To meet strict dependability requirements in hostile and highly-varying environments, IoT communication protocols need to be carefully tuned in relation to the expected environmental changes. However, this is difficult to attain, as every application has unique properties and requirements. Tuning communication protocols correctly requires indeed significant expertise as well as a clear understanding on how hardware and software components are affected by environmental changes. In this paper, we propose a novel framework to automate the parametrization of IoT communication protocols. The framework uses models of the environment as well as the employed hardware and protocols to predict the effects of environmental changes on network performance and to automatically select a configuration that meets user-specified dependability requirements. We demonstrate how to use this framework to configure a state-of-the-art MAC protocol for an IoT application deployed in a challenging outdoor environment and evaluate its accuracy in predicting how environmental changes affect network performance. We further evaluate the performance with different optimization strategies and show that the average run-time necessary to find a solution is sufficiently low to enable the use of our system in a typical IoT design process.

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Temperature has a strong impact on the operations of all electrical and electronic components. In wireless sensor nodes, temperature variations can lead to loss of synchronization, degradation of the link quality, or early battery depletion, and can therefore affect key network metrics such as throughput, delay, and lifetime. Considering that most outdoor deployments are exposed to strong temperature variations across time and space, a deep understanding of how temperature affects network protocols is fundamental to comprehend flaws in their design and to improve their performance. Existing testbed infrastructures, however, do not allow to systematically study the impact of temperature on wireless sensor networks. In this paper we present TempLab, an extension for wireless sensor network testbeds that allows to control the on-board temperature of sensor nodes and to study the effects of temperature variations on the network performance in a precise and repeatable fashion. TempLab can accurately reproduce traces recorded in outdoor environments with fine granularity, while minimizing the hardware costs and configuration overhead. We use TempLab to analyse the detrimental effects of temperature variations (i) on processing performance, (ii) on a tree routing protocol, and (iii) on CSMA-based MAC protocols, deriving insights that would have not been revealed using existing testbed installations.

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Temperature strongly affects the operation of integrated circuits, and its impact has been largely investigated on a device level. However, the impact of temperature variations on networks of multiple devices is far less understood and requires investigation. We aim to close this gap and analyse the impact of temperature fluctuations on low-power wireless sensor networks, a key enabling technology of pervasive computing. As we are moving forward into an era of human-centric safety-critical applications (e.g., smart health and intelligent transportation systems), it is particularly important to make sure that a networked system offers a reliable and deterministic performance despite all possible temperature changes over its deployment lifetime. In this demo, we present a testbed infrastructure based on infra-red heating lamps that allows to vary the on-board temperature of sensor nodes on a large scale in a repeatable fashion. Using this experimental infrastructure, we show the effects of temperature variations on network performance in two different ways. First, in a small-scale local testbed at PerCom, we highlight the degradation of the wireless link quality at high temperatures, and show that the performance of radio transceivers is temperature-dependent. We quantify this degradation and parametrize the dependency between temperature and link quality using the signal strength information captured between four wireless sensor nodes. Second, we connect remotely to our large-scale experimental infrastructure at TU Graz, and assess the impact of temperature variations on the performance of state-of-the-art network protocols, showing that the typical outdoor temperature fluctuations occurring during 24-hours do affect key network metrics such as throughput, delay, and lifetime.

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Wireless low-power transceivers used in sensor networks typically operate in unlicensed frequency bands that are subject to external radio interference caused by devices transmitting at much higher power.communication protocols should therefore be designed to be robust against such interference. A critical building block of many protocols at all layers is agreement on a piece of information among a set of nodes. At the MAC layer, nodes may need to agree on a new time slot or frequency channel, at the application layer nodes may need to agree on handing over a leader role from one node to another. Message loss caused by interference may break agreement in two different ways: none of the nodes uses the new information (time slot, channel, leader) and sticks with the previous assignment, or-even worse-some nodes use the new information and some do not. This may lead to reduced performance or failures. In this paper, we investigate the problem of agreement under external radio interference and point out the limitations of traditional message-based approaches. We propose JAG, a novel protocol that uses jamming instead of message transmissions to make sure that two neighbouring nodes agree, and show that it outperforms message-based approaches in terms of agreement probability, energy consumption, and time-to-completion. We further show that JAG can be used to obtain performance guarantees and meet the requirements of applications with real-time constraints.

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Radio interference drastically affects the performance of sensor-net communications, leading to packet loss and reduced energy-efficiency. As an increasing number of wireless devices operates on the same ISM frequencies, there is a strong need for understanding and debugging the performance of existing sensornet protocols under interference. Doing so requires a low-cost flexible testbed infrastructure that allows the repeatable generation of a wide range of interference patterns. Unfortunately, to date, existing sensornet testbeds lack such capabilities, and do not permit to study easily the coexistence problems between devices sharing the same frequencies. This paper addresses the current lack of such an infrastructure by using off-the-shelf sensor motes to record and playback interference patterns as well as to generate customizable and repeat-able interference in real-time. We propose and develop JamLab: a low-cost infrastructure to augment existing sensornet testbeds with accurate interference generation while limiting the overhead to a simple upload of the appropriate software. We explain how we tackle the hardware limitations and get an accurate measurement and regeneration of interference, and we experimentally evaluate the accuracy of JamLab with respect to time, space, and intensity. We further use JamLab to characterize the impact of interference on sensornet MAC protocols.

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Link quality estimation is a thorny problem in wireless sensor networks, because its accuracy affects the design and the efficiency of networking protocols and applications. Especially in the context of low-power wireless, estimating the link quality poses a sort of catch-22 dilemma, whereby a large number of packet samples are required to accurately estimate a channel, but only a few samples should be used due to limited energy resources. This paradox becomes even more severe in mobile wireless sensor networks, since the high variability of the medium imposes even stricter constraints on the timing in which the estimation has to be carried out. In this paper we propose the Triangle Metric, a metric that combines geometrically the information of PRR, LQI, and SNR into a robust estimator that guarantees a fast and reliable assessment of the link quality. Our evaluation shows that the triangle metric can identify the quality of links using as few as 10 packet samples, making it an eligible solution for highly mobile sensor networks.@en

Radio interference plays a central role for the performance of Wireless Sensor Networks (WSN). Interference not only leads to packet loss, but it also affects the function of MAC and routing protocols. Hitherto, testing the impact of interference on WSN experimentally has been difficult because of the unavailability of low-cost tools to create reproducible and well-controlled interference patterns. In this demo we present a simple and inexpensive method to generate controllable and repeatable interference patterns for 802.15.4 devices. The demo is presented as a game, where a user is required to achieve a given interference level.

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