Communication load is a limiting factor in many real-time systems. Event-triggered state estimation and eventtriggered learning methods reduce network communication by sending information only when it cannot be adequately predicted based on previously transmitted data. This paper proposes an event-triggered learning approach for nonlinear discretetime systems with cyclic excitation. The method automatically recognizes cyclic patterns in data -even when they change repeatedly -and reduces communication load whenever the current data can be accurately predicted from previous cycles. Nonetheless, a bounded error between original and received signal is guaranteed. The cyclic excitation model, which is used for predictions, is updated hierarchically, i.e., a full model update is only performed if updating a small number of model parameters is not sufficient. A nonparametric statistical test enforces that model updates happen only if the cyclic excitation changed with high probability. The effectiveness of the proposed methods is demonstrated using the application example of wireless realtime pitch angle measurements of a human foot in a feedbackcontrolled neuroprosthesis. The experimental results show that communication load can be reduced by 70 % while the rootmean-square error between measured and received angle is less than 1 • .Index Terms-sensor networks, statistical learning I. INTRODUCTIONM ANY applications require real-time transmission of signals over communication channels with bandwidth limitations. A typical example is given by wireless sensor networks in feedback-controlled systems. The number of agents (i.e., network nodes) and their communication rate is limited by the amount of information the wireless network can transmit in real-time. It is, therefore, desirable to reduce the communication load without compromising the accuracy of the transmitted signals.Well known approaches are event-based sampling [1]-[3] and event-triggered state estimation (ETSE [4]-[6], sometimes referred to as model-based event-based sampling [7]): At each sampling instant, the receiving agent independently predicts the state,

Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load via data compression and event-based communication approaches. The present paper focuses on the class of applications in which the signals exhibit unknown and potentially time-varying cyclic patterns. We review recently proposed event-triggered learning (ETL) methods that identify and exploit these cyclic patterns, we show how these methods can be applied to the nonlinear multivariable dynamics of three-dimensional orientation data, and we propose a novel approach that uses Gaussian process models. In contrast to other approaches, all three ETL methods work in real time and assure a small upper bound on the reconstruction error. The proposed methods are compared to several conventional approaches in experimental data from human subjects walking with a wearable inertial sensor network. They are found to reduce the communication load by 60–70%, which implies that two to three times more sensor nodes could be used at the same bandwidth.

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Location tracking with global navigation satellite systems (GNSS), such as GPS, is used in many applications, including the tracking of wild animals for research. Snapshot GNSS is a technique that only requires milliseconds of satellite signals to infer the position of a receiver. This is ideal for low-power applications such as animal tracking. However, there are few existing snapshot systems, none of which is open source.To address this, we developed SnapperGpS, a fully open-source, low-cost, and lowpower location tracking system designed for wildlife tracking. SnapperGpS comprises three parts, all of which are open-source: (i) a small, low-cost, and low-power receiver; (ii) a web application to configure the receiver via USB; and (iii) a cloud-based platform for processing recorded data. This paper presents the hardware side of this project.The total component cost of the receiver is under $30, making it feasible for field work with restricted budgets and low recovery rates. The receiver records very short and lowresolution samples resulting in particularly low power consumption, outperforming existing systems. It can run for more than a year on a 40 mAh battery.We evaluated SnapperGpS in controlled static and dynamic tests in a semi-urban environment where it achieved median errors of 12 m. Additionally, SnapperGpS has already been deployed for two wildlife tracking studies on sea turtles and sea birds. METADATA OVERVIEWMain design files: https://github.com/SnapperGPS/snappergps-pcb Target group: biologists tracking animal movement Skills required: PCB manufacturing and assembly (can be outsourced) -advanced; Replication: this hardware has been replicated by every author. See section "Build Details" for more detail.

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