A Review of Low Power Wide Area Technology in Licensed and Unlicensed Spectrum for IoT Use Cases

There are many platforms in licensed and license free spectrum that support LPWA (low power wide area) technology in the current markets. However, lack of standardization of the different platforms can be a challenge for an interoperable IoT environment. Therefore understanding the features of each technology platform is essential to be able to differentiate how the technology can be matched to a specific IoT application profile. This paper provides an analysis of LPWA underlying technology in licensed and unlicensed spectrum by means of literature review and comparative assessment of Sigfox, LoRa, NB-IoT and LTE-M. We review their technical aspect and discussed the pros and cons in terms of their technical and other deployment features. General IoT application requirements is also presented and linked to the deployment factors to give an insight of how different applications profiles is associated to the right technology platform, thus provide a simple guideline on how to match a specific application profile with the best fit connectivity features

Coexistence of Wi-Fi and 5G NR-U in the Unlicensed Band

The communications industry continues to evolve to meet the ever-growing demands of fast connectivity and higher energy-efficiency and has emerged the concept of Internet of Things (IoT) systems. IoT devices can be run on Wi-Fi or cellular network, helping businesses to receive higher return on investments. As billions of devices on cellular networks operate on the limited licensed spectrum, it is becoming scarcer. Mobile network operators are investigating to access the immense unlicensed spectrum, on which Wi-Fi is prominently operated. Managing this coexistence between the cellular and Wi-Fi networks poses several challenges. One challenge is the spectrum sharing that affects the network capacity and the spectrum efficiency by properly allocating the available resources for each technology. A second challenge is to maintain the quality of service (QoS) while maximizing the aggregated throughput. A final challenge is to reduce the power consumption of cellular base stations by creating a sleep/wakeup policy, thereby lowering the capital and operating expenses for the mobile network operators. To this end, this thesis proposes various optimization modeling for the coexistence mechanisms in the unlicensed spectrum, as well as intelligent techniques to manage the increasing power consumption with increased usage. First, this thesis develops optimization modeling techniques to properly allocate resources for the coexistence of the Wi-Fi and cellular networks by improving the aggregate throughput, while maintaining the minimum required power consumption. Next, this thesis implements the coexistence mechanism by simulating real-time traffic information to maximize the aggregate throughput, while satisfying the QoS for each user. Finally, this thesis investigates the use of machine learning techniques to predict the traffic behaviour of base stations; this will determine the sleep/wakeup schedule, thereby minimizing the power consumption while maintaining the QoS for each cellular user

Frequency hopping in wireless sensor networks

Wireless sensor networks (WSNs) are nowadays being used to collectively gather and spread information in different kinds of applications, for military, civilian, environmental as well as commercial purposes. Therefore the proper functioning of WSNs under different kinds of environmental conditions, especially hostile environments, is a must and a lot of research currently ongoing. The problems related to the initialization and deployment of WSNs under harsh and resource limited conditions are investigated in this thesis. Frequency hopping (FH) is a spread spectrum technique in which multiple channels are used, or hoped, for communications across the network. This mitigates the worst effects of interference with frequency agile communication systems rather than by brute force approaches. FH is a promising technique for achieving the coexistence of sensor networks with other currently existing wireless systems, and it is successful within the somewhat limited computational capabilities of the sensor nodes hardware radios. In this thesis, a FH scheme for WSNs is implemented for a pair of nodes on an application layer. The merits and demerits of the scheme are studied for different kinds of WSN environments. The implementation has been done using a Sensinode NanoStack, a communication stack for internet protocol (IP) based wireless sensor networks and a Sensinode Devkit, for an IPv6 over low power wireless personal area network (6LoWPAN). The measurements are taken from the developed test bed and channel simulator for different kinds of scenarios. The detailed analysis of the FH scheme is done to determine its usefulness against interference from other wireless systems, especially wireless local area networks (WLANs), and the robustness of the scheme to combat fading or frequency selective fading

Design of an Adaptive Frequency Hopping Algorithm Based On Probabilistic Channel Usage

Dealing with interference in the 2.4 GHz ISM band is of paramount importance due to an increase in the number of operating devices. For instance systems based on Bluetooth low energy technology are gaining lots of momentum due to their small size, reasonable cost and very low power consumptions. Thus the 2.4 GHz ISM band is becoming very hostile. Bluetooth specification enables the use of adaptive frequency hopping to improve performance in the presence of interference. This technique avoids the congested portions of the ISM band, however as the number of interferers increases for a given geographical environment, a greater number of bad channels are removed from the adapted hopping sequence. This results in longer channel occupancy, and consequently higher probability of collisions with coexisting devices, degrading their operation. At CoSa Research Group a novel algorithm, based on probabilistic channel usage of all channels (good and bad), is developed. The scheme is named Smooth Adaptive Frequency Hopping (SAFH) and uses an exponential smoothing filter to predict the conditions of the radio spectrum. Based on the predicted values, different usage probabilities are assigned to the channels, such as good channels are used more often than bad ones. The discrete probability distribution generated is then mapped to a set of frequencies, used for hopping. MATLAB/SIMULINK was used to investigate the performance of SAFH, in the presence of different types of interfering devices such as 802.11b , 802.15.4 and 802.15.1. Simulation study under different scenarios show, that our developed algorithm outperforms the conventional random frequency hopping as well as other adaptive hopping schemes. SAFH achieves lower average frame error rate and responds fast to changes in the channel conditions. Moreover it experiences smooth operation due to the exponential smoothing filter

