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The Internet of Things (IoT) consists of millions of devices deployed over hundreds of thousands of different networks, providing an ever-expanding resource to improve our understanding of and interactions with the physical world. Global service discovery is key to realizing the opportunities of the IoT, spanning disparate networks and technologies to enable the sharing, discovery, and utilisation of services and data outside of the context in which they are deployed. In this paper, we present Decentralised Service Registries (DSRs), a novel trustworthy decentralised approach to global IoT service discovery and interaction, building on DSF-IoT to allow users to simply create and share public and private service registries, to register and query for relevant services, and to access both current and historical data published by the services they discover. In DSR, services are registered and discovered using signed objects that are cryptographically associated with the registry service, linked into a signature chain, and stored and queried for using a novel verifiable DHT overlay. In contrast to existing centralised and decentralised approaches, DSRs decouple registries from supporting infrastructure, provide privacy and multi-tenancy, and support the verification of registry entries and history, service information, and published data to mitigate risks of service impersonation or the alteration of data. This decentralised approach is demonstrated through the creation and use of a DSR to register and search for real-world IoT devices and their data as well as qualified using a scalable cluster-based testbench for the high-fidelity emulation of peer-to-peer applications. DSRs are evaluated against existing approaches, demonstrating the novelty and utility of DSR to address key IoT challenges and enable the sharing, discovery, and use of IoT services.

Wireless networks are trending towards large scale systems, containing thousands of nodes, with multiple co-existing applications. Congestion is an inevitable consequence of this scale and complexity, which leads to inefficient use of the network capacity. This paper proposes an autonomous and adaptive wireless network management framework, utilising multi-agent deep reinforcement learning, to achieve efficient use of the network. Its novel reward function incorporates application awareness and fairness to address both node and network level objectives. Our experimental results demonstrate the proposed approach’s ability to be optimised for application-specific requirements, while optimising the fairness of the network. The results reveal significant performance benefits in terms of adaptive data rate and an increase in responsiveness compared to a single-agent approach. Some significant qualitative benefits of the multi-agent approach—network size independence, node-led priorities, variable iteration length, and reduced search space—are also presented and discussed.

Pedestrian dead reckoning is a common technique applied in indoor inertial navigation systems that is able to provide accurate tracking performance within short distances. Sensor drift is the main bottleneck in extending the system to long-distance and long-term tracking. In this paper, a hybrid system integrating traditional pedestrian dead reckoning based on the use of inertial measurement units, short-range radio frequency systems and particle filter map matching is proposed. The system is a drift-free pedestrian navigation system where position error and sensor drift is regularly corrected and is able to provide long-term accurate and reliable tracking. Moreover, the whole system is implemented on a commercial off-the-shelf smartphone and achieves real-time positioning and tracking performance with satisfactory accuracy.

Information fusion combining inertial navigation and radio frequency (RF) technologies, is commonly applied in indoor positioning systems (IPSs) to obtain more accurate tracking results. The performance of the inertial navigation system (INS) subsystem is affected by sensor drift over time and the RF-based subsystem aims to correct the position estimate using a fusion filter. However, the inherent sensor drift is usually not corrected during fusion, which leads to increasingly erroneous estimates over a short period of time. Among the inertial sensor drifts, gyroscope drift has the most significant impact in determining the correct orientation and accurate tracking. A gyroscope drift correction approach is proposed in this study and is incorporated in an INS and ultra-wideband (UWB) fusion IPS where only distance measurements from UWB subsystem are used. The drift correction approach is based on turn detection to account for the fact that gyroscope drift is accumulated during a turn. Practical pedestrian tracking experiments are conducted to demonstrate the accuracy of the drift correction approach. With the gyroscope drift corrected, the fusion IPS is able to provide more accurate tracking performance and achieve up to 64.52% mean position error reduction when compared to the INS only tracking result.