Wireless sensor networks for landslide monitoring: application and optimization by visibility analysis on 3D point clouds

Occurring in many geographical, geological and climatic environments, landslides represent a major geological hazard. In landslide prone areas, monitoring devices associated with Early Warning Systems are a cost-effective means to reduce the risk with a low environmental and economic impact, and in some cases, they can be the only solution. In this framework, particular interest has been reserved for Wireless Sensor Networks (WSNs), defined as networks of usually low-size and low-cost devices denoted as nodes, which are integrated with sensors that can gather information through wireless links. In this thesis, data from a new prototypical ground instability monitoring instrument called Wi-GIM (Wireless sensor network for Ground Instability Monitoring) have been analysed. The system consists in a WSN made by nodes able to measure their mutual inter-distances by calculating the time of flight of an Ultra-Wide Band impulse. Therefore, no sensors are implemented in the network, as the same signals used for transmission are also used for ranging. The system has been tested in a controlled outdoor environment and applied for the monitoring of the displacements of an actual landslide, the Roncovetro mudflow in Central Italy, where a parallel monitoring with a Robotic Total Station (RTS) allowed to validate the system. The outputs are displacement time series showing the distance of each couple of nodes belonging to the same cluster. Data retrieved from the tests revealed a precision of 2–5 cm and that measurements are influenced by the temperature. Since the correlation with this parameter has proved to be linear, a simple correction is sufficient to improve the precision and remove the effect of temperature. The campaign also revealed that measurements were not affected by rain or snow, and that the system can efficiently communicate up to 150 m with a 360° angle of view without affecting precision. Other key features of the implemented system are easy and quick installation, flexibility, low cost, real-time monitoring and acquisition frequency changeability. The comparison between Wi-GIM and RTS measurements pointed out the presence of an offset (in an order that vary from centimetric to decametric) constant for each single couple, due mainly to the presence of obstacles that can obstruct the Line Of Sight (LOS). The presence of vegetation is the main cause of the non-LOS condition between two nodes, which translates in a longer path of the signals and therefore to a less accurate distance measurements. To go further inside this issue, several tests have been carried out proving the strong influence of the vegetation over both data quantity and quality. To improve them, a MATLAB tool (R2018a, MAthWorks, Natick, MA, USA) called WiSIO (Wireless Sensor network Installation Optimizer) has been developed. The algorithm finds the best devices deployment following three criteria: (i) inter-visibility by means of a modified version of the Hidden Point Removal operator; (ii) equal distribution; (iii) positioning in preselected priority areas. With respect to the existing viewshed analysis, the main novelty is that it works directly with 3D point clouds, without rendering them or performing any surface. This lead to skip the process of generating surface models avoiding errors and approximations, that is essential when dealing with vegetation. A second installation of the Wi-GIM system has been therefore carried out considering the deployment suggested by WiSIO. The comparison of data acquired by the system positioned with and without the help of the proposed algorithm allowed to better comprehend the effectiveness of the tool. The presented results are very promising, showing how a simple elaboration can be essential to have more and more reliable data, improving the Wi-GIM system performances, making it even more usable in very complex environments and increasing its flexibility. The main left limitation of the Wi-GIM system is currently the precision. Such issue is connected to the aim of using only low-cost components, and it can be prospectively overcome if the system undergoes an industrialization process. Furthermore, since the system architecture is re-adaptable, it is prone to enhancements as soon as the technology advances and new low cost hardware enters the market

Image warfare in the war on terror: Image munitions and the continuation of war and politics by other means.

This thesis argues that the image as circulated within society has changed from what is broadly conceived of as a mass media society to that of an information society or a rhizomatic condition. This discontinuity is linked to changes that have taken place both within technology and the 'communications systems' that make up the media. This is theorized as a move from the 'mobilization of images' to the 'weaponization of images' and it takes the following form: the mobilization of images is connected to a twentieth century notion of propaganda and the rise of a mass society; whereas the weaponizing of images is understood as emerging through a networked/rhizomatic society connected with new media. It has also resulted in a paradigm shift from techno-war to image warfare. More specifically, this thesis is about exploring how American and British governments and militaries are failing to manage image warfare because they are operating with an outdated understanding that it is possible to 'control' images; whereas Al Qaeda appears to be understanding image warfare better. What I seek to show in this thesis is the disjuncture between this outdated idea of 'controlling' images (which Western governments and media continue to use) and a more dispersed or deterritorialized idea about how images operate in a rhizomatic condition. I explore this via my three conceptual terms: 'image munitions', 'counter-image munitions', 'remediation battles', with specific reference to the war on terror and specifically through four thematic case studies - political communications, suicides, executions and abuses - which allow exploration of different parts of this new theatre of war. In the conclusion I reflect on the implications of this analysis for understandings of contemporary and future warfare

