Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network

Internet of vehicles (IoVs) allows millions of vehicles to be connected and share information for various purposes. The main applications of IoVs are traffic management, emergency messages delivery, E-health, traffic, and temperature monitoring. On the other hand, IoVs lack in location awareness and geographic distribution, which is critical for some IoVs applications such as smart traffic lights and information sharing in vehicles. To support these topographies, fog computing was proposed as an appealing and novel term, which was integrated with IoVs to extend storage, computation, and networking. Unfortunately, it is also challenged with various security and privacy hazards, which is a serious concern of smart cities. Therefore, we can formulate that Fog-assisted IoVs (Fa-IoVs), are challenged by security threats during information dissemination among mobile nodes. These security threats of Fa-IoVs are considered as anomalies which is a serious concern that needs to be addressed for smooth Fa-IoVs network communication. Here, smooth communication refers to less risk of important data loss, delay, communication overhead, etc. This research work aims to identify research gaps in the Fa-IoVs network and present a deep learning-based dynamic scheme named CAaDet (Convolutional autoencoder Aided anomaly detection) to detect anomalies. CAaDet exploits convolutional layers with a customized autoencoder for useful feature extraction and anomaly detection. Performance evaluation of the proposed scheme is done by using the F1-score metric where experiments are carried out by exploiting a benchmark dataset named NSL-KDD. CAaDet also observes the behavior of fog nodes and hidden neurons and selects the best match to reduce false alarms and improve F1-score. The proposed scheme achieved significant improvement over existing schemes for anomaly detection. Identified research gaps in Fa-IoVs can give future directions to researchers and attract more attention to this new era

Layout Sequence Prediction From Noisy Mobile Modality

Trajectory prediction plays a vital role in understanding pedestrian movement for applications such as autonomous driving and robotics. Current trajectory prediction models depend on long, complete, and accurately observed sequences from visual modalities. Nevertheless, real-world situations often involve obstructed cameras, missed objects, or objects out of sight due to environmental factors, leading to incomplete or noisy trajectories. To overcome these limitations, we propose LTrajDiff, a novel approach that treats objects obstructed or out of sight as equally important as those with fully visible trajectories. LTrajDiff utilizes sensor data from mobile phones to surmount out-of-sight constraints, albeit introducing new challenges such as modality fusion, noisy data, and the absence of spatial layout and object size information. We employ a denoising diffusion model to predict precise layout sequences from noisy mobile data using a coarse-to-fine diffusion strategy, incorporating the RMS, Siamese Masked Encoding Module, and MFM. Our model predicts layout sequences by implicitly inferring object size and projection status from a single reference timestamp or significantly obstructed sequences. Achieving SOTA results in randomly obstructed experiments and extremely short input experiments, our model illustrates the effectiveness of leveraging noisy mobile data. In summary, our approach offers a promising solution to the challenges faced by layout sequence and trajectory prediction models in real-world settings, paving the way for utilizing sensor data from mobile phones to accurately predict pedestrian bounding box trajectories. To the best of our knowledge, this is the first work that addresses severely obstructed and extremely short layout sequences by combining vision with noisy mobile modality, making it the pioneering work in the field of layout sequence trajectory prediction.Comment: In Proceedings of the 31st ACM International Conference on Multimedia 2023 (MM 23

Transforming urban mobility with internet of things: public bus fleet tracking using proximity-based bluetooth beacons

In today’s fast-paced world, efficient and reliable public transportation systems are crucial for optimising time and reducing carbon dioxide emissions. However, developing countries face numerous challenges in their public transportation networks, including infrequent services, delays, inaccurate and unreliable arrival times, long waiting time, and limited real-time information available to the users. GPS-based systems have been widely used for fleet management, but they can be a significant infrastructure investment for smaller operators in developing countries. The accuracy of the GPS location can be easily affected by the weather condition and GPS signals are susceptible to spoofing attacks. When the GPS device is faulty, the entire location traces will be unavailable. This paper proposes the use of Internet-of-Things (IoT)-enabled Bluetooth Low Energy (BLE) systems as an alternative approach to fleet tracking for public bus service. The proposed approach offers simplicity and easy implementation for bus operators by deploying BLE proximity beacons on buses to track their journeys, with detection devices using Raspberry Pi (RPi) Zero strategically placed at terminals and selected stops. When the bus approaches and stops at the bus stops, the BLE advertisements emitted by the proximity beacons can be reliably detected by the RPi Zero. Experiment results show that the BLE signals can be detected up to 20 m in range when the RPi Zero is placed inside a metal enclosure. The location of the bus is then sent to the cloud to estimate the arrival times. A field trial of the proposed IoT-based BLE proximity sensing system involving two public bus services in southern Malaysian cities, namely, Johor Bahru, Iskandar Puteri and Kulai is presented. Based on the data collected, a bus arrival time estimation algorithm is designed. Our analysis shows that there was a 5–10 min reduction in journey time on public holidays as compared to a normal day. Overall, the paper emphasises the importance of addressing public transportation challenges. It also describes the challenges, experience, and mitigation drawn from the deployment of this real-world use case, demonstrating the feasibility and reliability of IoT-based proximity sensing as an alternative approach to tracking public bus services

