One of the latest protocols developed for the Internet of Things networks is IEEE 802.11ah, proposed by the WiFi Alliance. The new channel access mechanism in IEEE 802.11ah, which is called Restricted Access Window, aims at reducing the contention between the stations by allowing only selected stations to transmit data at certain time slots. Stations may exhibit selfish behavior to maximize their own throughput. This will come at the cost of the overall network quality of service. In this paper, we first analyze the default behavior of the IEEE 802.11ah protocol in terms of fairness. We then introduce various percentages of selfish stations and observe how the network’s quality of service in terms of fairness, throughput and packet-loss are affected. After establishing the inherent fairness of IEEE 802.11ah, we analyze applicability of two existing selfish behavior detection algorithms designed for IEEE 802.11 to the IEEE 802.11ah protocol. Due to their poor performance, we propose a new definition of ’selfish behavior’ specifically for IEEE 802.11ah, based on which we present a new algorithm for detecting selfish behavior. To combat selfish behavior and to create a better fairness, throughput and lower packet loss, we consequently present a novel mitigation algorithm called Selfish Stations Quarantine Punishment Algorithm (SSQPA). The proposed algorithm takes advantage of the RAW grouping to isolate selfish stations from the honest stations, thus mitigating the effect of the selfish behavior. SSQPA comes in two variants: honest stations-centric and network-centric. Our experimental results show that both variants can successfully mitigate selfish behavior effects in IEEE 802.11ah networks and either one can be used depending on the goal of the network.

This paper discusses a link layer protocol that enhances performance of the Transmission Control Protocol (TCP) over a last-hop wireless network (LHWN) to make it more suitable for delivery of a live video with a short play out deadline. The presented protocol provides preference control of the transmission of video data within the network and it maximizes network goodput (the successfully transmitted amount of useful video data) by maximizing reliability of transmission for a given playout deadline and a varying digital bandwidth. The protocol makes use of the skip function in the TCP receiver to maximize end-to-end throughput of the connection at the expense of reducing full reliability. It can be implemented in a wireless access point to improve quality of the received live video over an Internet connection to an office or home. The main ideas of the protocol are: hide packet losses from the sender by performing local retransmissions based on the information from the video application and according to a certain scheduling policy, drop stale segments before they arrive at the receiver and prevent end-to-end retransmissions of segments received by the access point. The performance of the TCP Reno protocol in combination with both the presented protocol and the skip function in the receiver is compared against the performance of TCP Reno, the TCP-Real-Time Mode (RTM) protocol, TCP Reno in combination with the Snoop protocol and TCP-RTM in combination with Snoop. The results of simulations show improvement of network goodput across a wide range of packet-error rates.

To minimize dependency on the availability of data labels, some WiFi-CSI based-gesture recognition solutions utilize an unsupervised representation learning phase prior to fine-tuning downstream task classifiers. In this case, however, the overall performance of the solution is negatively affected by domain factors present in the WiFi-CSI data used by the pre-training models. To reduce this negative effect, we propose an integration of the adversarial domain classifier in the pre-training phase. We consider this as an effective step towards automatic domain discovery during pre-training. We also experiment with multi-class and label versions of domain classification to improve situations, in which integrating a multi-class and single label-based domain classifier during pre-training fails to reduce the negative impact domain factors have on overall solution performance. For our extensive random and leave-out domain factor cross-validation experiments, we utilise (i) an end-to-end and unsupervised representation learning baseline, (ii) integration of both single- and multi-label domain classification, and (iii) so-called domain-aware versions of the aformentioned unsupervised representation learning baseline in (i) with two different datasets, i.e., Widar3 and SignFi. We also consider an input sample type that generalizes, in terms of overall solution performance, to both aforementioned datasets. Experiment results with the Widar3 dataset indicate that multi-label domain classification reduces domain shift in position (1.2% mean metric improvement and 0.5% variance increase) and orientation (0.4% mean metric improvement and 1.0% variance decrease) in domain factor leave-out cross-validation experiments. The results also indicate that domain shift reduction, when considering single- or multi-label domain classification during pre-training, is negatively impacted when a large proportion of negative view combinations contain views that originate from different domains within a substantial amount of mini-batches considered during pre-training. This is caused by the view contrastive loss repelling the aforementioned negative view combinations, eventually causing more domain shift in the intermediate feature space of the overall solution.