We propose a localization technique by fusing multiple classifiers based on received signal strengths (RSSs) of visible light in which different intensity-modulated sinusoidal signals emitted by LEDs are captured by photodiodes placed at various grid points. First, we obtain some approximate RSSs fingerprints by capturing the peaks of power spectral density of the received signals at each given grid point. Unlike the existing RSSs-based algorithms, several representative machine learning algorithms are adopted to train multiple classifiers based on these RSSs fingerprints. Then, two robust fusion localization algorithms, namely, grid-independent least square and grid-dependent least square (GD-LS), are proposed to combine the outputs of these classifiers. A singular value decomposition (SVD)-based LS (LS-SVD) method is proposed to mitigate the numerical stability problem when the prediction matrix is singular. Experiments conducted on an intensity-modulated direct detection system show that the probability of having mean square positioning error of less than 5 cm achieved by GD-LS is improved by 93.03% and 93.15%, respectively, as compared to those by the RSS ratio and RSS matching methods with the fast Fourier transform length of 2000.

In this paper, we propose a new paradigm in designing and realizing energy efficient wireless indoor access networks, namely, a hybrid system enabled by traditional RF access, such as WiFi, as well as the emerging visible light communication (VLC). VLC facilitates the great advantage of being able to jointly perform illumination and communications, and little extra power beyond illumination is required to empower communications, thus rendering wireless access with almost zero power consumption. On the other hand, when illumination is not required from the light source, the energy consumed by VLC could be more than that consumed by the RF. By capitalizing on the above properties, the proposed hybrid RF-VLC system is more energy efficient and more adaptive to the illumination conditions than the individual VLC or RF systems. To demonstrate the viability of the proposed system, we first formulate the problem of minimizing the power consumption of the hybrid RF-VLC system while satisfying the users requests and maintaining acceptable level of illumination, which is NP-complete. Therefore, we divide the problem into two subproblems. In the first subproblems, we determine the set of VLC access points (AP) that needs to be turned on to satisfy the illumination requirements. Given this set, we turn our attention to satisfying the users’ requests for real-time communications, and we propose a randomized online algorithm that, against an oblivious adversary, achieves a competitive ratio of log(N)log(M) with probability of success (1-1N), where N is the number of users and M is the number of VLC and RF APs. We also show that the best online algorithm to solve this problem can achieve a competitive ratio of log(M). Simulation results further demonstrate the advantages of the hybrid system.

Subterranean openings, including mines, present a unique and challenging environment for robots and autonomous exploration systems. Autonomous robots that are created today will be deployed in harsh and unexplored landscapes that humanity is increasingly encountering in its scientific and technological endeavors. Terrestrial and extraterrestrial environments pose significant challenges for both humans and robots: they are inhospitable and inaccessible to humans due to a lack of space or oxygen, poor or no illumination, unpredictable terrain, a GPS-denied environment, and a lack of satellite imagery or mapping information of any type. Underground mines provide a good physical simulation for these types of environments, and thus, can be useful for testing and developing highly sought-after autonomous navigation frameworks for autonomous agents. This review presents a collective study of robotic systems, both of individual and hybrid types, intended for deployment in such environments. The prevalent configurations, practices for their construction and the hardware equipment of existing multi-agent hybrid robotic systems will be discussed. It aims to provide a supplementary tool for defining the state of the art of coupled Unmanned Ground Vehicle (UGV)–Unmanned Aerial Vehicle (UAV) systems implemented for underground exploration and navigation purposes, as well as to provide some suggestions for multi-agent robotic system solutions, and ultimately, to support the development of a semi-autonomous hybrid UGV–UAV system to assist with mine emergency responses.

Visible light communication (VLC) backscatter has been proposed as a wireless access option for Internet of Things. However, the throughput of the state-of-the-art VLC backscatter is limited by simple single-carrier pulsed modulation scheme, such as ON-OFF keying (OOK). In this letter, a novel pixelated VLC backscatter is proposed and implemented to overcome the channel capacity limitation. In particular, multiple smaller VLC backscatters are integrated to generate multi-level signals, which enables the usage of advanced modulation schemes. Based on experimental results, rate adaptation at different communication distances can be employed to enhance the achievable data rate. Compared with OOK, the data rate can be tripled, when 8-pulse amplitude modulation is used at 2 m. In general, $n$ -fold throughput enhancement is realized by utilizing $n$ smaller VLC backscatters, while incurring negligible additional energy using the same device space as that of a single large backscatter.

In urban environments, tall buildings or structures can pose limits on the direct channel link between a base station (BS) and an Internet of Thing device (IoTD) for wireless communication. Unmanned aerial vehicles (UAVs) with a mounted reconfigurable intelligent surface (RIS), denoted as UAV-RIS, have been introduced in recent works to enhance the system throughput capacity by acting as a relay node between the BS and the IoTDs in wireless access networks. Uncoordinated UAVs or RIS phase shift elements will make unnecessary adjustments that can significantly impact the signal transmission to IoTDs in the area. The concept of Age of Information (AoI) is proposed in wireless network research to categorize the freshness of the received update message. To minimize the Average Sum of AoI (ASoA) in the network, two model-free deep reinforcement learning (DRL) approaches—Off-Policy deep Q -network (DQN) and On-Policy proximal policy optimization (PPO)—are developed to solve the problem by jointly optimizing the RIS phase shift, the location of the UAV-RIS, and the IoTD transmission scheduling for large-scale Internet of Things wireless networks. Analysis of loss functions and extensive simulations is performed to compare the stability and convergence performance of the two algorithms. The results reveal the superiority of the On-Policy approach, PPO, over the Off-Policy approach, DQN, in terms of stability, convergence speed, and under diverse environment settings.

Visible light communication (VLC)-based indoor localization approaches enjoy many advantages, such as utilizing ubiquitous lighting infrastructure, high location accuracy, and no interruption to RF-based devices. However, existing VLC-based localization methods lack a real-time backward channel from the device to landmarks and necessitate computation at the device, which make them unsuitable for real-time tracking of small IoT devices. In this paper, we propose and prototype RETRO, that establishes an almost zero-delay backward channel by retroreflection. RETRO localizes passive IoT devices without requiring computation and heavy sensing (e.g., camera) at the devices. Multiple photodiodes (i.e., landmarks) are mounted on any single unmodified light source to sense the retroreflected optical signal (i.e., location signature). We derive a closed-form expression, which is validated by experiments and ray tracing simulations, for the reflected optical power relative to the location and the orientation of the retroreflector. The expression is applied to a received signal strength indicator and trilateration based localization algorithm. Extensive experiments demonstrate centimeter-level location accuracy and single-digit angular error. For practicality concern, to mitigate the thickness problem of a single retroreflector, the capabilities of different retroreflector arrays are studied. The range of the localization system is theoretically evaluated for different light emission patterns.