Received signal interpolation for context discovery in cognitive radio

This paper addresses the context acquisition to characterize different features of a primary network based on the power measurements obtained by a sensor network. In particular, it presents a comparative study of the impact of natural neighbor, linear, and nearest neighbor interpolation techniques carried out over the measurements at different geographical positions. Extracted features include transmitter position, antenna pattern or propagation model. An evaluation is carried out in scenarios including the effect of both correlated and non-correlated shadowing.Peer ReviewedPostprint (author’s final draft

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

Fingerprinting-Based Positioning in Distributed Massive MIMO Systems

Location awareness in wireless networks may enable many applications such as emergency services, autonomous driving and geographic routing. Although there are many available positioning techniques, none of them is adapted to work with massive multiple-in-multiple-out (MIMO) systems, which represent a leading 5G technology candidate. In this paper, we discuss possible solutions for positioning of mobile stations using a vector of signals at the base station, equipped with many antennas distributed over deployment area. Our main proposal is to use fingerprinting techniques based on a vector of received signal strengths. This kind of methods are able to work in highly-cluttered multipath environments, and require just one base station, in contrast to standard range-based and angle-based techniques. We also provide a solution for fingerprinting-based positioning based on Gaussian process regression, and discuss main applications and challenges.Comment: Proc. of IEEE 82nd Vehicular Technology Conference (VTC2015-Fall

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Spatial In-Body Channel Characterization Using an Accurate UWB Phantom

"(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."Ultra-wideband (UWB) systems have emerged as a possible solution for future wireless in-body communications. However, in-body channel characterization is complex. Animal experimentation is usually restricted. Furthermore, software simulations can be expensive and imply a high computational cost. Synthetic chemical solutions, known as phantoms, can be used to solve this issue. However, achieving a reliable UWB phantom can be challenging since UWB systems use a large bandwidth and the relative permittivity of human tissues are frequency dependent. In this paper, a measurement campaign within 3.1-8.5 GHz using a new UWB phantom is performed. Currently, this phantom achieves the best known approximation to the permittivity of human muscle in the whole UWB band. Measurements were performed in different spatial positions, in order to also investigate the diversity of the in-body channel in the spatial domain. Two experimental in-body to in-body (IB2IB) and in-body to on-body (IB2OB) scenarios are considered. From the measurements, new path loss models are obtained. Besides, the correlation in transmission and reception is computed for both scenarios. Our results show a highly uncorrelated channel in transmission for the IB2IB scenario at all locations. Nevertheless, for the IB2OB scenario, the correlation varies depending on the position of the receiver and transmitter.This work was supported by the Ministerio de Economia y Competitividad, Spain, under Grant TEC2014-60258-C2-1-R and Grant TEC2014-56469-REDT and by the European FEDER Funds.Andreu Estellés, C.; Castelló Palacios, S.; García Pardo, C.; Fornés Leal, A.; Vallés Lluch, A.; Cardona Marcet, N. (2016). Spatial In-Body Channel Characterization Using an Accurate UWB Phantom. IEEE Transactions on Microwave Theory and Techniques. 64(11):3995-4002. doi:10.1109/TMTT.2016.2609409S39954002641