Low-Power Wide-Area Network (LPWAN) multi-Radio Access Technology (RAT) devices combine features of different low-power network technologies to flexibly manage heterogeneous requirements stemming from various Internet of Things (IoT) applications. IEEE 802.11ah is a novel technology that is envisioned to provide a “bridge” between Wi-Fi and s, thus it has been often considered as one of the supporting technologies in multi-RAT devices. In such multi- RAT scenarios, network discovery and handover procedures need to be utilized for determining the availability of a given technology and managing if the connection to the technology should be initiated. However, traditional discovery and handover procedures, such as beacon listening, have to be performed periodically and, therefore, consume substantial amount of energy, making them unsuitable for battery-powered IoT devices. To address this issue, we present a mechanism that is able to make more optimal discovery and handover decisions by leveraging the physical location information of the multi- RAT devices. We demonstrate how this approach is feasible in performing energy efficient handovers between a technology (NB-IoT) and IEEE 802.11ah based on estimated location. We do that by showing that the location-based procedure substantially reduces the energy consumption of the mobile device compared to the traditional discovery based on periodical listening for beacons, while maintaining comparable duration of the device’s association to IEEE 802.11ah. Moreover, we evaluate the energy and delay overheads caused by the localization service, showing only slight effects on the performance of the mechanism.

IEEE 802.11ah is a novel sub-GHz license-free WiFi technology that is of great interest for Smart Cities, primarily due to its long range, low energy consumption, reliability, and ubiquitousness. To guarantee a long lifetime of battery-powered IEEE 802.11ah Mobile Terminals (MTs), network discovery based on beacon listening or active probing should ideally be avoided. Alternative discovery approaches heavily rely on crowd-sourcing, which in turn relies on propagation modeling. Due to the novelty of IEEE 802.11ah, the accuracy of the available propagation models is currently all but clear, and this paper makes one of the first steps in bridging this gap. Specifically, for a number of Smart City-relevant environments we experimentally evaluate the accuracy of an exhaustive set of propagation models by comparing their outputs against real measurements obtained using IEEE 802.11ah-compatible hardware. Our results are encouraging, showing that the existing models are indeed suitable for IEEE 802.11ah. However, they also indicate that diverse models perform best in distinct types of deployment environments, suggesting the need for an environment-tailored design of IEEE 802.11ah-based systems that utilize propagation modeling.

Multi-Radio Access Technology (RAT) IoT devices are able to combine the high coverage of Low-Power Wide-Area (LPWA) technologies with the higher data-rates of shorter range technologies such as IEEE 802.11ah. In such scenarios, a discovery procedure has to be used for detecting the availability of a IEEE 802.11ah network. Currently, these procedures consume substantial energy, as the discovery has to be periodically performed, even if the IEEE 802.11ah technology is not available, which is undesirable for low-power Internet of Things (IoT) devices. We propose using the device's location information for making more optimized discovery and handover decisions. We demonstrate the feasibility of this approach in performing energy efficient handovers between various LPWA technologies and IEEE 802.11ah based on estimated location. We carry out our evaluation in terms of the energy consumption of the procedure and the duration of the device's association to IEEE 802.11ah. We show that the location-based procedure substantially reduces the energy consumption of the mobile device compared to the traditional discovery based on periodical listening for beacons.

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.

IEEE 802.11ah is a new Wi-Fi standard operating on unlicensed sub-GHz frequencies. It aims to provide long-range connectivity to Internet of Things (IoT) devices. The IEEE 802.11ah restricted access window (RAW) mechanism promises to increase throughput and energy efficiency in dense deployments by dividing stations into different RAW groups and allowing only one group to access the channel at a time. In this demo, we demonstrate the ability of the RAW mechanism to support a large number of densely deployed IoT stations with heterogeneous traffic requirements. Differentiated Quality of Service (QoS) is offered to a small set of high-throughput wireless cameras that coexist with thousands of best-effort sensor monitoring stations. The results are visualized in near real-time using our own developed IEEE 802.11ah visualizer running on top of the ns-3 event-based network simulator.