Poster: Indoor Navigation for Visually Impaired People with Vertex Colored Graphs

Visually impaired people face many daily encumbrances. Traditional visual enhancements do not suffice to navigate indoor environments. In this paper, we explore path finding algorithms such as Dijkstra and A* combined with graph coloring to find a safest and shortest path for visual impaired people to navigate indoors. Our mobile application is based on a database which stores the locations of several spots in the building and their corresponding label. Visual impaired people select the start and destination when they want to find their way, and our mobile application will show the appropriate path which guarantees their safety

A New Product Anti-Counterfeiting Blockchain Using a Truly Decentralized Dynamic Consensus Protocol

The growth of counterfeit goods has plagued the international community for decades. Nowadays, the battle against counterfeiting remains a significant challenge. Most of the current anti‐counterfeiting systems are centralized. Motivated by the evolution of blockchain technology, we propose (Block‐Supply), a decentralized anti‐counterfeiting supply chain that exploits NFC and blockchain technologies. This paper also proposes a new truly decentralized consensus protocol that, unlike most of the existing protocols, does not require PoW and randomly employs a different set of different size of validators each time a new block is proposed. Our protocol utilizes a game theoretical model to analyze the risk likelihood of the block\u27s proposing nodes. This risk likelihood is used to determine the number of validators involved in the consensus process. Additionally, the game model enforces the honest consensus nodes\u27 behavior by rewarding honest players and penalizing dishonest ones. Our protocol utilizes a novel, decentralized, dynamic mapping between the nodes that participate in the consensus process. This mapping ensures that the interaction between these nodes is executed anonymously and blindly. This way of mapping withstands many attacks that require knowing the identities of the participating nodes in advance, such as DDoS, Bribery, and Eclipse attacks

Towards Adaptive, Self-Configuring Networked Unmanned Aerial Vehicles

Networked drones have the potential to transform various applications domains; yet their adoption particularly in indoor and forest environments has been stymied by the lack of accurate maps and autonomous navigation abilities in the absence of GPS, the lack of highly reliable, energy-efficient wireless communications, and the challenges of visually inferring and understanding an environment with resource-limited individual drones. We advocate a novel vision for the research community in the development of distributed, localized algorithms that enable the networked drones to dynamically coordinate to perform adaptive beam forming to achieve high capacity directional aerial communications, and collaborative machine learning to simultaneously localize, map and visually infer the challenging environment, even when individual drones are resource-limited in terms of computation and communication due to payload restrictions

Zoom: A multi-resolution tasking framework for crowdsourced geo-spatial sensing

Abstract—As sensor networking technologies continue to de-velop, the notion of adding large-scale mobility into sensor networks is becoming feasible by crowd-sourcing data collection to personal mobile devices. However, tasking such networks at fine granularity becomes problematic because the sensors are heterogeneous, owned by the crowd and not the network operators. In this paper, we present Zoom, a multi-resolution tasking framework for crowdsourced geo-spatial sensor networks. Zoom allows users to define arbitrary sensor groupings over heterogeneous, unstructured and mobile networks and assign different sensing tasks to each group. The key idea is the separation of the task information ( what task a particular sensor should perform) from the task implementation ( code). Zoom consists of (i) a map, an overlay on top of a geographic region, to represent both the sensor groups and the task information, and (ii) adaptive encoding of the map at multiple resolutions and region-of-interest cropping for resource-constrained devices, allowing sensors to zoom in quickly to a specific region to determine their task. Simulation of a realistic traffic application over an area of 1 sq. km with a task map of size 1.5 KB shows that more than 90 % of nodes are tasked correctly. Zoom also outperforms Logical Neighborhoods, the state-of-the-art tasking protocol in task information size for similar tasks. Its encoded map size is always less than 50 % of Logical Neighborhood’s predicate size. I

Using Geospatial Information in Sensor Networks

This paper describes several ways sensor networks can benefit from geospatial information and identifies two research directions. First, better models of localization error, logical location, and communications costs are required to understand the interactions between spatial information and control and communications algorithms in sensor networks. Second, wider use of spatial information in densely deployed sensor networks will move sensor networking applications from simple tracking to object counting and area monitoring, and can enable data mining techniques sensor networks to accomplish "spatial sensor mining"

An Automated Ar-Based Annotation Tool for Indoor Navigation for Visually Impaired People

Low vision people face many daily encumbrances. Traditional visual enhancements do not suffice to navigate indoor environments, or recognize objects efficiently. In this paper, we explore how Augmented Reality (AR) can be leveraged to design mobile applications to improve visual experience and unburden low vision persons. Specifically, we propose a novel automated AR-based annotation tool for detecting and labeling salient objects for assisted indoor navigation applications like NearbyExplorer. NearbyExplorer, which issues audio descriptions of nearby objects to the users, relies on a database populated by large teams of volunteers and map-a-thons to manually annotate salient objects in the environment like desks, chairs, low overhead ceilings. This has limited widespread and rapid deployment. Our tool builds on advances in automated object detection, AR labeling and accurate indoor positioning to provide an automated way to upload object labels and user position to a database, requiring just one volunteer. Moreover, it enables low vision people to detect and notice surrounding objects quickly using smartphones in various indoor environments