Learning from Errors: Detecting ZigBee Interference in WiFi Networks

—In this work we show how to detect ZigBee inter- ference on commodity WiFi cards by monitoring the reception errors, such as synchronization errors, invalid header formats, too long frames, etc., caused by ZigBee transmissions. Indeed, in presence of non-WiFi modulated signals, the occurrence of these types of errors follows statistics that can be easily recognized. Moreover, the duration of the error bursts depends on the transmission interval of the interference source, while the error spacing depends on the receiver implementation. On the basis of these considerations, we propose the adoption of hidden Markov chains for characterizing the behavior of WiFi receivers in presence of controlled interference sources (training phase) and then run-time recognizing the most likely cause of error patterns. Experimental results prove the effectiveness of our approach for detecting ZigBee interference

Cross-technology WiFi/ZigBee communications: Dealing with channel insertions and deletions

In this paper we show how cross-technology in- terference can be exploited to set-up a low-rate bi-directional communication channel between heterogeneous WiFi and ZigBee networks. Because of the environment noise and receivers' imple- mentation, the cross-technology channel can be severely affected by insertions and deletions of symbols, whose effects need to be taken into account by the coding scheme and communication protocol

Exploratory approach for network behavior clustering in LoRaWAN

The interest in the Internet of Things (IoT) is increasing both as for research and market perspectives. Worldwide, we are witnessing the deployment of several IoT networks for different applications, spanning from home automation to smart cities. The majority of these IoT deployments were quickly set up with the aim of providing connectivity without deeply engineering the infrastructure to optimize the network efficiency and scalability. The interest is now moving towards the analysis of the behavior of such systems in order to characterize and improve their functionality. In these IoT systems, many data related to device and human interactions are stored in databases, as well as IoT information related to the network level (wireless or wired) is gathered by the network operators. In this paper, we provide a systematic approach to process network data gathered from a wide area IoT wireless platform based on LoRaWAN (Long Range Wide Area Network). Our study can be used for profiling IoT devices, in order to group them according to their characteristics, as well as detecting network anomalies. Specifically, we use the k-means algorithm to group LoRaWAN packets according to their radio and network behavior. We tested our approach on a real LoRaWAN network where the entire captured traffic is stored in a proprietary database. Quite important is the fact that LoRaWAN captures, via the wireless interface, packets of multiple operators. Indeed our analysis was performed on 997, 183 packets with 2169 devices involved and only a subset of them were known by the considered operator, meaning that an operator cannot control the whole behavior of the system but on the contrary has to observe it. We were able to analyze clusters’ contents, revealing results both in line with the current network behavior and alerts on malfunctioning devices, remarking the reliability of the proposed approach

A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels

In the last years, several machine learning-based techniques have been proposed to monitor human movements from Wi-Fi channel readings. However, the development of domain-adaptive algorithms that robustly work across different environments is still an open problem, whose solution requires large datasets characterized by strong domain diversity, in terms of environments, persons and Wi-Fi hardware. To date, the few public datasets available are mostly obsolete - as obtained via Wi-Fi devices operating on 20 or 40 MHz bands - and contain little or no domain diversity, thus dramatically limiting the advancements in the design of sensing algorithms. The present contribution aims to fill this gap by providing a dataset of IEEE 802.11 ac channel measurements over an 80 MHz bandwidth channel featuring notable domain diversity, through measurement campaigns that involved thirteen subjects across different environments, days, and with different hardware. Novel experimental data is provided by blocking the direct path between the transmitter and the monitor, and collecting measurements in a semi-anechoic chamber (no multi-path fading). Overall, the dataset - available on IEEE DataPort [1] - contains more than thirteen hours of channel state information readings (23.6 GB), allowing researchers to test activity/identity recognition and people counting algorithms

Exploratory approach for network behavior clustering in LoRaWAN

The interest in the Internet of Things (IoT) is increasing both as for research and market perspectives. Worldwide, we are witnessing the deployment of several IoT networks for different applications, spanning from home automation to smart cities. The majority of these IoT deployments were quickly set up with the aim of providing connectivity without deeply engineering the infrastructure to optimize the network efficiency and scalability. The interest is now moving towards the analysis of the behavior of such systems in order to characterize and improve their functionality. In these IoT systems, many data related to device and human interactions are stored in databases, as well as IoT information related to the network level (wireless or wired) is gathered by the network operators. In this paper, we provide a systematic approach to process network data gathered from a wide area IoT wireless platform based on LoRaWAN (Long Range Wide Area Network). Our study can be used for profiling IoT devices, in order to group them according to their characteristics, as well as detecting network anomalies. Specifically, we use the k-means algorithm to group LoRaWAN packets according to their radio and network behavior. We tested our approach on a real LoRaWAN network where the entire captured traffic is stored in a proprietary database. Quite important is the fact that LoRaWAN captures, via the wireless interface, packets of multiple operators. Indeed our analysis was performed on 997, 183 packets with 2169 devices involved and only a subset of them were known by the considered operator, meaning that an operator cannot control the whole behavior of the system but on the contrary has to observe it. We were able to analyze clusters\u2019 contents, revealing results both in line with the current network behavior and alerts on malfunctioning devices, remarking the reliability of the proposed approach

