Narrowband Internet Of Things Network Research Articles

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.

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.

Monitoring and control of smart urban drainage systems using NB-IoT cellular sensor networks.

Narrow-Band-Internet of Things (NB-IoT) is a promising wireless communication technology for harsh signal propagation conditions, including underground connectivity. The purpose of this study is to evaluate the real-life signal quality of an NB-IoT cellular sensor network for the monitoring and control of urban drainage systems. The implementation of the control systems toward smart infrastructures increase the need for low-cost and low-energy-consumption underground sensing devices. A measurement campaign was conducted in Rakvere, Estonia, to analyze the NB-IoT network's coverage and underground signal quality. The results were used to analyze the flood risk reduction potential in the pilot catchment, considering the quality of NB-IoT cellular network coverage. The signal measurements showed that in the case of plastic manholes, the best cellular transmitter placement is 0.5 m below the manhole cover, and at the ground level NB-RSRP reference signal strength of -104 dBm or better must be ensured to have a link budget with optimal energy consumption for communication. The results of flood risk reduction showed that excluding control locations with insufficient signal strength increased the number of major flooding nodes by 3.9% and mean flooding duration by 4.2% which is a relatively small change and characterizes the flexibility of the developed control system.

Positioning by Multicell Fingerprinting in Urban NB-IoT Networks.

Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from long-term evolution (LTE) are not yet widely available in existing networks and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning based on fingerprinting that use coverage and radio information from multiple cells. The proposed strategies were evaluated on two large-scale datasets made available under an open-source license that include experimental data from multiple NB-IoT operators in two large cities: Oslo, Norway, and Rome, Italy. Results showed that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell fingerprinting, with a minimum average positioning error of about 20 m when using data for a single operator that was consistent across the two datasets vs. about 70 m for the current state-of-the-art approaches. The combination of data from multiple operators and data smoothing further improved positioning accuracy, leading to a minimum average positioning error below 15 m in both urban environments.

A Four-Sided Matching Game for Energy-Efficient Scheduling in Narrowband-IoT Networks

This paper studies the uplink resource allocation problem in NB-IoT networks. We aim to maximize the network’s energy efficiency to serve devices in need for long battery lifetime. Also, the network energy efficiency is affected by the associated UEs to candidates, carrying the scheduling information for UEs. For this reason, we investigate the joint resource allocation and Candidate-UE association problem in the uplink (UL) in NB-IoT. The resource allocation problem allocates the UEs with the suitable scheduling delay value and subcarrier indication field. The Candidate-UE association problem selects the appropriate UE for each candidate. The optimization variables involved in the joint problem are divided into binary association variables and binary resource allocation variables. This makes it hard to solve the joint problem since its optimization variables are of combinatorial nature. To this end, we formulate the joint problem as a four-sided matching game that ranks the UEs, candidates, the scheduling delay values and subcarrier indication fields while considering maximizing the energy efficiency as a preference metric. We then describe a four-sided matching game algorithm to solve the problem. The simulation results highlight the proposed algorithm’s effectiveness compared to other algorithms in the obtained network energy efficiency.

Transmission Control in NB-IoT With Model-Based Reinforcement Learning

In Narrowband Internet of Things (NB-IoT), the control of uplink transmissions is a complex task involving device scheduling, resource allocation in the carrier, and the configuration of link-adaptation parameters. Existing heuristic proposals partially address the problem, but reinforcement learning (RL) seems to be the most effective approach a priori, given its success in similar control problems. However, the low sample efficiency of conventional (model-free) RL algorithms is an important limitation for their deployment in real systems. During their initial learning stages, RL agents need to explore the policy space selecting actions that are, in general, highly ineffective. In an NB-IoT access network this implies a disproportionate increase in transmission delays. In this paper, we make two contributions to enable the adoption of RL in NB-IoT: first, we present a multi-agent architecture based on the principle of task division. Second, we propose a new model-based RL algorithm for link adaptation characterized by its high sample efficiency. The combination of these two strategies results in an algorithm that, during the learning phase, is able to maintain the transmission delay in the order of hundreds of milliseconds, whereas model-free RL algorithms cause delays of up to several seconds. This allows our approach to be deployed, without prior training, in an operating NB-IoT network and learn to control it efficiently without degrading its performance.

