The Routing Protocol for Low power and Lossy Networks (RPL) is a wide-spread, flexible routing protocol specifically designed for energy-constrained networks such as Wireless Sensor Networks (WSNs). The resilience of RPL has been verified in several existing evaluations, but how a simple node reset can impact the energy efficiency has not been investigated so far. Thus, in this paper we examine the energy costs of transient node failures in a RPL network. We show that such temporary node failures can have a significant impact on the network's total energy usage. As transient node failures are common in WSNs this work reveals an important weakness of RPL which can even be exploited for attacks.

Being able to determine the location of a node is of great advantage in many IoT and WSN applications. For example, in health care scenarios or for autonomous configuration of IoT setups this information can be useful. One of the key challenges in localization is to estimate the distance between nodes. Most present indoor localization systems require additional hardware for this estimation which is costly in terms of money and energy consumption. To overcome this disadvantage, we developed a system which is able to perform distance measurements without adding any extra hardware and costs. It is based on phase measurements performed by the IEEE 802.15.4 transceiver chip that is normally solely used to realize communication. In the evaluation we investigate the performance of our system in different real world environments that are typical for IoT and WSN setups.

The energy consumption of Wireless Sensor Networks (WSNs) correlates with the voltage level at which the nodes are powered—a lowered voltage leads to a prolonged lifetime of nodes and networks. Operating nodes at voltage levels below the recommendation—so-called undervolting—saves energy but is accompanied by an increased risk of failures. In this article, we show that a WSN can still work reliably, even if the voltage recommendations are violated. We show that there is a correlation between temperature and error-proneness at the same voltage level and that ideal voltage levels depend on environmental conditions. Especially in outdoor deployments, temperatures fluctuate often and heavily. Additionally, we show that these ideal voltage levels are different for each individual node. To deal with these individual variations and varying temperatures, we present a supervised learning strategy that is able to keep the nodes in uncritical states even if environmental conditions are constantly and heavily changing while saving as much energy as possible by constantly adapting the voltage level of these nodes to an individually ideal level. All measurements were performed on sensor node prototypes that are also presented in this article. In detailed evaluations, it is shown (i) that a single node will never run in the same unsafe state twice, (ii) that only five known bias points are needed to predict nearly all temperature dependencies for an individual node, and, thus, (iii) that a system of undervolted nodes can be as reliable as a conventionally powered network while prolonging the lifetime by more than 40%.

Body area networks (BANs) consisting of small sensing and computing devices can help to improve medical care and well-being of humans. Obviously, the data recorded in BANs such as vital parameters are personal and should be private. To protect this privacy, such data are usually encrypted when transmitting it over a wireless link. In the past, many cryptographic algorithms and methods for the encryption and decryption of data have been proposed-and most of them have become obsolete. One-time pads (OTPs) were mathematically proven to be secure and impossible to crack. But, for most purposes, OTPs are complicated to handle, because if applied correctly, for each bit of plain text data, another bit of OTP must be available. Sensors in BANs usually do not generate an enormous amount of data. Hence, also the length of an OTP is not huge. Sustained progress in memory technology makes large amounts of nonvolatile memory available at cheap prices and small sizes. Thus, numerous OTPs can be preinstalled on BAN devices and ensure a long lasting and secure data transmission. In this paper, we present a concept for securing data transmission in BANs by utilizing OTPs. We delineate a system for generation, distribution, and utilization of OTPs in wireless sensor network (WSN) and BAN scenarios, and we show the implementation and evaluation of such a system.

Adapting the voltage level of a processing unit to the actual system load leads to an increased energy efficiency. The aim is to extend the battery lifetime, but existing voltage scaling approaches still act with reserve. To reduce the energy consumption even further, it is possible to power the electric circuits below the specified voltage levels. Considering the processing unit of wireless sensor nodes, this “undervolting” saves up to 42% of energy per clock cycle, and hence would lead to a significant longer lifetime of nodes and networks. Contrariwise, operating processors or nodes outside their specifications add some extra incertitude to the system. In this paper, we analyze the effects of undervolting for a typical wireless sensor node in theory and practice. Moreover, we give a detailed insight into the dependencies and used a prototype implementation to characterize the influence of lower than recommended voltage levels on the microcontroller unit (MCU). In addition, the impact of different temperatures is considered as well as the behavior of an undervolted transceiver unit and, therefore, the effects on the wireless communication. While classical computer applications may contain too many hazards to outweigh the improved energy consumption when using undervolting, we show that it is particularly suitable for wireless sensor networks (WSNs) with a huge potential of saving energy and the opportunity of novel power management approaches on every layer.

