Centimeter-level positioning accuracy provided by real-time kinematic (RTK) systems can support emerging IoT applications that require high-resolution spatial monitoring and precise geo-referenced sensor reporting. However, the high data rate requirements of traditional RTK protocols prevent their use in IoT networks with limited communication capacity and regulatory transmission constraints, such as LoRaWAN (Long Range Wide Area Network). We propose an RTK-over-LoRaWAN architecture that significantly reduces the data volume required for RTK positioning through adaptive RTCM scheduling. We conduct extensive field experiments with baseline distances up to 10km to validate that our method maintains centimeter-level accuracy under sparse RTCM delivery. The results show that RTK initialization requires only 500 B to 1000 B of total downlink payload, while extended RTK tracking is sustained with an effective downlink throughput below 15 B/s, reducing data requirements to under 5% compared to conventional RTK implementations. Our approach meets regulatory constraints such as those defined for the EU868 region, achieving duty cycles well below 1% per hour and enabling the practical deployment of RTK-based positioning in LoRaWAN networks.

Structural health monitoring and predictive maintenance promise to significantly reduce costs, increase availability, and improve safety of civil infrastructure and industrial facilities. Wireless and batteryless sensors fuel these applications with the required data. As metal structures often shield sensor nodes from electromagnetic waves, acoustic power and data transfer are promising alternatives. However, the metal channel suffers from severe multipath propagation, limiting data rates to typically less than 200 bits -1 . We investigate wideband pulses as alternative to commonly used continuous wave modulation schemes to increase robustness. In a simulation study, we first compare wideband modulation with narrowband modulation schemes and assess their robustness against noise and clock deviations. We then construct a wirelessly powered tag prototype to validate the simulation results in real-world metal channels. Furthermore, we propose a reader-based synchronization scheme to mitigate clock mismatch, which is inevitable with ultra-low-power tags. The results show that wideband pulse-position modulation is the most favorable modulation scheme, increasing the median data rates in many scenarios by 132% over continuous-wave schemes. Additionally, wideband pulses are advantageous for power transfer in the highly frequency-selective channel when optimal carrier frequencies are yet unknown. However, simultaneous power transfer and communication on the same transducer are shown to interfere with each other, complicating the receiver design.

Autonomous underwater vehicles (AUVs) are being researched for maritime applications such as ocean exploration and underwater maintenance. A swarm of AUVs is used for complex missions, with each AUV given a specific part of the mission. Energy-aware path planning is crucial for increasing operation time and reliability. This article discusses different path planning strategies for AUV swarms, comparing a particle swarm optimizer (PSO) and a greedy approach to determine the most energy-efficient route. One challenge here is to divide the total number of waypoints among the AUVs reasonably. For this purpose, we investigate a k-means algorithm and an extended variant of the PSO, in which we distribute the clusters dynamic according to a balanced metric. The aim is to find an algorithm that can be used for different applications and validated by different test scenarios focussing on energy consumption and mission fulfillment.

Energy-harvesting wireless sensors require energy management due to volatile energy sources. Existing energy managers lack adaptability to changing utility requirements or often rely on manually defined utility profiles. To address this, we study an autonomous energy manager that learns utility profiles dynamically, without the need for prior data. The new energy manager ensures that devices adapt to evolving utility needs, extending their operational capabilities in changing environments.

Navigating autonomous underwater vehicles (AUVs) in shallow and harbor waters is challenging and typically has higher accuracy requirements than navigation in the open sea. We investigate enhancements to underwater localization techniques based on Two-Way Ranging (TWR) using acoustic modems, which have great potential to meet localization accuracy requirements at lower cost and complexity than current systems. By modifying the Extended Kalman Filter, we account for dynamic positioning errors that occur during the movement of the localization target, i.e., the underwater vehicle, and the fact that distance measurements with acoustic modems are delayed in time. The method is evaluated numerically and experimentally showing an accuracy improvement of about 20 cm compared to the traditional EKF scheme. In real-world tests at ranges below 30 m, the absolute localization accuracy is assessed using an RTK-GPS reference, showing that a positioning error below 35 cm can be achieved in a quasi-static test, while in a dynamic test the tracking error is mostly below 75 cm.

Considering realistic characteristics of acoustic localization methods is crucial for roboticists when developing guidance and control algorithms for small and agile underwater robots. Current simulators either rely purely on geometric distancing, i.e. do not consider dynamic effects such as robot motion during acoustic signal propagation, or they are too complex for usage by non-communication experts and, thus, vulnerable to misconfiguration. We propose an open ROS-based framework that extends existing robot simulators (e. g. Gazebo) by simulating the effects of realistic acoustic ranging for underwater robot localization. Thus, our simulator enables realistic real-time analysis and evaluation of guidance, navigation, and control algorithms in software in-the-loop systems. For this purpose, we incorporate and encapsulate the non-trivial characteristics of acoustic communication and ranging such as robot motion during signal propagation, packet reception failure, and modem timings. This ensures the applicability of the tool by roboticists who are typically non-experts in acoustic communication and guarantees accurate and realistic simulation results. We demonstrate the functionality and performance of our framework and validate it on real-world experimental data on the example of a two-way ranging method. Our open-source release includes well-defined interfaces and parameters as well as a tutorial. This targets other roboticists who can either use our framework directly or easily adapt it to their individual setup, e. g., by adding further acoustic-ranging protocols.

