A New Routing Protocol for WMNs

Opportunistic routing is an emerging research area in Wireless Mesh Networks (WMNs), which exploits the broadcast nature of wireless networks to find the optimal routing solution that maximizes throughput and minimizes packet loss. Opportunistic routing protocols mainly suffer from computational overheads, as most of the protocols try to find the best next forwarding node. In this paper we address the key issue of computational overhead by designing new routing technique without using pre-selected list of potential forwarders. We propose a novel opportunistic routing technique for WMNs. We compare it with well-known protocols, such as AODV, OLSR, and ROMER based on throughput, delivery ratio, and average end to end delay. Simulation results show that proposed protocol, gives average throughput increase up to 32%, and increase in delivery ratio (from 10% to 20%). We also analyze the performance of proposed protocol and ROMER based on various parameters, such as duplicate transmissions and network collisions, by analysis depicts that proposed protocol reduces duplicate transmissions up to 70% and network collisions up to 30% DOI: 10.17762/ijritcc2321-8169.15026

Possible Challenges and Appropriate Measures for a Resilient WMN-Based Disaster Network

A wireless mesh network (WMN)-based disaster network shall provide an emergency communication infrastructure in case of a catastrophe destroyed any existing communication infrastructure. Since the hardware of the disaster network is deployed in an environment affected by the outcome of a catastrophe, events such as aftershocks and/or outbreaking fires are likely to occur and may destroy the hardware of the disaster network. To maintain its provided functionality and thus its usability, the network requires to be resilient to these and other events which are affecting the network infrastructure. To achieve a resilient network, the normal state of the network as well as possible challenges affecting the normal state need to be defined in prior. This scientific work deals with the derivation and definition of the required normal state of the WMN-based disaster network, as well as the definition of possible challenges resulting from environmental-based events. Since each possible challenge is influencing the network infrastructure of the WMN-based disaster network, possible measures for preventing and/or reducing the impact of each challenge are defined. In addition, emergency corrections capable of resolving the influences of an occurring challenge are defined

Self-organized backpressure routing for the wireless mesh backhaul of small cells

The ever increasing demand for wireless data services has given a starring role to dense small cell (SC) deployments for mobile networks, as increasing frequency re-use by reducing cell size has historically been the most effective and simple way to increase capacity. Such densification entails challenges at the Transport Network Layer (TNL), which carries packets throughout the network, since hard-wired deployments of small cells prove to be cost-unfeasible and inflexible in some scenarios. The goal of this thesis is, precisely, to provide cost-effective and dynamic solutions for the TNL that drastically improve the performance of dense and semi-planned SC deployments. One approach to decrease costs and augment the dynamicity at the TNL is the creation of a wireless mesh backhaul amongst SCs to carry control and data plane traffic towards/from the core network. Unfortunately, these lowcost SC deployments preclude the use of current TNL routing approaches such as Multiprotocol Label Switching Traffic Profile (MPLS-TP), which was originally designed for hard-wired SC deployments. In particular, one of the main problems is that these schemes are unable to provide an even network resource consumption, which in wireless environments can lead to a substantial degradation of key network performance metrics for Mobile Network Operators. The equivalent of distributing load across resources in SC deployments is making better use of available paths, and so exploiting the capacity offered by the wireless mesh backhaul formed amongst SCs. To tackle such uneven consumption of network resources, this thesis presents the design, implementation, and extensive evaluation of a self-organized backpressure routing protocol explicitly designed for the wireless mesh backhaul formed amongst the wireless links of SCs. Whilst backpressure routing in theory promises throughput optimality, its implementation complexity introduces several concerns, such as scalability, large end-to-end latencies, and centralization of all the network state. To address these issues, we present a throughput suboptimal yet scalable, decentralized, low-overhead, and low-complexity backpressure routing scheme. More specifically, the contributions in this thesis can be summarized as follows: We formulate the routing problem for the wireless mesh backhaul from a stochastic network optimization perspective, and solve the network optimization problem using the Lyapunov-driftplus-penalty method. The Lyapunov drift refers to the difference of queue backlogs in the network between different time instants, whereas the penalty refers to the routing cost incurred by some network utility parameter to optimize. In our case, this parameter is based on minimizing the length of the path taken by packets to reach their intended destination. Rather than building routing tables, we leverage geolocation information as a key component to complement the minimization of the Lyapunov drift in a decentralized way. In fact, we observed that the combination of both components helps to mitigate backpressure limitations (e.g., scalability,centralization, and large end-to-end latencies). The drift-plus-penalty method uses a tunable optimization parameter that weight the relative importance of queue drift and routing cost. We find evidence that, in fact, this optimization parameter impacts the overall network performance. In light of this observation, we propose a self-organized controller based on locally available information and in the current packet being routed to tune such an optimization parameter under dynamic traffic demands. Thus, the goal of this heuristically built controller is to maintain the best trade-off between the Lyapunov drift and the penalty function to take into account the dynamic nature of semi-planned SC deployments. We propose low complexity heuristics to address problems that appear under different wireless mesh backhaul scenarios and conditions..