The ever increasing proliferation of wireless objects and consistent connectivity demands are creating significant challenges for battery-constrained wireless devices. The vision of massive IoT, involving billions of smart objects to be connected to the cellular network, needs to address the problem of uninterrupted power consumption while taking advantage of emerging high-frequency 5G communications. The problem of limited battery power motivates us to utilize radio frequency (RF) signals as the energy source for battery-free communications in next-generation wireless networks. Backscatter communication (BackCom) makes it possible to harvest energy from incident RF signals and reflect back parts of the same signals while modulating the data. Ambient BackCom (Amb-BackCom) is a type of BackCom that can harvest energy from nearby WiFi, TV, and cellular RF signals to modulate information. The objective of this article is to review BackCom as a solution to the limited battery life problem and enable future battery-free communications for combating the energy issues for devices in emerging wireless networks. We first highlight the energy constraint in existing wireless communications. We then investigate BackCom as a practical solution to the limited battery life problem. Subsequently, in order to take the advantages of omnipresent radio waves, we elaborate BackCom tag architecture and various types of BackCom. To understand encoding and data extraction, we demonstrate signal processing aspects that cover channel coding, interference, decoding, and signal detection schemes. Moreover, we also describe BackCom communication modes, modulation schemes, and multiple access techniques to accommodate maximum users with high throughput. Similarly, to mitigate the increased network energy, adequate data and power transfer schemes for BackCom are elaborated, in addition to reliability, security, and range extension. Finally, we highlight BackCom applications with existing research challenges and future directions for next-generation 5G wireless networks.

Recent developments in the field of preventive healthcare have received considerable attention due to the effective management of various chronic diseases including diabetes, heart stroke, obesity, and cancer. Various automated systems are being used for activity and food recognition in preventive healthcare. The automated systems lack sophisticated segmentation techniques and contain multiple sensors, which are inconvenient to be worn in real-life settings. To monitor activity and food together, our work presents a novel wearable system that employs the motion sensors in a smartwatch together with a piezoelectric sensor embedded in a necklace. The motion sensor generates distinct patterns for eight different physical activities including eating activity. The piezoelectric sensor generates different signal patterns for six different food types as the ingestion of each food is different from the others owing to their different characteristics: hardness, crunchiness, and tackiness. For effective representation of the signal patterns of the activities and foods, we employ dynamic segmentation. A novel algorithm called event similarity search (ESS) is developed to choose a segment with dynamic length, which represents signal patterns with different complexities equally well. Amplitude-based features and spectrogram-generated images from the segments of activity and food are fed to convolutional neural network (CNN)-based activity and food recognition networks, respectively. Extensive experimentation showed that the proposed system performs better than the state of the art methods for recognizing eight activity types and six food categories with an accuracy of 94.3% and 91.9% using support vector machine (SVM) and CNN, respectively.

Recent research on single-image super-resolution (SISR) using deep convolutional neural networks has made a breakthrough and achieved tremendous performance. Despite their significant progress, numerous convolutional neural networks (CNN) are limited in practical applications, owing to the requirement of the heavy computational cost of the model. This paper proposes a multi-path network for SISR, known as multi-path deep CNN with residual inception network for single image super-resolution. In detail, a residual/ResNet block with an Inception block supports the main framework of the entire network architecture. In addition, remove the batch normalization layer from the residual network (ResNet) block and max-pooling layer from the Inception block to further reduce the number of parameters to preventing the over-fitting problem during the training. Moreover, a conventional rectified linear unit (ReLU) is replaced with Leaky ReLU activation function to speed up the training process. Specifically, we propose a novel two upscale module, which adopts three paths to upscale the features by jointly using deconvolution and upsampling layers, instead of using single deconvolution layer or upsampling layer alone. The extensive experimental results on image super-resolution (SR) using five publicly available test datasets, which show that the proposed model not only attains the higher score of peak signal-to-noise ratio/structural similarity index matrix (PSNR/SSIM) but also enables faster and more efficient calculations against the existing image SR methods. For instance, we improved our method in terms of overall PSNR on the SET5 dataset with challenging upscale factor 8× as 1.88 dB over the baseline bicubic method and reduced computational cost in terms of number of parameters 62% by deeply-recursive convolutional neural network (DRCN) method.

In this paper, we present an architecture of a personalized glucose monitoring system (PGMS). PGMS consists of both invasive and non-invasive sensors on a single device. Initially, blood glucose is measured invasively and non-invasively, to train the machine learning models. Then, paired data and corresponding errors are divided scientifically into six different clusters based on blood glucose ranges as per the patient’s diabetic conditions. Each cluster is trained to build the unique error prediction model using an adaptive boosting (AdaBoost) algorithm. Later, these error prediction models undergo personalized calibration based on the patient’s characteristics. Once, the errors in predicted non-invasive values are within the acceptable error range, the device gets personalized for a patient to measure the blood glucose non-invasively. We verify PGMS on two different datasets. Performance analysis shows that the mean absolute relative difference (MARD) is reduced exceptionally to 7.3% and 7.1% for predicted values as compared to 25.4% and 18.4% for measured non-invasive glucose values. The Clarke error grid analysis (CEGA) plot for non-invasive predicted values shows 97% data in Zone A and 3% data in Zone B for dataset 1. Moreover, for dataset 2 results echoed with 98% and 2% in Zones A and B, respectively.

The expected rise of small devices in the Internet of Things (IoT) require these devices to operate without any power interruption. The replacement and recharging of batteries in these widely deployed small IoT devices is a challenging task. Recently, Ambient Backscatter Communications (AmB-Com) is introduced, which enables passive IoT devices to harvest power from nearby Radio Frequency (RF) signals. AmB-Com allows the passive devices to harvest energy and simultaneously transmit their information using the reflection of incident RF signals. This survey paper covers various types of ambient RF signals as the source of energy to energize passive IoT devices. Then, we show the utilization of multiple frequency bands to power up passive devices. In order to keep the balance between energy harvesting and information transfer, we discuss efficient power transfer schemes for AmB-Com in addition to energy management, resource allocation, and optimization techniques. Finally, we cover the performance of AmB-Com systems and highlight the research challenges in AmB-Com.

5G is expected to deal with high data rates for different types of wireless traffic. To enable high data rates, 5G employs beam searching operation to align the best beam pairs. Beam searching operation along with high order modulation techniques in 5G, exhausts the battery power of user equipment (UE). LTE network uses discontinuous reception (DRX) with fixed sleep cycles to save UE energy. LTE-DRX in current form cannot work in 5G network, as it does not consider multiple beam communication and the length of sleep cycle is fixed. On the other hand, artificial intelligence (AI) has a tendency to learn and predict the packet arrival-time values from real wireless traffic traces. In this paper, we present AI based DRX (AI-DRX) mechanism for energy efficiency in 5G enabled devices. We propose AI-DRX algorithm for multiple beam communications, to enable dynamic short and long sleep cycles in DRX. AI-DRX saves the energy of UE while considering delay requirements of different services. We train a recurrent neural network (RNN) on two real wireless traces with minimum root mean square error (RMSE) of 5 ms for trace 1 and 6 ms for trace 2. Then, we utilize the trained RNN model in AI-DRX algorithm to make dynamic short or long sleep cycles. As compared to LTE-DRX, AI-DRX achieves $69\%$ higher energy efficiency on trace 1 and $55\%$ more energy efficiency on trace 2, respectively. The AI-DRX attains $70\%$ improvement in energy efficiency for trace 2 compared with Poisson packet arrival model for $\lambda =1/20$ .