Nowadays, personal positioning systems are more necessary to build many location-based services. Pedestrian Dead Reckoning (PDR), which is a pedestrian positioning technique using the accelerometer sensor to recognize pattern of steps, is an alternative method that has advantages in terms of infrastructure-independent. However, the variation of walking pattern on each individual will make some difficulties for the system to detect displacement. This is really interested authors to develop a sensor-based positioning system that applied generally to all individuals. In the test, 15 test subjects was taken with the distance of each 10m, 20m and 30m. Experiment begins with the feasibility test of accelerometer sensor. In this work, a smartphone with average sampling rate 63.79 Hz and standard deviation of 1.293 is used to records the acceleration. Then, the acceleration data are analyzed to detect step and to estimate the travelled distance using several methods. Detection of steps are able to make an average error of 2.925%, while the most nearly correct displacement estimation is using Scarlet experimental method which is make a distance average error of 1.39metres at all the traveled distance.

Modern Smart Buildings will utilize sensor technologies to obtain current information of the occupants and use it to provide automatic services to improve the safety, efficient use, productivity, and comfort. Energy saving is one of the promises of Smart Buildings. This can be achieved by controlling the HVAC system, lights, and other energy consuming appliances in relation to the activities of people in the building. In particular, presence is an important factor to consider. By using beacons such as those based on Bluetooth Low Energy (BLE) and mobile phones, one can deduct occupancy information by interacting, over WiFi, with a central server. In the present study, we investigate BLE utilization for transmitting the occupancy data to the server using off-the-self devices with several parameters, e.g., distance, packet size, and line-of-sight state. Furthermore, we propose a utilization of size-constrained BLE packets for transmitting occupancy data, which was previously only possible by means of HTTP over WiFi. The result shows that it is possible to transmit occupancy data via BLE, despite of its resource constraints. Bluetooth is also shown to be about 30% more energy efficient than WiFi to perform occupancy data transmission, i.e., the mobile phone has 16 hours and 38 minutes of battery life when running on BLE communication scheme and 14 hours and 46 minutes in WiFi communication.

Smart spaces are those that are aware of their state and can act accordingly. Among the central elements of such a state is the presence of humans and their number. For a smart office building, such information can be used for saving energy and safety purposes. While acquiring presence information is crucial, using sensing techniques that are highly intrusive, such as cameras, is often not acceptable for the building occupants. In this paper, we illustrate a proposal for occupancy detection which is low intrusive; it is based on equipment typically available in modern offices such as room-level power-metering and an app running on workers’ mobile phones. For power metering, we collect the aggregated power consumption and disaggregate the load of each device. For the mobile phone, we use the Received Signal Strength (RSS) of BLE (Bluetooth Low Energy) nodes deployed around workspaces to localize the phone in a room. We test the system in our offices. The experiments show that sensor fusion of the two sensing modalities gives 87–90% accuracy, demonstrating the effectiveness of the proposed approach.

Indoor occupancy provides information about human occupation in the closed space, most notably, office and residential buildings. This information is useful in dwindling unnecessary energy usage, such as consumption in unoccupied spaces or energy-wasting due to unnecessarily active appliances. We present an empirical experiment on office occupancy detection using simple office sensors. We choose generic power meters and mobile phones. First, we classify beacon signals received by mobile phones into a room location. A workspace map is assumed to be available to facilitate the mapping between room locations and the occupancy state of users' workspace. Second, we infer the individual occupancy state utilizing the aggregated electricity consumption of occupant-related devices (i.e., monitors) in shared offices. The later solution helps to keep costs and intrusiveness level low compared to deploying a power meter for each device or user. We experiment in an work environment with two shared offices, a personal office, and a social corner involving five volunteers. Given the acquired data, three techniques based on machine learning, optimization, and probabilistic approach are implemented and compared to evaluate their performance. The results indicate that localization and occupancy based on beaconing works best for three of the five volunteers, reaching 95% F-measure. Further findings shows that occupancy inference based on the aggregated power consumption performs well for the four volunteers when using Decision Tree classification, reaching more than 90% F-measure. Our effort on the fusion of two modalities gives a positive result for all five volunteers, ranging from 92% to 99% F-measure.

Precise fine-grained office occupancy detection can be exploited for energy savings in buildings. Based on such information one can optimally regulate lighting and climatization based on the actual presence and absence of users. Conventional approaches are based on movement detection, which are cheap and easy to deploy, but are imprecise and offer coarse information. We propose a power monitoring system as a source of occupancy information.The approach is based on sub-metering at the level of room circuit breakers. The proposed method tackles the problem of indoor, office occupancy detection based on statistical approaches based on power consumption information thus contributing to building context awareness which, in turn, is a crucial stepping stone for energy efficient buildings. The key advantage of the proposed approach is to be low intrusive, especially when compared with image or tag based solutions, while still being sufficiently precise in its classification. Such classification is based on nearest neighbors and neural networks machine learning approaches, both in sequential and non-sequential implementations. To test the viability, precision, and saving potential of the proposed approach we deploy in an actual office over several months. We find that the room-level sub- metering can acquire precise, fine-grained occupancy context for up to three people, with averaged kappa measures of 93-95% using either the nearest neighbors or neural networks based approaches.

Counterfeit products have become a pervasive problem in the global marketplace, necessitating effective strategies to protect both consumers and brands. This study examines the role of cybersecurity in addressing counterfeiting issues, specifically focusing on a multi-level grayscale watermark-based authentication system. The system comprises a generator responsible for creating a secure 2D code, and an authenticator designed to extract watermark information and verify product authenticity. To authenticate the secure 2D code, we propose various features, including the analysis of the spatial domain, frequency domain, and grayscale watermark distribution. Furthermore, we emphasize the importance of selecting appropriate interpolation methods to enhance counterfeit detection. Our proposed approach demonstrates remarkable performance, achieving precision, recall, and specificities surpassing 84.8%, 83.33%, and 84.5%, respectively, across different datasets.