Use of Specialized Devices to Power Flow Control in Power Systems

This article discusses specialized devices forthe power flow control in power systems. Today it is anactual topic because of permanent increasing power flow inthe transmission lines. This trend can lead to overloading oftransmission lines and can endanger the security of electricenergy supply

Energy-Efficient Communication in Wireless Networks

This chapter describes the evolution of, and state of the art in, energy‐efficient techniques for wirelessly communicating networks of embedded computers, such as those found in wireless sensor network (WSN), Internet of Things (IoT) and cyberphysical systems (CPS) applications. Specifically, emphasis is placed on energy efficiency as critical to ensuring the feasibility of long lifetime, low‐maintenance and increasingly autonomous monitoring and control scenarios. A comprehensive summary of link layer and routing protocols for a variety of traffic patterns is discussed, in addition to their combination and evaluation as full protocol stacks

Dragon: processing node discovery protocol based on static attributes for homogeneous and heterogeneous wireless sensor networks

Wireless Sensor Networks (WSNs) are networks consisting of small, battery-powered computers with short-range radio communication and sensing capabilities. These computers (referred to as nodes) are used to sense one or more variables using one or more sensors and report these readings to a base-station via a multi-hop communication. Often, these WSNs are deployed to detect a phenomenon. Detection of this phenomenon usually depends on readings from several sensors in different locations. Therefore, sensor readings are periodically collected at the base-station which processes these data or forwards them to a cloud. This base-station also represents a gateway for users to access and communicate with the WSN. It allows a user to submit a query, whose execution retrieves data from relevant sensor nodes and the result of the computation over these data is detection of a phenomenon. In a typical node, radio is responsible for far more energy consumption when compared to the CPU or most of the sensors. Therefore, it has always been researchers’ intention to lower the network communication to the lowest possible level. Because nodes closer to the base-station transfer more data, their batteries are depleted faster which may lead to part of the network being unreachable. Additionally, because a user accesses the WSN via a base-station, it represents a single point of failure. One of the solutions to overcome this problem is to allow a user to communicate and submit a query via any node in the network. However, building a fully decentralised and energy-efficient framework allowing any node to accept and execute a query submitted by a user brings several new challenges. First, a node needs to be able to communicate with any other node in the network, not only the base-station, without relying on any central entity. Second, any node must be able to identify all the nodes which monitor the same phenomenon. And third, a node which processes the data must be chosen in such way, that the overall communication of the whole network is minimised. In this thesis we present Dragon, a framework for in-network data stream processing. Dragon allows communication among any pair of nodes via optimal or near optimal routes. This is achieved without the need to first discover or establish a path between two communicating nodes. Dragon also allows any node to find a list of all other nodes fulfilling given static criteria. The search for these nodes requires communication with only close (possibly multi-hop) neighbourhood. Finding a list of nodes observing the same phenomenon and requesting data directly from these nodes allows any node in the network to accept and execute a snapshot iii(one-time) query with a very low network overhead and in a timely manor. Finally, Dragon introduces a distributed algorithm for discovery of a processing node for continuous queries in WSNs. The algorithm follows the cost gradient to the node with the lowest communication cost, hence decreasing the overall network traffic and communication delay.Open Acces

Efficient In-Network Processing for a Hardware-Heterogeneous IoT

As the number of small, battery-operated, wireless-enabled devices deployed in various applications of Internet of Things (IoT), Wireless Sensor Networks (WSN), and Cyber-physical Systems (CPS) is rapidly increasing, so is the number of data streams that must be processed. In cases where data do not need to be archived, centrally processed, or federated, in-network data processing is becoming more common. For this purpose, various platforms like D RAGON , Innet, and CJF were proposed. However, these platforms assume that all nodes in the network are the same, i.e. the network is homogeneous. As Moore’s law still applies, nodes are becoming smaller, more powerful, and more energy efficient each year; which will continue for the foreseeable future. Therefore, we can expect that as sensor networks are extended and updated, hardware heterogeneity will soon be common in networks - the same trend as can be seen in cloud computing infrastructures. This heterogeneity introduces new challenges in terms of choosing an in-network data processing node, as not only its location, but also its capabilities, must be considered. This paper introduces a new methodology to tackle this challenge, comprising three new algorithms - Request, Traverse, and Mixed - for efficiently locating an in-network data processing node, while taking into account not only position within the network but also hardware capabilities. The roposed algorithms are evaluated against a naïve approach and achieve up to 90% reduction in network traffic during long-term data processing, while spending a similar amount time in the discovery phase

