Cooperative sensing of spectrum opportunities

Reliability and availability of sensing information gathered from local spectrum sensing (LSS) by a single Cognitive Radio is strongly affected by the propagation conditions, period of sensing, and geographical position of the device. For this reason, cooperative spectrum sensing (CSS) was largely proposed in order to improve LSS performance by using cooperation between Secondary Users (SUs). The goal of this chapter is to provide a general analysis on CSS for cognitive radio networks (CRNs). Firstly, the theoretical system model for centralized CSS is introduced, together with a preliminary discussion on several fusion rules and operative modes. Moreover, three main aspects of CSS that substantially differentiate the theoretical model from realistic application scenarios are analyzed: (i) the presence of spatiotemporal correlation between decisions by different SUs; (ii) the possible mobility of SUs; and (iii) the nonideality of the control channel between the SUs and the Fusion Center (FC). For each aspect, a possible practical solution for network organization is presented, showing that, in particular for the first two aspects, cluster-based CSS, in which sensing SUs are properly chosen, could mitigate the impact of such realistic assumptions

A mixed approach to similarity metric selection in affinity propagation-based WiFi fingerprinting indoor positioning

The weighted k-nearest neighbors (WkNN) algorithm is by far the most popular choice in the design of fingerprinting indoor positioning systems based on WiFi received signal strength (RSS). WkNN estimates the position of a target device by selecting k reference points (RPs) based on the similarity of their fingerprints with the measured RSS values. The position of the target device is then obtained as a weighted sum of the positions of the k RPs. Two-step WkNN positioning algorithms were recently proposed, in which RPs are divided into clusters using the affinity propagation clustering algorithm, and one representative for each cluster is selected. Only cluster representatives are then considered during the position estimation, leading to a significant computational complexity reduction compared to traditional, flat WkNN. Flat and two-step WkNN share the issue of properly selecting the similarity metric so as to guarantee good positioning accuracy: in two-step WkNN, in particular, the metric impacts three different steps in the position estimation, that is cluster formation, cluster selection and RP selection and weighting. So far, however, the only similarity metric considered in the literature was the one proposed in the original formulation of the affinity propagation algorithm. This paper fills this gap by comparing different metrics and, based on this comparison, proposes a novel mixed approach in which different metrics are adopted in the different steps of the position estimation procedure. The analysis is supported by an extensive experimental campaign carried out in a multi-floor 3D indoor positioning testbed. The impact of similarity metrics and their combinations on the structure and size of the resulting clusters, 3D positioning accuracy and computational complexity are investigated. Results show that the adoption of metrics different from the one proposed in the original affinity propagation algorithm and, in particular, the combination of different metrics can significantly improve the positioning accuracy while preserving the efficiency in computational complexity typical of two-step algorithms

Performance evaluation of non-prefiltering vs. time reversal prefiltering in distributed and uncoordinated IR-UWB ad-hoc networks

Time Reversal (TR) is a prefiltering scheme mostly analyzed in the context of centralized and synchronous IR-UWB networks, in order to leverage the trade-off between communication performance and device complexity, in particular in presence of multiuser interference. Several strong assumptions have been typically adopted in the analysis of TR, such as the absence of Inter-Symbol / Inter-Frame Interference (ISI/IFI) and multipath dispersion due to complex signal propagation. This work has the main goal of comparing the performance of TR-based systems with traditional non-prefiltered schemes, in the novel context of a distributed and uncoordinated IR-UWB network, under more realistic assumptions including the presence of ISI/IFI and multipath dispersion. Results show that, lack of power control and imperfect channel knowledge affect the performance of both non-prefiltered and TR systems; in these conditions, TR prefiltering still guarantees a performance improvement in sparse/low-loaded and overloaded network scenarios, while the opposite is true for less extreme scenarios, calling for the developement of an adaptive scheme that enables/disables TR prefiltering depending on network conditions

Coverage and Deployment Analysis of Narrowband Internet of Things in the Wild

Narrowband Internet of Things (NB-IoT) is gaining momentum as a promising technology for massive Machine Type Communication (mMTC). Given that its deployment is rapidly progressing worldwide, measurement campaigns and performance analyses are needed to better understand the system and move toward its enhancement. With this aim, this paper presents a large scale measurement campaign and empirical analysis of NB-IoT on operational networks, and discloses valuable insights in terms of deployment strategies and radio coverage performance. The reported results also serve as examples showing the potential usage of the collected dataset, which we make open-source along with a lightweight data visualization platform.Comment: Accepted for publication in IEEE Communications Magazine (Internet of Things and Sensor Networks Series