Spectrum Sensing Security in Cognitive Radio Networks

This thesis explores the use of unsupervised machine learning for spectrum sensing in cognitive radio (CR) networks from a security perspective. CR is an enabling technology for dynamic spectrum access (DSA) because of a CR's ability to reconfigure itself in a smart way. CR can adapt and use unoccupied spectrum with the help of spectrum sensing and DSA. DSA is an efficient way to dynamically allocate white spaces (unutilized spectrum) to other CR users in order to tackle the spectrum scarcity problem and improve spectral efficiency. So far various techniques have been developed to efficiently detect and classify signals in a DSA environment. Neural network techniques, especially those using unsupervised learning have some key advantages over other methods mainly because of the fact that minimal preconfiguration is required to sense the spectrum. However, recent results have shown some possible security vulnerabilities, which can be exploited by adversarial users to gain unrestricted access to spectrum by fooling signal classifiers. It is very important to address these new classes of security threats and challenges in order to make CR a long-term commercially viable concept. This thesis identifies some key security vulnerabilities when unsupervised machine learning is used for spectrum sensing and also proposes mitigation techniques to counter the security threats. The simulation work demonstrates the ability of malicious user to manipulate signals in such a way to confuse signal classifier. The signal classifier is forced by the malicious user to draw incorrect decision boundaries by presenting signal features which are akin to a primary user. Hence, a malicious user is able to classify itself as a primary user and thus gains unrivaled access to the spectrum. First, performance of various classification algorithms are evaluated. K-means and weighted classification algorithms are selected because of their robustness against proposed attacks as compared to other classification algorithm. Second, connection attack, point cluster attack, and random noise attack are shown to have an adverse effect on classification algorithms. In the end, some mitigation techniques are proposed to counter the effect of these attacks

Coexistence and interference mitigation for WPANs and WLANs from traditional approaches to deep learning: a review

More and more devices, such as Bluetooth and IEEE 802.15.4 devices forming Wireless Personal Area Networks (WPANs) and IEEE 802.11 devices constituting Wireless Local Area Networks (WLANs), share the 2.4 GHz Industrial, Scientific and Medical (ISM) band in the realm of the Internet of Things (IoT) and Smart Cities. However, the coexistence of these devices could pose a real challenge—co-channel interference that would severely compromise network performances. Although the coexistence issues has been partially discussed elsewhere in some articles, there is no single review that fully summarises and compares recent research outcomes and challenges of IEEE 802.15.4 networks, Bluetooth and WLANs together. In this work, we revisit and provide a comprehensive review on the coexistence and interference mitigation for those three types of networks. We summarize the strengths and weaknesses of the current methodologies, analysis and simulation models in terms of numerous important metrics such as the packet reception ratio, latency, scalability and energy efficiency. We discover that although Bluetooth and IEEE 802.15.4 networks are both WPANs, they show quite different performances in the presence of WLANs. IEEE 802.15.4 networks are adversely impacted by WLANs, whereas WLANs are interfered by Bluetooth. When IEEE 802.15.4 networks and Bluetooth co-locate, they are unlikely to harm each other. Finally, we also discuss the future research trends and challenges especially Deep-Learning and Reinforcement-Learning-based approaches to detecting and mitigating the co-channel interference caused by WPANs and WLANs

A Dynamic Interference-Avoidance Algorithm for Frequency Hopping Systems

In this paper we investigate an algorithm for the Adaptive Frequency Hopping mechanism that is used by frequency dynamic systems to mitigate interference from othersystems. With this algorithm we introduce several improvements in relation to the existing algorithms that are based on the approach of using Packet Error Rate as the means for channel classification. One is the use of a single criterion for channel classification regardless of the dynamics of interfering systems, which adds more flexibility and reduces the risk of erroneous channel classification. The second is the introduction of the concept of channel probing which ensures that channels that areexcluded from the hopset are not used until they are clear from interference. The third improvement is the parameterization of the algorithm, which enables the control of the trade off between the main achievements of the algorithm: throughput and quickness of adaptation to changing interference. We show these achievements of the proposed algorithm through simulation