CSI (Channel State Information) of WiFi systems contains the environment channel response between the transmitter and the receiver, so the people/objects and their movement in between can be sensed. To get CSI, the receiver performs channel estimation based on the pre-known training field of the transmitted WiFi signal. CSI related technology is useful in many cases, but it also brings concerns on privacy and security. In this paper, we open sourced a CSI fuzzer to enhance the privacy and security of WiFi CSI applications. It is built and embedded into the transmitter of openwifi, which is an open source full-stack WiFi chip design, to prevent unauthorized sensing without sacrificing the WiFi link performance. The CSI fuzzer imposes an artificial channel response to the signal before it is transmitted, so the CSI seen by the receiver will indicate the actual channel response combined with the artificial response. Only the authorized receiver, that knows the artificial response, can calculate the actual channel response and perform the CSI sensing. Another potential application of the CSI fuzzer is covert channels based on a set of pre-defined artificial response patterns. Our work resolves the pain point of implementing the anti-sensing idea based on the commercial off-the-shelf WiFi devices.

Open source Software Defined Radio (SDR) project, such as srsLTE and Open Air Interface (OAI), has been widely used for 4G/5G research. However the SDR implementation of the IEEE802.11 (Wi-Fi) is still difficult. The Wi-Fi Short InterFrame Space (SIFS) requires acknowledgement (ACK) packet being sent out in 10μs/16μs (2.4GHz/5GHz) after receiving a packet successfully, thus the Personal Computer (PC) based SDR architecture hardly can be used due to the latency (≥ 100μs) between PC and Radio Frequency (RF) front-end. Researchers have to do simulation, hack a commercial chip or buy an expensive reference design to test their ideas. To change this situation, we have developed an open-source full-stack IEEE802.11a/g/n SDR implementation - openwifi. It is based on Xilinx Zynq Systemon-Chip (SoC) that includes Field Programmable Gate Array (FPGA) and ARM processor. With the low latency connection between FPGA and RF front-end, the most critical SIFS timing is achieved by implementing Physical layer (PHY) and low level Media Access Control (low MAC) in FPGA. The corresponding driver is implemented in the embedded Linux running on the ARM processor. The driver instantiates Application Programming Interfaces (APIs) defined by Linux mac80211 subsystem, which is widely used for most SoftMAC Wi-Fi chips. Researchers could study and modify openwifi easily thanks to the modular design. Compared to PC based SDR, the SoC is also a better choice for portable and embedded scenarios.

The new medium access method of IEEE 802.11ah, called restricted access window (RAW), divides stations into different groups, and only allows stations in the same group to access the channel simultaneously, in order to reduce collisions and thus achieve better performance (e.g., throughput). However, the existing station grouping strategies only support homogeneous scenarios where all stations use the same modulation and coding scheme (MCS) and packet size. A surrogate model is an efficient mathematical model that represents the behavior of a complex system, trained with a limited set of labeled input-output data samples. In this article, we present a surrogate model that can accurately predict RAW performance under a given RAW configuration in heterogeneous networks. Different from the homogeneous scenario, heterogeneous networks are defined by a large number of parameters, leading to an enormous design space, i.e., the order of 10(17) possible data points. This is too big to achieve feasible training convergence. In this article, we present a novel training methodology that leads to a new design space with highly reduced size, i.e., the order of 10(5) data points. The surrogate model converges when less than 6000 labeled data points are used for training, which is only a tiny portion of the whole design space. The results show that, the relative error between model prediction and simulation results is less than 0.1 for 95% of the data points, in the areas of the design space studied. Its low complexity and high precision make the proposed model a valuable tool to develop real-time RAW optimization algorithms for heterogeneous IEEE 802.11ah networks.

The Restricted Access Window (RAW) mechanism proposed by IEEE 802.11ah promises to address one of the major problems of the Internet of Things (IoT): high channel contention in large-scale densely deployed sensor networks. The RAW feature allows the Access Point (AP) to divide stations into different groups, with only the stations in the same group being allowed to access the channel simultaneously. Existing station grouping strategies only support homogeneous scenarios, where all sensor stations have the same fixed data transmission interval, modulation and coding scheme (MCS) and packet size. In this paper, we present two contributions to address this issue. First, a surrogate model that predicts RAW performance given specific network conditions and RAW configuration parameters. It is fast to train and can be solved in real-time. Second, the Model-Based RAW Optimization Algorithm (MoROA), which uses the surrogate model to determine the optimal RAW configuration in real-time, for heterogeneous stations and dynamic traffic. We compare the accuracy of our surrogate model to simulation results. Performance of MoROA is compared to existing RAW optimization algorithms and traditional 802.11 channel access methods. The results shows that the trained surrogate model can accurately predict RAW performance with a relative error less than 7% and 10% for 95% and 98% of the RAW configurations respectively. MoROA achieves a throughput up to twice as high as traditional 802.11 channel access functions in dense heterogeneous networks.

