CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies <s> RF-Based Human Activity Recognition <s> Activity monitoring in home environments has become increasingly important and has the potential to support a broad array of applications including elder care, well-being management, and latchkey child safety. Traditional approaches involve wearable sensors and specialized hardware installations. This paper presents device-free location-oriented activity identification at home through the use of existing WiFi access points and WiFi devices (e.g., desktops, thermostats, refrigerators, smartTVs, laptops). Our low-cost system takes advantage of the ever more complex web of WiFi links between such devices and the increasingly fine-grained channel state information that can be extracted from such links. It examines channel features and can uniquely identify both in-place activities and walking movements across a home by comparing them against signal profiles. Signal profiles construction can be semi-supervised and the profiles can be adaptively updated to accommodate the movement of the mobile devices and day-to-day signal calibration. Our experimental evaluation in two apartments of different size demonstrates that our approach can achieve over 96% average true positive rate and less than 1% average false positive rate to distinguish a set of in-place and walking activities with only a single WiFi access point. Our prototype also shows that our system can work with wider signal band (802.11ac) with even higher accuracy. <s> BIB001 </s> CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies <s> RF-Based Human Activity Recognition <s> Some pioneer WiFi signal based human activity recognition systems have been proposed. Their key limitation lies in the lack of a model that can quantitatively correlate CSI dynamics and human activities. In this paper, we propose CARM, a CSI based human Activity Recognition and Monitoring system. CARM has two theoretical underpinnings: a CSI-speed model, which quantifies the correlation between CSI value dynamics and human movement speeds, and a CSI-activity model, which quantifies the correlation between the movement speeds of different human body parts and a specific human activity. By these two models, we quantitatively build the correlation between CSI value dynamics and a specific human activity. CARM uses this correlation as the profiling mechanism and recognizes a given activity by matching it to the best-fit profile. We implemented CARM using commercial WiFi devices and evaluated it in several different environments. Our results show that CARM achieves an average accuracy of greater than 96%. <s> BIB002 </s> CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies <s> RF-Based Human Activity Recognition <s> We present WFID, a passive device-free indoor human identification system with one pair of WiFi signal transmitter and receiver. WFID design is motivated by the observation that PHY layer Channel State Information (CSI) is capable of capturing the frequency diversity of wideband channel, such that the human body curve may be uniquely identified by learning the feature pattern of CSI. Different from many CSI-based techniques focusing on phase shift, we propose a novel feature of subcarrier-amplitude frequency (SAF). Based on this feature, WFID realizes human identification through a linear-kernel SVM. We have implemented a prototype of WFID with a commercial AP and a computer equipped with one Intel 5300 NIC. WFID is evaluated in two typical indoor scenarios. The results confirm that WFID achieves high classification accuracy which is permanent over several days under two typical indoor scenarios, with low computation cost. This reveals the potential for WFID to realize real-time indoor human identification. <s> BIB003 </s> CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies <s> RF-Based Human Activity Recognition <s> Device-free passive indoor localization is playing a critical role in many applications such as elderly care, intrusion detection, smart home, etc. However, existing device-free localization systems either suffer from labor-intensive offline training or require dedicated special-purpose devices. To address the challenges, we present our system named MaTrack, which is implemented on commodity off-the-shelf Intel 5300 Wi-Fi cards. MaTrack proposes a novel Dynamic-MUSIC method to detect the subtle reflection signals from human body and further differentiate them from those reflected signals from static objects (furniture, walls, etc.) to identify the human target's angle for localization. MaTrack does not require any offline training compared to existing signature-based systems and is insensitive to changes in environment. With just two receivers, MaTrack is able to achieve a median localization accuracy below 0.6 m when the human is walking, outperforming the state-of-the-art schemes. <s> BIB004 </s> CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies <s> RF-Based Human Activity Recognition <s> Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively leveraging commodity WiFi devices. However, is it always possible to sense human respiration no matter where the subject stays and faces? What affects human respiration sensing and what's the theory behind? In this paper, we first introduce the Fresnel model in free space, then verify the Fresnel model for WiFi radio propagation in indoor environment. Leveraging the Fresnel model and WiFi radio propagation properties derived, we investigate the impact of human respiration on the receiving RF signals and develop the theory to relate one's breathing depth, location and orientation to the detectability of respiration. With the developed theory, not only when and why human respiration is detectable using WiFi devices become clear, it also sheds lights on understanding the physical limit and foundation of WiFi-based sensing systems. Intensive evaluations validate the developed theory and case studies demonstrate how to apply the theory to the respiration monitoring system design. <s> BIB005 </s> CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies <s> RF-Based Human Activity Recognition <s> Injuries that are caused by falls have been regarded as one of the major health threats to the independent living for the elderly. Conventional fall detection systems have various limitations. In this work, we first look for the