Innovative Wireless Localization Techniques and Applications

Innovative methodologies for the wireless localization of users and related applications are addressed in this thesis. In last years, the widespread diffusion of pervasive wireless communication (e.g., Wi-Fi) and global localization services (e.g., GPS) has boosted the interest and the research on location information and services. Location-aware applications are becoming fundamental to a growing number of consumers (e.g., navigation, advertising, seamless user interaction with smart places), private and public institutions in the fields of energy efficiency, security, safety, fleet management, emergency response. In this context, the position of the user - where is often more valuable for deploying services of interest than the identity of the user itself - who. In detail, opportunistic approaches based on the analysis of electromagnetic field indicators (i.e., received signal strength and channel state information) for the presence detection, the localization, the tracking and the posture recognition of cooperative and non-cooperative (device-free) users in indoor environments are proposed and validated in real world test sites. The methodologies are designed to exploit existing wireless infrastructures and commodity devices without any hardware modification. In outdoor environments, global positioning technologies are already available in commodity devices and vehicles, the research and knowledge transfer activities are actually focused on the design and validation of algorithms and systems devoted to support decision makers and operators for increasing efficiency, operations security, and management of large fleets as well as localized sensed information in order to gain situation awareness. In this field, a decision support system for emergency response and Civil Defense assets management (i.e., personnel and vehicles equipped with TETRA mobile radio) is described in terms of architecture and results of two-years of experimental validation

Multimodal Sensing for Robust and Energy-Efficient Context Detection with Smart Mobile Devices

Adoption of smart mobile devices (smartphones, wearables, etc.) is rapidly growing. There are already over 2 billion smartphone users worldwide [1] and the percentage of smartphone users is expected to be over 50% in the next five years [2]. These devices feature rich sensing capabilities which allow inferences about mobile device user’s surroundings and behavior. Multiple and diverse sensors common on such mobile devices facilitate observing the environment from different perspectives, which helps to increase robustness of inferences and enables more complex context detection tasks. Though a larger number of sensing modalities can be beneficial for more accurate and wider mobile context detection, integrating these sensor streams is non-trivial. This thesis presents how multimodal sensor data can be integrated to facilitate ro- bust and energy efficient mobile context detection, considering three important and challenging detection tasks: indoor localization, indoor-outdoor detection and human activity recognition. This thesis presents three methods for multimodal sensor inte- gration, each applied for a different type of context detection task considered in this thesis. These are gradually decreasing in design complexity, starting with a solution based on an engineering approach decomposing context detection to simpler tasks and integrating these with a particle filter for indoor localization. This is followed by man- ual extraction of features from different sensors and using an adaptive machine learn- ing technique called semi-supervised learning for indoor-outdoor detection. Finally, a method using deep neural networks capable of extracting non-intuitive features di- rectly from raw sensor data is used for human activity recognition; this method also provides higher degree of generalization to other context detection tasks. Energy efficiency is an important consideration in general for battery powered mo- bile devices and context detection is no exception. In the various context detection tasks and solutions presented in this thesis, particular attention is paid to this issue by relying largely on sensors that consume low energy and on lightweight computations. Overall, the solutions presented improve on the state of the art in terms of accuracy and robustness while keeping the energy consumption low, making them practical for use on mobile devices

