The increasing interest for ubiquitous networking, and the tremendous popularity gained by IEEE 802.11 Wireless Local Area Networks (WLANs) in recent years, is leading to very dense deployments where high levels of channel contention may prevent to meet the increasing users' demands. To mitigate the negative effects of channel contention, the Target Wake Time (TWT) mechanism included in the IEEE 802.11ax amendment can have a significant role, as it provides an extremely simple but effective mechanism to schedule transmissions in time. Moreover, in addition to reduce the contention between stations, the use of TWT may also contribute to take full advantage of other novel mechanisms in the IEEE 802.11 universe, such as multiuser transmissions, multi-AP cooperation, spatial reuse and coexistence in high-density WLAN scenarios. Overall, we believe TWT may be a first step towards a practical collision-free and deterministic access in future WLANs.1 It is expected that the IEEE 802.11ax amendment will be published in 2019.

a b s t r a c tWireless multi-hop networks often experience severe performance degradations when legacy routing algorithms are employed, because they are not optimized to take advantage of the peculiarities of wireless links. Indeed, the wireless channel is intrinsically a broadcast medium, making a point-to-point link abstraction not suitable. Furthermore, channel conditions may significantly differ both in time and space, making routing over predetermined paths inadequate to adapt the forwarding process to the channel variability. Motivated by these limitations, the research community has started to explore novel routing paradigms and design principles dealing with the wireless diversity as an opportunity rather than a shortcoming. Within this large body of research, opportunistic routing and network coding are emerging as two of the most promising approaches to exploit the intrinsic characteristics of multi-hop wireless networks, such as multi-user diversity. The aim of this survey is to examine how opportunistic forwarding and network coding can achieve performance gains by performing hop-by-hop route construction and by encoding data packets at intermediate nodes. To this end, we present a taxonomy of existing solutions, and we describe their most representative features, benefits and design challenges. We also discuss open issues in this research area, with a special attention to the ones most related to wireless mesh networks.

Vehicles provide an ideal platform for urban sensing applications, as they can be equipped with all kinds of sensing devices that can continuously monitor the environment around the travelling vehicle. In this work we are particularly concerned with the use of vehicles as building blocks of a multimedia mobile sensor system able to capture camera snapshots of the streets to support traffic monitoring and urban surveillance tasks. However, cameras are high data-rate sensors while wireless infrastructures used for vehicular communications may face performance constraints. Thus, data redundancy mitigation is of paramount importance in such systems. To address this issue in this paper we exploit sub-modular optimisation techniques to design efficient and robust data collection schemes for multimedia vehicular sensor networks. We also explore an alternative approach for data collection that operates on longer time scales and relies only on localised decisions rather than centralised computations. We use network simulations with realistic vehicular mobility patterns to verify the performance gains of our proposed schemes compared to a baseline solution that ignores data redundancy. Simulation results show that our data collection techniques can ensure a more accurate coverage of the road network while significantly reducing the amount of transferred data.

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Monitoring Wireless Sensor Networks (WSNs) are composed of sensor nodes that report temperature, relative humidity, and other environmental parameters. The time between two successive measurements is a critical parameter to set during the WSN configuration because it can impact the WSN's lifetime, the wireless medium contention and the quality of the reported data. As trends in monitored parameters can significantly vary between scenarios and within time, identifying a sampling interval suitable for several cases is also challenging. In this work, we propose a dynamic sampling rate adaptation scheme based on reinforcement learning, able to tune sensors' sampling interval on-the-fly, according to environmental conditions and application requirements. The primary goal is to set the sampling interval to the best value possible so as to avoid oversampling and save energy, while not missing environmental changes that can be relevant for the application. In simulations, our mechanism could reduce up to 73% the total number of transmissions compared to a fixed strategy and, simultaneously, keep the average quality of information provided by the WSN. The inherent flexibility of the reinforcement learning algorithm facilitates its use in several scenarios, so as to exploit the broad scope of the Internet of Things.

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