correlations between different radio signal variations and activities by analyzing radio propagation model. Based on our observation, we propose WiFall, a truly unobtrusive fall detection system. WiFall employs physical layer Channel State Information (CSI) as the indicator of activities. It can detect fall of the human without hardware modification, extra environmental setup, or any wearable device. We implement WiFall on desktops equipped with commodity 802.11n NIC, and evaluate the performance in three typical indoor scenarios with several layouts of transmitter-receiver (Tx-Rx) links. In our area of interest, WiFall can achieve fall detection for a single person with high accuracy. As demonstrated by the experimental results, WiFall yields 90 percent detection precision with a false alarm rate of 15 percent on average using a one-class SVM classifier in all testing scenarios. It can also achieve average 94 percent fall detection precisions with 13 percent false alarm using Random Forest algorithm. <s> BIB006 </s> CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies <s> RF-Based Human Activity Recognition <s> User authentication is a critical process in both corporate and home environments due to the ever-growing security and privacy concerns. With the advancement of smart cities and home environments, the concept of user authentication is evolved with a broader implication by not only preventing unauthorized users from accessing confidential information but also providing the opportunities for customized services corresponding to a specific user. Traditional approaches of user authentication either require specialized device installation or inconvenient wearable sensor attachment. This paper supports the extended concept of user authentication with a device-free approach by leveraging the prevalent WiFi signals made available by IoT devices, such as smart refrigerator, smart TV and thermostat, etc. The proposed system utilizes the WiFi signals to capture unique human physiological and behavioral characteristics inherited from their daily activities, including both walking and stationary ones. Particularly, we extract representative features from channel state information (CSI) measurements of WiFi signals, and develop a deep learning based user authentication scheme to accurately identify each individual user. Extensive experiments in two typical indoor environments, a university office and an apartment, are conducted to demonstrate the effectiveness of the proposed authentication system. In particular, our system can achieve over 94% and 91% authentication accuracy with 11 subjects through walking and stationary activities, respectively. <s> BIB007
RF-based Human Activity Recognition is to leverage the RF signals, e.g., WiFi, RFID, and Radar signals to recognize a set of activities performed within the detection range of RF signals. The key insight that RF signals can be applied for human activity recognition is that the human presence could affect the propagation of the wireless signal from the transmitter to receiver. In addition, different activities can have different effects on the RF signals so that the pattern in the received signals could be distinctive for different activities. Compared with vision-based and sensor-based approaches, people do not need to wear any sensors on the body, and no pictures will be taken that protect people's privacy to some degree. RF-based human activity recognition can be classified into two categories, namely data-based approaches and model-based approaches. Data-based approaches mainly rely on the discovering of the pattern from the data or extracted features of the RF signals based on the machine learning algorithms BIB001 BIB006  BIB003 BIB007 . Some of the extracted features can be interpreted by the behavior while some of them may be hard to explain, e.g., those apply deep learning to extracted features. We do not need to build complex models to describe the behavior; we can learn from the data, so it has broad application scenarios. However, large amounts of training data sets are required to be collected, which is quite labor-intensive. In addition, the wireless signals are sensitive to the environmental changes. So, the training sets will lose its effectiveness when the environment changes. Model-based approaches are proposed to address the limitations of data-based approaches BIB004 BIB002 BIB005 . For human activities, some physical quantities of the behavior will be extracted through the wireless communication models, such as angle of arrival (AoA), time of flight (ToF), and speed. Human activities can be interpreted by the physical quantities and then be recognized. However, many commercial devices do not support extracting the precise physical quantities directly from the devices. Therefore, people either need to pay a lot for expensive and specialized devices or perform calibration on the RF signals. Meanwhile, since some activities could be too complicated to be modeled, model-based approaches have limited application scenarios. The general architecture for human activity recognition can be summarized as shown in Figure 4 . In the future, not only the object monitoring but also the physical and physiological information of human will be the focus of cyber-physical systems, and, for the industry 4.0, human activity recognition will be integrated to each part of the smart warehouse to provide more intelligent services. To summarize, the above three approaches for human activity recognition all have their advantages and limitations, as listed in Table 2 . Vision-based approaches show encouraging performance for human activity recognition. However, there are still many challenging problems for real-world deployment. Using wearable sensors is a straightforward way for human activity recognition. Ever since the person is performing any activity, there will be a signal reflecting the behavior. However, since people have to wear different kinds of sensors on their body, it could be intrusive for the users. RF-based human activity recognition has attracted much attention since our environment has been covered by wireless signals. Thus, we need to leverage the RF signals around expanding its applications. However, there are still many challenging issues for RF-based human activity recognition. There is still a long way to make it deployed for daily and industrial use. 