and leverages Long Term Evolution (LTE) technology. The Amplify-and-Forward (AF) optimization technique is used to improve the performance of some notably poor-performing terminals in the scenario. Bit-Error-Rate (BER) tests show the terminals’ overall performance before and after optimization. An improvement of 17% is achieved in BER. The signal quality of the channels is analyzed as well as the Cumulative Distribution Function (CDF) is used to showcase the effective throughput performance of the NB-IoT terminals.

Narrowband IoT

Narrowband Internet of things (NBIoT) is a low energy and low resource consuming version of IoT. As its name suggests, it needs a narrow bandwidth for its operations. Its energy consumption is also very low, and thus it is suitable for low energy applications. It is compatible with all types of cellular communication infrastructure such as 2G, 3G, 4G and 5G. It is also possible to deploy NBIoT in the standalone mode where cellular networks are not available. It can cover a large area with a very small amount of power. So, it is a popular low power wide area (LPWA) technology. Due to its LPWA features, it is popular for the connected living applications at home and workplace surroundings. Its LPWA features make it a popular green technology for digital transformation. In this article, the authors provide the main characteristics of NBIoT, its standards, its potentials, and applications in different domains.

A Reliable Algorithm for Efficient Water Delivery and Smart Metering in Water-Scarce Regions

Smart water meters play a significant part in the interactive control of water supply networks required for smart cities. Opposed to completely mechanical water meters, electro-mechanical water meters or fully electronic water meters can capture real-time data via an automated meter reading process, making them more appropriate for smart city applications. However, the depletion of fresh water supplies as a result of climate change, as well as the rising demand associated with population increase, are some growing concern in many regions globally. Monitoring water distribution and delivery is a critical strategy for increasing distribution efficiency. This paper presents a wireless transceiver unit that can be integrated with existing electronic water meters. The unit utilises a reliable communication algorithm for real-time data exchange. Metering data is transmitted using cutting-edge narrow-band Internet of Things technology that operates using low power whilst covering a wide range with effective penetration.

Field-Programmable Gate Array-Based Implementation of Zero-Trust Stream Data Encryption for Enabling 6G-Narrowband Internet of Things Massive Device Access

With the advent of 6G Narrowband IoT (NB-IoT) technology, IoT security faces inevitable challenges due to the application requirements of Massive Machine-Type Communications (mMTCs). In response, a 6G base station (gNB) and User Equipment (UE) necessitate increased capacities to handle a larger number of connections while maintaining reasonable performance during operations. To address this developmental trend and overcome associated technological hurdles, this paper proposes a hardware-accelerated and software co-designed mechanism to support streaming data transmissions and secure zero-trust inter-endpoint communications. The proposed implementations aim to offload processing efforts from micro-processors and enhance global system operation performance by hardware and software co-design in endpoint communications. Experimental results demonstrate that the proposed secure mechanism based on the use of non-repeating keys and implemented in FPGA, can save 85.61%, 99.71%, and 95.68% of the micro-processor’s processing time in key block generations, non-repeating checks, and data block transfers, respectively.

A Dual-Mode CMOS Power Amplifier with an External Power Amplifier Driver Using 40 nm CMOS for Narrowband Internet-of-Things Applications

The narrowband Internet-of-Things (NB-IoT) has been developed to provide low-power, wide-area IoT applications. The efficiency of a power amplifier (PA) in a transmitter is crucial for a longer battery lifetime, satisfying the requirements for output power and linearity. In addition, the design of an internal complementary metal-oxide semiconductor (CMOS) PA is typically required when considering commercial applications to include the operation of an optional external PA. This paper presents a dual-mode CMOS PA with an external PA driver for NB-IoT applications. The proposed PA supports an external PA mode without degrading the performances of output power, linearity, and stability. In the operation of an external PA mode, the PA provides a sufficient gain to drive an external PA. A parallel-combined transistor method is adopted for a dual-mode operation and a third-order intermodulation distortion (IMD3) cancellation. The proposed CMOS PA with an external PA driver was implemented using 40 nm-CMOS technology. The PA achieves a gain of 20.4 dB, a saturated output power of 28.8 dBm, and a power-added efficiency (PAE) of 57.8% in high-power (HP) mode at 920 MHz. With an NB-IoT signal (200 kHz π/4-differential quadrature phase shift keying (DQPSK)), the proposed PA achieves 24.2 dBm output power (Pout) with a 31.0% PAE, while satisfying −45 dBc adjacent channel leakage ratio (ACLR). More than 80% of the current consumption at 12 dBm Pout could be saved compared to that in HP mode when the proposed PA operates in low-power (LP) mode. The implemented dual-mode CMOS PA provides high linear output power with high efficiency, while supporting an external PA mode. The proposed PA is a good candidate for NB-IoT applications.