Optimisation of electrochemical sensors based on molecularly imprinted polymers: from OFAT to machine learning

: Molecularly imprinted polymers (MIPs) rely on synthetic engineered materials able to selectively bind and intimately recognise a target molecule through its size and functionalities. The way in which MIPs interact with their targets, and the magnitude of this interaction, is closely linked to the chemical properties derived during the polymerisation stages, which tailor them to their specific target. Hence, MIPs are in-deep studied in terms of their sensitivity and cross-reactivity, further being used for monitoring purposes of analytes in complex analytical samples. As MIPs are involved in sensor development within different approaches, a systematic optimisation and rational data-driven sensing is fundamental to obtaining a best-performant MIP sensor. In addition, the closer integration of MIPs in sensor development requires that the inner properties of the materials in terms of sensitivity and selectivity are maintained in the presence of competitive molecules, which focus is currently opened. Identifying computational models capable of predicting and reporting the best-performant configuration of electrochemical sensors based on MIPs is of immense importance. The application of chemometrics using design of experiments (DoE) is nowadays increasingly adopted during optimisation problems, which largely reduce the number of experimental trials. These approaches, together with the emergent machine learning (ML) tool in sensor data processing, represent the future trend in design and management of point-of-care configurations based on MIP sensing. This review provides an overview on the recent application of chemometrics tools in optimisation problems during development and analytical assessment of electrochemical sensors based on MIP receptors. A comprehensive discussion is first presented to cover the recent advancements on response surface methodologies (RSM) in optimisation studies of MIPs design. Therefore, the recent advent of machine learning in sensor data processing will be focused on MIPs development and analytical detection in sensors

Accuracy improvement in the TDR-based localization of water leaks

A time domain reflectometry (TDR)-based system for the localization of water leaks has been recently developed by the authors. This system, which employs wire-like sensing elements to be installed along the underground pipes, has proven immune to the limitations that affect the traditional, acoustic leak-detection systems.Starting from the positive results obtained thus far, in this work, an improvement of this TDR-based system is proposed. More specifically, the possibility of employing a low-cost, water-absorbing sponge to be placed around the sensing element for enhancing the accuracy in the localization of the leak is addressed.To this purpose, laboratory experiments were carried out mimicking a water leakage condition, and two sensing elements (one embedded in a sponge and one without sponge) were comparatively used to identify the position of the leak through TDR measurements. Results showed that, thanks to the water retention capability of the sponge (which maintains the leaked water more localized), the sensing element embedded in the sponge leads to a higher accuracy in the evaluation of the position of the leak. Keywords: Leak localization, TDR, Time domain reflectometry, Water leaks, Underground water pipe

Reflectometric System for Continuous and Automated Monitoring of Irrigation in Agriculture

In this work, a time domain reflectometry (TDR)-based system for continuous and diffused monitoring of soil water content in agriculture is presented. The proposed TDR-based system employs elongate sensing elements (SEs). In practical application, each wire-like SE is buried along the cultivation row to be monitored, and through a single TDR measurement it is possible to retrieve the water content profile of the cultivation along the length of the SE. By connecting the TDR-based monitoring system to the irrigation machines, it would be possible to automatically start/stop irrigation based on the actual water requirement of the cultivations, thus favoring precision agriculture and enhancing irrigation efficiency. To demonstrate the feasibility of the proposed monitoring solution, a dedicated hardware+software platform was developed and the TDR-based system was experimented in open-field cultivations

Microwave reflectometric systems and monitoring apparatus for diffused-sensing applications

Most sensing networks rely on punctual/local sensors; they thus lack the ability to spatially resolve the quantity to be monitored (e.g. a temperature or humidity profile) without relying on the deployment of numerous inline sensors. Currently, most quasi-distributed or distributed sensing technologies rely on the use of optical fibre systems. However, these are generally expensive, which limits their large-scale adoption. Recently, elongated sensing elements have been successfully used with time-domain reflectometry (TDR) to implement diffused monitoring solutions. The advantage of TDR is that it is a relatively low-cost technology, with adequate measurement accuracy and the potential to be customised to suit the specific needs of different application contexts in the 4.0 era. Based on these considerations, this paper addresses the design, implementation and experimental validation of a novel generation of elongated sensing element networks, which can be permanently installed in the systems that need to be monitored and used for obtaining the diffused profile of the quantity to be monitored. Three applications are considered as case studies: monitoring the irrigation process in agriculture, leak detection in underground pipes and the monitoring of building structures