A comprehensive survey of V2X cybersecurity mechanisms and future research paths

Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.This work is supported by the H2020-INSPIRE-5Gplus project (under Grant agreement No. 871808), the ”Ministerio de Asuntos Económicos y Transformacion Digital” and the European Union-NextGenerationEU in the frameworks of the ”Plan de Recuperación, Transformación y Resiliencia” and of the ”Mecanismo de Recuperación y Resiliencia” under references TSI-063000-2021-39/40/41, and the CHIST-ERA-17-BDSI-003 FIREMAN project funded by the Spanish National Foundation (Grant PCI2019-103780).Peer ReviewedPostprint (published version

Effective and Efficient Communication and Collaboration in Participatory Environments

Participatory environments pose significant challenges to deploying real applications. This dissertation investigates exploitation of opportunistic contacts to enable effective and efficient data transfers in challenged participatory environments. There are three main contributions in this dissertation: 1. A novel scheme for predicting contact volume during an opportunistic contact (PCV); 2. A method for computing paths with combined optimal stability and capacity (COSC) in opportunistic networks; and 3. An algorithm for mobility and orientation estimation in mobile environments (MOEME). The proposed novel scheme called PCV predicts contact volume in soft real-time. The scheme employs initial position and velocity vectors of nodes along with the data rate profile of the environment. PCV enables efficient and reliable data transfers between opportunistically meeting nodes. The scheme that exploits capacity and path stability of opportunistic networks is based on PCV for estimating individual link costs on a path. The total path cost is merged with a stability cost to strike a tradeoff for maximizing data transfers in the entire participatory environment. A polynomial time dynamic programming algorithm is proposed to compute paths of optimum cost. We propose another novel scheme for Real-time Mobility and Orientation Estimation for Mobile Environments (MOEME), as prediction of user movement paves way for efficient data transfers, resource allocation and event scheduling in participatory environments. MOEME employs the concept of temporal distances and uses logistic regression to make real time estimations about user movement. MOEME relies only on opportunistic message exchange and is fully distributed, scalable, and requires neither a central infrastructure nor Global Positioning System. Indeed, accurate prediction of contact volume, path capacity and stability and user movement can improve performance of deployments. However, existing schemes for such estimations make use of preconceived patterns or contact time distributions that may not be applicable in uncertain environments. Such patterns may not exist, or are difficult to recognize in soft-real time, in open environments such as parks, malls, or streets

Napredna (edge computing) softverska arhitektura za upravljanje resursima i unutrašnje pozicioniranje

In Part I, this thesis aims to shed light on IoT and edge com-puting systems and accompanying computing and architectural paradigms, their definition, areas of application, and common use-cases, as well as operational, business, economical, social challenges and benefits. It illustrates modern needs and requests in building IoT systems and current State-of-The-Art (SoTA) approaches to designing them. Additionally, it discusses the security and privacy topics of IoT and edge computing systems. It also encompasses research, design, and implementation of an MQTT-based Resource Management Framework for Edge Com-puting systems that handle: resource management, failover detection and handover administration, logical and physical workload balancing and protection, and monitoring of physical and logical system resources designed for a real-world IoT platform. The thesis offers insights into modern requests for such frameworks, current SoTA approaches, and offer a solution in the form of a software framework, with minimal implementation and communication overhead. In Part II, the thesis elaborates on IPS, their definition, deploy-ment types, commonly used positioning techniques, areas of application, and common use-cases, as well as operational, business, economic, social challenges, and benefits. It specifically discusses designing IPS for the typical IoT infrastructure. It offers insights to modern IPS requests, current SoTA in solving them, and under-line original approaches from this thesis. It elaborates on the research, design and authors’ implementation of an IPS for the IoT – Bluetooth LowEnergyMicrolocation Asset Tracking (BLEMAT), including its software engines (collections of software components) for: indoor positioning, occupancy detection, visualization, pattern discovery and prediction, geofencing, movement pattern detection, visualization, discovery and prediction, social dynamics analysis, and indoor floor plan layout detection.Deo I teze ima je za cilj da rasvetli IoT i edge computing računarske sisteme i prateće računarske paradigme softverskih arhitektura, njihovu definiciju, područja primene i slučajeve uobičajene upotrebe, kao i operativne, poslovne, ekonomske, i socijalne izazove i koristi. Teza ilustruje savremene potrebe i zahtevi u izgradnji IoT sistema i najsavremeniji pristupi u njihovom dizajniranju. Raspravlja se o temama bezbednosti i privatnosti u IoT i edge computing računarskim sistemima. Kao još jedan glavni zadatak, teza je obuhvata istraživanje, dizajn i implementaciju softverske arhitekture za upravljanje resursima zasnovanim na MQTT komunikacionom protokolu za edge computing računarske sisteme koja se bavi: upravljanjem resursima, detekcijom prestanka rada upravljačkih algoritama i administracijom primopredaje tj. transporta upravljačkih algoritama, i logičkim i fizičkim balansiranjem i zaštitom radnog opterećenja sistema. Diskutuju se savremeni zahtevi za takve softverske arhitekture, trenutni pristupi. Na kraju, prikazuje se rešenje sa minimalnim troškovima implementacije i  komunikacije. Deo II teze ima za cilj da objasni sisteme za unutrašnje pozicioniranje, njihovu definiciju, vrste primene, najčešće korišćene tehnike pozicioniranja, područja primene i uobičajene slučajeve upotrebe, kao i operativne, poslovne, ekonomske, i socijalne izazove i koristi. Posebno se diskutuje o dizajniranju ovakvih sistema za tipičnu IoT infrastrukturu. Nudi se uvid u savremene zahteve sisteme za unutrašnje pozicioniranje, trenutne pristupe u rešavanju istih, i naglašeni su originalni pristupe iz ove teze. Dalje je fokus na istraživanju, dizajniranju i implementaciji sistema za unutrašnje pozicioniranje (BLEMAT), uključujući njegove softverske podsisteme (kolekcije softverskih komponenti) za: pozicioniranje u zatvorenom prostoru, detekciju zauzeća prostorija, vizualizaciju, otkrivanje i predviđanje obrazaca kretanja, geofencing, vizualizaciju i analizu društvene dinamike i detekciju rasporeda prostorija unutrašnjeg prostora

