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Sensor networks are made up of small battery-powered sensing devices with wireless communication capabilities, enabling the network to monitor the environment in which it is deployed. Through their flexible and cable-free design these networks open up for new deployment scenarios that were previously not plausible such as during a natural disaster. Motivated by scenarios where centralized oversight is not possible the focus of this thesis is to equip nodes with further adaptability to changes in the links it has with other nodes. This is achieved through contributions in three areas focusing on observations from a node-link perspective.

First, the impact the local environment has on the nodes is explored by deploying a sensor network outdoors next to a meteorological station to correlate the variations in link quality to the changes in the environment. The work identifies temperature as the main factor, where through further investigations in a controlled setting, a linear relationship between the decrease in signal quality and the increase in temperature is described.

Secondly, the thesis address how nodes in a sensor network can be motivated to exchange data by modeling it as a game. The game theoretic design is motivated by the absence of any centralized control and focus on the nodes as individual users in the network. The presented design motivates the selfish nodes to participate in the exchange of sensor data, showing that it is the best strategy.

Lastly, by exploring and understanding how connections in a mobile sensor network occur, nodes are given more flexibility to determine how to send and sample sensor data. This adaptability to contact occurrences is shown to provide better ways of sending data by selecting higher quality links as well as making sampling more energy preserving by reducing the rate in the vicinity of other nodes.

Opportunistic communication coupled with a sensing task enables the collection and spreading of sensory information in areas without global connectivity, providing useful information in challenging environments. In this paper, we consider an opportunistic sensor network where the mobility of users enables both the measurement and spreading of sensor data. We motivate user participation through a game theoretic approach, which is designed to ensure a fair and efficient exchange of sensor messages.The message exchange is modeled as a two-player game where sensor measurements are exchanged between nodes in a contrite tit-for-tat manner. The proposed game captures the nodes desire to limit energy consumption while at the same time obtaining messages containing useful information.We show that the best response in the game is a Pareto optimal subgame perfect equilibrium. The game is evaluated through simulation in a realistic scenario and compared with three other approaches, generating the best overall efficiency by striking a balance between size and content of messages.

Opportunistic networks, often characterized by their intermittent connectivity and sparsity of nodes, has sparked routing in these networks to focus on isolated contact opportunities. Routing has predominantly been viewed as an exchange of messages between a pair of nodes. In this paper, we take a new look at contact opportunities, specifically focusing on the occurrence of having multiple simultaneous node contacts. Multi-contact encounters warrants a more holistic view of routing decisions, where more factors than just the features of a message-node tuple can be considered. We discuss these aspects and propose a protocol addition to leverage multi-contact opportunities with the notion of heterogeneous link quality, in order to limit energy consumption. The approach, implemented for the Epidemic, Spray-and-Wait and PRoPHETv2 protocols, re-evaluates routing decisions, weighting the routing metrics value against the estimated cost of the relay. Results indicate a two to three fold decrease in the number of messages lost, as well as a reduction in message relays, while maintaining a high delivery ratio for all three protocols.

Accurate estimation of precipitation and its spatial variability is crucial for reliable discharge simulations. Although radar and satellite based techniques are becoming increasingly widespread, quantitative precipitation estimates based on point rain gauge measurement interpolation are, and will continue to be in the foreseeable future, widely used. However, the ability to infer spatially distributed data from point measurements is strongly dependent on the number, location and reliability of measurement stations.

In this study we quantitatively investigated the effect of rain gauge network configurations on the spatial interpolation by using the operational hydrometeorological sensor network in the Thur river basin in north-eastern Switzerland as a test case. Spatial precipitation based on a combination of radar and rain gauge data provided by MeteoSwiss was assumed to represent the true precipitation values against which the precipitation interpolation from the sensor network was evaluated. The performance using scenarios with both increased and decreased station density were explored. The catchment-average interpolation error indices significantly improve up to a density of 24 rain gauges per 1000 km², beyond which improvements were negligible. However, a reduced rain gauge density in the higher parts of the catchment resulted in a noticeable decline of the performance indices. An evaluation based on precipitation intensity thresholds indicated a decreasing performance for higher precipitation intensities. The results of this study emphasise the benefits of dense and adequately distributed rain gauge networks.

Temperature is known to have a significant effect on the performance of radio transceivers: the higher the temperature, the lower the quality of links. Analysing this effect is particularly important in sensor networks because several applications are exposed to harsh environmental conditions. Daily or hourly changes in temperature can dramatically reduce the throughput, increase the delay, or even lead to network partitions. A few studies have quantified the impact of temperature on low-power wireless links, but only for a limited temperature range and on a single radio transceiver. Building on top of these preliminary observations, we design a low-cost experimental infrastructure to vary the onboard temperature of sensor nodes in a repeatable fashion, and we study systematically the impact of temperature on various sensornet platforms. We show that temperature affects transmitting and receiving nodes differently, and that all platforms follow a similar trend that can be captured in a simple first-order model. This work represents an initial stepping stone aimed at predicting the performance of a network considering the particular temperature profile of a given environment.

Wireless sensor networks have been deployed outdoors ever since their inception. They have been used in areas such as precision farming, tracking wildlife, and monitoring glaciers. These diverse application areas all have different requirements and constraints, shaping the way in which the sensor network communicates. Yet something they all share is the exposure to an outdoor environment, which at times can be harsh, uncontrolled and difficult to predict. Therefore, understanding the implications of an outdoor environment is an essential step towards reliable wireless sensor network operations.

In this thesis we consider aspects of how the environment influence outdoor wireless sensor networks. Specifically, we experimentally study how meteorological factors impact radio links, and find that temperature is most significant. This motivates us to further study and propose a first order model describing the impact of temperature on wireless sensor nodes. We also analyze transmission errors in an outdoor wireless sensor networks, identifying and explaining patterns in the way data gets corrupted. The findings lead to a design and evaluation of an approach for probabilistic recover of corrupt data in outdoor wireless sensor networks. Apart from the experimental findings we have conducted two different outdoor deployments for which large data sets has been collected, containing both link and meteorological measurements.