Offset-Aware Resource Allocation in NB-IoT Networks

Narrowband Internet of Things (NB-IoT) is a critical technology for fifth-generation (5G) and next sixth-generation (6G) networks to meet the high density and latency requirements for massive devices. Before data transmission or reception in NB-IoT networks, a device needs to monitor a search space in a narrowband physical downlink control channel (NPDCCH). When the NPDCCH resource is exhausted, neither data transmission in the narrowband uplink shared channel (NPUSCH) nor data reception in the narrowed downlink shared channel (NPDSCH) is possible, even if the two channels still have available radio resources. To solve the problem, the 3rd generation partnership project (3GPP) puts forth an NPDCCH offset mechanism for NB-IoT, which can set different offset values for different devices. Devices using different NPDCCH offset values can postpone different time intervals for monitoring their NPDCCH search spaces so that the devices do not need to contend for the same NPDCCH search space. However, this article finds that improper use of the NPDCCH offset mechanism will hurt the system’s performance. Therefore, this article investigates how to use the NPDCCH offset mechanism in uplink and downlink resource allocation. The objective is to minimize the consumption of uplink and downlink radio resources while each device can transmit and/or receive its data. We propose an offset-aware resource allocation algorithm to solve the problem. Compared with two resource allocation algorithms with and without using the NPDCCH offset, the simulation results provide several insights and show that the proposed algorithm effectively reduces radio resource consumption.

Measurement-Based Optimization of Cell Selection in NB-IoT Networks

Narrowband-Internet of Things (NB-IoT) is an emerging cellular communication technology designed for low-power wide-area applications. Cell selection determines the channel of user device and hence is an important issue in cellular networks. In this article, we make the first attempt to examine and optimize the cell selection in NB-IoT networks by field measurement. We conduct measurements at 30 different locations which involve five typical application scenarios of NB-IoT. Two kinds of NB-IoT modules and two network operators are also involved in the measurements. We find four potential issues on the cell selection of the User Equipment (UE) through the measurements. We propose an adaptive cell selection approach to optimize the cell selection of UE. The simulation test based on real-world measurement data shows that the cell selected by the adaptive approach can improve the coverage level and reduce the power consumption for UE.

RACH Success Probability Analysis and Optimization in NB-IoT Networks

Narrow Band-Internet of Things (NB-IoT) is a novel technology to provide a high coverage area for massive low-cost devices with latency tolerance and long battery life. However, existing random access techniques with fixed repetition values will cause severe interference and low resource utilization. In order to provide more reliable transmission for NB-IoT devices, the Random Access CHannel (RACH) success probability is first analyzed in this paper. An exact closed-form expression for the RACH success probability of a random user, including both the repetition values and received Signal to Interference plus Noise Ratio (SINR) values, is derived. We further formulate the joint optimization problem, including sub-carrier assignment, repetition value and start TS allocation, to maximize the RACH success probability. By mapping the two-dimensional resource table as a one-dimensional resource block vector, the many-to-one matching algorithm is invoked to solve this problem. The improved utility function is further designed to eliminate the “externalities” effects in the matching process. Numerical results demonstrate that the proposed optimization outperforms existing algorithm with fixed repetition value.