A model personal energy meter

Every day each of us consumes a significant amount of energy, both directly through transport, heating and use of appliances, and indirectly from our needs for the production of food, manufacture of goods and provision of services. This dissertation investigates a personal energy meter which can record and apportion an individual's energy usage in order to supply baseline information and incentives for reducing our environmental impact. If the energy costs of large shared resources are split evenly without regard for individual consumption each person minimises his own losses by taking advantage of others. Context awareness offers the potential to change this balance and apportion energy costs to those who cause them to be incurred. This dissertation explores how sensor systems installed in many buildings today can be used to apportion energy consumption between users, including an evaluation of a range of strategies in a case study and elaboration of the overriding principles that are generally applicable. It also shows how second-order estimators combined with location data can provide a proxy for fine-grained sensing. A key ingredient for apportionment mechanisms is data on energy usage. This may come from metering devices or buildings directly, or from profiling devices and using secondary indicators to infer their power state. A mechanism for profiling devices to determine the energy costs of specific activities, particularly applicable to shared programmable devices is presented which can make this process simpler and more accurate. By combining crowdsourced building-inventory information and a simple building energy model it is possible to estimate an individual's energy use disaggregated by device class with very little direct sensing. Contextual information provides crucial cues for apportioning the use and energy costs of resources, and one of the most valuable sources from which to infer context is location. A key ingredient for a personal energy meter is a low cost, low infrastructure location system that can be deployed on a truly global scale. This dissertation presents a description and evaluation of the new concept of inquiry-free Bluetooth tracking that has the potential to offer indoor location information with significantly less infrastructure and calibration than other systems. Finally, a suitable architecture for a personal energy meter on a global scale is demonstrated using a mobile phone application to aggregate energy feeds based on the case studies and technologies developed

Integrated human exposure to air pollution

The book “Integrated human exposure to air pollution” aimed to increase knowledge about human exposure in different micro-environments, or when citizens are performing specific tasks, to demonstrate methodologies for the understanding of pollution sources and their impact on indoor and ambient air quality, and, ultimately, to identify the most effective mitigation measures to decrease human exposure and protect public health. Taking advantage of the latest available tools, such as internet of things (IoT), low-cost sensors and a wide access to online platforms and apps by the citizens, new methodologies and approaches can be implemented to understand which factors can influence human exposure to air pollution. This knowledge, when made available to the citizens, along with the awareness of the impact of air pollution on human life and earth systems, can empower them to act, individually or collectively, to promote behavioral changes aiming to reduce pollutants’ emissions. Overall, this book gathers fourteen innovative studies that provide new insights regarding these important topics within the scope of human exposure to air pollution. A total of five main areas were discussed and explored within this book and, hopefully, can contribute to the advance of knowledge in this field