Backscatter communication enables miniature, batteryless, and low-cost wireless sensors. Since electromagnetic waves are strongly attenuated in several scenarios, backscatter communication in metals via acoustic waves can leverage various applications, e. g., in structural health monitoring. When backscattering, the Tag has little control over the modulation it performs on the carrier wave. Therefore, existing approaches commonly employ differential binary modulation schemes, limiting the achievable data rates. To overcome this limitation, we derive a channel model that accurately describes the modulation in an acoustic backscatter channel---as, e. g., found in steel beams---and leverage it to achieve higher-order load modulation. We present an open-source Reader and Tag pair prototype based on COTS components that we have developed for communication and on-the-fly channel characterization. We explore the influence of various parameters on communication performance on different channels. Moreover, (i) we are the first to demonstrate that acoustic backscatter is feasible in guided-wave channels, covering up to 3 meters, and that (ii) our modulation scheme achieves up to 211% higher data rates than binary modulation schemes, and (iii) provides reliable communication through channel coding.

The recent development of small, cheap AUVs enables a plethora of underwater near- and inshore applications. Among these are monitoring of wind parks, detection of pollution sources, water-quality inspection, and the support of divers during disaster management. These tasks profit from online reporting, control, and AUV swarm interaction; yet they require underwater communication. Unfortunately, commercial devices are prohibitively expensive and typically closed-source, hampering their application in affordable products and research. Therefore, we developed the open-source ahoi acoustic modem. It is (i) small enough to be carried by micro AUVs, (ii) consumes little enough energy to not diminish operation times of its host, (iii) comes at an attractive unit cost below $600, (iv) can reliably communicate at distances of 150 m and more, and (v) supports ranging without additional hardware. Due to its modular build, the modem can be customized and is suitable as research platform to analyze, e.g., MAC and routing protocols. We conducted extensive real-world studies and present results of communication range, packet reception rate, ranging accuracy, and efficient and reliable self-localization. Finally, we draw conclusions regarding acoustic communication, ranging, and localization with inexpensive and low-power devices that go beyond a particular device. Our study, hence, encompasses general insights, observations, and recommendations.

The restricted energy budget of energy-harvesting sensor nodes demands algorithms for adaptive duty-cycling. However, their comparison and development is hindered by the lack of reproducibility of environmental conditions. We enable replaying recorded light conditions by building an affordable light box. Our self-developed control circuit and high power LEDs allow us to repeatedly replay real environmental illumination data through current and voltage traces. This allows us to directly compare the behavior of nodes running different energy-aware and -predictive algorithms.

On-board commercial passenger aircraft Wireless Sensor Networks (WSNs) are anticipated to be used for implementing machine-to-machine communication also referred to as Wireless Avionics Intra-Communications (WAIC). These systems enable safety-related wireless avionics and aim to reduce electrical wiring harness contributing by 5% of the total weight of an aircraft. The globally harmonized frequency band designated for WAIC usage is shared with aeronautical Radio Altimeters (RAs). Literature lacks consideration of the impact of on-board RAs on WAIC systems; thus, we close this gap by performing a detailed study and propose two mitigation techniques based on channel hopping. Our simulations show that harmful RA signals infer doubled to tripled delays as well as packet error rates up to 90% when WAIC systems use the frequency band without applying appropriate techniques for increasing communication robustness. With the developed mitigation techniques, we show delays can be kept at levels comparable to non-interfered performance while increasing the usable spectrum by 50% simultaneously. Our evaluations show that the presented mitigation techniques enable reliable usage of WAIC systems in commercial aircraft allowing increased spectrum usage.

Complementing the topics of ACM SenSys 2015, the 3rd International Workshop on Energy Harvesting and Energy Neutral Sensing Systems (ENSsys) 2015 brings together researchers to explore the challenges, issues and opportunities in the research, design, and engineering of energy-harvesting and energy-neutral sensing systems. These are an enabling technology for future applications in smart energy, transportation, environmental monitoring and smart cities. Innovative solutions in hardware for energy scavenging, adaptive algorithms, and power management policies are needed to enable uninterrupted operation. This one-day workshop features invited and peer reviewed talks on these areas, and provides a forum for feedback, discussion and networking.

Many underwater monitoring tasks, such as submarine life studies and pipeline inspections, are usually performed manually. Automated underwater monitoring has the potential to increase safety, improve timeliness, and decrease costs. We propose a hybrid solution of stationary sensor buoys and swarms of autonomous underwater vehicles (AUV) and report on our current progress of its realization. Our solution is based on sensor network technology and a small mobile underwater robot developed in our institute.

Traditionally, wireless sensor networks collect readings from distributed embedded sensing systems and forward them to one or more sink nodes. While many energy-efficient data collection protocols have emerged as a result, the transmission of control commands from a sink to individual nodes in the network is generally unsupported by these solutions. We present how the receiver-initiated opportunistic ORiNoCo data collection protocol can be extended to allow for the reconfiguration of nodes at minimal additional energy overhead. When our solution is being applied, adaptations of the sensor sampling rates or node sleep cycles can be easily controlled by the base station during runtime.

A sustainable, uniform, and utility-maximizing operation of energy-harvesting sensor networks requires methods for aligning consumption with harvest. This article presents a lightweight algorithm for online load adaptation of energy-harvesting sensor nodes using supercapacitors as energy buffers. The algorithm capitalizes on the elementary relationship between state of charge and voltage that is characteristic for supercapacitors. It is particularly designed to handle the nonlinear system model, and it is lightweight enough to run on low-power sensor node hardware. We define two energy policies, evaluate their performance using real-world solar-harvesting traces, and analyze the influence of the supercapacitor’s capacity and imprecisions in harvest forecasts. To show the practical merit of our algorithm, we devise a load adaptation scheme for multihop data collection sensor networks and run a 4-week field test. The results show that (i) choosing a duty cycle a priori is infeasible, (ii) our algorithm increases the achievable work load of a node when using forecasts, (iii) uniform and steady operation is achieved, and (iv) depletion can be prevented in most cases.