Denial-of-Sleep Attack Detection in NB-IoT Using Deep Learning

With increasing Internet-of-Things (IoT) protocols and connectivity, a growing number of attacks are emerging in the associated networks. This work presents approaches using deep learning (DL) to detect attacks in an IoT environment, particularly in narrowband Internet-of-Things (NB-IoT). By virtue of its low cost, low complexity and limited energy, an NB-IoT device will not likely permit cutting-edge security mechanisms, leaving it vulnerable to, for example, denial-of-sleep (DoSl) attacks. For performance analysis, a NB-IoT network was simulated, using ns-3, to generate a novel dataset to represent an implementation of DoSl attacks. After preprocessing, the dataset was presented to a collection of machine learning (ML) models to evaluate their performance. The considered DL recurrent neural network (RNN) models have proven capable of reliably classifying traffic, with very high accuracy, into either a DoSl attack or a normal record. The performance of a long short-term memory (LSTM) classifier has provided accuracies up to 98.99%, with a detection time of 2.54 x 10-5 second/record, surpassing performance of a gated recurrent unit (GRU). RNN DL models have superior performance in terms of accuracy of detecting DoSl attacks in NB-IoT networks, when compared with other ML algorithms, including support vector machine, Gaussian naïve-Bayes, and logistic regression.

SmartCon: Deep Probabilistic Learning-Based Intelligent Link-Configuration in Narrowband-IoT Toward 5G and B5G

To enhance the coverage and transmission reliability, repetitions adopted by Narrowband Internet of Things (NB-IoT) allow repeating transmissions several times. However, this results in a waste of radio resources when the signal strength is high. In addition, in low signal quality, the selection of a higher modulation and coding scheme (MCS) level leads to a huge packet loss in the network. Moreover, the number of physical resource blocks (PRBs) per-user needs to be chosen dynamically, such that the utilization of radio resources can be improved on per-user basis. Therefore, in NB-IoT systems, dynamic adaptation of repetitions, MCS, and radio resources, known as auto link-configuration, is crucial. Accordingly, in this paper, we propose SmartCon which is a Generative Adversarial Network (GAN)-based deep learning approach for auto link-configuration during uplink or downlink scheduling, such that the packet loss rate is significantly reduced in NB-IoT networks. For the training purpose of the GAN, we use a Multi-Armed Bandit (MAB)-based reinforcement learning mechanism that intelligently tunes its output depending on the present network condition. The performance of SmartCon is thoroughly evaluated through simulations where it is shown to significantly improve the performance of NB-IoT systems compared to baseline schemes.

Effect of Number of Interference on Long-Term Evolution Throughput with Minimum Mean Square Error-Interference Rejection Combining Receiver

Different interference measurement methodologies for the Narrowband Internet of Things network are depicted and examined in this article. The Co-Channel Interference is a unit of the master exporter of interference, which happens mostly because of the frequency reuse concept. Co-Channel Interference has a stronger influence in Code Division Multiple Access based approaches since the frequency reuse ratio in those systems is identical. The impact of various interference measurement methodologies on Narrowband Internet of Things link adaptability and throughput performance is investigated. Different interference measuring approaches are described, and evaluated, their core process ideas, including technique overhead aspects, are reviewed, and an itemized performance assessment and comparison is made. In order to reduce the effects of interference signals and increase user throughput, Minimum Mean Square Error-Interference Rejection Combining receivers are deployed as a mobile station interference rejection and inhibition technology. The presented data show that one interference approach outperforms the others in terms of throughput.

On the Performance of a UAV-Aided Wireless Network Based on NB-IoT

In recent years, interest in Unmanned Aerial Vehicles (UAVs) as a means to provide wireless connectivity has substantially increased thanks to their easy, fast and flexible deployment. Among the several possible applications of UAV networks explored by the current literature, they can be efficiently employed to collect Internet-of-Things (IoT) data because the non-stringent latency and small-size traffic type is particularly suited for UAVs’ inherent characteristics. However, the implications coming from the implementation of existing technology in such kinds of nodes are not straightforward. In this article, we consider a Narrow Band IoT (NB-IoT) network served by a UAV base station. Because of the many configurations possible within the NB-IoT standard, such as the access structure and numerology, we thoroughly review the technical aspects that have to be implemented and may be affected by the proposed UAV-aided IoT network. For proper remarks, we investigate the network performance jointly in terms of the number of successful transmissions, access rate, latency, throughput and energy consumption. Then, we compare the obtained results on different and known trajectories in the research community and study the impact of varying UAV parameters such as speed and height. Moreover, the numerical assessment allows us to extend the discussion to the potential implications of this model in different scenarios. Thus, this article summarizes all the main aspects that must be considered in planning NB-IoT networks with UAVs.

