Because of the ubiquity of Internet of Things (IoT) devices, the power consumption and security of IoT systems have become very important issues. Advanced Encryption Standard (AES) is a block cipher algorithm is commonly used in IoT devices. In this paper, the power consumption and cryptographic calculation requirement for different payload lengths and AES encryption types are analyzed. These types include software-based AES-CB, hardware-based AES-ECB (Electronic Codebook Mode), and hardware-based AES-CCM (Counter with CBC-MAC Mode). The calculation requirement and power consumption for these AES encryption types are measured on the Texas Instruments LAUNCHXL-CC1310 platform. The experimental results show that the hardware-based AES performs better than the software-based AES in terms of power consumption and calculation cycle requirements. In addition, in terms of AES mode selection, the AES-CCM-MIC64 mode may be a better choice if the IoT device is considering security, encryption calculation requirement, and low power consumption at the same time. However, if the IoT device is pursuing lower power and the payload length is generally less than 16 bytes, then AES-ECB could be considered.

As the technology of Internet of Things (IoT) becomes popular, the number of sensor nodes also increases. The network coverage, extensibility, and reliability are also the key points of technical development. To address the challenge of environmental restriction and deployment cost, most sensor nodes are powered by batteries. Therefore, the low-power consumption becomes an important issue because of the finite value of battery capacity. In addition, significant interference occurs in the environment, thereby complicating reliable wireless communication. This study proposes a fuzzy-based adaptive data rate for the transmission power control in wireless sensor networks to balance the communication quality and power consumption. The error count and error interval perform the inputs of a fuzzy system and the corresponding fuzzy system output is guard that is utilized for limiting the upper bounds of data rate and transmission power. The long-term experimental results are introduced to demonstrate that the control algorithm can overcome environmental interference and obtain low-power performance. The sensor nodes have reliable communication under an ultra-low-power consumption. The experimental results show that the total power consumption of the proposed approach has been improved 73% compared with the system without executing the algorithm and also indicate the Packet Error Rate (PER) is close to 1%. Therefore, the proposed method is suitable for the battery supply IoT system.

This study aims to implement a system-on-chip (SoC) detection system for tool wear monitoring and alarms for high-precision machining processes. The proposed deep learning approach is trained by the collected sensors from real performed in a three-axial computer numerical control (CNC) machine center combined with different conditions of spindle speed and tightening torque. The corresponding vibrational and sound signals were collected by a three-axial accelerometer and micro-electro-mechanical-system (MEMS) microphone, and then the tool flank wear was measured by a camera. In this study, the flank wear was designed for early detection according to the ISO 8688-2:1989 standard. A deep learning model with frequency spectrum inputs of the collected signals was developed for tool wear prediction. In addition, to treat the machining variation for detection, two sensor fusion approaches are presented and implemented on an SoC-board (Pocket Beagle) for landing and cost reduction. The corresponding average detection accuracies were approximately 99.7% and 87.75% for the single and merged models, respectively. The results demonstrated the effectiveness and performance of the proposed approach.

This paper introduces a real-time processing and classification of raw sensor data using a convolutional neural network (CNN). The established system is a microcontroller-unit (MCU) implementation of an intelligent gripper for shape identification of grasped objects. The pneumatic gripper has two embedded accelerometers to sense acceleration (in the form of vibration signals) on the jaws for identification. The raw data is firstly transferred into images by short-time Fourier transform (STFT), and then the CNN algorithm is adopted to extract features for classifying objects. In addition, the hyperparameters of the CNN are optimized to ensure hardware implementation. Finally, the proposed artificial intelligent model is implemented on a MCU (Renesas RX65N) from raw data to classification. Experimental results and discussions are introduced to show the performance and effectiveness of our proposed approach.

User behaviour and choice is a significant parameter in the consumption patterns of energy in the built environment. This paper introduces a behavior-based approach for developing smart energy applications. With the rapid development of wireless sensor networks and the Internet of Things (IoT), human-computer interfaces can be created through the mapping of user experiences. These applications can provide users with dynamic feedback on their energy consumption patterns in their built environment. The paper describes a “Sensible Energy System” (SENS) that is based on user experience design methods with sensor network technology. Through SENS, solar energy simulation is combined with device consumption data in order to achieve an IoT network to facilitate the interaction between user behaviors and electricity consumption. The interaction between users and devices through SENS can not only optimize power consumption, but also provide consumers with additional choice and dynamic decision making regarding their consumption. This article provides an (1) understanding and analysis of users’ spatial interaction, explains the (2) planning of the new smart environment design and user experiences, discusses (3) designing a suitable Wireless sensor network (WSN) agent and energy connection, describes (4) the information that has been collected, and (5) incorporates a rooftop solar potential simulation for predicting energy outputs into the sensor network model.