Coverage and mobile sensor placement for vehicles on predetermined routes: a greedy heuristic approach

Road potholes are not only nuisance but can also damage vehicles and pose serious safety risks for drivers. Recently, a number of approaches have been developed for automatic pothole detection using equipment such as accelerometers, image sensors or LIDARs. Mounted on vehicles, such as taxis or buses, the sensors can automatically detect potholes as the vehicles carry out their normal operation. While prior work focused on improving the performance of a standalone device, it simply assumed that the sensors would be installed on the entire fleet of vehicles. When the number of sensors is limited it is important to select an optimal set of vehicles to make sure that they do not cover similar routes in order to maximize the total coverage of roads inspected by sensors. The paper investigates this problem for vehicles that follow pre-determined routes, formulates it as a linear optimization problem and proposes a solution based on a greedy heuristic. The proposed approach has been tested on an official London bus route dataset containing 713 routes and showed up to 78% improvement compared to a random sensor placement selected as a baseline algorithm

Wireless magnetic sensor network for road traffic monitoring and vehicle classification

Efficiency of transportation of people and goods is playing a vital role in economic growth. A key component for enabling effective planning of transportation networks is the deployment and operation of autonomous monitoring and traffic analysis tools. For that reason, such systems have been developed to register and classify road traffic usage. In this paper, we propose a novel system for road traffic monitoring and classification based on highly energy efficient wireless magnetic sensor networks. We develop novel algorithms for vehicle speed and length estimation and vehicle classification that use multiple magnetic sensors. We also demonstrate that, using such a low-cost system with simplified installation and maintenance compared to current solutions, it is possible to achieve highly accurate estimation and a high rate of positive vehicle classification

Passive localization through light flicker fingerprinting

In this paper, we show that the flicker waveforms of various CFL and LED lamp models exhibit distinctive waveform patterns due to harmonic distortions of rectifiers and voltage regulators, the key components of modern lamp drivers. We then propose a passive localization technique based on fingerprinting these distortions that occur naturally in indoor environments and thus requires no infrastructure or additional equipment. The novel technique uses principal component analysis (PCA) to extract the most important signal features from the flicker frequency spectra followed by kNN clustering and neural net- work classifiers to identify a light source based on its flicker signature. The evaluation on 39 flicker patterns collected from 8 residential locations demonstrates that the technique can identify a location within a house with up to 90% accuracy and identify an individual house from a set of houses with an average accuracy of 86.3%

Practical Hash-based Anonymity for MAC Addresses

Given that a MAC address can uniquely identify a person or a vehicle, continuous tracking over a large geographical scale has raised serious privacy concerns amongst governments and the general public. Prior work has demonstrated that simple hash-based approaches to anonymization can be easily inverted due to the small search space of MAC addresses. In particular, it is possible to represent the entire allocated MAC address space in 39 bits and that frequency-based attacks allow for 50% of MAC addresses to be enumerated in 31 bits. We present a practical approach to MAC address anonymization using both computationally expensive hash functions and truncating the resulting hashes to allow for k-anonymity. We provide an expression for computing the percentage of expected collisions, demonstrating that for digests of 24 bits it is possible to store up to 168,617 MAC addresses with the rate of collisions less than 1%. We experimentally demonstrate that a rate of collision of 1% or less can be achieved by storing data sets of 100 MAC addresses in 13 bits, 1,000 MAC addresses in 17 bits and 10,000 MAC addresses in 20 bits.Comment: Accepted at the 17th International Conference on Security and Cryptography (SECRYPT 2020). To be presented between 8-10 July 202

Fountain coding in ad hoc wireless networks

PhD programme overviewThe aim of this PhD is to investigate innovative means of achieving real-time communications over an ad hoc wireless network by using Fountain coding, and more specifically as a novel means of message routing

On the impact of mobility on battery-less RF energy harvesting system performance

