Over the past decade, visible light positioning has become increasingly important for precise localization systems, yet its widespread adoption is limited due to the necessity of modifying existing lighting systems. This article presents HueLoc, a novel method that bypasses this issue by using inherent features of light, such as the dominant colors in white light-emitting diode (LED) lights, and employs affordable, energy-efficient hue sensors for location services. We propose that by extracting the power at dominant wavelengths of LEDs, these can be uniquely identified using a specifically designed signature. The unique signatures can be used by mobile objects for spatial awareness and further localization using the proposed regression-based learning approach. Our experiments demonstrate that HueLoc attains a location-mapping accuracy of 100% and achieves decimeter-level localization precision with a moving object in uncontrolled lighting conditions. Moreover, these unique signatures can be combined with other RF-based technologies to enhance their localization accuracy. As an example, this article details the integration of Bluetooth features with light signatures using a three-stage incremental learning approach. The experimental results show that this fusion method significantly improves Bluetooth localization by over 75%, overcoming challenges associated with severe indoor multipath and achieving highly precise location accuracy within decimeters.

The Open RAN API and interface standards facilitate the new ecosystems where distinct hardware and software components are brought together to build 5G systems. Key to this concept is the seamless and efficient integration and monetization process among stakeholders. A marketplace serves as a means to realize this collaborative revenue sharing, eliminating the need for intricate proprietary agreements or contracts between each participant. This demo presents the marketplace strategy emphasizing software integration across diverse deployment settings, utilizing the API-centric integration Platform-as-a-Service (iPaas) model aligned with Open RAN standards.

Recently, the concept of digital twins (DTs) has received significant attention within the realm of 5G/6G. This demonstration shows an innovative DT design and implementation framework tailored toward integration within the 5G infrastructure. The proposed DT enables near real-time anomaly detection capability pertaining to user connectivity. It empowers the 5G system to proactively execute decisions for resource control and connection restoration.

Designing a reliable and highly accurate indoor localization system is challenging due to the non-uniformity of indoor spaces, multipath fading, and satellite signal blockage. To address these issues, we propose a Deep Neural Network-based localization system that combines passive Visible Light Positioning (p-VLP) and Bluetooth Low Energy (BLE) technologies to achieve stable, energy-efficient, and accurate indoor localization. Our solution leverages incremental learning to fuse data from visible light and BLE, overcoming their individual limitations and achieving centimeter-level localization accuracy. We build a prototype using low-cost S9706 hue sensors for p-VLP and low-power nrf52830 BLE boards to collect data simultaneously from both technologies in a 25m2 testbed. Our approach demonstrates a significant localization accuracy improvement of approximately 47% and 64% compared to individual p-VLP and BLE technologies, respectively, achieving a mean localization error of 20 cm.

In LED-to-Smartphone Camera communication, the camera detects the status of the light source (ON or OFF) in each frame to receive information. If the light source is modulated faster than a single frame duration, there are multiple white & dark stripes corresponding to the transmitted information bits. Further, the receiver’s region of interest in LED-to-Smartphone Camera communication is limited by the size of the light source in the frame in terms of the number of pixels. As the distance between the LED and the camera increases, the light source does not cover the whole frame, resulting in a significant amount of data loss. Therefore, LED-to-Smartphone Camera communication is limited by distance, making it difficult to transmit data from several meters away. On the other hand, there is a loss of data even at relatively short ranges due to small region of interest, especially for tiny light sources. In this work, we enhance the smartphone camera with a telephoto lens that provides optical zoom on the light source and increases the region of interest. The optical zoom from a telephoto lens helps the user focus on the light source and reduces data loss. We compare the data transfer rate with and without a telephoto lens at short distances (15-80 cm). The initial results show that a telephoto lens improves the data transfer rate at short distances and also expands the possible range for LED-to-Smartphone Camera links.

This paper provides details of the collaborative robotics testbed platform that has been developed within the UMBRELLA project. The testbed is part of a larger open Industrial IoT testbed which is currently being deployed in the UK. The aim of the testbed is to permit flexible experimentation using different end devices (including smart city sensing and robot nodes) to evaluate algorithms or new practical application scenarios. For the collaborative robotics testbed this relates to warehouse robotics, which can move pallets of different sizes and shapes. The testbed also includes simulator facilities for validation of algorithms prior to deployment on the robot nodes. The nodes support a rich set of sensors, actuators, and wireless communication technologies. The software architecture is based on Docker containers and ROS2 DDS middleware for flexible and extensible evolution to support future sensors or network technologies. This will be provided as an open testbed to support research, experimentation and evaluation of swarm robotics and other Industrial IoT use-cases.

