The Internet of Things (IoT) architecture is quickly becoming popular even outside of its originating scenario of home automation. This paper reports the design, implementation, and performance of an IoT hardware and software architecture conceived for the continuous monitoring of livestock located in barns and during grazing. We have adopted the LoRa low power wide area network (LPWAN) technology to cover the diverse environments, and a suitable configuration of web services to perform data storage, analysis, and visualization. Since the LoRa LPWAN (LoRaWAN) medium access control (MAC) layer does not provide a listen-before-talk (LBT) mechanism, we propose a custom MAC layer with LBT-based carrier-sense multiple access with collision avoidance (CSMA/CA). The devised system has been implemented using off-the-shelf hardware, and its performance has also been estimated with the help of a C++ event-based simulator. The preliminary results of our HW implementation on the field confirm the stability of the conceived system and its reliability.

Due to the growing number of devices accessing the Internet through wireless networks, the radio spectrum has become a highly contended resource. The availability of low cost radio spectrum monitoring sensors enables a geographically distributed, real-time observation of the spectrum to spot inefficiencies and to develop new strategies for its utilization. The potentially large number of sensors to be deployed and the intrinsic nature of data make this task a Big Data problem. In this work we design, implement, and validate a hardware and software architecture for wideband radio spectrum monitoring inspired to the Lambda architecture. This system offers Spectrum Sensing as a Service to let end users easily access and process radio spectrum data. To minimize the latency of services offered by the platform, we fine tune the data processing chain. From the analysis of sensor data characteristics, we design the data models for MongoDB and Cassandra, two popular NoSQL databases. A MapReduce job for spectrum visualization has been developed to show the potential of our approach and to identify the challenges in processing spectrum sensor data. We experimentally evaluate and compare the performance of the two databases in terms of application processing time for different types of queries applied on data streams with heterogeneous generation rate. Our experiments show that Cassandra outperforms MongoDB in most cases, with some exceptions depending on data stream rate.

This article proposes new approximations for the bit error probability of several LoRa-based schemes, such as phase-shift keying (PSK) LoRa, in-phase and quadrature (IQ) chirp spread spectrum (CSS), discrete chirp rate keying (DCRK) CSS, and CSS with index modulation (IM). We include a new scheme that combines IQ, CSS, and IM, denoted as IQ chirp index modulation (IQCIM). Our analysis, which assumes coherent detection in additive white Gaussian noise, compares the LoRa-based schemes also in terms of spectral efficiency, computational complexity, and peak-to-average power ratio (PAPR). Noncoherent detection of CSS with IM is analyzed as well. Our performance analysis shows that the proposed IQCIM produces a significant increase of spectral efficiency (SE) compared to LoRa with similar complexity, at an energy cost of approximately 0.9 dB. When compared to the other LoRa-based schemes, for a fixed decoding latency, the SE of IQCIM is still the largest, with reduced computational cost per transmitted bit. In this context, we also propose a maximum-likelihood (ML) coherent detector for PSK-LoRa and a novel noncoherent detector for IQCSS.

This paper concerns the analysis of adjacent channel interference of the 3GPP Long Term Evolution (LTE) E-UTRA (Evolved Universal Terrestrial Radio Access) mobile systems into Digital Video Broadcasting - Terrestrial (DVB-T) systems. The simulated performance of both systems allows us to define recommendations for minimizing the interference effects. The subjective quality of the received TV signal has been evaluated experimentally in terms of picture failure (PF). We also investigated the importance of the selection of suitable Spectral Emission Masks (SEMs) of the LTE downlink transmission. Results show that, by using the ECC 148 SEM, both the protection ratio and the minimum distance between LTE towers and DVB-T receivers can be significantly decreased.

The seamless integration of multiple radio access technologies (multi-RAT) and cloud/edge resources is pivotal for advancing future networks, which seek to unify distributed and heterogeneous computing and communication resources into a cohesive continuum system, tailored for mobile applications. Many research projects and focused studies are proposing solutions in this area, the impact of which is undoubtedly increased by moving from theoretical and simulation studies to experimental validations. To this aim, this paper proposes a testbed architecture that combines contemporary communication and cloud technologies to provide microservice-based mobile applications with the ability to offload part of their tasks to cloud/edge data centers connected by multi-RAT cellular networks. The testbed leverages Kubernetes, Istio service mesh, OpenFlow, public 5G networks, and IEEE 802.11ad mmWave (60 GHz) Wi-Fi access points. The architecture is validated through a use case in which a ground robot autonomously follows a moving object by using an artificial intelligence-driven computer vision application. Computationally intensive navigation tasks are offloaded by the robot to microservice instances, which are executed on demand within cloud and edge data centers that the robot can exploit during its journey. The proposed testbed is flexible and can be reused to assess communication and cloud innovations focusing on multi-RAT cloud continuum scenarios.

The delivery of video over wireless, error-prone transmission channels requires careful allocation of channel and source code rates, given the available bandwidth. In this paper, we present a theoretical framework to find an optimal joint channel and source code rate allocation, by considering an intra-coded video compression standard such as Motion JPEG 2000 and an error-prone wireless transmission channel. Lagrangian optimization is used to find the optimal code rate allocation, from a PSNR perspective, starting from commonly available source coding outputs, such as intermediate rate-distortion traces. The algorithm is simple and adaptive both on the available bandwidth and on the transmission channel conditions, and it has a low computational complexity. Simulation results, using Reed-Solomon (R-S) coding, show that the achieved performance, in terms of PSNR and MSSIM, is comparable with that of other methods reported in literature. In addition, a simplified and sub-optimal expression for determining the channel code assignment is also provided.