The ease of use and flexibility provided by drones or Unmanned Aerial vehicles (UAV) is attracting different industries and researchers across domains (e.g., delivery, agriculture, security, etc.). Although maintaining a reliable and secure command and control communication channel is still an open challenge and primary limitation for using drones. Satellite and 5G are considered viable solutions for drone communication. In this survey paper, we have explored specifications and proposed enhancements in cellular technology specified by 3GPP to command and control UAVs. It also describes the required network Quality of Service (QoS) parameters for drone communication. Such as end-to-end latency to send and receive a command and control message (C2), reliability, and message size. Along with these, it also emphasizes defining the reliability in terms of communication and navigation of UAVs, based on cellular technology 5G additional investigation and standardization should be executed.
The increase in computing power and integration of specialized hardware for Artificial Intelligence (AI) acceleration like Tensor Processing Units (TPU) enable complex machine learning at edge devices in the Internet of Things (IoT). However, wireless portable systems are limited in computing power and battery lifetime. To increase the battery lifetime of edge devices and accelerate inference of IoT systems, many developments focus on combining or outsourcing AI algorithms to a cloud via wireless links e.g. wireless LAN IEEE 802.11ac or mobile network 4G/5G. Due to limitations of restricted wireless transmissions in rural areas mainly below 50 MBit/s, resulting longer transfer times can significantly affect inference latency and energy consumption from the perspective of the IoT edge device and deteriorate the response time of the application. In this work, we provide a prototype setup for image processing via Convolutional Neural Networks (CNN) and investigate inference latency and energy consumption of an IoT edge device with a varying wireless link. The complexity of selected pre-trained CNN models is between 300 MFLOPs to 19.6 GFLOPs where FLOPs are Floating Point Operations. The first experiments address the latency and energy consumption by processing CNN models on the IoT device with and without TPU as edge AI accelerator. Following experiments address the latency and energy consumption on the IoT device in cloud processing mode with and without Graphics Processing Unit (GPU) as cloud AI accelerator. The edge device sends input data and receives the results via wireless link from 1 MBit/s to 50 MBit/s. For CNN models with ≤564 MFLOPs edge processing with AI acceleration performs better than cloud processing regarding latency and energy efficiency. Even for complex CNN models with 7.6 GFLOPs edge processing can be useful at limited wireless link data rates up to 14 MBit/s. Edge processing without AI acceleration is only an option for low complexity ( ≤300 MFLOPs) and low expected wireless link data rates.
Bluetooth Low Energy (BLE) is one of the technology for indoor positioning. The coordinates in indoor positioning are calculated from the radio signal strength, although distance errors occur between the measured and calculated positions. This paper proposes the method for selecting BLE beacons in multi-point positioning for distance error reduction. The proposed method consists of Learning and Calculation Phase. The correct values used in the proposed method are the values measured with the tape measure. The Learning Phase obtains features from the RSSI distribution of each beacon and uses them to compute the coordinates in the Calculation Phase. The Calculation Phase is used for navigation with an allowable distance error of 0.5 [m] based on a person’s shoulder width. The proposed method calculates the typical value from the Learning Phase based on the probability that it fits within the distance error range of 0.5 [m]. The calculated position uses the logarithmic approximation to convert the beacon’s radio signal strength to the distance. The experiment for measuring the distance error compares the proposed method calculated, the existing method calculated by two-dimensional three-point positioning, and the tape measure-based method. The number of combinations in three-point positioning was also changed to compare the accuracy. The mean distance error compared to the value measured with a tape measure was 1.337 [m] for the existing method and 0.449 [m] for the proposed method. The proposed method is 66.3% less distance error than the existing methods.
This paper addresses the development of a persuasive IoT system for stress detection and management in students during classroom situations. An emotion-aware persuasive architecture is developed with four modules: Context Acquisition, Context Manager, Persuasion Manager and Context-Aware Applications. By using the galvanic skin response biomarker, the real-time stress level is measured by the wearable wristband Empatica E4. The data, processed and classified on discrete stress levels from 0 to 5, is sent to the context module that identifies situations of interest where the students need positive reinforcement from the persuasive system. Based on the situation of interest and the user’s profile, the persuasive module composes personalized persuasive messages displayed in a mobile application. The persuasive system was evaluated through an exploratory study during a class session, with encouraging results in detecting stress levels and the positive effect of persuasive messages on students.
Atrial fibrillation (AF) is a cardiac arrhythmia occurring when the atria lose their normal rhythm causing the heart to beat erratically. The estimated number of individuals with atrial fibrillation globally in 2010 was 33.5 million. Despite continued research in this area there is no universal standard for detecting atrial fibrillation. The majority of published detectors rely on manual classification techniques that are implemented on standalone devices. This paper proposes a dual convolutional neural network (CNN) based AF detection system. The proposed system transforms 5 s windows of electrocardiogram data to two-dimensional images via a stationary wavelet transform to serve as CNN inputs. The dual CNN system implements a model tailored for an IoT gateway device to prescreen arrhythmia cases locally. Less obvious arrhythmia cases are transferred to a secondary model hosted on a cloud server for further prediction. Local classification of AF reduces the overheads for cloud storage capacity and transfer of data. The proposed runtime system ultimately received an F1 score of 0.94 when evaluated using previously unseen data.