This paper studies the indoor localisation of WiFi devices based on a commodity chipset and standard channel sounding. First, we present a novel shallow neural network (SNN) in which features are extracted from the channel state information (CSI) corresponding to WiFi subcarriers received on different antennas and used to train the model. The single-layer architecture of this localisation neural network makes it lightweight and easy-to-deploy on devices with stringent constraints on computational resources. We further investigate for localisation the use of deep learning models and design novel architectures for convolutional neural network (CNN) and long-short term memory (LSTM). We extensively evaluate these localisation algorithms for continuous tracking in indoor environments. Experimental results prove that even an SNN model, after a careful handcrafted feature extraction, can achieve accurate localisation. Meanwhile, using a well-organised architecture, the neural network models can be trained directly with raw data from the CSI and localisation features can be automatically extracted to achieve accurate position estimates. We also found that the performance of neural network-based methods are directly affected by the number of anchor access points (APs) regardless of their structure. With three APs, all neural network models proposed in this paper can obtain localisation accuracy of around 0.5 metres. In addition the proposed deep NN architecture reduces the data pre-processing time by 6.5 hours compared with a shallow NN using the data collected in our testbed. In the deployment phase, the inference time is also significantly reduced to 0.1 ms per sample. We also demonstrate the generalisation capability of the proposed method by evaluating models using different target movement characteristics to the ones in which they were trained.

High accuracy localisation technologies exist but are prohibitively expensive to deploy for large indoor spaces such as warehouses, factories, and supermarkets to track assets and people. However, these technologies can be used to lend their highly accurate localisation capabilities to low-cost, commodity, and less-accurate technologies. In this paper, we bridge this link by proposing a technology-agnostic calibration framework based on artificial intelligence to assist such low-cost technologies through highly accurate localisation systems. A single-layer neural network is used to calibrate a less accurate technology using a more accurate one such as Bluetooth Low Energy (BLE) using Ultra-Wideband (UWB) and UWB using a professional motion tracking system. On a real indoor testbed, we demonstrate an improvement in accuracy of approximately 70% for BLE and 50% for UWB. Not only does the proposed approach require a very short measurement campaign, the low complexity of a single-layer neural network makes it ideal for deployment on constrained devices typically used for localisation purposes.

Industrial IoT applications necessitate reliable and efficient wireless solutions that can adapt to the environment in which they are deployed to meet the stringent application requirements. This is particularly essential when these systems coexist within unlicensed spectrum bands. This paper examines proactive adaptive frequency hopping and routing in order that the wireless systems can coexist while still meeting these requirements. The two aspects considered, adaptive frequency hopping and proactive routing, are implemented in a combined and coordinated manner to effectively address interference or frequency selective fading avoidance and mitigation of radio propagation anomalies (such as shadow fading) respectively.

Radio Environment Maps (REMs) are becoming an increasingly popular method for optimizing radio coverage, interference management and resource assignment. The reason for this is that REMs can be formed without the need for surveys or complex calibration processes which are costly and time consuming. This is achieved by combining measurements and other relevant radio related knowledge and predictions into a spatial database. The main problem with REM formation is that direct interpolation techniques on the data are sensitive to errors and indirect techniques can overly simplify the extrapolation of radio propagation. In this paper the impact of errors in radio measurements and propagation assumptions are considered on direct and indirect approaches respectively. Novel approaches to estimating the impact of errors, based on similarity of models, are considered and a method for refinement of estimates is proposed.

This paper examines the benefit of using a Radio Environment Map (REM) exploiting measurement based signal level prediction as a means to detect and avoid the hidden node problem within wireless networks. Practical implementation considerations are taken into account, such as support for multi-Radio Access Technology (RAT) deployments, unknown transmit power levels, radio measurement inaccuracies and propagation anomalies (such as fading), which can reduce the reliability of REM based predictions. The approach considered within this paper manages to overcome these limitations and the expected performance is assessed in a representative deployment scenario. The novelty of the proposed approach lies in the ability to combine measurements to perform signal localization and a predicted signal space model using measurements taken by a grid of sensors. The model is then used for prediction of hidden node conditions and can be exploited in existing and future networks employing carrier sensing based medium access control or dynamic frequency re-use, or to other radio interference and cognitive radio related problems.

Radio Environment Map (REM) is an integrated database consisting of multi-domain information including the spectrum availability profile, geographical features, regulations, relevant policies, radio equipment profile, past experiences, etc. In this paper we analyse the integration of REM with the wider communication system and how it interfaces with the relevant network entities. REM is primarily designed to facilitate network establishment and to improve operational performance and radio resource management. The REM computes complex temporal and spatial aspects of radio environment by gathering information from various spectrum sensing sources, and thus has interface with heterogeneous devices. In this paper we particularly look at the different interfaces of REM with other potential entities in the architecture. The interface design must take into consideration the stakeholders providing REM facilities and the architecture in which the functions are mapped. These are necessarily scenario specific as they depend on the motivation, benefits and incentives for using the REM approach. For selected use case scenarios, we draw architectural interface design considerations and network mapping.