Deep Grid Scheduler for 5G NB-IoT Uplink Transmission

Since the birth of narrowband Internet of Things (NB-IoT), the Internet of Things (IoT) industry has made a considerable progress in the application for smart cities, smart manufacturing, and healthcare. Therefore, the number of UEs is increasing exponentially, which brings considerable pressure to the efficient resource allocation for the bandwidth and power constrained NB-IoT networks. In view of the conventional algorithms that cannot dynamically adjust resource allocation, resulting in a low resource utilization and prone to resource fragmentation, this paper proposes a double deep Q-network (DDQN)-based NB-IoT dynamic resource allocation algorithm. It first builds an NB-IoT environment model based on the real environment. Then, the DDQN algorithm interacts with the NB-IoT environment model to learn and optimize resource allocation strategies until it converges to the optimum. Finally, the simulation results show that the DDQN-based NB-IoT dynamic resource allocation algorithm is better than the traditional algorithm in the resource utilization, average transmission rate, and UE average queuing time.

Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights.

The Internet of Things (IoT) and its applications in industrial settings are set to bring in the fourth industrial revolution. The industrial environment consisting of high profile manufacturing plants and a variety of equipment is inherently characterized by high reflectiveness, causing significant multi-path components that affect the propagation of wireless communications—a challenge among others that needs to be resolved. This paper provides a detailed insight into Narrow-Band IoT (NB-IoT), Industrial IoT (IIoT), and Wireless Sensor Networks (WSN) within the context of indoor industrial environments. It presents the applications of NB-IoT for industrial settings, such as the challenges associated with these applications. Furthermore, future research directions were put forth in the areas of NB-IoT network management using self-organizing network (SON) technology, edge computing for scalability enhancement, security in NB-IoT generated data, and proposing a suitable propagation model for reliable wireless communications.

Learning Driven Resource Allocation and SIC Ordering in EH Relay Aided NB-IoT Networks

Integrating the energy-harvesting (EH) relay and non-orthogonal multiple access (NOMA) technologies into narrow band internet of things (NB-IoT) networks can efficiently improve the energy and spectrum efficiency of the network and the quality-of-service of edge users. Therefore, we consider an EH relay aided NOMA NB-IoT network in this letter. To reduce the rate variance among NB-IoT devices, we aim to maximize the proportional fairness of data rate across all NB-IoT devices through jointly optimizing the communication resource allocation and successive interference cancellation (SIC) ordering subject to the minimum data rate requirements. Considering the non-convexity of this optimization problem, we propose a deep reinforcement learning based online optimization algorithm to obtain the sub-optimal solution. Simulation results demonstrate that the proposed algorithm can efficiently improve the proportional fairness and the total throughput among NB-IoT devices, in comparison with orthogonal multiple access techniques.

Downlink Connection Density Maximization for NB-IoT Networks Using NOMA With Perfect and Partial CSI

We address the issue of maximizing the number of connected devices in a Narrowband Internet-of-Things (NB-IoT) network using nonorthogonal multiple access (NOMA) in the downlink. We first propose an optimal joint subcarrier and power allocation strategy assuming perfect channel state information (CSI) called stratified device allocation (SDA), which maximizes the connectivity under data rate, power, and bandwidth constraints. Then, we generalize the connectivity maximization problem to the case of partial CSI, where only the distance-dependent path-loss component of the channel gain is available at the base station (BS). We introduce a novel framework called the stochastic connectivity optimization (SCO) framework. In this framework, we propose a heuristic improvement to SDA, namely, SDA with excess power (SDA-EP) algorithm for operation under partial CSI. Furthermore, we derive a concave approximation (SCO-CA) algorithm of near-optimal performance to SCO given the same amount of CSI. Through computer simulations, we show that SDA-EP and SCO-CA outperform conventional NOMA and OMA schemes in the presence of partial CSI over a wide range of service scenarios.

