Exploiting programmable architectures for WiFi/ZigBee inter-technology cooperation

The increasing complexity of wireless standards has shown that protocols cannot be designed once for all possible deployments, especially when unpredictable and mutating interference situations are present due to the coexistence of heterogeneous technologies. As such, flexibility and (re)programmability of wireless devices is crucial in the emerging scenarios of technology proliferation and unpredictable interference conditions. In this paper, we focus on the possibility to improve coexistence performance of WiFi and ZigBee networks by exploiting novel programmable architectures of wireless devices able to support run-time modifications of medium access operations. Differently from software-defined radio (SDR) platforms, in which every function is programmed from scratch, our programmable architectures are based on a clear decoupling between elementary commands (hard-coded into the devices) and programmable protocol logic (injected into the devices) according to which the commands execution is scheduled. Our contribution is two-fold: first, we designed and implemented a cross-technology time division multiple access (TDMA) scheme devised to provide a global synchronization signal and allocate alternating channel intervals to WiFi and ZigBee programmable nodes; second, we used the OMF control framework to define an interference detection and adaptation strategy that in principle could work in independent and autonomous networks. Experimental results prove the benefits of the envisioned solution

Predicting lorawan behavior. How machine learning can help

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the use of k-means, Long Short-Term Memory Neural Networks and Decision Trees. We test these approaches on a real large-scale LoRaWAN network where the overall captured traffic is stored in a proprietary database. Our study shows how profiling techniques enable a machine learning prediction algorithm even when training is not possible because of high error rates perceived by some devices. In this challenging case, the prediction of the inter-arrival time of packets has an error of about 3.5% for 77% of real sequence cases

Sub-GHz LPWAN network coexistence, management and virtualization : an overview and open research challenges

The IoT domain is characterized by many applications that require low-bandwidth communications over a long range, at a low cost and at low power. Low power wide area networks (LPWANs) fulfill these requirements by using sub-GHz radio frequencies (typically 433 or 868 MHz) with typical transmission ranges in the order of 1 up to 50 km. As a result, a single base station can cover large areas and can support high numbers of connected devices (> 1000 per base station). Notorious initiatives in this domain are LoRa, Sigfox and the upcoming IEEE 802.11ah (or "HaLow") standard. Although these new technologies have the potential to significantly impact many IoT deployments, the current market is very fragmented and many challenges exists related to deployment, scalability, management and coexistence aspects, making adoption of these technologies difficult for many companies. To remedy this, this paper proposes a conceptual framework to improve the performance of LPWAN networks through in-network optimization, cross-technology coexistence and cooperation and virtualization of management functions. In addition, the paper gives an overview of state of the art solutions and identifies open challenges for each of these aspects

Renovating the iPMU via Internet of Things for Pollutant Emission Estimations in Poultry Facilities

The emissions of ammonia (NH3), particulate matter (PM2.5), and carbon dioxide (CO2) are major concerns in poultry facilities. They can pose environmental concerns and nuances. Robust and affordable measurement systems are needed to accurately measure in-barn concentrations and quantify the emissions. The Intelligent Portable Monitoring Unit (iPMU or PMU3) developed in 2016 was reconstructed into PMU4 to include upgraded NH3 and PM2.5 sensors and wireless connectivity for a low-cost, robust, and accurate air quality monitoring device with contactless data transfer using the concept of Internet of Things (IoT). In addition, a user-friendly web-based interface was developed for 1) real-time and historical data visualization, and 2) estimation of NH3 and PM2.5 emissions with suitable ventilation measurement. The PMU4 device developed in this project features back-up data stores and leverages cloud computing for data analysis and visualization. It was designed to collect and store sensor data (temperature, relative humidity, NH3, PM2.5, and CO2) on an SD card, and simultaneously send the data to a secure server. The PMU4 device uses Wi-Fi for internet access, but it was programmed to tolerate internet outages by queueing data and automatically transmitting the data queue to the secure server when the internet restores. The PMU4 device was deployed in the Robert T. Hamilton Poultry Teaching & Research Facility (Iowa State University, Ames, IA) for 13 days (September 15 – September 27, 2023) for field evaluation. The results showed that the mean temperature and relative humidity were 23.2 ± 1.8 ˚C and 62.1 ± 11.8% respectively. The mean concentrations of NH3, PM2.5, and CO2 were 0.47 ± 0.27 ppm, 12.0 ± 12.6 µg/m3, and 581.3 ± 134.2 ppm. The NH3 and PM2.5 per bird emission rates for the monitoring period were estimated using facility temperature, NH3 and PM2.5 concentrations, atmospheric barometric pressure obtained from the nearest airport, and ventilation and chicken inventory obtained from the facility manger. The mean per bird emission rates for NH3 and PM2.5 were 0.23 g/d/hen and 7.44 mg/d/hen, respectively. Advisor: Yijie Xion