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.

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.

The global population is aging; projections show that by 2050, more than 20% of the population will be aged over 64. This will lead to an increase in aging related illness, a decrease in informal support, and ultimately issues with providing care for these individuals. Assistive smart homes provide a promising solution to some of these issues. Nevertheless, they currently have issues hindering their adoption. To help address some of these issues, this study introduces a novel approach to implementing assistive smart homes. The devised approach is based upon an intention recognition mechanism incorporated into an intelligent agent architecture. This approach is detailed and evaluated. Evaluation was performed across three scenarios. Scenario 1 involved a web interface, focusing on testing the intention recognition mechanism. Scenarios 2 and 3 involved retrofitting a home with sensors and providing assistance with activities over a period of 3 months. The average accuracy for these three scenarios was 100%, 64.4%, and 83.3%, respectively. Future will extend and further evaluate this approach by implementing advanced sensor-filtering rules and evaluating more complex activities.

This paper introduces an ontology-based integrated framework for activity modeling, activity recognition and activity model evolution. Central to the framework is ontological activity modeling and semantic-based activity recognition, which is supported by an iterative process that incrementally improves the completeness and accuracy of activity models. In addition, the paper presents a service-oriented architecture for the realization of the proposed framework which can provide activity context-aware services in a scalable distributed manner. The paper further describes and discusses the implementation and testing experience of the framework and services in the context of smart home based assistive living.