Visible light technology shows great potential in the underground mine positioning today. Most visible light-based underground positioning technology deploy the receiver, such as photodiodes (PDs), on the mine tunnel ground and consider the miner’s lamp on the ceiling as the transmitter. This incurs the unreliable reception of visible light signals and the difficult implementation of VLP systems due to the complex underground mine environment, such as the receiver being obscured or trampled by objects (e.g., Minecarts and Ore). We present a reliable underground positioning algorithm with visible light considering the actual mine environment. The key innovation is that we design a novel reverse mechanism, i.e., utilizing the existing miner’s headlamp as the transmitter for broadcasting the specific light beacon representing the mine’s identity information, and the PDs deployed on the ceiling of the mine tunnel as the receiver for optical signal reception. We can infer the current locations of all miners using a series of base stations constructed by PDs. If some special mine environment limits the large-scale deployment of base stations and causes the positioning blind spots, the inertial navigation is combined to achieve all miners’ real-time positioning. We overcome some technical challenges for this new mechanism and the underground mine environment, such as optimal frequency selection, robust frequency identification, and accurate base station positioning. We conduct extensive experiments on our algorithm in a simulated mine tunnel, and the experimental results verify that our algorithm exhibits good performance.

The prevalence of illumination equipment and the inherent advantages of the Visible Light Communication (VLC) technique have resulted in a growing interest in Visible Light Positioning (VLP). There exist many excellent VLP techniques over the past several years. However, one limitation of most VLP survey works is that they mainly focus on the analysis from the perspective of techniques but ignore the equally important hardware aspect, since the hardware part directly affects the performance and cost of VLP systems and also determines whether it can be put into practical use. Different from most surveys concentrating on a single perspective, we provide an intensive overview of VLP systems from software algorithms to hardware devices. A novel‐innovative classification method is used in the software algorithms, while the hardware aspect is introduced in terms of transmitters, modems, and receivers, making up for the deficiencies of the previous works. Massive papers including pioneering papers and the state‐of‐the‐art ones in related areas are gathered and categorized. These solutions have also been evaluated in terms of accuracy, cost, range, and complexity. Furthermore, current open issues and tendencies regarding VLP are also illustrated in this paper.

Wireless sensor networks (WSNs) have been used in many fields due to its wide applicability. In this kind of network, each node is independent of each other and has its own local clock and communicates wirelessly. Time synchronization plays a vital role in WSNs and it can ensure accuracy requirements for coordination and data reliability. However, two key challenges exist in large-scale WSNs that are severe resource constraints overhead and multihop time synchronization errors. To address these issues, this paper proposes a novel unmanned aerial vehicle (UAV)-assisted low-consumption time synchronization algorithm based on cross-technology communication (CTC) for a large-scale WSN. This algorithm uses a UAV to send time synchronization data packets for calibration. Moreover, to ensure coverage and a high success rate for UAV data transmission, we use CTC for time synchronization. Without any relays, a high-power time synchronization packet can be sent by a UAV to achieve the time synchronization of low-power sensors. This algorithm can achieve accurate time synchronization with almost zero energy consumption for the sensor nodes. Finally, we implemented our algorithm with 30 low-power RF-CC2430 ZigBee nodes and a Da Jiang Innovations (DJI) M100 UAV on a 1 km highway and an indoor site. The results show that time synchronization can be achieved accurately with almost zero energy consumption for the sensor nodes, and the time synchronization error is less than 30 $\mathsf{\mu }$s in 99% of cases.

The mine cage has an important role in the production of coal mines. It has many safety problems in the transportation of people, such as overloading of personnel and illegal outreach of human limbs. However, the harsh mine environment makes it very difficult to monitor personnel overload and limb extension. To solve these two problems, we propose a novel safety monitoring algorithm of the mine cage based on visible light. With visible light technology, our algorithm cleverly utilizes the existing underground lighting equipment (i.e., miner’s headlamp and the miner’s lamp deployed on the mine cage) as the transmitter to broadcast the light beacons representing unique identity information through visible light frequency modulation. Next, cheap photodiodes deployed in the mine cage are used as the receiver to perceive the modulated optical signals. Then we use the frequency matching method for personnel counting and the frequency power comparison method for illegal limb extension monitoring. Moreover, a novel method of monitoring the delineated safe area of the mine cage is also proposed to ensure that all the miners are in the delineated safe area. Finally, we conducted extensive experiments with a simulated mine cage model. Results show that our algorithm has superior performance. With the photodiode SD5421-002, the accuracy of personnel overload judgment and safe area monitoring of our algorithm can reach 99%, and the accuracy of limb extension monitoring is more than 96%.

One of the key challenges for wireless sensing systems is how to efficiently enable wireless sensing capabilities for multiple devices while leveraging existing wireless communication resources. In this paper, we propose DEWS, a distributed channel measurement scheme that allows multiple transmitters to perform sensing tasks simultaneously, which considers three key issues in wireless sensing tasks: multi-device sensing resolution, multi-device sensing reliability, and multi-device sensing accuracy. First, we use a carefully designed distributed Resource Unit (dRU) allocation scheme based on OFDMA to ensure that multiple devices perform sensing tasks simultaneously with the entire bandwidth, thereby improving the sensing resolution. Then, a subcarrier linear phase shift scheme is designed to avoid signal interference between different transmitting devices and improve the reliability of multi-device sensing. This scheme realizes the fine-grained Time Division Multiplexing (TDM) of multiple devices, which can also increase the number of simultaneous access of sensing devices. Finally, a sensing accuracy improvement algorithm combining a single antenna-based motion recovery method utilizing Independent Component Analysis (ICA) and sampling frequency offset (SFO) correction is proposed, to further help DEWS to break the inherent bandwidth limits and recover different motions at similar distances. We verify the effectiveness of DEWS through extensive experimental testing using commercial USRPs.

Clock synchronization is still a vital and challenging task for underground coal wireless internet of things (IoT) due to the uncertainty of underground environment and unreliability of communication links. Instead of considering on-demand driven clock synchronization, this paper proposes a novel Adaptive Robust Synchronization (ARS) scheme with packets loss for mine wireless environment. A clock synchronization framework that is based on Kalman filtering is first proposed, which can adaptively adjust the sampling period of each clock and reduce the communication overhead in single-hop networks. The proposed scheme also solves the problem of outliers in data packets with time-stamps. In addition, this paper extends the ARS algorithm to multi-hop networks. Additionally, the upper and lower bounds of error covariance expectation are analyzed in the case of incomplete measurement. Extensive simulations are conducted in order to evaluate the performance. In the simulation environment, the clock accuracy of ARS algorithm is improved by 7.85% when compared with previous studies for single-hop networks. For multi-hop networks, the proposed scheme improves the accuracy by 12.56%. The results show that the proposed algorithm has high scalability, robustness, and accuracy, and can quickly adapt to different clock accuracy requirements.