An Indoor and Outdoor Navigation System for Visually Impaired People

In this paper, we present a system that allows visually impaired people to autonomously navigate in an unknown indoor and outdoor environment. The system, explicitly designed for low vision people, can be generalized to other users in an easy way. We assume that special landmarks are posed for helping the users in the localization of pre-defined paths. Our novel approach exploits the use of both the inertial sensors and the camera integrated into the smartphone as sensors. Such a navigation system can also provide direction estimates to the tracking system to the users. The success of out approach is proved both through experimental tests performed in controlled indoor environments and in real outdoor installations. A comparison with deep learning methods has been presented

WiSHFUL : enabling coordination solutions for managing heterogeneous wireless networks

The paradigm shift toward the Internet of Things results in an increasing number of wireless applications being deployed. Since many of these applications contend for the same physical medium (i.e., the unlicensed ISM bands), there is a clear need for beyond-state-of-the-art solutions that coordinate medium access across heterogeneous wireless networks. Such solutions demand fine-grained control of each device and technology, which currently requires a substantial amount of effort given that the control APIs are different on each hardware platform, technology, and operating system. In this article an open architecture is proposed that overcomes this hurdle by providing unified programming interfaces (UPIs) for monitoring and controlling heterogeneous devices and wireless networks. The UPIs enable creation and testing of advanced coordination solutions while minimizing the complexity and implementation overhead. The availability of such interfaces is also crucial for the realization of emerging software-defined networking approaches for heterogeneous wireless networks. To illustrate the use of UPIs, a showcase is presented that simultaneously changes the MAC behavior of multiple wireless technologies in order to mitigate cross-technology interference taking advantage of the enhanced monitoring and control functionality. An open source implementation of the UPIs is available for wireless researchers and developers. It currently supports multiple widely used technologies (IEEE 802.11, IEEE 802.15.4, LTE), operating systems (Linux, Windows, Contiki), and radio platforms (Atheros, Broadcom, CC2520, Xylink Zynq,), as well as advanced reconfigurable radio systems (IRIS, GNURadio, WMP, TAISC)

Wi-Dia: Data-Driven Wireless Diagnostic Using Context Recognition

The recent densification of Wi-Fi networks is exacerbating the effects of well-known pathologies including hidden nodes and flow starvation. This paper provides an automatic diagnostic tool for detecting the source roots of performance impairments by recognizing the wireless operating context. Our tool for Wi-Fi diagnostic, named Wi-Dia, exploits machine learning methods and uses features related to network topology and channel utilization, without impact on regular network operations and working in real-time. Real-time per-link Wi-Fi diagnosis enables recovering actions for context-specific treatments. Wi-Dia classifier recognizes different classes of interference; it is jointly trained using simulated and experimental data, taking advantage of the flexibility of the first and the realistic details of the latter. Wi-Dia has been validated in a large European wireless testbed; it provides the right detection of Wi-Fi pathological conditions in real complex scenarios

Error-Based Interference Detection in WiFi Networks

In this paper we show that inter-technology interference can be recognized by commodity WiFi devices by monitoring the statistics of receiver errors. Indeed, while for WiFi standard frames the error probability varies during the frame reception in different frame fields (PHY, MAC headers, payloads) protected with heterogeneous coding, errors may appear randomly at any point during the time the demodulator is trying to receive an exogenous interfering signal. We thus detect and identify cross-technology interference on off-the-shelf WiFi cards by monitoring the sequence of receiver errors (bad PLCP, bad PCS, invalid headers, etc.) and develop an Artificial Neural Network (ANN) to recognize the source of interference. The result is quite impressive, reaching an average accuracy of almost 99% in recognizing ZigBee, Microwave and LTE (in unlicensed spectrum) interference

Enabling a win-win coexistence mechanism for WiFi and LTE in unlicensed bands

The problem of WiFi and LTE coexistence has been significantly debated in the last years, with the emergence of LTE extensions enabling the utilization of unlicensed spectrum for carrier aggregation. Since the two technologies employ com-pletely different access protocols and frame transmission times, supporting coexistence with minimal modifications on existing protocols is not an easy task. Current solutions are often based on LTE unilateral adaptations, being LTE in unlicensed bands still under definition. In this paper, we demonstrate that it is possible to avoid a subordinated role for WiFi nodes, by simply equipping WiFi nodes with a sensing mechanism based on adaptive tunings of the ambient noise thresholds (as indeed considered by several commercial cards). Under this assumption, we propose a win-win coexistence mechanism between the two technologies, that does not require modifications on legacy WiFi access operations. We model the interactions between the two technologies in terms of a game and demonstrate the feasibility of the approach in simulation and in real experiments