Predicting lorawan behavior. How machine learning can help

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the use of k-means, Long Short-Term Memory Neural Networks and Decision Trees. We test these approaches on a real large-scale LoRaWAN network where the overall captured traffic is stored in a proprietary database. Our study shows how profiling techniques enable a machine learning prediction algorithm even when training is not possible because of high error rates perceived by some devices. In this challenging case, the prediction of the inter-arrival time of packets has an error of about 3.5% for 77% of real sequence cases

Predicting lorawan behavior. How machine learning can help

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the use of k-means, Long Short-Term Memory Neural Networks and Decision Trees. We test these approaches on a real large-scale LoRaWAN network where the overall captured traffic is stored in a proprietary database. Our study shows how profiling techniques enable a machine learning prediction algorithm even when training is not possible because of high error rates perceived by some devices. In this challenging case, the prediction of the inter-arrival time of packets has an error of about 3.5% for 77% of real sequence cases

Exploratory approach for network behavior clustering in LoRaWAN

The interest in the Internet of Things (IoT) is increasing both as for research and market perspectives. Worldwide, we are witnessing the deployment of several IoT networks for different applications, spanning from home automation to smart cities. The majority of these IoT deployments were quickly set up with the aim of providing connectivity without deeply engineering the infrastructure to optimize the network efficiency and scalability. The interest is now moving towards the analysis of the behavior of such systems in order to characterize and improve their functionality. In these IoT systems, many data related to device and human interactions are stored in databases, as well as IoT information related to the network level (wireless or wired) is gathered by the network operators. In this paper, we provide a systematic approach to process network data gathered from a wide area IoT wireless platform based on LoRaWAN (Long Range Wide Area Network). Our study can be used for profiling IoT devices, in order to group them according to their characteristics, as well as detecting network anomalies. Specifically, we use the k-means algorithm to group LoRaWAN packets according to their radio and network behavior. We tested our approach on a real LoRaWAN network where the entire captured traffic is stored in a proprietary database. Quite important is the fact that LoRaWAN captures, via the wireless interface, packets of multiple operators. Indeed our analysis was performed on 997, 183 packets with 2169 devices involved and only a subset of them were known by the considered operator, meaning that an operator cannot control the whole behavior of the system but on the contrary has to observe it. We were able to analyze clusters’ contents, revealing results both in line with the current network behavior and alerts on malfunctioning devices, remarking the reliability of the proposed approach

Exploratory approach for network behavior clustering in LoRaWAN

The interest in the Internet of Things (IoT) is increasing both as for research and market perspectives. Worldwide, we are witnessing the deployment of several IoT networks for different applications, spanning from home automation to smart cities. The majority of these IoT deployments were quickly set up with the aim of providing connectivity without deeply engineering the infrastructure to optimize the network efficiency and scalability. The interest is now moving towards the analysis of the behavior of such systems in order to characterize and improve their functionality. In these IoT systems, many data related to device and human interactions are stored in databases, as well as IoT information related to the network level (wireless or wired) is gathered by the network operators. In this paper, we provide a systematic approach to process network data gathered from a wide area IoT wireless platform based on LoRaWAN (Long Range Wide Area Network). Our study can be used for profiling IoT devices, in order to group them according to their characteristics, as well as detecting network anomalies. Specifically, we use the k-means algorithm to group LoRaWAN packets according to their radio and network behavior. We tested our approach on a real LoRaWAN network where the entire captured traffic is stored in a proprietary database. Quite important is the fact that LoRaWAN captures, via the wireless interface, packets of multiple operators. Indeed our analysis was performed on 997, 183 packets with 2169 devices involved and only a subset of them were known by the considered operator, meaning that an operator cannot control the whole behavior of the system but on the contrary has to observe it. We were able to analyze clusters’ contents, revealing results both in line with the current network behavior and alerts on malfunctioning devices, remarking the reliability of the proposed approach