Typical home automation systems consist of dense populated sensors and actuators in wireless networks. In apartment houses, networks are often deployed closely to each other by different neighbors. In such scenarios wireless networks suffer from packet loss caused by interference with other networks. Packet loss causes unnecessary delay and energy consumption. To overcome this, transmission power control can be used to lower the interference. However, reducing the interference is a rarely used capability of transmission power control. This paper presents transmission power control based on a P-controller. The feedback loop for the signal-to-noise ratio is realized by modifying the link layer acknowledgments of IEEE 802.15.4. The evaluation of the system is performed in a real-world testbed. It shows the benefit of reducing the transmission power in dense networks. The application layer loss can be lowered by 5%.

In mobile applications, like wireless sensor networks, it is often inevitable to use a location-independent source of energy. Wherever the batteries capacity or inefficient energy harvesting limits the lifetime, other solutions have to be implemented to increase the energy efficiency. Dynamic Voltage Scaling (DVS) is a well known technique with the ability to adapt the voltage level to the actual system load to save energy. While it has been used in many application areas, DVS has not been studied sufficiently for wireless sensor nodes. In this paper, we present a method to estimate the gain of DVS. We study DVS theoretically as well as in a practical manner, i.e., we using a prototype implementation of a DVS capable wireless sensor node. With the theoretical considerations we were able to optimize DVS for an 8-Bit ULP micro controller by a combination of DVS and dynamic power management (DPM). In addition, we proved the calculated results with an extensive practical evaluation of the concept using a prototypical implementation. Moreover, we analysed the effect of voltage scaling on other components of the sensor node (transceiver, sensors and memory).

In mobile applications, like wireless sensor networks, it is often inevitable to use a location-independent source of energy. Wherever the batteries capacity or inefficient energy harvesting limits the lifetime, other solutions have to be implemented to increase the energy efficiency. Dynamic Voltage Scaling (DVS) is a well known technique with the ability to adapt the voltage level to the actual system load to save energy. While it has been used in many application areas, DVS has not been studied sufficiently for wireless sensor nodes. In this demo, we show the benefits of DVS. We were able to optimize DVS for an 8-Bit ULP micro controller by a combination of DVS and dynamic power management (DPM). Moreover, we analyzed the effect of voltage scaling on other components of the sensor node (transceiver, sensors and memory).

Today, many sensor nodes are equipped with a microSD slot to provide a cost effective way to store large amounts of data. When using the FAT file system, data collected by a node can be easily read by a PC without the need for any special software or communication protocol. While several FAT implementations for microcontrollers do exist, they are not suited for real-time applications. For a WSN in an industrial scenario where a node needs to run a closed loop control program, logging to a non-real-time capable persistent storage system is not an option. In this paper we present RATFAT, an efficient implementation of a flexible real-time capable FAT file system for Contiki, that can be used for applications requiring real-time guarantees.

While for an automatic and autonomous indoor fall detection an enormous amount of different sensors is conceivable, for an outdoor fall detection only wearable sensors are applicable. For practical uses cases, it would be very beneficial if after a detected fall a medical alert is initiated. In indoor environments, a base station connected to a telephone line can handle these calls. On the road, this can be achieved by mobile phones or smartphones. In this paper we first estimate the suitability of several systems for an outdoor fall detection. Then, we propose a system for a mobile and stationary fall detection and alerting system. We implemented and evaluated this system, which is consisting of a smartphone and a wireless sensor node.

