Cross-layer design of multi-hop wireless networks

MULTI -hop wireless networks are usually defined as a collection of nodes equipped with radio transmitters, which not only have the capability to communicate each other in a multi-hop fashion, but also to route each others’ data packets. The distributed nature of such networks makes them suitable for a variety of applications where there are no assumed reliable central entities, or controllers, and may significantly improve the scalability issues of conventional single-hop wireless networks. This Ph.D. dissertation mainly investigates two aspects of the research issues related to the efficient multi-hop wireless networks design, namely: (a) network protocols and (b) network management, both in cross-layer design paradigms to ensure the notion of service quality, such as quality of service (QoS) in wireless mesh networks (WMNs) for backhaul applications and quality of information (QoI) in wireless sensor networks (WSNs) for sensing tasks. Throughout the presentation of this Ph.D. dissertation, different network settings are used as illustrative examples, however the proposed algorithms, methodologies, protocols, and models are not restricted in the considered networks, but rather have wide applicability. First, this dissertation proposes a cross-layer design framework integrating a distributed proportional-fair scheduler and a QoS routing algorithm, while using WMNs as an illustrative example. The proposed approach has significant performance gain compared with other network protocols. Second, this dissertation proposes a generic admission control methodology for any packet network, wired and wireless, by modeling the network as a black box, and using a generic mathematical 0. Abstract 3 function and Taylor expansion to capture the admission impact. Third, this dissertation further enhances the previous designs by proposing a negotiation process, to bridge the applications’ service quality demands and the resource management, while using WSNs as an illustrative example. This approach allows the negotiation among different service classes and WSN resource allocations to reach the optimal operational status. Finally, the guarantees of the service quality are extended to the environment of multiple, disconnected, mobile subnetworks, where the question of how to maintain communications using dynamically controlled, unmanned data ferries is investigated

Optimal Real-time Spectrum Sharing between Cooperative Relay and Ad-hoc Networks

Optimization based spectrum sharing strategies have been widely studied. However, these strategies usually require a great amount of real-time computation and significant signaling delay, and thus are hard to be fulfilled in practical scenarios. This paper investigates optimal real-time spectrum sharing between a cooperative relay network (CRN) and a nearby ad-hoc network. Specifically, we optimize the spectrum access and resource allocation strategies of the CRN so that the average traffic collision time between the two networks can be minimized while maintaining a required throughput for the CRN. The development is first for a frame-level setting, and then is extended to an ergodic setting. For the latter setting, we propose an appealing optimal real-time spectrum sharing strategy via Lagrangian dual optimization. The proposed method only involves a small amount of real-time computation and negligible control delay, and thus is suitable for practical implementations. Simulation results are presented to demonstrate the efficiency of the proposed strategies.Comment: One typo in the caption of Figure 5 is correcte

In-network computation in sensor networks

Sensor networks are an important emerging class of networks that have many applications. A sink in these networks acts as a bridge between the sensor nodes and the end-user (which may be automated and/or part of the sink). Typically, convergecast is performed in which all the data collected by the sensors is relayed to the sink, which in turn presents the relevant information to the end-user. Interestingly, some applications require the sink to relay just a function of the data collected by the sensors. For instance, in a fire alarm system, the sinks needs to monitor the maximum of the temperature readings of all the sensors. For these applications, instead of performing convergecast, we can let the intermediate nodes process the data they receive, to significantly reduce the volume of traffic transmitted and increase the rate at which the data is collected and processed at the sink: this is known as in-network computation. Most of the current literature on this novel technique focuses on asymptotic results for large networks and for very elementary functions. In this dissertation, we study a new class of functions for which we want to compute explicit solutions for networks of practical size. We consider the applications where the sink is interested in the first M statistical moments of the data collected at a certain time. The k-th statistical moment is defined as the expectation of the k-th power of the data. The M=1 case represents the elementary functions like MAX, MIN, MEAN, etc. that are commonly considered in the literature. For this class of functions, we are interested in explicitly computing the maximum achievable throughput including routing, scheduling and queue management for any given network when in-network computation is allowed. Flow models have been routinely used to solve optimal joint routing and scheduling problems when there is no in-network computation and they are typically tractable for relatively large networks. However, deriving such models is not obvious when in-network computation is allowed. Considering a single rate wireless network and the physical model of interference, we develop a discrete-time model for the real-time network operation and perform two transformations to obtain a flow model that keeps the essence of in-network computation. This model gives an upper bound on the maximum achievable throughput. To show the tightness of that upper bound, we derive a numerical lower bound by computing a feasible solution to the discrete-time model. This lower bound turns out to be close to the upper bound proving that the flow model is an excellent approximation to the discrete-time model. We then adapt the flow model to a wired multi-rate network with asynchronous transmissions on links with different capacities. To compute the lower bound for wired networks, we propose a heuristic strategy involving the generation of multiple trees and effective queue management that achieves a throughput close to the one computed by the flow model. This cross validates the tightness of the upper bound and the goodness of our heuristic strategy. Finally, we provide several engineering insights on what in-network computation can achieve in both types of networks

Stochastic Performance Trade-offs in the Design of Real-Time Wireless Sensor Networks

Future sensing applications call for a thorough evaluation of network performance trade-offs so that desired guarantees can be provided for the realization of real-time wireless sensor networks (WSNs). Recent studies provide insight into the performance metrics in terms of first-order statistics, e.g., the expected delay. However, WSNs are characterized by the stochastic nature of the wireless channel and the queuing processes, which result in non-deterministic delay, throughput, and network lifetime. For the design of WSNs with predictable performance, probabilistic analysis of these performance metrics and their intrinsic trade-offs is essential. Moreover, providing stochastic guarantees is crucial since each deployment may result in a different realization. In this paper, the trade-offs between delay, throughput, and lifetime are quantified through a stochastic network design approach. To this end, two novel probabilistic network design measures, quantile and quantile interval, are defined to capture the dependability and predictability of the performance metrics, respectively. Extensive evaluations are conducted to explore the performance trade-offs in real-time WSNs