There is the long-standing assumption that radio communication in the range of hundreds of meters needs to consume mWs of power at the transmitting device. In this paper, we demonstrate that this is not necessarily the case for some devices equipped with backscatter radios. We present LOREA an architecture consisting of a tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing systems such as Computational Radio Frequency Identification (CRFID). LOREA achieves this by: First, generating narrow-band backscatter transmissions that improve receiver sensitivity. Second, mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. An off-the-shelf implementation of LOREA costs 70 USD, a drastic reduction in price considering commercial RFID readers cost 2000 USD. LOREA's range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4 km when the tag is located at 1 m distance from a 28 dBm carrier source while consuming 70 μW at the tag. When the tag is equidistant from the carrier source and the receiver, we can communicate upto 75 m, a significant improvement over existing RFID readers.

The sensing capabilities of an Internet-of-Things (IoT) network are usually fixed at deployment. Adding new sensing modalities is a cumbersome process because it requires altering the deployed hardware. We introduce passive sensor tags that allow to easily and seamlessly add new sensors to existing IoT deployments without requiring hardware modifications or additional energy sources. Passive sensor tags employ backscatter communication to generate transmissions that can be decoded by the radio transceivers present in today's IoT devices. Furthermore, unlike recent works, our approach does not require dedicated infrastructure to generate the unmodulated carrier used for backscatter communication.

Visual codes are used to embed digital data in physical objects, or they are shown in video sequences to transfer data over screen/camera links. Existing codes either carry limited data to make them robust against a range of channel conditions (e.g., low camera quality or long distances), or they support a high data capacity but only work over a narrow range of channel conditions. We present FOCUS, a new code design that does not require this explicit trade-off between code capacity and the reader's channel quality. Instead, FOCUS builds on concepts from OFDM to encode data at different levels of spatial detail. This enables each reader to decode as much data from a code as its channel quality allows. We build a prototype of FOCUS devices and evaluate it experimentally. Our results show that FOCUS gracefully adapts to the reader's channel, and that it provides a significant performance improvement over recently proposed designs, including Strata and PixNet.

Visual codes, such as QR codes, offer a low-cost alternative to RF technology when digital data needs to be embedded in objects in the physical world. However, in order to support receivers with a poor visual channel, e.g. low-resolution cameras, most visual codes are designed for low data capacity and short reading distances. We present our work on Focus, a visual code that avoids earlier work's explicit trade-off between code capacity and channel quality. Rather than encoding the payload directly into individual pixels, Focus encodes the payload over a range of spatial frequencies. As a result, even a receiver with a very poor visual channel (e.g., a low-resolution camera, or a camera experiencing motion blur) can still partly decode a Focus code, because the code's low-frequency components are robust to common channel impairments. A receiver with a good channel can decode all data from the same code.

Sensor networks that operate in the unlicensed 2.4~GHz frequency band suffer cross-technology radio interference from a variety of devices, e.g., Bluetooth headsets, laptops using WiFi, or microwave ovens. Such interference has been shown to significantly degrade network performance. We present SoNIC, a system that enables resource-limited sensor nodes to detect the type of interference they are exposed to and select an appropriate mitigation strategy. The key insight underlying SoNIC is that different interferers disrupt individual 802.15.4 packets in characteristic ways that can be detected by sensor nodes. In contrast to existing approaches to interference detection, SoNIC does not rely on active spectrum sampling or additional hardware, making it lightweight and energy-efficient.

Outdoor wireless sensor networks are all exposed to a constantly changing environment that influences the performance of the network. In this paper, we study how variations in meteorological conditions influence IEEE 802.15.4 links. We show that the performance varies over both long and short periods of time, and correlate these variations to changes in meteorological conditions. The case study is based on six months of data from a sensor network deployed next to a meteorological research station running a continuous experiment, collecting both high-quality link and meteorological measurements. We present observations from the deployment, highlighting variations in packet reception ratio and signal strength. Furthermore, we show how the variations correlate with four selected meteorological factors, temperature, absolute humidity, precipitation and sunlight. Our results show that packet reception ratio and signal strength correlate the most with temperature and the correlation with other factors are less pronounced. We also identify a diurnal cycle as well as a seasonal variation in the packet reception ratio aggregated over all links. We discuss the implication of the findings and how they can be used when designing wireless sensor networks.

The main task of most deployed wireless sensor networks is data collection. While a number of solutions have been designed for static networks, there are currently no widely used data collection algorithms for mobile sensor networks. In this paper, we concentrate on scenarios where many nodes, both data sources and sinks, move along a certain track in one direction, a scenario that is common in sports events. Rather than designing a new protocol from scratch, we extend an existing data collection protocol with lightweight mechanisms to make it efficient for mobility. Our extensive simulations and results in a test bed that includes mobile robots demonstrate that our solution is able to achieve high packet delivery rates at low energy consumption. For our target scenario, our solution more than doubles packet delivery rates when the network is sparse. Our solution also works well in scenarios with a higher degree of mobility where nodes move according to a more demanding random waypoint model.

Many sensor network application scenarios include mobile nodes, such as a moving sink. Evaluation of such applications in a testbed is challenging as the testbed has to support mobile nodes. Here, we present Sensei-UU, a sensor network testbed that supports mobile sensor nodes. The testbed is inexpensive, relocatable and possible to reproduce by other researchers. Its primary design objectives are to support experiments with repeatable mobility and relocating the testbed deployment to different locations. Mobile sensor nodes are carried by robots that use floor markings for navigation and localization. The testbed can be used to evaluate applications in which sensor nodes move in the order of meters rather than millimeters, e.g. when a human carries a mobile phone that collects data while passing stationary sensor nodes. To investigate the repeatability of robot movements, we measure the achieved precision and the timing of the robots, and find that our robot localization is accurate to ±1 cm. Furthermore, we investigate variations in radio signal strengths between mobile and stationary nodes. We study the impact of imprecise movements, external sources of interference and environmental influences. We conclude that Sensei-UU supports experiments in which these variations are acceptably low to capture small-scale fading phenomena in IEEE 802.15.4.

The main purpose of wireless sensor networks (WSNs) is to obtain information about their environment. However, WSNs often produce imprecise and incorrect sensor data, e.g. because of sensor failure or unreliable radio communication. We propose a system for WSN applications that allows to assess the quality of sensor data and further allows to fuse data based on their estimated quality. Our system comprises local and distributed heuristics to estimate the quality of sensor data, with a focus on data accuracy and data consistency. In the fusion step, the most plausible value of the measured quantity is inferred from multiple sensor readings by use of the Dempster-Shafer theory of evidence. Both quality assessment and data fusion are carried out within the network and thus do not rely on a powerful sink node. We demonstrate the effectiveness of our system by means of a wireless game controller for the game Pong, built from multiple sensor nodes. The controller can detect and reject incorrect sensor readings and thus improve the player's control over the in-game paddle.