Exploratory approach for network behavior clustering in LoRaWAN

The interest in the Internet of Things (IoT) is increasing both as for research and market perspectives. Worldwide, we are witnessing the deployment of several IoT networks for different applications, spanning from home automation to smart cities. The majority of these IoT deployments were quickly set up with the aim of providing connectivity without deeply engineering the infrastructure to optimize the network efficiency and scalability. The interest is now moving towards the analysis of the behavior of such systems in order to characterize and improve their functionality. In these IoT systems, many data related to device and human interactions are stored in databases, as well as IoT information related to the network level (wireless or wired) is gathered by the network operators. In this paper, we provide a systematic approach to process network data gathered from a wide area IoT wireless platform based on LoRaWAN (Long Range Wide Area Network). Our study can be used for profiling IoT devices, in order to group them according to their characteristics, as well as detecting network anomalies. Specifically, we use the k-means algorithm to group LoRaWAN packets according to their radio and network behavior. We tested our approach on a real LoRaWAN network where the entire captured traffic is stored in a proprietary database. Quite important is the fact that LoRaWAN captures, via the wireless interface, packets of multiple operators. Indeed our analysis was performed on 997, 183 packets with 2169 devices involved and only a subset of them were known by the considered operator, meaning that an operator cannot control the whole behavior of the system but on the contrary has to observe it. We were able to analyze clusters\u2019 contents, revealing results both in line with the current network behavior and alerts on malfunctioning devices, remarking the reliability of the proposed approach

A holistic review of cybersecurity and reliability perspectives in smart airports

Advances in the Internet of Things (IoT) and aviation sector have resulted in the emergence of smart airports. Services and systems powered by the IoT enable smart airports to have enhanced robustness, efficiency and control, governed by real-time monitoring and analytics. Smart sensors control the environmental conditions inside the airport, automate passenger-related actions and support airport security. However, these augmentations and automation introduce security threats to network systems of smart airports. Cyber-attackers demonstrated the susceptibility of IoT systems and networks to Advanced Persistent Threats (APT), due to hardware constraints, software flaws or IoT misconfigurations. With the increasing complexity of attacks, it is imperative to safeguard IoT networks of smart airports and ensure reliability of services, as cyber-attacks can have tremendous consequences such as disrupting networks, cancelling travel, or stealing sensitive information. There is a need to adopt and develop new Artificial Intelligence (AI)-enabled cyber-defence techniques for smart airports, which will address the challenges brought about by the incorporation of IoT systems to the airport business processes, and the constantly evolving nature of contemporary cyber-attacks. In this study, we present a holistic review of existing smart airport applications and services enabled by IoT sensors and systems. Additionally, we investigate several types of cyber defence tools including AI and data mining techniques, and analyse their strengths and weaknesses in the context of smart airports. Furthermore, we provide a classification of smart airport sub-systems based on their purpose and criticality and address cyber threats that can affect the security of smart airport\u27s networks

A holistic review of cybersecurity and reliability perspectives in smart airports

Advances in the Internet of Things (IoT) and aviation sector have resulted in the emergence of smart airports. Services and systems powered by the IoT enable smart airports to have enhanced robustness, efficiency and control, governed by real-time monitoring and analytics. Smart sensors control the environmental conditions inside the airport, automate passenger-related actions and support airport security. However, these augmentations and automation introduce security threats to network systems of smart airports. Cyber-attackers demonstrated the susceptibility of IoT systems and networks to Advanced Persistent Threats (APT), due to hardware constraints, software flaws or IoT misconfigurations. With the increasing complexity of attacks, it is imperative to safeguard IoT networks of smart airports and ensure reliability of services, as cyber-attacks can have tremendous consequences such as disrupting networks, cancelling travel, or stealing sensitive information. There is a need to adopt and develop new Artificial Intelligence (AI)-enabled cyber-defence techniques for smart airports, which will address the challenges brought about by the incorporation of IoT systems to the airport business processes, and the constantly evolving nature of contemporary cyber-attacks. In this study, we present a holistic review of existing smart airport applications and services enabled by IoT sensors and systems. Additionally, we investigate several types of cyber defence tools including AI and data mining techniques, and analyse their strengths and weaknesses in the context of smart airports. Furthermore, we provide a classification of smart airport sub-systems based on their purpose and criticality and address cyber threats that can affect the security of smart airport\u27s networks