Hybrid intelligent decision support system for distributed detection based on ad hoc integrated WSN & RFID

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe real time monitoring of environment context aware activities, based on distributed detection, is becoming a standard in public safety and service delivery in a wide range of domains (child and elderly care and supervision, logistics, circulation, and other). The safety of people, goods and premises depends on the prompt immediate reaction to potential hazards identified in real time, at an early stage to engage appropriate control actions. Effective emergency response can be supported only by available and acquired expertise or elaborate collaborative knowledge in the domain of distributed detection that include indoor sensing, tracking and localizing. This research proposes a hybrid conceptual multi-agent framework for the acquisition of collaborative knowledge in dynamic complex context aware environments for distributed detection. This framework has been applied for the design and development of a hybrid intelligent multi-agent decision system (HIDSS) that supports a decentralized active sensing, tracking and localizing strategy, and the deployment and configuration of smart detection devices associated to active sensor nodes wirelessly connected in a network topology to configure, deploy and control ad hoc wireless sensor networks (WSNs). This system, which is based on the interactive use of data, models and knowledge base, has been implemented to support fire detection and control access fusion functions aimed at elaborating: An integrated data model, grouping the building information data and WSN-RFID database, composed of the network configuration and captured data, A virtual layout configuration of the controlled premises, based on using a building information model, A knowledge-based support for the design of generic detection devices, A multi-criteria decision making model for generic detection devices distribution, ad hoc WSNs configuration, clustering and deployment, and Predictive data models for evacuation planning, and fire and evacuation simulation. An evaluation of the system prototype has been carried out to enrich information and knowledge fusion requirements and show the scope of the concepts used in data and process modelling. It has shown the practicability of hybrid solutions grouping generic homogeneous smart detection devices enhanced by heterogeneous support devices in their deployment, forming ad hoc networks that integrate WSNs and radio frequency identification (RFID) technology. The novelty in this work is the web-based support system architecture proposed in this framework that is based on the use of intelligent agent modelling and multi-agent systems, and the decoupling of the processes supporting the multi-sensor data fusion from those supporting different context applications. Although this decoupling is essential to appropriately distribute the different fusion functions, the integration of several dimensions of policy settings for the modelling of knowledge processes, and intelligent and pro-active decision making activities, requires the organisation of interactive fusion functions deployed upstream to a safety and emergency response.Saudi government, represented by the Ministry of Interior and General Directorate of Civil Defenc

The future roadmap of in-vehicle network processing: a HW-centric (R-)evolution

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The automotive industry is undergoing a deep revolution. With the race towards autonomous driving, the amount of technologies, sensors and actuators that need to be integrated in the vehicle increases exponentially. This imposes new great challenges in the vehicle electric/electronic (E/E) architecture and, especially, in the In-Vehicle Network (IVN). In this work, we analyze the evolution of IVNs, and focus on the main network processing platform integrated in them: the Gateway (GW). We derive the requirements of Network Processing Platforms that need to be fulfilled by future GW controllers focusing on two perspectives: functional requirements and structural requirements. Functional requirements refer to the functionalities that need to be delivered by these network processing platforms. Structural requirements refer to design aspects which ensure the feasibility, usability and future evolution of the design. By focusing on the Network Processing architecture, we review the available options in the state of the art, both in industry and academia. We evaluate the strengths and weaknesses of each architecture in terms of the coverage provided for the functional and structural requirements. In our analysis, we detect a gap in this area: there is currently no architecture fulfilling all the requirements of future automotive GW controllers. In light of the available network processing architectures and the current technology landscape, we identify Hardware (HW) accelerators and custom processor design as a key differentiation factor which boosts the devices performance. From our perspective, this points to a need - and a research opportunity - to explore network processing architectures with a strong HW focus, unleashing the potential of next-generation network processors and supporting the demanding requirements of future autonomous and connected vehicles.Peer ReviewedPostprint (published version