Narrowband-Internet of Things Device-to-Device Simulation: An Open-Sourced Framework.

Narrowband-Internet of Things (NB-IoT) displays high-quality connectivity underpinned by fifth-generation (5G) networks to cover a wide array of IoT applications. The devices’ development and integration into different smart systems require permanent control, supervision, and the study of power consumption models to determine the performance of the network topology and allow for the measurement of the efficiency of the network topology’s application. This paper reports on an architecture and open-sourced simulation that was developed to study NB-IoT in Device-to-Device (D2D) mode, which includes the Physical (PHY), network, and application layers, as well as a queuing model, the model for uplink and downlink delays, the throughput, the overall NB-IoT D2D network performance, and the energy consumption based on the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. Our results prove that the suggested framework contributes to a reduction in power consumption, a minimization of queuing delays, a decrease in communication cost, a reduction in inter-cluster collisions, and the prevention of attacks from malicious nodes. Consequently, the framework manages the battery’s State of Charge (SOC), improves the battery’s State of Health (SOH), and maximizes the whole network lifetime. The proposed framework, the code of which has been open-sourced, can be effectively used for scientific research and development purposes to evaluate different parameters and improve the planning of NB-IoT networks.

Implementation of Downlink Physical Channel Processing Architecture for NB-IoT Using LTE/5G Networks

The monumental success over the years, in wireless communication and mobile communication have overcome the challenges such as fading, multipath, interferences and spectrum limitations due to the advancement in the communication system with higher peak data rate with reduced latency thus most of research work are being focused from long term evolution (LTE) along with reduced delay, area and power consumption. Narrowband Internet of Things (NB-IoT) is an emerging domain with a vast range of applications. The conventional NB-IoT network uses orthogonal frequency division multiplexing (OFDM) for data transmission over the communication channel. LTE downlink physical layer has three control channels which are PCFICH, PDCCH, and PHICH uses in channel processing. The processing step involves scrambling, modulation, layer mapping, precoding and resource element mapping at the transmitter. The receiver end comprising of demapping from the source elements and detection of data occurs in physical downlink channels of LTE. 5G will make sure that, one can download an app of any MB in size at greater speed when compared with existing system. The entire scheme has been simulated using Model sim and realized in Plan Ahead Tool in Virtex 5 tool.

Analysis of Random Access in NB-IoT Networks With Three Coverage Enhancement Groups: A Stochastic Geometry Approach

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks. To provide reliable connections with extended coverage, a repetition transmission scheme and up to three Coverage Enhancement (CE) groups are introduced into NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure, where each CE group is configured with different repetition values and transmission resources. To characterize the RACH performance of the NB-IoT network with three CE groups, this paper develops a novel traffic-aware spatio-temporal model to analyze the RACH success probability, where both the preamble transmission outage and the collision events of each CE group jointly determine the traffic evolution and the RACH success probability. Based on this analytical model, we derive the analytical expression for the RACH success probability of a randomly chosen IoT device in each CE group over multiple time slots with different RACH schemes, including baseline, back-off (BO), access class barring (ACB), and hybrid ACB and BO schemes (ACB&BO). Our results have shown that the RACH success probabilities of the devices in three CE groups outperform that of a single CE group network but not for all the groups, which is affected by the choice of the categorizing parameters.This mathematical model and analytical framework can be applied to evaluate the performance of multiple group users of other networks with spatial separations.