Wayfinding and navigation can present substantial challenges to visually impaired (VI) people. Some of the significant aspects of these challenges arise from the difficulty of knowing the location of a moving person with enough accuracy. Positioning and localization in indoor environments require unique solutions. Furthermore, positioning is one of the critical aspects of any navigation system that can assist a VI person with their independent movement. The other essential features of a typical indoor navigation system include pathfinding, obstacle avoidance, and capabilities for user interaction. This work focuses on the positioning of a VI person with enough precision for their use in indoor navigation. We aim to achieve this by utilizing only the capabilities of a typical smartphone. More specifically, our proposed approach is based on the use of the accelerometer, gyroscope, and magnetometer of a smartphone. We consider the indoor environment to be divided into microcells, with the vertex of each microcell being assigned two-dimensional local coordinates. A regression-based analysis is used to train a multilayer perceptron neural network to map the inertial sensor measurements to the coordinates of the vertex of the microcell corresponding to the position of the smartphone. In order to test our proposed solution, we used IPIN2016, a publicly-available multivariate dataset that divides the indoor environment into cells tagged with the inertial sensor data of a smartphone, in order to generate the training and validating sets. Our experiments show that our proposed approach can achieve a remarkable prediction accuracy of more than 94%, with a 0.65 m positioning error.

Indoor navigation is an active area to tackle the problems related to locate an object or person and to explore several domains ranging from emergency response to improving marketing strategies in micro indoor spaces. This paper aims to provide the review of emerging indoor technologies explored to resolve indoor navigation for Visually Impaired people. This paper discusses various positioning enabled wireless technologies and algorithms used in real-world scenarios for providing indoor navigation with a comprehensive study about their advantages and disadvantages.

To overcome the limitation of Global positioning system (GPS) in indoor environments, various indoor positioning system have been developed using Wi-Fi, Bluetooth, Ultrawideband (UWB) and radio-frequency identification (RFID). Amongst them, Wi-Fi technologies are most commonly used for indoor navigation. Wi-Fi signals may be unavailable in some areas due to obstacles and unreachable coverages. Despite of it, the accuracy achieved by Wi-Fi is between 5–15 m that is unfavorable for visually impaired people. The popularity of beacons for positioning and smartphones with built-in inertial sensors plays a vital role in developing potential indoor navigation system. This paper presents a framework for visually impaired person (VIP) based on inertial sensors of smartphones and Bluetooth beacons. Beacons/proximity sensors in a building can help a pedestrian to navigate between two landmarks/points of interest via turn-by-turn navigation. However, there are certain areas in the building where external sensing is absent in a big hallway or dark alley. This model demonstrates that inertial sensors are useful to track a VIP in dark areas. Also. minimizes the use of external sensors between two landmarks/beacons. The performance of the proposed framework with the fusion algorithm in an android application is examined by conducting trajectory test on a smartphone. The experimental results of the walking traces show that the system has high accuracy with almost 1.5-2 m mean position error which could be improved further by implementing magnetometer based position learning techniques.

Indoor navigation is highly challengingfor visually impaired, particularly when visiting an unknown environment with complex design. In addition, a person at the entrance of the building might not be aware of distant changes/disruption in the path to the destination. Internet of Things devices can become the foundation infrastructure for scanning the dynamic changes in such an environment. With the sensory data of the scanned nodes, a dynamic pathfinding algorithm can provide guided route considering the changes to the destination. There are various pathfinding algorithms proposed for indoor environment including A*, Dijkstra's, probabilistic roadmap, recursive tree and orthogonal jump point search. However, there is no study done to find if these algorithms are suited to the special requirements of low vision people. We have carried out simulations in MATLAB to evaluate the performance of these algorithms based on parameters such as distance and nodes travelled execution time and safety. The results provide strong conclusion to implement most suitable orthogonal jump point search to achieve optimal and safe path for low vision people in complex buildings.

Traditionally, pathfinding is solved using classical search algorithms such as the Dijkstra's, A*, probabilistic roadmaps and jump point search. These algorithms are still more practical in a familiar environment that has minimum changes. However, these generated navigation path may lose their appropriateness as they cannot handle dynamic changes in the complex environment, restricting independent living of visually impaired people. Nowadays, a network of smart physical devices called Internet of Things has become a foundation infrastructure for indoor navigation and pathfinding. Although, variations in the environment are identified and stored by sensors, there is absence of a reasonable system that adapts to the variable circumstances and learns to react to the changes. In this paper, we introduce a learning based autonomous system DynaPATH that classifies events of dynamic environments and adapts to the changes. We have performed simulation in order to evaluate the effectiveness of our approach. The results of our approach are compared with performance of different pathfinding algorithms for VIP people. The simulation results display strong conclusion that our proposed system has high stability and is VIP friendly for navigation in a complex environment.

Security is an essential requirement in mobile ad hoc networks to provide protected communication between mobile nodes. Due to unique characteristics of MANETS, it creates a number of consequential challenges to its security design. To overcome the challenges, there is a need to build a multifence security solution that achieves both broad protection and desirable network performance. MANETs are vulnerable to various attacks, blackhole, is one of the possible attacks. Black hole is a type of routing attack where a malicious node advertise itself as having the shortest path to all nodes in the environment by sending fake route reply. By doing this, the malicious node can deprive the traffic from the source node. It can be used as a denial-of-service attack where it can drop the packets later. In this paper, we proposed a DPRAODV (Detection, Prevention and Reactive AODV) to prevent security threats of blackhole by notifying other nodes in the network of the incident. The simulation results in ns2 (ver- 2.33) demonstrate that our protocol not only prevents blackhole attack but consequently improves the overall performance of (normal) AODV in presence of black hole attack.