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

Dysregulation of CXCL9 and reduced tumor growth in Egr-1 deficient mice

<p>Abstract</p> <p>Background</p> <p>Early growth response-1 (Egr-1) is an immediate-early transcription factor inducible in the vasculature in response to injury, shear stress, and other stimuli. Mice lacking Egr-1 have a profound deficit in the ability to recover from femoral artery ligation, suggesting a role in neovascularization. Previous studies have shown that manipulating Egr-1 expression can have either positive or negative effects on tumor growth. We hypothesized that Egr-1 knockout mice might exhibit reduced tumor growth, possibly due to a reduced capacity to respond to angiogenic signals from a growing tumor.</p> <p>Results</p> <p>We injected 10⁶Lewis lung carcinoma (LLC1) cells subcutaneously in the flank of wild type and Egr-1 knockout mice. The average mass of tumors from wild type mice at 12 days after implantation was 413 +/- 128 mg, while those from Egr-1^-/-mice was 219 +/- 81 mg (p = 0.001, mean +/- SD). However, sectioning the tumors and staining with anti-CD31 antibodies revealed no difference in the vascularity of the tumors and there was no difference in angiogenic growth factor expression. Expression of the chemokine Mig (CXCL9) was increased 2.8-fold in tumors from knockout mice, but no increase was found in serum levels of Mig. Natural killer cells have a 1.7-fold greater prevalence in the CD45⁺cells found in tumors from Egr-1^-/-mice compared to those from wild type mice. Immunohistochemical staining suggests that Mig expression in the tumors comes from invading macrophages.</p> <p>Conclusion</p> <p>Mice deficient in Egr-1 exhibit reduced growth of LLC1 tumors, and this phenomenon is associated with overexpression of Mig locally within the tumor. There are no obvious differences in tumor vascularity in the knockout mice. Natural killer cells accumulate in the tumors grown in Egr-1^-/-mice, providing a potential mechanism for the reduction in growth.</p

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

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

Energy efficiency in short and wide-area IoT technologies—A survey

In the last years, the Internet of Things (IoT) has emerged as a key application context in the design and evolution of technologies in the transition toward a 5G ecosystem. More and more IoT technologies have entered the market and represent important enablers in the deployment of networks of interconnected devices. As network and spatial device densities grow, energy efficiency and consumption are becoming an important aspect in analyzing the performance and suitability of different technologies. In this framework, this survey presents an extensive review of IoT technologies, including both Low-Power Short-Area Networks (LPSANs) and Low-Power Wide-Area Networks (LPWANs), from the perspective of energy efficiency and power consumption. Existing consumption models and energy efficiency mechanisms are categorized, analyzed and discussed, in order to highlight the main trends proposed in literature and standards toward achieving energy-efficient IoT networks. Current limitations and open challenges are also discussed, aiming at highlighting new possible research directions

ViFi: virtual fingerprinting WiFi-based indoor positioning via multi-wall multi-floor propagation model

Widespread adoption of indoor positioning systems based on WiFi fingerprinting is at present hindered by the large efforts required for measurements collection during the offline phase. Two approaches were recently proposed to address such issue: crowdsourcing and RSS radiomap prediction, based on either interpolation or propagation channel model fitting from a small set of measurements. RSS prediction promises better positioning accuracy when compared to crowdsourcing, but no systematic analysis of the impact of system parameters on positioning accuracy is available. This paper fills this gap by introducing ViFi, an indoor positioning system that relies on RSS prediction based on Multi-Wall Multi-Floor (MWMF) propagation model to generate a discrete RSS radiomap (virtual fingerprints). Extensive experimental results, obtained in multiple independent testbeds, show that ViFi outperforms virtual fingerprinting systems adopting simpler propagation models in terms of accuracy, and allows a sevenfold reduction in the number of measurements to be collected, while achieving the same accuracy of a traditional fingerprinting system deployed in the same environment. Finally, a set of guidelines for the implementation of ViFi in a generic environment, that saves the effort of collecting additional measurements for system testing and fine tuning, is proposed

Antennas and photovoltaic panels: Toward a green Internet of Things

The perspective of a wide use of green power motivates the scientific community to study the possibility of fabricating integrated stand-alone devices. In particular, solar energy is one of the most promising renewable powers, and it is widely used in autonomous wireless communication systems. Specifically, integration of sensors and antennas in a solar panel represents a challenge for future technology. In this paper, the feasibility of a single integrated autonomous device equipped with WiFi capability is analyzed, discussing its potentiality in the framework of the Internet of Things