We consider the recognition of activities from passive entities by analysing radio-frequency (RF)-channel fluctuation. In particular, we focus on the recognition of activities by active Software-defined-radio (SDR)-based Device-free Activity Recognition (DFAR) systems and investigate the localisation of activities performed, the generalisation of features for alternative environments and the distinction between walking speeds. Furthermore, we conduct case studies for Received Signal Strength (RSS)-based active and continuous signal-based passive systems to exploit the accuracy decrease in these related cases. All systems are compared to an accelerometer-based recognition system.

Delay or disruption tolerant protocols can be used in several application areas. In the area of Ambient Assisted Living (AAL) or healthcare, these protocols are well-suited to provide a seamless handover between online and offline monitoring of vital parameters or activity data; this also implies a synchronization of offline gathered data. In this paper, we analyze the expected data rates for several medical sensors and applications and give an insight into how to dimension such disruption tolerant protocols and systems. We also developed a scenario for activity recognition in which we constantly monitor the acceleration data of a wearable sensor node. We implemented and evaluated this scenario with our disruption tolerant protocol for wireless sensor nodes and show its overall functionality, its utility, and its limits.

Today's industrial facilities, such as oil refineries, chemical plants, and factories, rely on wired sensor systems to monitor and control the production processes. The deployment and maintenance of such cabled systems is expensive and inflexible. It is, therefore, desirable to replace or augment these systems using wireless technology, which requires us to overcome significant technical challenges. Process automation and control applications are mission-critical and require timely and reliable data delivery, which is difficult to provide in industrial environments with harsh radio environments. In this article, we present the GINSENG system which implements performance control to allow us to use wireless sensor networks for mission-critical applications in industrial environments. GINSENG is a complete system solution that comprises on-node system software, network protocols, and back-end systems with sophisticated data processing capability. GINSENG assumes that a deployment can be carefully planned. A TDMA-based MAC protocol, tailored to the deployment environment, is employed to provide reliable and timely data delivery. Performance debugging components are used to unintrusively monitor the system performance and identify problems as they occur. The article reports on a real-world deployment of GINSENG in an especially challenging environment of an operational oil refinery in Sines, Portugal. We provide experimental results from this deployment and share the experiences gained. These results demonstate the use of GINSENG for sensing and actuation and allow an assessment of its ability to operate within the required performance bounds. We also identify shortcomings that manifested during the evaluation phase, thus giving a useful perspective on the challenges that have to be overcome in these harsh application settings.

In most of today's healthcare and AAL scenarios a multitude of data is recorded by different sensors for various purposes. By definition, this data is mostly personal and can be assigned to certain persons, which again makes it interesting for shady data miners, hackers, or even health insurance companies. Thus, this data needs special care regarding privacy and security. On the other hand, if the systems are spread over a larger area, a remote access to such systems is essential for the operator. At least a basic system monitoring should be possible and backups to a distant location are beneficial if the distributed systems fail. In this paper we provide concepts and solutions how in distributed installations of personal AAL platforms privacy and security can be preserved, while remote backup and restore functionality and remote system monitoring can be provided.

Delay Tolerant Networking (DTN) enables transfer of data where conventional network protocols fail to deliver data because no continuous end-to-end connectivity is available. While the Bundle Protocol (BP) has been established as the standard DTN protocol in many application areas, Wireless Sensor Networks (WSN) often use proprietary protocols with a subset of the BP features. In this paper we use an exemplary application to demonstrate how the BP can be beneficial for many WSN-based projects. We show, how low-power sensor nodes can transport bundles by exploiting existing movement in the environment. More importantly we show, how 8-bit WSN nodes can seamlessly interact with standard BP implementations running on standard PCs. Our application, a sensor that is installed on the roof of a 15-story building, is using an elevator to transport bundles carrying measured values to our lab. We analytically compare the BP to existing protocols for WSNs, evaluate our application scenario and give insight into principal limitations.

In this demo, we present some exemplary applications for INGA, a new and affordable wireless sensor node for general applications. INGA's hardware design is completely under open source license and it runs the open source operating system Contiki "out of the box". Several up-to-date sensors are included in the basic design and we developed it for research, medical applications and universal usage.