Survey on diversity-based routing in wireless mesh networks: Challenges and solutions

Wireless 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 wire- less 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 var- iability. Motivated by these limitations, the research community has started to explore novel routing par- adigms 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 net- works, 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

wireless real time monitoring system for the implementation of intelligent control in subways

This chapter looks into the technical features of state-of-the-art wireless sensors networks for environmental monitoring. Technology advances in low-power and wireless devices have made the deployment of those networks more and more affordable. In addition, wireless sensor networks have become more flexible and adaptable to a wide range of situations. Hence, a framework for their correct implementation will be provided. Then, one specific application about real-time environmental monitoring in support of a modelbased predictive control system installed in a metro station will be described. In these applications, filtering, resampling, and post-processing functions must be developed, in order to convert raw data into a dataset arranged in the right format, so that it can inform the algorithms of the control system about the current state of the domain under control. Finally, the whole architecture of the model-based predictive control and its final performances will be reported

Novel routing paradigms for wireless Mesh Networks

The increasing desire of ubiquitous Internet access has recently promoted the deployment of wireless multi-hop networks in several application domains. Wireless Mesh Networks (WMNs) provide significant benefits over existing wirelessmulti-hop networking paradigms, offering a suitable solution for a wide range of application scenarios, spanning frompublic safety communications to community-based networks and metro scale municipal networks. Routing design is crucial to guarantee robust communication through the mesh backbone. Traditional unicast routing has shown to be ineffective when dealing with highly variable wireless channels. One of the most critical aspects is the wire- less diversity, intended as the reception of a packet at multiple forwarders, causing collisions and interference due to the broadcast nature of the wireless medium. A set of innovative routing approaches has recently been proposed as a valuable alternative to classical routing, thanks to their ability to deal with the wireless diversity as an opportunity rather than a shortcoming. The primary goal of this thesis is to deeply investigate wireless diversity-based routing inWMNs, proposing novel solutions able to significantly improve WMN performance. We extensively describe the main features of this strategy and provide a classification of themost representative solutions in literature, discussing their most relevant characteristics, advantages and disadvantages. Then, we focus on one of the most promising categories: Opportunistic Routing (OR). It exploits the multiple packet recipients offered by the wireless transmission to incrementally build a path, selecting the best next hop only after packet reception. Then, we propose a novel opportunistic routing algorithm, able to select at each hop the forwarders that maximize the throughput gain. In contrast to the common opportunistic approach, the proposed algorithm avoids any form of a priori constraint on route selection, fully leveraging all the transmission opportunities encountered during path construction. To improve its efficiency in multi-flow environments, we extend its routing strategy with an opportunistic packet scheduling algorithm and a prioritized channel access scheme, so as to facilitate the transmission of the packets that are traversing the paths providing higher performance gains. To ensure high performance in all the typical WMN application scenarios, we need to consider that in these environments channel quality may significantly vary in time and space, requiring a high degree of flexibility in the path construction process. Most of the existing solutions performlocal decisions (i.e. hop-by-hop) based on end-to-end principles. In contrast, we propose a novel routing algorithm that combines end-to-end with localized data, so as to adapt routing decisions to channel conditions at the time of packet transmission. This ensures higher reliability even in the most challenging application scenarios. The key factors determining opportunistic improvements are not clear yet, making hard to identify the conditions under which this paradigm outperforms classical unicast routing. Hence, we propose a novel routing architecture that relies on a configurable machine learning-based agent to properly select, at each node, the most suitable routing algorithm within a set of available solutions, according to network conditions and traffic characteristics. This solution represents a further step towards the definition of a wireless diversity-based routing paradigm able to ensure high performance in all WMN application scenarios