Localization over distributed mobile adaptive networks based on coarsely quantized data

The localization is a very important task in many internet of things (IoT) based applications. Due to high requirements of communication cost and power consumption, however, existing localization methods are hard to apply for practical IoT applications with low power and narrow frequency. To answer this problem, in this paper, we propose a fully distributed low energy localization method by incorporating the distributed quantization aware least mean square (DQA-LMS) algorithm in a mobile diffusion narrowband IoT (NB-IoT) network. Each mobile network node in this case is assumed to use a low-power and low-resolution analog to digital converter (ADC). First, we address the performance analysis of the mobile networks in the presence of the quantized data. Second, we evaluate the localization performance of the proposed method with differently leveled quantization data and confirm that it can perform suitably even in the one-bit data case. Finally, we collate the localization performance of the Mobile DQA-LMS with the mobile distributed LMS (Mobile DLMS) and show that for all the quantized data cases, the performance of the mobile DQA-LMS is better than the conventional DLMS in terms of mean square deviation (MSD).

Narrow band of vehicular things communication system using hybrid pelican-beetle swarm optimization approach for intelligent transportation system

Congestion is caused by the continually expanding number of vehicles worldwide. Intelligent Transportation Systems (ITSs) could be used to build a system that efficiently uses the existing infrastructure and current technological advancements. Several Vehicular applications use the restricted vehicle battery in V2X systems, and a key concern is communication secrecy. Earlier research has focused on channel measurement, autonomous piloting, high-speed transmissions, secretive communications, and QoS analysis but battery-limited V2X has received far less attention. Therefore, resolving the abovementioned complications in the current model is imperative. Thus, a new V2X communication model is developed based on NB-IoT (NarrowBand Internet of Things) approaches. NB -IoT is utilized in the V2X communication model to offer low-cost, high-range, and minimal power to the communication environment. The objective of the proposed NarrowBand of Vehicular Things Communication (NBVTCS)System is to analyze the performance metrics of vehicular communication including energy efficiency rate with different SNR, bit error rate with different vehicular positions, and overall throughput of the system. The simulation was carried out to attain an effective energy efficiency rate, the system's transmit power, bit error rate, and overall system throughput. The proposed vehicular architecture also optimized the abovementioned vehicular metrics with the help of the proposed Hybrid Pelican Beetle Swarm Optimization (HPBSO) approach to achieve a higher accuracy in communication parameters. Finally, the simulation outcome demonstrates that the developed vehicular model is achieved with a minimal energy consumption rate and maximum throughput and also compares the proposed HPBSO technique with other optimization techniques including DOX, EFO, POA, and BSO.

The Impact of Impulsive Traffic on Cellular Internet of Things Network Performance Indicators.

The use of wireless sensor networks and the Internet of Things has increased dramatically in the last decade. The sensors measure the required parameters and send them to the data processing centers using one of the various wireless transmission technologies (often using cellular infrastructure) to make the appropriate decision. Files containing measurement information can arrive in bursts simultaneously, which is a critical issue to be aware of. The purpose of this work is to develop and analyze a model to evaluate the effectiveness of an LTE (Long-Term Evolution) cell in serving requests from NB-IoT (Narrowband Internet of things) devices when these requests are received in bursts rather than individually. In the article, the common uses of the Internet of Things in our modern era were discussed, the NB-IoT technology was paid attention to, and a mathematical model to evaluate the performance of an LTE cell in the case of impulsive arrivals of NB-IoT requests was built. Finally, the computational algorithm and numerical evaluation were introduced.

Energy-efficient non-anchor channel allocation in NB-IoT cellular networks

In Narrowband Internet of Things (NB-IoT), a base station can provide multiple non-anchor channels to release the burden of anchor channels. NB-IoT devices can use non-anchor channels to execute data transmission to save energy. However, we creatively observe a new important property in the NB-IoT; allocating more non-anchor channels to a base station may even cause an increase in the energy consumption of devices during the data transmission procedure. Unlike traditional channel assignment problems, this new property in the NB-IoT leads to the non-anchor channel allocation being a non-convex problem. This article studies the energy-efficient non-anchor channel allocation in the NB-IoT to minimize the energy consumption of devices to finish their data requests. Because of the non-convex property, this article proposes a dynamic programming algorithm without considering frequency reuse to determine how many non-anchor channels each base station should use. We prove that the dynamic programming algorithm can get an optimal solution. Then, we propose an energy-efficient non-anchor channel reuse algorithm to minimize the energy consumption of devices. Compared with two baselines, the simulation results show that the proposed algorithms reduce the energy consumption by 66%, and blind decoding consumes the most energy of a device.