This paper underscores the crucial role of video anonymization in smart ports, were data privacy and security hold critical importance. It introduces the Matrix Platform, specifically designed for smart environments, and highlights its strong video anonymization capabilities. The platform employs advanced Video Anonymization techniques to effectively balance the preservation of data confidentiality with the enhancement of port security. Furthermore, the paper discusses how object detection and anonymization methods are strategically employed to protect sensitive information while still allowing access to critical operational details such as cargo and vessel types. Emphasis is placed on video anonymization's pivotal role in strengthening port security by concealing high-value assets and minimizing the risk of exposing sensitive data. By integrating the Matrix Platform, ports gain the capability to proactively manage security risks, safeguard assets, and secure information. This adaptable platform can be deployed in both Edge and Cloud environments, ensuring alignment with the specific needs of smart ports, with a primary focus on data anonymization. In conclusion, as the port industry continues to evolve, this paper asserts that the adoption of video anonymization techniques is fundamental for future growth and development, providing assurance of privacy and security in this dynamic landscape.

Over the last number of years Unmanned Aerial Vehicles (UAVs) have become increasingly popular in several fields such as agriculture and medicine. Recently UAVs have been used for the deployment within Emergency Response for visual scouting of an impacted area in addition to delivering supplies via payload. UAVs are also capable of acting as networking nodes using incorporated technology or by attaching independent hardware via the UAVs payload capability. The application of UAVs as a network node(s) can enable increased performance within the network as UAV based nodes can alter their current position due to their unique nature of mobility. Therefore, this study aims to assess the performance of aerial communication using UVAs which incorporate the use of a small Bluetooth 5.0 (BLE 5.0) node as a payload. In this research, the impact generated via Air-Ground Propagation on the Received Signal Strength Indicator (RSSI) measurement was investigated. In addition to this, this paper also investigates any performance difference displayed between different Transmission Power Profiles as this in turn affects the overall power consumption of the transmitting device. When operating at heights of 5, 10, and 15 Metres the maximum average loss was found to be 2.37 dBm among the Standard Transmission Power profile and a maximum average loss of 1.18 dBm occurring with the Enhanced Transmission Power Profile.

IoT ecosystems are rapidly expanding, and device management is emerging as a key challenge due to the scale, complexity, and dynamism of IoT systems. The adoption of autonomous techniques shows promise to alleviate key issues including maintaining organisational security when large volumes of IoT devices are being added and removed from a telecommunications network. Here we propose a proactive IoT device management approach that addresses the need to control network access in a risk-based manner. The proposed system comprises of two novel core components, a Management Platform for IoT (MP-IoT) component and an Intent-Based Microsegmentation (IBMS) component. The MP-IoT component carries out a risk management role and combines with IBMS to provide risk-based network segmentation. The two components work together to proactively manage risks by migrating devices between isolated network segments according to a dynamic assessment of the risk to the system from an individual device. Self-healing techniques may then be used to mitigate risks associated with a device and consequently change the network segment that it resides in. Here we present the key challenges associated with typical IoT environments and demonstrate how they are addressed by the proposed Proactive IoT Device Management architecture. A prototype implementation is also presented to validate the operation of the proposed architecture.

This paper presents an approach to autonomous IoT device lifecycle management for our developed Matrix IoT platform. We discuss our approach for zero touch onboarding, IoT device failure, device end-of-life offboarding and SLAs to support device lifecycle management. We collected timings on the key stages of our proposed onboarding process. The total onboarding time takes on average 6.4 seconds to onboard a device. Therefore, when scaled to many hundreds of devices, there is a very significant time saving benefit to onboarding devices automatically, along with the benefits of reducing human error.