Defending Against IoT-Enabled DDoS Attacks at Critical Vantage Points on the Internet

The number of Internet of Things (IoT) devices continues to grow every year. Unfortunately, with the rise of IoT devices, the Internet is also witnessing a rise in the number and scale of IoT-enabled distributed denial-of-service (DDoS) attacks. However, there is a lack of network-based solutions targeted directly for IoT networks to address the problem of IoT-enabled DDoS. Unlike most security approaches for IoT which focus on hardening device security through hardware and/or software modification, which in many cases is infeasible, we introduce network-based approaches for addressing IoT-enabled DDoS attacks. We argue that in order to effectively defend the Internet against IoT-enabled DDoS attacks, it is necessary to consider network-wide defense at critical vantage points on the Internet. This dissertation is focused on three inherently connected and complimentary components: (1) preventing IoT devices from being turned into DDoS bots by inspecting traffic towards IoT networks at an upstream ISP/IXP, (2) detecting DDoS traffic leaving an IoT network by inspecting traffic at its gateway, and (3) mitigating attacks as close to the devices in an IoT network originating DDoS traffic. To this end, we present three security solutions to address the three aforementioned components to defend against IoT-enabled DDoS attacks

Managing Smartphone Testbeds with SmartLab

The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. In this paper, we make three major contributions: First, we propose a comprehensive architecture, coined SmartLab1, for managing a cluster of both real and virtual smartphones that are either wired to a private cloud or connected over a wireless link. Second, we propose and describe a number of Android management optimizations (e.g., command pipelining, screen-capturing, file management), which can be useful to the community for building similar functionality into their systems. Third, we conduct extensive experiments and microbenchmarks to support our design choices providing qualitative evidence on the expected performance of each module comprising our architecture. This paper also overviews experiences of using SmartLab in a research-oriented setting and also ongoing and future development efforts

Security and Privacy for IoT Ecosystems

Smart devices have become an integral part of our everyday life. In contrast to smartphones and laptops, Internet of Things (IoT) devices are typically managed by the vendor. They allow little or no user-driven customization. Users need to use and trust IoT devices as they are, including the ecosystems involved in the processing and sharing of personal data. Ensuring that an IoT device does not leak private data is imperative. This thesis analyzes security practices in popular IoT ecosystems across several price segments. Our results show a gap between real-world implementations and state-of-the-art security measures. The process of responsible disclosure with the vendors revealed further practical challenges. Do they want to support backward compatibility with the same app and infrastructure over multiple IoT device generations? To which extent can they trust their supply chains in rolling out keys? Mature vendors have a budget for security and are aware of its demands. Despite this goodwill, developers sometimes fail at securing the concrete implementations in those complex ecosystems. Our analysis of real-world products reveals the actual efforts made by vendors to secure their products. Our responsible disclosure processes and publications of design recommendations not only increase security in existing products but also help connected ecosystem manufacturers to develop secure products. Moreover, we enable users to take control of their connected devices with firmware binary patching. If a vendor decides to no longer offer cloud services, bootstrapping a vendor-independent ecosystem is the only way to revive bricked devices. Binary patching is not only useful in the IoT context but also opens up these devices as research platforms. We are the first to publish tools for Bluetooth firmware and lower-layer analysis and uncover a security issue in Broadcom chips affecting hundreds of millions of devices manufactured by Apple, Samsung, Google, and more. Although we informed Broadcom and customers of their technologies of the weaknesses identified, some of these devices no longer receive official updates. For these, our binary patching framework is capable of building vendor-independent patches and retrofit security. Connected device vendors depend on standards; they rarely implement lower-layer communication schemes from scratch. Standards enable communication between devices of different vendors, which is crucial in many IoT setups. Secure standards help making products secure by design and, thus, need to be analyzed as early as possible. One possibility to integrate security into a lower-layer standard is Physical-Layer Security (PLS). PLS establishes security on the Physical Layer (PHY) of wireless transmissions. With new wireless technologies emerging, physical properties change. We analyze how suitable PLS techniques are in the domain of mmWave and Visible Light Communication (VLC). Despite VLC being commonly believed to be very secure due to its limited range, we show that using VLC instead for PLS is less secure than using it with Radio Frequency (RF) communication. The work in this thesis is applied to mature products as well as upcoming standards. We consider security for the whole product life cycle to make connected devices and IoT ecosystems more secure in the long term

