Cybersecurity is becoming an increasingly important aspect in ensuring maritime data protection and operational continuity. Ships, ports, surveillance and navigation systems, industrial technology, ...cargo, and logistics systems all contribute to a complex maritime environment with a significant cyberattack surface. To that aim, a wide range of cyberattacks in the maritime domain are possible, with the potential to infect vulnerable information and communication systems, compromising safety and security. The use of navigation and surveillance systems, which are considered as part of the maritime OT sensors, can improve maritime cyber situational awareness. This survey critically investigates whether the fusion of OT data, which are used to provide maritime situational awareness, may also improve the ability to detect cyberincidents in real time or near-real time. It includes a thorough analysis of the relevant literature, emphasizing RF but also other sensors, and data fusion approaches that can help improve maritime cybersecurity.
Indoor user localization and tracking are instrumental to a broad range of services and applications in the Internet of Things (IoT) and particularly in Body Sensor Networks (BSN) and Ambient ...Assisted Living (AAL) scenarios. Due to the widespread availability of IEEE 802.11, many localization platforms have been proposed, based on the Wi-Fi Received Signal Strength (RSS) indicator, using algorithms such as
-Nearest Neighbour (KNN), Maximum A Posteriori (MAP) and Minimum Mean Square Error (MMSE). In this paper, we introduce a hybrid method that combines the simplicity (and low cost) of Bluetooth Low Energy (BLE) and the popular 802.11 infrastructure, to improve the accuracy of indoor localization platforms. Building on KNN, we propose a new positioning algorithm (dubbed i-KNN) which is able to filter the initial fingerprint dataset (i.e., the radiomap), after considering the proximity of RSS fingerprints with respect to the BLE devices. In this way, i-KNN provides an optimised small subset of possible user locations, based on which it finally estimates the user position. The proposed methodology achieves fast positioning estimation due to the utilization of a fragment of the initial fingerprint dataset, while at the same time improves positioning accuracy by minimizing any calculation errors.
This research work investigates how RSS information fusion from a single, multi-antenna access point (AP) can be used to perform device localization in indoor RSS based localization systems. The ...proposed approach demonstrates that different RSS values can be obtained by carefully modifying each AP antenna orientation and polarization, allowing the generation of unique, low correlation fingerprints, for the area of interest. Each AP antenna can be used to generate a set of fingerprint radiomaps for different antenna orientations and/or polarization. The RSS fingerprints generated from all antennas of the single AP can be then combined to create a multi-layer fingerprint radiomap. In order to select the optimum fingerprint layers in the multilayer radiomap the proposed methodology evaluates the obtained localization accuracy, for each fingerprint radio map combination, for various well-known deterministic and probabilistic algorithms (Weighted k-Nearest-Neighbor-WKNN and Minimum Mean Square Error-MMSE). The optimum candidate multi-layer radiomap is then examined by calculating the correlation level of each fingerprint pair by using the "Tolerance Based-Normal Probability Distribution (TBNPD)" algorithm. Both steps take place during the offline phase, and it is demonstrated that this approach results in selecting the optimum multi-layer fingerprint radiomap combination. The proposed approach can be used to provide localisation services in areas served only by a single AP.
In the near future, the fifth-generation wireless technology is expected to be rolled out, offering low latency, high bandwidth and multiple antennas deployed in a single access point. This ecosystem ...will help further enhance various location-based scenarios such as assets tracking in smart factories, precise smart management of hydroponic indoor vertical farms and indoor way-finding in smart hospitals. Such a system will also integrate existing technologies like the Internet of Things (IoT), WiFi and other network infrastructures. In this respect, 5G precise indoor localization using heterogeneous IoT technologies (Zigbee, Raspberry Pi, Arduino, BLE, etc.) is a challenging research area. In this work, an experimental 5G testbed has been designed integrating C-RAN and IoT networks. This testbed is used to improve both vertical and horizontal localization (3D Localization) in a 5G IoT environment. To achieve this, we propose the DEep Learning-based co-operaTive Architecture (DELTA) machine learning model implemented on a 3D multi-layered fingerprint radiomap. The DELTA begins by estimating the 2D location. Then, the output is recursively used to predict the 3D location of a mobile station. This approach is going to benefit use cases such as 3D indoor navigation in multi-floor smart factories or in large complex buildings. Finally, we have observed that the proposed model has outperformed traditional algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).