The future of Internet of Things (IoT) envisions billions of sensors integrated with the physical environment. At the same time, recharging and replacing batteries on this infrastructure could result not only in high maintenance costs, but also large amounts of toxic waste due to the need to dispose of old batteries. Recently, battery-free sensor platforms have been developed that use supercapacitors as energy storage, promising maintenance-free and perpetual sensor operation. While prior work focused on supercapacitor characterization, modelling and supercapacitor-aware scheduling, the impact of mobility on capacitor charging and overall sensor application performance has been largely ignored. We show that supercapacitor size is critical for mobile system performance and that selecting an optimal value is not trivial: small capacitors charge quickly and enable the node to operate in low energy environments, but cannot support intensive tasks such as communication or reprogramming; increasing the capacitor size, on the other hand, enables the support for energy-intensive tasks, but may prevent the node from booting at all if the node navigates in a low energy area. The paper investigates this problem and proposes a hybrid storage solution that uses an adaptive learning algorithm to predict the amount of available ambient energy and dynamically switch between two capacitors depending on the environment. The evaluation based on extensive simulations and prototype measurements showed up to 40% and 80% improvement compared to a fixed-capacitor approach in terms of the amount of harvested energy and sensor coverage

A Smart Air Conditioner using Internet of Things

The local remote control is the traditional mechanism in which the end user controls the air conditioner. In the absence of this mechanism, the user loses the control. This thesis aimed to design and implement a smart air conditioner using Internet of Things (IoT) technology. Recent literatures were reviewed to select the most optimal platform to design and implement the project. The design of the project was then developed based on the selected platform

Battery-assisted Electric Vehicle Charging: Data Driven Performance Analysis

As the number of electric vehicles rapidly increases, their peak demand on the grid becomes one of the major challenges. A battery-assisted charging concept has emerged recently, which allows to accumulate energy during off-peak hours and in-between charging sessions to boost-charge the vehicle at a higher rate than available from the grid. While prior research focused on the design and implementation aspects of battery-assisted charging, its impact at large geographical scales remains largely unexplored. In this paper we analyse to which extent the battery-assisted charging can replace high-speed chargers using a dataset of over 3 million EV charging sessions in both domestic and public setting in the UK. We first develop a discrete-event EV charge model that takes into account battery capacity, grid supply capacity and power output among other parameters. We then run simulations to evaluate the battery-assisted charging performance in terms of delivered energy, charging time and parity with conventional high-speed chargers. The results indicate that in domestic settings battery-assisted charging provides 98% performance parity of high-speed chargers from a standard 3 kW grid connection with a single battery pack. For non-domestic settings, the battery-assisted chargers can provide 92% and 99% performance parity of high-speed chargers with 10 battery packs using 3kW and 7kW grid supply respectively.Comment: Paper presented at 2020 IEEE PES ISGT Conference (26-28 October 2020

Revolutionising Higher Education:Unleashing the Potential of Large Language Models for Strategic Transformation

This paper investigates the transformative potential of Large Language Models (LLMs) within higher education, highlighting their capacity to reshape the academic landscape. By examining the complex impact of LLMs across critical areas of Higher Education Institutions (HEIs), including the role of HEIs as gatekeepers of knowledge, providers of credentials, research centres, incubators of innovation, drivers of social change and employers. In addition to academic integrity, the future of higher education, intellectual property, and public perception. The findings of this paper indicate that LLMs can empower transformation in HEIs by revolutionising various aspects of academia. The aim is to unveil the profound implications of integrating these cutting-edge technologies. The comprehensive study in this paper reveals the significant impacts and challenges associated with using LLMs in academic settings, which is achieved through a detailed analysis of current literature. The core findings suggest that LLMs hold the promise to trigger significant advancements in higher education. This paper also discusses the innovative potential of LLMs, and it outlines a path for their effective use in HEIs, emphasising the importance of a thoughtful approach to maximise their educational benefits. HEIs must address these challenges thoughtfully, ensuring that the integration of LLMs aligns with their fundamental objectives of promoting education, critical thinking, and personal growth