Summer Research Fellowship Project Descriptions 2022

A summary of research done by Smith College’s 2021 Summer Research Fellowship (SURF) Program participants. Ever since its 1967 start, SURF has been a cornerstone of Smith’s science education. Supervised by faculty mentor-advisors drawn from the Clark Science Center and connected to its eighteen science, mathematics, and engineering departments and programs and associated centers and units. At summer’s end, SURF participants were asked to summarize their research experiences for this publication.https://scholarworks.smith.edu/clark_womeninscience/1012/thumbnail.jp

Human-sensor dialogue in participatory sensing

Participatory sensing is an emerging field that uses pervasive technology to create new forms of sensing networks combining people, personal devices, and other sensors. Pervasive computing technology forms an essential component, used to report data and coordinate activities. This thesis reviews research in participatory sensing and key fields related to it: pervasive computing, observation networks and public engagement with science. After examining wider issues in sensor-based interaction from pervasive computing literature, this thesis investigates human-sensor dialogue; specifically how to develop new forms of dialogue in future participatory sensing experiences. The term 'dialogue' is used in broad sense, encompassing affordances and ongoing relationships between sensors and users. The thesis examines participatory sensing activities centring on two studies involving groups of young people collecting and visualising environmental sensor data using automatic and manual sensors. Participant observation methods are used for in-situ, naturalistic evaluation using observations, video footage and system logs and data. A framework for human-sensor dialogue is developed as a tool to help analyse the dialogue in participatory sensing experiences and inspire new forms of dialogue in future experiences. It highlights five activities to which dialogue can relate: planning, testing, navigation, capture and reflection. These are interleaved throughout an experience, affecting how it takes shape and resulting from the design of the devices and the whole experience. The framework is demonstrated by applying it to the experiences in the previous two studies. The framework is used to prototype a new experience intended for longer term engagement. It is used to elicit requirements for the new experience, structuring the activity and highlighting the desired transitions. The resulting prototype application is described, outlining the activity setup, key features and technical details. This application uses handheld devices as mobile sensors, wirelessly connected to fixed environmental sensors, which collect, process, and store the restating data

Algorithmes de localisation distribués en intérieur pour les réseaux sans fil avec la technologie IEEE 802.15.4