Experimental wireless network research is often very time consuming and requires knowledge of multiple experimentation platforms (JFED, OMF, etc.), thereby hindering innovation specially from non-testbed experts. To foster innovation, this paper presents an intuitive wireless experimentation using the Node-RED framework. Within the framework, drag and drop components are combined to set-up wireless experiments in simulation and testbed environments, configure network stack and execute series of experiments. Furthermore, the intuitiveness of the Node-RED framework is demonstrated by using drag and drop components to optimize multiple conflicting objectives in simulation and in a real-testbed, without requiring advanced testbed knowledge.

Wireless network deployments in industry often grow organically with new technologies added over time, among which many use the non-licensed spectrum to avoid licensing costs. As a result, technologies competing for the same spectrum end up deployed in the same area, causing coexistence problems to manifest themselves at a later stage. To avoid unexpected performance degradation, there is a need to evaluate the impact of additional wireless technologies on an existing network before the actual deployment. This paper proposes to simplify the impact assessment by emulating the signals of the potential wireless network with a single software-defined radio. To evaluate the emulator's performance, the impact of Bluetooth on Wi-Fi technology is considered as the reference scenario. A series of real-life experiments with configurable traffic load and network scale are conducted to estimate the impact of Bluetooth network on a Wi-Fi link, and the corresponding measurements are repeated with the emulated Bluetooth signals. To the best of the authors' knowledge, we are the first to propose such a solution, and it is shown that the use of our emulator gives a reliable indication of the expected impact at the location of the Wi-Fi link. As such, this paper provides an important step toward a simple, cost efficient, and reliable solution, to assess the impact of a wireless network prior to its deployment.

Wireless communication is an enabling technology for industrial automation. For mobile industrial devices operating in large areas, the performance of the wireless handover process is crucial. For the welfare of industrial processes short time communication outage must be ensured, especially for time-critical traffic. This paper assesses the handover performance for three industrial real-life use cases with different requirements. It covers handover performance under heavy interference, its impact on time-critical traffic and on broadcast traffic latency, followed by lessons learned and opportunities for further research.

Wireless systems often need to optimize multiple conflicting objectives (low delay, high reliability, and low cost), which are difficult to fulfill simultaneously. In such cases, the wireless system exhibits multiple optimal operation points, referred to as the optimal Pareto front (OPF). However, due to the large number of parameter settings to be evaluated and the time-consuming nature of performing wireless experiments, it is typically not possible to identify the OPF by exhaustively evaluating all possible settings. Instead, for many use cases, an approximation is good enough. To this end, this paper applies a multi-objective surrogate-based optimization (MOSBO) toolbox to efficiently optimize wireless systems and approximate the OPF using a limited number of iterations. Moreover, a real Wi-Fi conferencing scenario is optimized that has two conflicting objectives (exposure and audio quality) and four configurable parameters (Tx-Power, Tx-Rate, Codec Bit-Rate, and Codec Frame-Length). The benefits of using the MOSBO approach for such a network problem is demonstrated by approximating the OPF using 94 iterations instead of requiring the exploration of 6528 different parameter combinations, while still dominating 96.58% of the complete design space.

A large amount of research focuses on experimentally optimizing the performance of wireless solutions. Finding the optimal performance settings typically requires investigating all possible combinations of design parameters, while the number of required experiments increases exponentially for each considered design parameter. The aim of this paper is to analyze the applicability of global optimization techniques to reduce the optimization time of wireless experimentation. In particular, the paper applies the Efficient Global Optimization (EGO) algorithm implemented in the SUrrogate MOdeling (SUMO) toolbox inside a wireless testbed. Moreover, to cope with the unpredictable nature of wireless testbeds, the paper applies an experiment outlier detection which monitors outside interference and verifies the validity of conducted experiments. The proposed techniques are implemented and evaluated in a wireless testbed using a realistic wireless conferencing scenario. The performance gain and experimentation time of a SUMO optimized experiment is compared against an exhaustively searched experiment. In our proof of concept, it is shown that the proposed SUMO optimizer reaches 99.79% of the global optimum performance while requiring 8.67 times less experiments compared to the exhaustive search experiment.

A large amount of research focuses on experimentally optimizing performance of wireless solutions. Finding the optimal performance settings typically requires investigating all possible combinations of design parameters, and as a result the number of required experiments increases exponentially for each considered design parameter. However, Efficient Global Optimization (EGO) algorithms overcome this limitation and arrive at the global optimum performance in a very short time compared to the exhaustive search technique. In this demo, we apply the SUrrogate MOdeling (SUMO) toolbox, an efficient implementation of EGO algorithms, in order to improve the experimentation time of a realistic wireless conference solution. By tuning a speaker's transmit power and channel parameters, the SUMO toolbox searches for an improved listeners' audio quality having minimum transmission exposure. Moreover, the SUMO experiment is compared to an exhaustive search experiment and it is found that SUMO reached 99.51\% of the global optimum performance while requiring 10 times less experiments.