Mobile Crowd-Sensing (MCS) has appeared as a prospective solution for large-scale data collection, leveraging built-in sensors and social applications in mobile devices that enables a variety of Internet of Things (IoT) services. However, the human involvement in MCS results in a high possibility for unintentionally contributing corrupted and falsified data or intentionally spreading disinformation for malevolent purposes, consequently undermining IoT services. Therefore, recruiting trustworthy contributors plays a crucial role in collecting highquality data and providing better quality of services while minimizing the vulnerabilities and risks to MCS systems. In this article, a novel trust model called Experience-Reputation (E-R) is proposed for evaluating trust relationships between any two mobile device users in an MCS platform. To enable the E-R model, virtual interactions among the users are manipulated by considering an assessment of the quality of contributed data from such users. Based on these interactions, two indicators of trust called Experience and Reputation are calculated accordingly. By incorporating the Experience and Reputation trust indicators (TIs), trust relationships between the users are established, evaluated and maintained. Based on these trust relationships, a novel trust-based recruitment scheme is carried out for selecting the most trustworthy MCS users to contribute to data sensing tasks. In order to evaluate the performance and effectiveness of the proposed trust-based mechanism as well as the E-R trust model, we deploy several recruitment schemes in an MCS testbed which consists of both normal and malicious users. The results highlight the strength of the trust-based scheme as it delivers better quality for MCS services while being able to detect malicious users. We believe that the trust-based user recruitment offers an effective capability for selecting trustworthy users for various MCS systems and, importantly, the proposed mechanism is practical to deploy in the real world.

The theft of medical data, which is intrinsically valuable, can lead to loss of patient privacy and trust. With increasing requirements for valuable and accurate information, patients need to be confident that their data is being stored safely and securely. However, medical devices are vulnerable to attacks from the digital domain, with many devices transmitting data unencrypted wirelessly to electronic patient record systems. As such, it is now becoming more necessary to visualise data patterns and trends in order identify erratic and anomalous data behaviours. In this paper, a system design for modelling data flow within healthcare infrastructures is presented. The system assists information security officers within healthcare organisations to improve the situational awareness of cyber security risks. In addition, a visualisation of TCP Socket Connections using real-world network data is put forward, in order to demonstrate the framework and present an analysis of potential risks.

Current interference management solutions for dense IEEE 802.11 Wireless Local Area Networks (WLANs) rely on locally measuring the cumulative interference at the Acess Point (AP) in charge of adjusting the spectrum resources to its clients. These solutions often result in coarse-grained spectrum allocation that often leaves many wireless users unsatisfied and increases the spectrum congestion problem instead of easing it. In this paper we present a centralised interference management algorithm that treats the network-wide interference impact of each channel individually and allows the controller to adjust the radio resource of each AP while it is utilised. This coordinated allocation takes into account the Quality of Service (QoS) requirements of downlink flows while minimising its effect on neighbouring APs. Therefore, this paper proposes a novel approach for quantifying the interference impact of each employed channel and jointly addressing the user-side quality requirements and the network-side interference management. The algorithm is tailored for operator-agnostic Software-Defined Networking (SDN)-based Radio Resource Management (RRM) in dense Wireless Fidelity (Wi-Fi) networks and adopts a fine-grained per-flow approach. Simulation results show that our algorithm outperforms existing solutions in terms of reducing the overall interference, increasing the capacity of the wireless channel and improving the users' satisfaction.

In spite of the enormous popularity of Wi-Fi-enabled devices, the utilisation of Wi-Fi radio resources (e.g. RF spectrum and transmission power levels) at Access Points (APs) is degraded in current decentralised Radio Resource Management (RRM) schemes. Most state of the art centralised control solutions apply configurations in which the network-wide impacts of the involved parameters and their mutual relationships are ignored. In this paper, we propose an algorithm for jointly adjusting the transmission power levels and optimising the RF channel assignment of APs by taking into account the flows' required qualities while minimising their interference impact throughout the network. The proposed solution is tailored for an operator-agnostic and Software Defined Wireless Networking (SDWN)-based centralised RRM in dense Wi-Fi networks. Our extensive simulation results validate the performance improvements of the proposed algorithm compared to the main state of the art alternative by showing more than 25% higher spectrum efficiency, satisfying the users' demands and further mitigating the network-wide interference through flow-based and quality-oriented power level adjustment.

With the widespread deployment of 5G gaining pace, there is increasing interest in deploying this technology beyond traditional Mobile Network Operators (MNO) into private and community scenarios. These deployments leverage the flexibility of 5G itself to support private networks that sit alongside or even on top of existing public 5G. By utilizing a range of virtualisation and slicing techniques in the 5G Core (5GC) and heterogeneous Radio Access Networks (RAN) at the edge, a wide variety of use cases can be supported by 5G. However, these non-typical deployments may experience different performance characteristics as they adapt to their specific scenario. In this paper we present the results of our work to model and predict the performance of millimeter wave (mmWave) backhaul links that were deployed as part of the Liverpool 5G network. Based on the properties of the 802.11ad protocol and the physical characteristics of the environment, we simulate how each link will perform with different signal-to-noise ratio (SNR) and Packet Error Rate (PER) values and verify them against real-world deployed links. Our results show good convergence between simulated and real results and provide a solid foundation for further network planning and optimization.