92 lines
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5.6 KiB
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92 lines
No EOL
5.6 KiB
TeX
\chapter{Introduction}
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In recent years, \acp{WSN} have been widely adopted as a means for applications
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such as industrial monitoring \cite{ding2010sensing} \cite{rfc5673}, wild-life
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tracking \cite{cassens2017automated} and public infrastructure \cite{rfc5867}.
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One significant trend is the rise of networked embedded devices that contain
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sensors and actors for performing dedicated tasks, which is often referred to as
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the \acf{IoT}.
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Many of these applications have in common that they need some means of
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transporting commands to these embedded devices and in turn receive sensor data
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from the devices. The way such a transport channel is achieved is often through
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creating a wireless mesh network, since deployment is comparatively convenient
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and cost efficient compared to wired networks. At the same time, such devices
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will often be battery powered, since at the deployment side a connection to the
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power grid might not be available with reasonable effort. In total, these
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restrictions lead to different constraints on the software that creates the mesh
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routing.
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When nodes are supplied by a battery, the maintenance interval of the node
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itself depends on how efficiently the node manages its limited amount of energy.
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In extreme cases (e.g. where the sink depletes its battery first) the total
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lifetime of the network may even depend on the minimum lifetime of any node in
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the network. For routing in \ac{WSN} the amount of energy it takes to establish
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a routing topology within the network of devices is largely due to the time the
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radio is powered for transmitting messages, which is proportional to the number
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and sizes of messages that need to be exchanged to create and maintain the
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routing topology. The amount of energy used also depends on the efficiency of
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the resulting routing topology when transmitting messages through the network.
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There is often a trade-off between the energy consumption of a network and the
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performance in terms of network latency and throughput. For this, different
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metrics can be applied and have to be carefully selected based on the conditions
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the network operates in.
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A problem that often occurs, especially in harsh environmental conditions (e.g.
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outdoor deployments, wildlife monitoring) or with faulty software, is from
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single or repeated node restarts. When a node resets, it has to reconfigure its
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routing information by exchanging message with surrounding nodes, which makes up
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a great part of the energy costs associated with \ac{WSN}. Such restarts do not
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only affect the specific resetting node, but also nodes that depend on the
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resetting node inside the routing topology. Previous work has shown that this
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behavior of transitively failing nodes can have a large impact on the energy
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efficiency of \acp{WSN} using \ac{RPL} \cite{kulau2017energy},
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\cite{mueller2017}.
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The \ac{RPL} is a protocol for routing messages in wireless mesh networks
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and has explicitly been designed for use with networks of nodes with low power
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consumption and lossy wireless links, as is typical for many deployment
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scenarios. It has become the de-facto standard routing protocol for wireless
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sensor networks and, as such, its behavior in conditions where transient node
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failures occur is of great interest to this work.
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One aspect of the effectiveness of a mesh routing protocol is how it deals with
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such transient node failures. It has to quickly detect these failures, decide
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how to invalidate the routes using the failed node and create and announce
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alternative routes in the network.
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In subsequent work \cite{mueller2017}, a hardened implementation of \ac{RPL} for
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the \emph{Contiki} operating system has been developed, that managed to reduce
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the influence of node restarts, by restoring a previous state that has been
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stored in the flash memory of the node. This hardened implementation has
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previously been evaluated both using simulations and within a limited test
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network.
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The \fitlab is a shared \ac{IoT} test network that features over 2000 nodes and
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provides an interface for the remote configuration and scheduling of
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experiments. Measurement data can be obtained for a variety of variables including
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\ac{RSSI}, \acp{PCAP}, serial interface output, and event logs for each node.
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The goal of this work is to further verify the findings concerning the effect of
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node restarts on the performance of \ac{RPL} and to evaluate the effectiveness
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of the hardened implementation for reducing the energy impact of transient node
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failures on a larger scale. In addition to the parameters needed for the
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verification of previous work \cite{mueller2017}, some further variables have
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been recorded (see \autoref{tab:params}) and evaluated that allow some
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conclusion about the performance \ac{RPL}.
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In \autoref{chap:relwork} an introduction to the mechanisms of \ac{RPL} is given
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and other work discussing the protocol performance of \ac{RPL} is presented
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including an analysis of the effect of transient node failures in \ac{WSN}. From
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the literature, different attempts for hardening \ac{RPL} against various forms
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of attacks on the protocol and other extensions that aim to add security and
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better performance are presented.
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The topology of test network and the hardware used for the sensor nodes is
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descibed in \autoref{chap:hardware}, while in \autoref{chap:setup} the different
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software components are further described including how the data for the
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individual variables is recorded, aggregated and analyzed.
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In \autoref{chap:evaluation} a description of the different configurations the
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experiments were run in, how parameters were controlled is given for and the
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analysis of the network topology, network performance and energy consumption is
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are presented. |