Smart city is able to make the city source and infrastructure more efficiently utilized, which improves the quality of life for citizens. In this framework, wireless sensor networks (WSNs) play an important role to collect, process and analyze the corresponding information. However, the massive deployment of WSNs consume a significant energy consumption, which has raised the growing demand for green WSNs for smart cities. Exploiting the recent advance in collaborative energy and information transfer to power the WSNs and transmit the data has been considered a promising approach to realize the green WSNs for smart cities. We propose an architecture design of the green WSNs for smart cities, by exploiting the collaborative energy and information transfer protocol, and illustrate the challenging issues in this design. To achieve green system design, the sensor nodes in WSNs harvest the energy simultaneously with the information decoding from the received RF signals. Specifically, the energy-constrained sensor nodes partitions the received signals into two independent groups to perform energy harvesting (EH) and information decoding (ID). The sensor nodes use the harvested energy to amplify and forward the information signals. We study the joint optimization of subcarrier grouping, subcarrier pairing, and power allocation such that the transmission rate performance is maximized with the EH constraint. The joint optimization problem is solved via dual decomposition after transforming it into an equivalent convex optimization problem. Simulation results tested with the real WSNs system data indicate that the performance of our proposed protocol can be significantly improved.

This letter investigates an active intelligent reflecting surface (IRS)-aided integrated sensing and communication (ISAC) system in the scenario of cloud radio access network (C-RAN). In the system, the active IRS is deployed to amplify and reflect the joint communication and sensing signals from the remote radio heads (RRHs). Our goal is to form the desired reflective beam pattern from the active IRS towards the sensing targets located in non-line-of-sight (NLoS) areas for the RRHs, while satisfying the signal-to-interference-plus-noise ratio (SINR) requirement for the communication users and the capacity limit for the fronthaul links between the baseband unit (BBU) pool and RRHs. Specifically, we propose an efficient joint design for the fronthaul compression and the beamforming of the RRHs and the active IRS. Via numerical simulations, the effectiveness of the proposed design is verified.

Due to the single and limited prior information in electromagnetic tomography (EMT), the existing reconstruction methods are often unable to reconstruct the image with high accuracy for different kinds of flow patterns. In this article, an image reconstruction algorithm based on the sequential Monte Carlo principle for EMT is proposed. It can increase the diversity of samples to extract more image features for EMT images reconstruction and maintain a high image reconstruction effect for different flow patterns. First, the EMT image reconstruction process is modeled as a dynamic time-varying system to search for the optimal estimation in state space. At the same time, controllable adjustment of probability density distribution can improve the reconstruction effect under different flow patterns. Second, samples are obtained from the initial sample space, and their weights are updated according to the samples’ information. The numbers of effective samples and the diversity of samples are increased by resampling. Finally, the system state estimation is calculated according to the samples and their updated weights, and the system state is updated iteratively until the optimal solution is obtained. Simulation results have shown that the proposed method is superior to other existing methods in image error and correlation coefficient, and has better imaging quality.

The internet of things (IoT) is becoming more indispensable in modern society as the further development and maturity of information technology progresses. However the exponential growth of IoT devices leads to severe energy consumption. As a technology with broad application prospects, simultaneous wireless information and power transfer (SWIPT) enables IoT devices to harvest energy from receiving radio frequency (RF) signals while ensuring information transmission. In this paper, we investigate the transmission rate optimization problem for a dual-hop multi-relay IoT system, where a decode-and-forward (DF) relay supports the SWIPT technique. We jointly optimize the resource including power and subcarrier allocation, to maximize the system transmission rate. The time-sharing strategy and Lagrange dual method are used to solve this optimization problem. Simulation results reveal that the proposed algorithm has a larger transmission rate than other benchmark algorithms when ensuring each relay has no additional energy supply. Specifically, the proposed algorithm improves the information transmission rate by 2.8%, 3.4% and 43% compared with other algorithms in the case of five relays when the source’s power is equal to 0.5 W, respectively.

To prevent lifestyle diseases, portable heartbeat detection systems by electrocardiograms (ECGs) for daily life monitoring have attracted attentions. For portable systems, power consumption is critical and it is thus beneficial to reduce the sampling frequency at the point of acquisition. Recent developed variable pulsewidth finite rate of innovation (VPW-FRI) framework provides an effective way for low-rate sampling and compression of ECG signals. Unfortunately, noise and model mismatch will induce inaccuracies to such scheme in the reconstruction process. In this article, a sub-Nyquist sampling scheme based on the extension of VPW model is proposed for ECG signals, which reduce the model dismatch error, and improve the reconstruction quality under model mismatch and noise scenario. In the proposed scheme, ECG signal is modeled as the differentiated VPW model to reduce the model mismatch error. Then a FRI sampling framework based on the differentiated VPW model is proposed to sample the ECG signal at a sub-Nyquist rate. After the ECG signal is sampled, some consecutive Fourier series coefficients are calculated according to the samples. Then a modified annihilating filter algorithm is proposed to estimate parameters of the ECG signal and reconstruct the ECG signal. Finally, simulation experiments with real ECG signals from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database have shown that the proposed scheme outperformed the traditional methods for model mismatch. The signal-to-residual ratio (SRR) of the reconstructed signal of the proposed scheme is higher than that of the VPW-FRI scheme.

Owing to the inherent central information processing and resource management ability, the cloud radio access network (C-RAN) is a promising network structu