Performance Modelling and Optimisation of Multi-hop Networks

A major challenge in the design of large-scale networks is to predict and optimise the total time and energy consumption required to deliver a packet from a source node to a destination node. Examples of such complex networks include wireless ad hoc and sensor networks which need to deal with the effects of node mobility, routing inaccuracies, higher packet loss rates, limited or time-varying effective bandwidth, energy constraints, and the computational limitations of the nodes. They also include more reliable communication environments, such as wired networks, that are susceptible to random failures, security threats and malicious behaviours which compromise their quality of service (QoS) guarantees. In such networks, packets traverse a number of hops that cannot be determined in advance and encounter non-homogeneous network conditions that have been largely ignored in the literature. This thesis examines analytical properties of packet travel in large networks and investigates the implications of some packet coding techniques on both QoS and resource utilisation. Specifically, we use a mixed jump and diffusion model to represent packet traversal through large networks. The model accounts for network non-homogeneity regarding routing and the loss rate that a packet experiences as it passes successive segments of a source to destination route. A mixed analytical-numerical method is developed to compute the average packet travel time and the energy it consumes. The model is able to capture the effects of increased loss rate in areas remote from the source and destination, variable rate of advancement towards destination over the route, as well as of defending against malicious packets within a certain distance from the destination. We then consider sending multiple coded packets that follow independent paths to the destination node so as to mitigate the effects of losses and routing inaccuracies. We study a homogeneous medium and obtain the time-dependent properties of the packet’s travel process, allowing us to compare the merits and limitations of coding, both in terms of delivery times and energy efficiency. Finally, we propose models that can assist in the analysis and optimisation of the performance of inter-flow network coding (NC). We analyse two queueing models for a router that carries out NC, in addition to its standard packet routing function. The approach is extended to the study of multiple hops, which leads to an optimisation problem that characterises the optimal time that packets should be held back in a router, waiting for coding opportunities to arise, so that the total packet end-to-end delay is minimised

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Distributed Detection and Estimation in Wireless Sensor Networks

In this article we consider the problems of distributed detection and estimation in wireless sensor networks. In the first part, we provide a general framework aimed to show how an efficient design of a sensor network requires a joint organization of in-network processing and communication. Then, we recall the basic features of consensus algorithm, which is a basic tool to reach globally optimal decisions through a distributed approach. The main part of the paper starts addressing the distributed estimation problem. We show first an entirely decentralized approach, where observations and estimations are performed without the intervention of a fusion center. Then, we consider the case where the estimation is performed at a fusion center, showing how to allocate quantization bits and transmit powers in the links between the nodes and the fusion center, in order to accommodate the requirement on the maximum estimation variance, under a constraint on the global transmit power. We extend the approach to the detection problem. Also in this case, we consider the distributed approach, where every node can achieve a globally optimal decision, and the case where the decision is taken at a central node. In the latter case, we show how to allocate coding bits and transmit power in order to maximize the detection probability, under constraints on the false alarm rate and the global transmit power. Then, we generalize consensus algorithms illustrating a distributed procedure that converges to the projection of the observation vector onto a signal subspace. We then address the issue of energy consumption in sensor networks, thus showing how to optimize the network topology in order to minimize the energy necessary to achieve a global consensus. Finally, we address the problem of matching the topology of the network to the graph describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R. Chellapa and S. Theodoridis, Eds., Elsevier, 201

Gossip Algorithms for Distributed Signal Processing

Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This article presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page

Efficient Information Access in Data-Intensive Sensor Networks

Recent advances in wireless communications and microelectronics have enabled wide deployment of smart sensor networks. Such networks naturally apply to a broad range of applications that involve system monitoring and information tracking (e.g., fine-grained weather/environmental monitoring, structural health monitoring, urban-scale traffic or parking monitoring, gunshot detection, monitoring volcanic eruptions, measuring rate of melting glaciers, forest fire detection, emergency medical care, disaster response, airport security infrastructure, monitoring of children in metropolitan areas, product transition in warehouse networks etc.).Meanwhile, existing wireless sensor networks (WSNs) perform poorly when the applications have high bandwidth needs for data transmission and stringent delay constraints against the network communication. Such requirements are common for Data Intensive Sensor Networks (DISNs) implementing Mission-Critical Monitoring applications (MCM applications).We propose to enhance existing wireless network standards with flexible query optimization strategies that take into account network constraints and application-specific data delivery patterns in order to meet high performance requirements of MCM applications.In this respect, this dissertation has two major contributions: First, we have developed an algebraic framework called Data Transmission Algebra (DTA) for collision-aware concurrent data transmissions. Here, we have merged the serialization concept from the databases with the knowledge of wireless network characteristics. We have developed an optimizer that uses the DTA framework, and generates an optimal data transmission schedule with respect to latency, throughput, and energy usage. We have extended the DTA framework to handle location-based trust and sensor mobility. We improved DTA scalability with Whirlpool data delivery mechanism, which takes advantage of partitioning of the network. Second, we propose relaxed optimization strategy and develop an adaptive approach to deliver data in data-intensive wireless sensor networks. In particular, we have shown that local actions at nodes help network to adapt in worse network conditions and perform better. We show that local decisions at the nodes can converge towards desirable global network properties e.g.,high packet success ratio for the network. We have also developed a network monitoring tool to assess the state and dynamic convergence of the WSN, and force it towards better performance

A Physical Estimation based Continuous Monitoring Scheme for Wireless Sensor Networks

Data estimation is emerging as a powerful strategy for energy conservation in sensor networks. In this thesis is reported a technique, called Data Estimation using Physical Method (DEPM), that efficiently conserves battery power in an environment that may take a variety of complex manifestations in real situations. The methodology can be ported easily with minor changes to address a multitude of tasks by altering the parameters of the algorithm and ported on any platform. The technique aims at conserving energy in the limited energy supply source that runs a sensor network by enabling a large number of sensors to go to sleep and having a minimal set of active sensors that may gather data and communicate the same to a base station. DEPM rests on solving a set of linear inhomogeneous algebraic equations which are set up using well-established physical laws. The present technique is powerful enough to yield data estimation at an arbitrary number of point-locations, and provides for easy experimental verification of the estimated data by using only a few extra sensors

Optimal scheduling and fair servicepolicy for STDMA in underwater networks with acoustic communications

In this work, a multi-hop string network with a single sink node is analyzed. A periodic optimal scheduling for TDMA operation that considers the characteristic long propagation delay of the underwater acoustic channel is presented. This planning of transmissions is obtained with the help of a new geometrical method based on a 2D lattice in the space-time domain. In order to evaluate the performance of this optimal scheduling, two service policies have been compared: FIFO and Round-Robin. Simulation results, including achievable throughput, packet delay, and queue length, are shown. The network fairness has also been quantified with the Gini index