Planning and deploying a functional large scale Wireless Sensor Network (WSN) or a Network of Internet of Things (IoTs) is a challenging task, especially in complex urban environments. A main network ...design bottleneck is the existence and/or correct usage of appropriate cross layer simulators that can generate realistic results for the scenario of interest. Existing network simulators tend to overlook the complexity of the physical radio propagation layer and consequently do not realistically simulate the main radio propagation conditions that take place in urban or suburban environments, thus passing inaccurate results between Open Systems Interconnection (OSI) layers. This work demonstrates through simulations and measurements that, by correctly passing physical information to higher layers, the overall simulation process produces more accurate results at the network layer. It is demonstrated that the resulting simulation methodology can be utilized to accomplish realistic wireless planning and performance analysis of the deployed nodes, with results that are very close to those of real test-beds, or actual WSN deployments.
Satellite images can provide valuable information about the presented urban landscape scenes to remote sensing and telecommunication applications. Obtaining information from satellite images is ...difficult since all the objects and their surroundings are presented with feature complexity. The shadows cast by buildings in urban scenes can be processed and used for estimating building heights. Thus, a robust and accurate building shadow detection process is important. Region-based active contour models can be used for satellite image segmentation. However, spectral heterogeneity that usually exists in satellite images and the feature similarity representing the shadow and several non-shadow regions makes building shadow detection challenging. In this work, a new automated method for delineating building shadows is proposed. Initially, spectral and spatial features of the satellite image are utilized for designing a custom filter to enhance shadows and reduce intensity heterogeneity. An effective iterative procedure using intensity differences is developed for tuning and subsequently selecting the most appropriate filter settings, able to highlight the building shadows. The response of the filter is then used for automatically estimating the radiometric property of the shadows. The customized filter and the radiometric feature are utilized to form an optimized active contour model where the contours are biased to delineate shadow regions. Post-processing morphological operations are also developed and applied for removing misleading artefacts. Finally, building heights are approximated using shadow length and the predefined or estimated solar elevation angle. Qualitative and quantitative measures are used for evaluating the performance of the proposed method for both shadow detection and building height estimation.
In Canadian mathematics education, dominant colonial narratives highlight an achievement disparity between Indigenous and non-Indigenous students in a way that often re-inscribes perceived deficits ...of Indigenous students, ignores the educational aspirations of Indigenous peoples, and sidelines Indigenous cultural and linguistic representations of knowledge in the classroom. Intentions of Indigenizing curriculum include challenging and reversing racist and colonial ideologies that hinder Indigenous education, providing meaningful alternatives within school cultures that foreground essential aspects of Indigenous education, and supporting the dynamic learning of Indigenous students. In my research described in this article, I used a narrative inquiry to describe how two Cree elementary school teachers shared promising practices of holistic assessments in school mathematics that centered their Cree language,
miyō-pimōhtēwin
, and
kamskénow
.
Creating this chapter brought us together as a diverse group of scholars to think deeply about a process of reflection in teacher education that centers on ethical relationality. To show our coming ...alongside adult learners attentive to reflection that centers ethical relationality, we inquire into both the Assessment as Pimosayta courses that Murphy, Cardinal, and Huber teach and into Stavrou's experiences teaching and enacting assessment in his practice. The body of our chapter is structured by the five design elements foregrounded by Stavrou and Murphy's recent bringing of critical race theory and anti-racist education to narrative inquiry: beginning with experience; carrying theoretical frameworks into an inquiry; negotiating theoretical frameworks with participants; using narrative threads to show the complexity of experience; ending in experience. Centering ethical relationality as we come alongside pre- and in-service teachers as they imagine coming alongside Indigenous children, youth, families, and communities lifts the long-termness of our work, including that this long-termness entails interactions and responsibilities with other humans and more-than-human beings.
Obtaining the segmentation of building footprints from satellite images is a complex process since building areas and their surroundings are presented with various colour intensity values and complex ...features. Active contour region-based segmentation methods can be used to establish the corresponding boundary of building structures. Typically, these methods divide the image into regions that exhibit a certain similarity and homogeneity. However, using the traditional active contour algorithms for building structures detection, in several cases where spectral heterogeneity exists, over-detection or under-detection are usually noticed. In this work, the Red, Green and Blue (RGB) representation and the properties of the Hue, Saturation and Value (HSV) colour space have been analysed and used to optimize the extraction of buildings from satellite images in an active contour segmentation framework. Initially, the satellite image was processed by applying a clustering technique using colour features to eliminate vegetation areas and shadows that may adversely affect the performance of the algorithm. Subsequently, the HSV representation of the image was used and a new active contour model was developed and applied for building extraction, utilizing descriptors derived from the value and saturation images. A new energy term is encoded for biasing the contours to achieve better segmentation results. An effective procedure has been designed and incorporated in the proposed model for the active contour initialization. This process enhances the performance of the model, leading to lower computational cost and higher building detection accuracy. Additionally, statistical measures are used for designing optimum morphological filters to eliminate any misleading information that may still exist. Qualitative and quantitative measures are used for evaluating the performance of the proposed method.
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