Average consensus theory is intensely popular for building time synchronization in wireless sensor networks (WSN). However, the average consensus-based time synchronization algorithm is based on iteration that poses challenges for efficiency, as they entail high communication cost and long convergence time in large-scale WSN. Based on the suggestion that the greater the algebraic connectivity leads to the faster the convergence, a novel multi-hop average consensus time synchronization (MACTS) is developed with innovative implementation in this paper. By employing multi-hop communication model, it shows that virtual communication links among multi-hop nodes are generated and algebraic connectivity of network increases. Meanwhile, a multihop controller is developed to balance the convergence time, accuracy, and communication complexity. Moreover, the accurate relative clock offset estimation is obtained by delay compensation. Implementing the MACTS based on the popular one-way broadcast model and taking multi-hop over short distances, we can easily achieve the fast convergence MACTS.

Accurate and fast-convergent time synchronization is very important for wireless sensor networks. The flooding time synchronization converges fast, but its transmission delay and by-hop error accumulation seriously reduce the synchronization accuracy. In this paper, a rapid-flooding multiple one-way broadcast time-synchronization (RMTS) protocol for large-scale wireless sensor networks is proposed. To minimize the by-hop error accumulation, the RMTS uses maximum likelihood estimations for clock skew estimation and clock offset estimation, and quickly shares the estimations among the networks. As a result, the synchronization error resulting from delays is greatly reduced, while faster convergence and higher-accuracy synchronization is achieved. Extensive experimental results demonstrate that, even over 24-hops networks, the RMTS is able to build accurate synchronization at the third synchronization period, and moreover, the by-hop error accumulation is slower when the network diameter increases.

Clock skew compensation is essential for accurate time synchronization in wireless networks. However, contemporary clock skew estimation is based on inaccurate transmission time measurement, which makes credible estimation challenging. Based on one-way broadcast synchronization, this article presents a novel maximum likelihood estimation (MLE) with an innovative implementation to minimize the clock skew estimation error caused by delay. A multiple one-way broadcast model is developed for observation and set collection, while the distribution of actual delay is discussed. A clock skew MLE based on Gaussian delay is then proposed and its Cramer-Rao lower bound provided. The developed MLE is independent of the clock offset estimation, and requires only two synchronization periods to generate an accurate estimation. Extensive experimental results indicate that the performance of the proposed MLE is better than the methods popularly used in existing time synchronization protocols.

Radio-frequency-energy-powered cognitive radio network (RF-CRN) is being taken seriously in Connected Vehicles, especially in 5G network, which can better address the challenges of energy limitation and spectrum scarcity. However, the energy efficiency (EE) of the RF-CRN wherein multiple secondary users (SUs) share the same channel is rarely presented. In this article, we consider a RF-CRN in which SUs first harvest energy from RF signals originating from a primary network (PN) and then utilize the available energy in the battery to transmit data. Since all SUs can access the authorized spectrum for transmission simultaneously, co-frequency interference (Co-FI) occurs among SUs. Given the quality of service (QoS) requirement, our goal is to achieve the maximum EE of the RF-CRN by jointly optimizing transmission time and power control. To this end, a resource allocation scheme referred to as approximate convex policy for co-frequency interference (CO-ACP) is proposed. Specifically, the EE problem is firstly converted into a convex one by CO-ACP. Then, we utilize Frank-Wolfe (FW) and one-dimensional linear programming to obtain the optimal solution. Simulation results demonstrate that a tight lower-bound optimum solution for the non-convex EE maximization can be achieved by CO-ACP. Moreover, the CO-ACP provides meaningful system features, such as the number of SUs, energy harvesting efficiency, and the battery energy state of the SUs under different RF-CRN scenarios, providing a clear reference for future deployment of RF-CRN.

Average-consensus protocol is one of the ways to develop distributed time-synchronization algorithms in Internet of Things (IoT) networks. However, the large number of iteration leads to a common time notion issue in nodes. This poses a critical challenge in the convergence of the time-synchronization algorithm and resulting asymptotic convergence in the average consensus protocol. In this paper, a parameter-sharing-based average-consensus time-synchronization (PACTS) algorithm is proposed. For fast convergence, the proposed PACTS quickly forwards the time information to multi-hop nodes and employs multi-hop average-consensus instead of single-hop average-consensus. Specifically, a node asynchronously and periodically broadcasts the relative clock offset estimation of neighbors with its local time information. Meanwhile, the relative clock offset estimation of the multi-hop node is calculated and used to estimate the average value. Consequently, an average-consensus among local multi-hop nodes is obtained. As a result, the iteration number and convergence time are significantly reduced over the network. Finally, the experimental results indicate that the proposed PACTS algorithm has low complexity, high accuracy, and quick convergence.

Wireless sensor networks (WSNs) have been widely used to collect valuable information in Structural Health Monitoring (SHM) of bridges, using various sensors, such as temperature, vibration and strain sensors. Since multiple sensors are distributed on the bridge, accurate time synchronization is very important for multi-sensor data fusion and information processing. Based on shape of the bridge, a spanning tree is employed to build linear topology WSNs and achieve time synchronization in this paper. Two-way time message exchange (TTME) and maximum likelihood estimation (MLE) are employed for clock offset estimation. Multiple TTMEs are proposed to obtain a subset of TTME observations. The time out restriction and retry mechanism are employed to avoid the estimation errors that are caused by continuous clock offset and software latencies. The simulation results show that the proposed algorithm could avoid the estimation errors caused by clock drift and minimize the estimation error due to the large random variable delay jitter. The proposed algorithm is an accurate and low complexity time synchronization algorithm for bridge health monitoring.