IoT Device Identification at the Edge

The code is written in Python and can be used to transform pcap files into trainable dataset using Fast Fourier Transform. The generated dataset can be used to train and evaluate binary classification models. The code also contains various scripts to process the final results and generate summary statistics

Scalable Device Identification for IoT Networks using Binary Classification Models at the Edge

With the proliferation of IoT devices in households, network-level management is essential for users' security and control. Identifying IoT devices through their network profiles enables the detection of anomalies, such as hacking attempts, misconfigurations, or firmware updates. However, the variability in the types and numbers of devices across households makes training separate models for each household or a single global model impractical. Solutions based on single multiclass classification are not scalable considering the diverse range of devices in households and the constant introduction of new devices. In this technical report, we propose a system that employs separate binary classification models for each device. We evaluate its accuracy in classifying the network traffic over a long period of time. We also investigate the decline in accuracy over time and propose mitigation strategies. Furthermore, we assess the models' sensitivity to changes in network traffic patterns. To address this, our system utilizes scalable binary classification models that can be adjusted to individual households by downloading only the necessary device-specific models. The system is protocol-agnostic and capable of classifying network traffic, whether it is local network communication or over the Internet. Our results show a promising F1 score of 90-95% on the trained dataset, with accuracy remaining around 80% even after three months. This indicates that periodic model retraining every three months is sufficient. Additionally, we evaluate the system's ability to detect non-device traffic and find that it can detect deviations even when only 30\% of the traffic does not belong to the device. This demonstrates the system's sensitivity to changes in an IoT device's network communication profile, providing users with potential insights into device-related issues

Scalable Device Identification for IoT Networks using Binary Classification Models at the Edge

With the proliferation of IoT devices in households, network-level management is essential for users' security and control. Identifying IoT devices through their network profiles enables the detection of anomalies, such as hacking attempts, misconfigurations, or firmware updates. However, the variability in the types and numbers of devices across households makes training separate models for each household or a single global model impractical. Solutions based on single multiclass classification are not scalable considering the diverse range of devices in households and the constant introduction of new devices. In this technical report, we propose a system that employs separate binary classification models for each device. We evaluate its accuracy in classifying the network traffic over a long period of time. We also investigate the decline in accuracy over time and propose mitigation strategies. Furthermore, we assess the models' sensitivity to changes in network traffic patterns. To address this, our system utilizes scalable binary classification models that can be adjusted to individual households by downloading only the necessary device-specific models. The system is protocol-agnostic and capable of classifying network traffic, whether it is local network communication or over the Internet. Our results show a promising F1 score of 90-95% on the trained dataset, with accuracy remaining around 80% even after three months. This indicates that periodic model retraining every three months is sufficient. Additionally, we evaluate the system's ability to detect non-device traffic and find that it can detect deviations even when only 30\% of the traffic does not belong to the device. This demonstrates the system's sensitivity to changes in an IoT device's network communication profile, providing users with potential insights into device-related issues

Anonymising Video Data Collection at the Edge Using DeepDish

Increase in popularity of various deep learning methodologies has led to a huge increase in demand for training data. These training data often contain video featuring people but these videos often cannot be used due to privacy concerns as people are identifiable. We present an extension to the DeepDish object tracking system running on a Raspberry Pi. DeepDish performs object detection in the Raspberry Pi itself, avoiding the need to ship raw video data off the device. Our extension adds online face detection and blurring to preserve privacy. We implement and evaluate four different face detection algorithms, achieving 83\% accuracy using YuNet, while increasing the latency by 75\%. We then implement and evaluate three different object tracking algorithms to reduce the latency increase by running the simpler object tracking algorithm, reducing the number of calls to the expensive face detection algorithm. The result is a significant decrease in latency at the cost of a small decrease in accuracy. We also implemented and compared three different face obfuscation algorithms, one of which achieves differential privacy