Edge computing has recently emerged as a promising paradigm to support mobile access in Internet of Things (IoT) multinetworks, where heterogeneous wireless communication solutions coexist. Meanwhile, software-defined networking (SDN) presents a potential infrastructure to monitor and manage mobile edge computing. However, resilient access in the integrated IoT-Edge-SDN environment is a key challenge. In this article, we present location-aware spatio-temporal solution (LASTS) as an edge computing-empowered software-defined system to scalably control mobile IoT access and detect sequential anomaly. LASTS utilizes a Personal access point protocol to enable switching between multiple networks. In addition, it supports efficient control and transfer of the mobile device’s spatio-temporal context. This context information plays an important role in a deep learning model employed for sequential anomaly detection in the LASTS system. Realistic testbed experiments confirm that LASTS can successfully achieve scalable access control and sequential anomaly detection in mobile IoT.

This study presents an innovative offset-trimmed helicoids (OTH) structure, featuring a tunable deformation center that emulates the flexibility of human fingers. This design significantly reduces the actuation force needed for larger elastic deformations, particularly when dealing with harder materials like thermoplastic polyurethane (TPU). The incorporation of two helically routed tendons within the finger enables both in- plane bending and lateral out-of-plane transitions, effectively expanding its workspace and allowing for variable curvature along its length. Compliance analysis indicates that the compliance at the fingertip can be fine-tuned by adjusting the mounting placement of the fingers. This customization enhances the gripper's adaptability to a diverse range of objects. By leveraging TPU's substantial elastic energy storage capacity, the gripper is capable of dynamically rotating objects at high speeds, achieving approximately 60° in just 15 milliseconds. The three-finger gripper, with its high dexterity across six degrees of freedom, has demonstrated the capability to successfully perform intricate tasks. One such example is the adept spinning of a rod within the gripper's grasp.

To exploit the compliant capabilities of soft robot arms we require controller which can exploit their physical capabilities. Teleoperation, leveraging a human in the loop, is a key step towards achieving more complex control strategies. Whilst teleoperation is widely used for rigid robots, for soft robots we require teleoperation methods where the configuration of the whole body is considered. We propose a method of using an identical ‘physical twin’, or demonstrator of the robot. This tendon robot can be back-driven, with the tendon lengths providing configuration perception, and enabling a direct map-ping of tendon lengths for the execture. We demonstrate how this teleoperation across the entire configuration of the robot enables complex interactions with exploit the envrionment, such as squeezing into gaps. We also show how this method can generalize to robots which are a larger scale that the physical twin, and how, tuneability of the stiffness properties of the physical twin simplify its use.

Modular soft robot arms (MSRAs) are composed of multiple modules connected in a sequence, and they can bend at different angles in various directions. This capability allows MSRAs to perform more intricate tasks than single-module robots. However, the modular structure also induces challenges in accurate planning and control. Nonlinearity and hysteresis complicate the physical model, while the modular structure and increased DOFs further lead to cumulative errors along the sequence. To address these challenges, we propose a versatile configuration space planning and control strategy for MSRAs, named$S2C2A$(State to Configuration to Action). Our approach formulates an optimization problem,$S2C$(State to Configuration planning), which integrates various loss functions and a forward model based on biLSTM to generate configuration trajectories based on target states. A configuration controller$C2A$(Configuration to Action control) based on biLSTM is implemented to follow the planned configuration trajectories, leveraging only inaccurate internal sensing feedback. We validate our strategy using a cable-driven MSRA, demonstrating its ability to perform diverse offline tasks such as position and orientation control and obstacle avoidance. Furthermore, our strategy endows MSRA with online interaction capability with targets and obstacles. Future work focuses on addressing MSRA challenges, such as more accurate physical models.

With the development of artificial intelligence and the improvement of hardware computing power, deep learning models have become widely used in the Internet of Things (IoT) field, especially for analyzing spatiotemporal data collected by wireless sensors. Recurrent neural networks (RNNs) such as long short-term memory (LSTM) network are generally used for these time-series data. Hyperparameter settings of model training are regarded as essential factors for the performance of deep learning models. Manually optimizing hyperparameters not only cost more resources but also be more likely to set hyperparameters that follow stereotypes, resulting in unreasonable hyperparameter settings and poor model performance. As one of the most important fields in automated machine learning research, automated hyperparameter optimization (HPO) mainly includes grid search, hyperparameter search based on genetic algorithm, etc. Whereas these methods have their own drawbacks. In this article, an automated HPO method based on parallel genetic algorithm (PGA) is proposed. According to the process of PGA, this article divided HPO into several stages, including population initialization, fitness function, tournament selection, crossover operators, mutation operators, subgroup exchange, and end of evolution. Then, the proposed HPO method is implemented in LSTM models and tested on two different time-series datasets collected by real-world IoT sensors. By comparing our proposed method with other mainstream HPO methods in different datasets, it is proved that our HPO method based on PGA shows a better performance on both time costs and prediction results.