Wildlife Monitoring on the Edge: A Performance Evaluation of Embedded Neural Networks on Microcontrollers for Animal Behavior Classification

Monitoring animals’ behavior living in wild or semi-wild environments is a very interesting subject for biologists who work with them. The difficulty and cost of implanting electronic devices in this kind of animals suggest that these devices must be robust and have low power consumption to increase their battery life as much as possible. Designing a custom smart device that can detect multiple animal behaviors and that meets the mentioned restrictions presents a major challenge that is addressed in this work. We propose an edge-computing solution, which embeds an ANN in a microcontroller that collects data from an IMU sensor to detect three different horse gaits. All the computation is performed in the microcontroller to reduce the amount of data transmitted via wireless radio, since sending information is one of the most power-consuming tasks in this type of devices. Multiples ANNs were implemented and deployed in different microcontroller architectures in order to find the best balance between energy consumption and computing performance. The results show that the embedded networks obtain up to 97.96% ± 1.42% accuracy, achieving an energy efficiency of 450 Mops/s/watt.

Improved authentication method in embedded networks systems. An autonomous vehicle approach

By adding connectivity to in-vehicle networks (including multimedia devices) and external networks (e.g. wireless and Internet) the attack surface was dramatically extended. In this context, there are several types of attacks already been demonstrated on automotive/AV control networks using compromised network connection or physical manipulation. Subsequently, the success attacks violate safety requirements, being able to disrupt system operation or even to take over operational control. In order to avoid false authentication or identity theft of devices or IoT, this paper proposes a time-based authentication method. The proposed method increases the degree of cybersecurity and allows its implementation on independent, low power mobile devices. Finally, a critical conclusion regarding the proposed authentication methods is presented.

Growth and arrest of topological cycles in small physical networks

The chordless cycle sizes of spatially embedded networks are demonstrated to follow an exponential growth law similar to random graphs if the number of nodesNxis below a critical valueN*. For covalent polymer networks, increasing the network size, as measured by the number of cross-link nodes, beyondN*results in a crossover to a new regime in which the characteristic size of the chordless cyclesh*no longer increases. From this result, the onset and intensity of finite-size effects can be predicted from measurement ofh*in large networks. Although such information is largely inaccessible with experiments, the agreement of simulation results from molecular dynamics, Metropolis Monte Carlo, and kinetic Monte Carlo suggests the crossover is a fundamental physical feature which is insensitive to the details of the network generation. These results show random graphs as a promising model to capture structural differences in confined physical networks.

A Survey on Frameworks Used for Robustness Analysis on Interdependent Networks

The analysis of network robustness tackles the problem of studying how a complex network behaves under adverse scenarios, such as failures or attacks. In particular, the analysis of interdependent networks’ robustness focuses on the specific case of the robustness of interacting networks and their emerging behaviors. This survey systematically reviews literature of frameworks that analyze the robustness of interdependent networks published between 2005 and 2017. This review shows that there exists a broad range of interdependent network models, robustness metrics, and studies that can be used to understand the behaviour of different systems under failure or attack. Regarding models, we found that there is a focus on systems where a node in one layer interacts with exactly one node at another layer. In studies, we observed a focus on the network percolation. While among the metrics, we observed a focus on measures that count network elements. Finally, for the networks used to test the frameworks, we found that the focus was on synthetic models, rather than analysis of real network systems. This review suggests opportunities in network research, such as the study of robustness on interdependent networks with multiple interactions and/or spatially embedded networks, and the use of interdependent network models in realistic network scenarios.