The demand for higher data rates, capacity and better quality-of-service is constantly growing for home networks. Therefore, there is a pressing need for efficient use of wireless network resources. In this context, the application of cognitive radio principles that enable network nodes to characterize their environment and control their resources based on the acquired knowledge, is the prominent solution for next generation home networks. In this paper we present an architecture and a prototype implementation based on these principles. The proposed system is able to autonomously optimize the performance of network nodes in a dynamic environment according to the goals, restrictions and policy regulations formulated by network stakeholders. The obtained results show the momentous and suitability of the cognitive framework for home networking.

There is growing interest within industry and standardisation bodies to permit wireless local area networks to utilise the scarce radio resources more efficiently and intelligently. One approach is to increase the physical layer data rate and exploit multi-path and spatial diversity using multi antenna systems, the other, and complementary, approach is to use more intelligent dynamic channel and mode selection. Both approaches are inter-related, but in this paper we focus on techniques for the latter. In particular we address the dynamic channel and mode selection problem from a terminal point of view and have implemented a solution that provides significant performance enhancements over conventional techniques when applied to existing wireless local area networks.

Abstract This paper explores radio link enhancement utilising dynamic run‐time software download and configuration. In particular, the insertion of virtual medium access control (MAC), hybrid automatic repeat request (ARQ) and compression enhancement layers are considered in this paper. This is made possible with a flexible protocol stack framework, which permits the insertion of protocol layers and configuring them in a platform‐independent manner. A general purpose central processing unit (CPU) environment is used in order to assess performance and power consumption of this proposed approach. The results presented indicate that there are scenarios in which this approach is attractive and that execution environment optimisations for performing generic protocol functions can increase the applicability to more scenarios. Currently protocols, and in particular link layer protocols, are designed to support specific traffic and underlying radio propagation characteristics and network topologies that are expected to be observed in a range of deployment scenarios in a manner that is determined at a system design time. As a result, this leads to many different solutions that are tailored to the particular and quite specific requirements such as short range personal area networks (PAN) based on bluetooth or ultra wide band (UWB), local area networks (LAN) based on 802.11 and of course many wide area cellular networks. These solutions cannot be changed to support traffic characteristics and radio performance observed in scenarios that were not envisaged at the time of standardisation. Hence, many new releases of standards are generated to incorporate these new and often optional features that often cannot be used because not all devices support these features. A flexible protocol stack framework has been proposed to provide the ability to insert and configure new software protocol implementations at run time. The overall benefit of this is that new optional features can be exploited before or without standardisation. In addition, dynamic optimisation can be performed to tailor the protocol configuration to specific scenarios and adapt to the changing radio environment. Copyright © 2005 John Wiley & Sons, Ltd.

Re-configurable terminals, by definition, are intended to work across multiple telecommunications systems and standards. One of the requirements to be able to achieve this is the ability to monitor levels of coverage in alternative systems. There are limited opportunities for performing alternative mode monitoring within existing standards; therefore other approaches need to be investigated. This paper investigates the concept of co-operative mode monitoring (CoMM), whereby measurements taken by "donor" terminals in the destination mode are presented to the "roaming" terminal.

To date, research into reconfigurable mobile communications has predominantly focussed on the software radio concept, and specifically on the hardware technologies required to move physical layer processing into a programmable environment. Although an interesting and necessary challenge, this only represents a fraction of the overall support and technology required to realise the potential of the concept. Other necessary developments include network/terminal cooperation for seamless inter-standard handoff, QoS management, a secure software download mechanism, terminal software architecture supporting reconfiguration, configuration management, capability negotiation. Summarising results from early IST-TRUST (Transparently Reconfigurable Ubiquitous terminal) project deliverables, this paper describes the likely overall system environment, and the key technical challenges to be researched in TRUST for realising a reconfigurable terminal to meet the needs of users within that environment.

The reconfigurable software radio concept has been an active research topic for many years. This was driven initially by military requirements for flexibility, but more recently by the commercial pressure to take advantage of economies of scale, reductions in maintenance costs and more efficient utilisation of spectrum. Therefore, initial projects looking at this concept have focussed on the technology for providing multi-mode and software reconfigurable terminals. The Transparently Reconfigurable Ubiquitous Terminal (TRUST) project differs in that it examines the user requirements in order to identify the frameworks and systems that are needed to support software reconfigurable radios from the users' perspective. The primary goal of the TRUST project is to realise the user potential of reconfigurable radio systems, which will provide network connectivity and services when and where they are required. The project will achieve this through a four-step process: (1) understand the users' requirements from their perspective of the system: "the terminal" (2) translate these users' requirements into technology requirements (3) advance the terminal and network technologies through research and innovation (4) validate, promote and disseminate these advancements and technologies.