The Internet of Things is finally blooming through diverse applications, from home automation and monitoring to health tracking and quantified-self movement. Consumers deploy more and more low-rate and low-power connected devices that provide complex services. In this scenario, positioning these intelligent objects in their environment is necessary to provide geo-localized services, as well as to optimize the network operation. However, indoor positioning of devices using only their radio interface is still very imprecise. Indoor wireless localization techniques often deduce from the Radio frequency (RF) signal attenuation the distances that separate a mobile node from a set of reference points called landmarks. The received signal strength indicator (RSSI), which reflects this attenuation, is known in the literature to be inaccurate and unreliable when it comes to distance estimation, due to the complexity of indoor radio propagation (shadowing, multi-path fading). However, it is the only metric that will certainly be available in small and inexpensive smart objects. In this thesis, we therefore seek algorithmic solutions to the following problem: is it possible to achieve a fair localization using only the RSSI readings provided by low-quality hardware? To this extent, we first study the behavior of the RSSI, as reported by real hardware like IEEE 802.15.4 sensor nodes, in several indoor environments with different sizes and configurations , including a large scale wireless sensor network. Such experimental results confirm that the relationship between RSSI and distance depends on many factors; even the battery pack attached to the devices increases attenuation. In a second step, we demonstrate that the classical log-normal shadowing propagation model is not well adapted in indoor case, because of the RSSI values dispersion and its lack of obvious correlation with distance. We propose to correct the observed inconsistencies by developing algorithms to filter irrelevant samples. Such correction is performed by biasing the classical log-normal shadowing model to take into account the effects of multipath propagation. These heuristics significantly improved RSSI-based indoor localization accuracy results. We also introduce an RSSI-based positioning approach that uses a maximum likelihood estimator conjointly with a statistical model based on machine learning. In a third step, we propose an accurate distributed and cooperative RSSI-based localization algorithm that refines the set of positions estimated by a wireless node. This algorithm is composed of two on-line steps: a local update of position¿s set based on stochastic gradient descent on each new RSSI measurement at each sensor node. Then an asynchronous communication step allowing each sensor node to merge their common local estimates and obtain the agreement of the refined estimated positions. Such consensus approach is based on both a distributed local gradient step and a pairwise gossip protocol. This enables each sensor node to refine its initial estimated position as well as to build a local map of itself and its neighboring nodes. The proposed algorithm is compared to multilateration, Multi Dimensional Scaling (i.e. MDS) with modern majorization problem and classical MDS. Simulation as well as experimental results obtained on real testbeds lead to a centimeter-level accuracy. Both landmarks and blind nodes communicate in the way that the data processing and computation are performed by each sensor node without any central computation point, tedious calibration or intervention from a human.L¿internet des objets se développe à travers diverses applications telles que la domotique, la surveillance à domicile, etc. Les consommateurs s¿intéressent à ces applications dont les objets interagissent avec des dispositifs de plus en plus petits et connectés. La localisation est une information clé pour plusieurs services ainsi que pour l¿optimisation du fonctionnement du réseau. En environnement intérieur ou confiné, elle a fait l¿objet de nombreuses études. Cependant, l¿obtention d¿une bonne précision de localisation demeure une question difficile, non résolue. Cette thèse étudie le problème de la localisation en environnement intérieur appliqué aux réseaux sans fil avec l¿utilisation unique de l¿atténuation du signal. L¿atténuation est mesurée par l¿indicateur de l¿intensité du signal reçu (RSSI). Le RSSI est connu dans la littérature comme étant imprécis et peu fiable en ce qui concerne l¿estimation de la distance, du fait de la complexité de la propagation radio en intérieur : il s¿agit des multiples trajets, le shadowing, le fading. Cependant, il est la seule métrique directement mesurable par les petits objets communicants et intelligents. Dans nos travaux, nous avons amélioré la précision des mesures du RSSI pour les rendre applicables à l¿environnement interne dans le but d¿obtenir une meilleure localisation. Nous nous sommes également intéressés à l¿implémentation et au déploiement de solutions algorithmiques relatifs au problème suivant : est-il possible d¿obtenir une meilleure précision de la localisation en utilisant uniquement les mesures de RSSI fournies par les n¿uds capteurs sans fil IEEE 802.15.4 ? Dans cette perspective, nous avons d¿abord étudié le comportement du RSSI dans plusieurs environnements intérieurs de différentes tailles et selon plusieurs configurations , y compris un réseau de capteurs sans fil à grande échelle (SensLAB). Pour expliquer les résultats des mesures, nous avons caractérisé les objets communicants que nous utilisons, les n¿uds capteurs Moteiv TMote Sky, par une série d¿expériences en chambre anéchoïque. Les résultats expérimentaux confirment que la relation entre le RSSI et la distance dépend de nombreux facteurs même si la batterie intégrée à chaque n¿ud capteur produit une atténuation. Ensuite, nous avons démontré que le modèle de propagation log-normal shadowing n¿est pas adapté en intérieur, en raison de la dispersion des valeurs de RSSI et du fait que celles-ci ne sont pas toujours dépendantes de la distance. Ces valeurs devraient être considérées séparément en fonction de l¿emplacement de chaque n¿ud capteur émetteur. Nous avons proposé des heuristiques pour corriger ces incohérences observées à savoir les effets de la propagation par trajets multiples et les valeurs aberrantes. Nos résultats expérimentaux ont confirmé que nos algorithmes améliorent significativement la précision de localisation en intérieur avec l¿utilisation unique du RSSI. Enfin, nous avons étudié et proposé un algorithme de localisation distribué, précis et coopératif qui passe à l¿échelle et peu consommateur en termes de temps de calcul. Cet algorithme d¿approximation stochastique utilise la technique du RSSI tout en respectant les caractéristiques de l¿informatique embarquée des réseaux de capteurs sans fil. Il affine l¿ensemble des positions estimées par un n¿ud capteur sans fil. Notre approche a été comparée à d¿autres algorithmes distribués de l¿état de l¿art. Les résultats issus des simulations et des expériences en environnements internes réels ont révélé une meilleure précision de la localisation de notre algorithme distribué. L¿erreur de localisation est de l¿ordre du centimètre sans aucun n¿ud ou unité centrale de traitement, ni de calibration fastidieuse ni d¿intervention humaine