Wireless Sensor Node with Passive RFID for Indoor Monitoring System

This paper discusses the development of an indoor monitoring system based on passive radio frequency identification (RFID) system and Raspberry Pi 3. There are two algorithms designed for this project where the first is to link the RFID module to the Raspberry Pi 3, and the other one is to send the data obtained to a database over wireless network via UDOO Quad as a secondary router. The result is then displayed on a localhost generated using XAMPP. The objective of this project is to realize a monitoring system that incorporates different systems such as Raspberry Pi 3, UDOO Quad, and also RFID module by designing algorithms using Python and C programming language. Plus, the performance of the system is also analyzed using different type of antennas such as the Raspberry Pi 3 Antenna, monopole antenna, and a Yagi Uda antenna in terms of power received versus distance in both line of sight position and non-line of sight position. Finally, antenna that produces the best performance for line-of-sight (LOS) propagation is Yagi Uda antenna while monopole antenna is better when it comes to non-line-of-sight (NLOS) propagation

Indoor navigation for the visually impaired : enhancements through utilisation of the Internet of Things and deep learning

Wayfinding and navigation are essential aspects of independent living that heavily rely on the sense of vision. Walking in a complex building requires knowing exact location to find a suitable path to the desired destination, avoiding obstacles and monitoring orientation and movement along the route. People who do not have access to sight-dependent information, such as that provided by signage, maps and environmental cues, can encounter challenges in achieving these tasks independently. They can rely on assistance from others or maintain their independence by using assistive technologies and the resources provided by smart environments. Several solutions have adapted technological innovations to combat navigation in an indoor environment over the last few years. However, there remains a significant lack of a complete solution to aid the navigation requirements of visually impaired (VI) people. The use of a single technology cannot provide a solution to fulfil all the navigation difficulties faced. A hybrid solution using Internet of Things (IoT) devices and deep learning techniques to discern the patterns of an indoor environment may help VI people gain confidence to travel independently. This thesis aims to improve the independence and enhance the journey of VI people in an indoor setting with the proposed framework, using a smartphone. The thesis proposes a novel framework, Indoor-Nav, to provide a VI-friendly path to avoid obstacles and predict the user s position. The components include Ortho-PATH, Blue Dot for VI People (BVIP), and a deep learning-based indoor positioning model. The work establishes a novel collision-free pathfinding algorithm, Orth-PATH, to generate a VI-friendly path via sensing a grid-based indoor space. Further, to ensure correct movement, with the use of beacons and a smartphone, BVIP monitors the movements and relative position of the moving user. In dark areas without external devices, the research tests the feasibility of using sensory information from a smartphone with a pre-trained regression-based deep learning model to predict the user s absolute position. The work accomplishes a diverse range of simulations and experiments to confirm the performance and effectiveness of the proposed framework and its components. The results show that Indoor-Nav is the first type of pathfinding algorithm to provide a novel path to reflect the needs of VI people. The approach designs a path alongside walls, avoiding obstacles, and this research benchmarks the approach with other popular pathfinding algorithms. Further, this research develops a smartphone-based application to test the trajectories of a moving user in an indoor environment

AN ENERGY EFFICIENT CROSS-LAYER NETWORK OPERATION MODEL FOR MOBILE WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) are modern technologies used to sense/control the environment whether indoors or outdoors. Sensor nodes are miniatures that can sense a specific event according to the end user(s) needs. The types of applications where such technology can be utilised and implemented are vast and range from households’ low end simple need applications to high end military based applications. WSNs are resource limited. Sensor nodes are expected to work on a limited source of power (e.g., batteries). The connectivity quality and reliability of the nodes is dependent on the quality of the hardware which the nodes are made of. Sensor nodes are envisioned to be either stationary or mobile. Mobility increases the issues of the quality of the operation of the network because it effects directly on the quality of the connections between the nodes

AN ENERGY EFFICIENT CROSS-LAYER NETWORK OPERATION MODEL FOR MOBILE WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) are modern technologies used to sense/control the environment whether indoors or outdoors. Sensor nodes are miniatures that can sense a specific event according to the end user(s) needs. The types of applications where such technology can be utilised and implemented are vast and range from households’ low end simple need applications to high end military based applications. WSNs are resource limited. Sensor nodes are expected to work on a limited source of power (e.g., batteries). The connectivity quality and reliability of the nodes is dependent on the quality of the hardware which the nodes are made of. Sensor nodes are envisioned to be either stationary or mobile. Mobility increases the issues of the quality of the operation of the network because it effects directly on the quality of the connections between the nodes

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202

An investigation of eyes-free spatial auditory interfaces for mobile devices: supporting multitasking and location-based information

Auditory interfaces offer a solution to the problem of effective eyes-free mobile interactions. However, a problem with audio, as opposed to visual displays, is dealing with multiple simultaneous information streams. Spatial audio can be used to differentiate between different streams by locating them into separate spatial auditory streams. In this thesis, we consider which spatial audio designs might be the most effective for supporting multiple auditory streams and the impact such spatialisation might have on the users' cognitive load. An investigation is carried out to explore the extent to which 3D audio can be effectively incorporated into mobile auditory interfaces to offer users eyes-free interaction for both multitasking and accessing location-based information. Following a successful calibration of the 3D audio controls on the mobile device of choice for this work (the Nokia N95 8GB), a systematic evaluationof 3D audio techniques is reported in the experimental chapters of this thesis which considered the effects of multitasking, multi-level displays, as well as differences between egocentric and exocentric designs. One experiment investigates the implementation and evaluation of a number of different spatial (egocentric) and non-spatial audio techniques for supporting eyes-free mobile multitasking that included spatial minimisation. The efficiency and usability of these techniques was evaluated under varying cognitive load. This evaluation showed an important interaction between cognitive load and the method used to present multiple auditory streams. The spatial minimisation technique offered an effective means of presenting and interacting with multiple auditory streams simultaneously in a selective-attention task (low cognitive load) but it was not as effective in a divided-attention task (high cognitive load), in which the interaction benefited significantly from the interruption of one of the stream. Two further experiments examine a location-based approach to supporting multiple information streams in a realistic eyes-free mobile environment. An initial case study was conducted in an outdoor mobile audio-augmented exploratory environment that allowed for the analysis and description of user behaviour in a purely exploratory environment. 3D audio was found to be an effective technique to disambiguate multiple sound sources in a mobile exploratory environment and to provide a more engaging and immersive experience as well as encouraging an exploratory behaviour. A second study extended the work of the previous case study by evaluating a number of complex multi-level spatial auditory displays that enabled interaction with multiple location-based information in an indoor mobile audio-augmented exploratory environment. It was found that a consistent exocentric design across levels failed to reduce workload or increase user satisfaction, so this design was widely rejected by users. However, the rest of spatial auditory displays tested in this study encouraged an exploratory behaviour similar to that described in the previous case study, here further characterised by increased user satisfaction and low perceived workload