Performance evaluation of a Call Admission Control scheme in IEEE 802.11s WMNs

Le reti wireless mesh (Wireless Mesh Network) forniscono molti vantaggi rispetto ad altre reti wireless come l’estensione del range di copertura, la capacità di auto-configurazione, i costi contenuti e la semplicità di diffusione, contribuendo alla riduzione del Digital Divide. In questo panorama di crescente interesse, molte sono ancora le questioni aperte. Focalizzando l’attenzione sul Traffic Engineering, uno schema di Call Admission Control è stato implementato ed applicato su reti mesh definite dalla proposta di standard IEEE 802.11s, con l’obiettivo fondamentale di bilanciamento del traffico. Partendo dal protocollo di routing di default (HWMP) definito nella proposta di standard, è stato implementato un Bandwidth Broker per far sì che il nodo Root definito nel protocollo HWMP potesse svolgere anche le funzioni di Bandwidth Broker, consentendo l’apertura di flussi di traffico basata sulla conoscenza della topologia, della distribuzione del carico nella rete e di possibili intereferenze tra nodi. Wireless Mesh Networks are gaining increasing attention due to the advantages over other networks including extended coverage, self-configuration, low cost, easy of incremental deployment. However many issues are still under investigation. Focusing on Traffic Engineering, a Call Admission Control scheme has been implemented and applied on IEEE 802.11s Wireless Mesh Networks with the main goal of traffic balancing. Based on the default mandatory routing protocol (HWMP) defined in the joint proposal to IEEE 802.11s Task Group, a Bandwidth Broker has been implemented, so as to make the Root node defined in HWMP acting as a Bandiwdth Broker within the network. The Call Admission Control scheme can be applied on IEEE 802.11s Wireless Mesh Networks to allow the Bandwidth Broker to setup traffic flows based on the information about topology, traffic load and interferences among nodes

Target wake time: scheduled access in IEEE 802.11ax WLANs

The increasing interest in ubiquitous networking, and the tremendous popularity gained by IEEE 802.11 WLANs in recent years, is leading to very dense deployments where high levels of channel contention may prevent meeting the increasing user demands. To mitigate the negative effects of channel contention, the 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 reducing the contention between stations, the use of TWT may also contribute to taking 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 toward a practical collision-free and deterministic access in future WLANs.This work has been partially supported by a Gift from the Cisco University Research Program Fund (CG #890107, Towards Deterministic Channel Access in High-Density WLANs), a corporate advised fund of the Silicon Valley Community Foundation, and by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM- 2015-0502), Catalan Government (SGR-2017- 1188). We would also like to acknowledge the constructive comments received from the anonymous reviewers

Target wake time: scheduled access in IEEE 802.11ax WLANs

The increasing interest in ubiquitous networking, and the tremendous popularity gained by IEEE 802.11 WLANs in recent years, is leading to very dense deployments where high levels of channel contention may prevent meeting the increasing user demands. To mitigate the negative effects of channel contention, the 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 reducing the contention between stations, the use of TWT may also contribute to taking 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 toward a practical collision-free and deterministic access in future WLANs.This work has been partially supported by a Gift from the Cisco University Research Program Fund (CG #890107, Towards Deterministic Channel Access in High-Density WLANs), a corporate advised fund of the Silicon Valley Community Foundation, and by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM- 2015-0502), Catalan Government (SGR-2017- 1188). We would also like to acknowledge the constructive comments received from the anonymous reviewers

Opportunistic Packet Scheduling and Routing in Wireless Mesh Networks

No abstract availableOpportunistic routing has emerged as a promising approach to cope with unreliable and unpredictable wireless links in static multi-hop wireless networks. Most existing opportunistic schemes force strict timing between the potential forwarders in order to reduce the number of redundant packet copies generated during the forwarding process. Alternative opportunistic schemes have been also proposed, which adopt a randomized forwarding process to better exploit path diversity. However, both approaches suffer from performance degradations when the number of flows increases. To address this issue, in this paper we describe PacketOPP, a novel opportunistic routing protocol that combines randomized opportunistic forwarding with opportunistic packet scheduling to improve the support of multiple simultaneous flows. Specifically, PacketOPP implements a packet scheduler to award a higher priority to the packets that are expected to deliver the maximum opportunistic gain. Using extensive ns-2 simulations we show that PacketOPP outperforms state-of-the-art non-scheduled opportunistic routing protocols, such as ROMER and MaxOPP, as well as a traditional shortest path routing protoco

Adapting sampling interval of sensor networks using on-line reinforcement learning

Comunicació presentada al 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), celebrat els deis 12 a 14 de desembre de 2016 a Reston, Virginia.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.This work has been partially supported by the Catalan Government through the project SGR-2014-1173, the European Union through the project FP7-SME-2013-605073-ENTOMATIC, and by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502)

Adapting sampling interval of sensor networks using on-line reinforcement learning

Comunicació presentada al 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), celebrat els deis 12 a 14 de desembre de 2016 a Reston, Virginia.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.This work has been partially supported by the Catalan Government through the project SGR-2014-1173, the European Union through the project FP7-SME-2013-605073-ENTOMATIC, and by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502)