A holistic review of cybersecurity and reliability perspectives in smart airports

Advances in the Internet of Things (IoT) and aviation sector have resulted in the emergence of smart airports. Services and systems powered by the IoT enable smart airports to have enhanced robustness, efficiency and control, governed by real-time monitoring and analytics. Smart sensors control the environmental conditions inside the airport, automate passenger-related actions and support airport security. However, these augmentations and automation introduce security threats to network systems of smart airports. Cyber-attackers demonstrated the susceptibility of IoT systems and networks to Advanced Persistent Threats (APT), due to hardware constraints, software flaws or IoT misconfigurations. With the increasing complexity of attacks, it is imperative to safeguard IoT networks of smart airports and ensure reliability of services, as cyber-attacks can have tremendous consequences such as disrupting networks, cancelling travel, or stealing sensitive information. There is a need to adopt and develop new Artificial Intelligence (AI)-enabled cyber-defence techniques for smart airports, which will address the challenges brought about by the incorporation of IoT systems to the airport business processes, and the constantly evolving nature of contemporary cyber-attacks. In this study, we present a holistic review of existing smart airport applications and services enabled by IoT sensors and systems. Additionally, we investigate several types of cyber defence tools including AI and data mining techniques, and analyse their strengths and weaknesses in the context of smart airports. Furthermore, we provide a classification of smart airport sub-systems based on their purpose and criticality and address cyber threats that can affect the security of smart airport\u27s networks

AI-driven, Context-Aware Profiling for 5G and Beyond Networks

In the era of Industrial Internet of Things (IIoT) and Industry 4.0, an immense volume of heterogeneous network devices will coexist and contend for shared network resources, in order to satisfy the very challenging IIoT applications, requiring ultra-reliable and ultra-low latency communications. Although novel key enablers, such as Network Slicing, Software Defined Networking (SDN) and Network Function Virtualization (NFV) have already offered significant advantages towards more efficient and flexible network and resource management approaches, the particular characteristics of IIoT applications pose additional burdens, mainly due to the complex wireless environments, high number of heterogeneous network devices, sensors, user equipments (UEs), etc., which may stochastically demand and contend for the -often scarce -computing and communication resources of industrial environments. To this end, this paper introduces PRIMATE, a novel, Artificial Intelligence (AI)-driven framework for the profiling of the networking behavior of such UEs, devices, users and things, which is able to operate in conjunction with already standardized or forthcoming, AI-based network resource management processes towards further gains. The novelty and potential of the proposed work lies on the fact that instead of attempting to either predict raw network metrics in a reactive manner, or predict the behavior of specific network entities/devices in an isolated manner, a big data-driven classification approach is introduced, which models the behavior of any network device/user from both a macroscopic, as well as service-specific perspective. The extended evaluation at the last part of this work shows the validity and viability of the proposed framework.This work has been partially supported by EC H2020 5GPPP 5Growth project (Grant 856709)

Machine Learning Based Transmission Scheduling For LoRaWANs.

PhD Theses.First and foremost, I would like to thank the government of the Kingdom of Saudi Arabia for the opportunity to pursue my research programme. It has been a great honour to work alongside such an elite team, for which I would like to express my sincerest gratitude to my research supervision panel. Firstly, to my primary supervisor Dr. Kok Keong Chai for his limitless support and continual guidance throughout my research progress. Secondly, to Prof. Yue Chen and Dr. Jesus Requena for their influential feedback and advice. I owe a deep sense of gratitude to Prof. Talib Alukaidey for his genuine support and long-standing ties before and during my research at Queen Mary University of London. Also a special thank you to Dr. Atm Alam for his generous efforts and enlightenment throughout my research. The deepest heartfelt thank you to my father Maj. Gen. Ali Alenezi who taught me the meaning of perseverance, persistence and discipline, to my mother Bahiah Alsharif who taught me the value of seeking knowledge and for always believing in me, to my wife, to my brothers and sisters for the love they have given. Their presence and moral support has made it all possible, for which I am forever indebted to you all. Lastly, thank you to my friends and colleagues for being part of this once in a lifetime experience