Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Management of a taxi services company through use of GPS positioning and GPRS data transfer

The article describes methods of management and use of the GPS systems in taxi companies through application of advanced information technologies, such as GPS positioning and GPRS data transfer. All these systems are united in an integrated solution – taxi information system (TIS), which includes all aspects of the activities of the taxi companies

Distributed drone base station positioning for emergency cellular networks using reinforcement learning

Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network

A Comparison of Mobile Scanning to a Total Station Survey at the I-35 and IA 92 Interchange in Warren County, Iowa, August 15, 2012

The purpose of this project was to investigate the potential for collecting and using data from mobile terrestrial laser scanning (MTLS) technology that would reduce the need for traditional survey methods for the development of highway improvement projects at the Iowa Department of Transportation (Iowa DOT). The primary interest in investigating mobile scanning technology is to minimize the exposure of field surveyors to dangerous high volume traffic situations. Issues investigated were cost, timeframe, accuracy, contracting specifications, data capture extents, data extraction capabilities and data storage issues associated with mobile scanning. The project area selected for evaluation was the I-35/IA 92 interchange in Warren County, Iowa. This project covers approximately one mile of I-35, one mile of IA 92, 4 interchange ramps, and bridges within these limits. Delivered LAS and image files for this project totaled almost 31GB. There is nearly a 6-fold increase in the size of the scan data after post-processing. Camera data, when enabled, produced approximately 900MB of imagery data per mile using a 2- camera, 5 megapixel system. A comparison was done between 1823 points on the pavement that were surveyed by Iowa DOT staff using a total station and the same points generated through the MTLS process. The data acquired through the MTLS and data processing met the Iowa DOT specifications for engineering survey. A list of benefits and challenges is included in the detailed report. With the success of this project, it is anticipate[d] that additional projects will be scanned for the Iowa DOT for use in the development of highway improvement projects

RF Localization in Indoor Environment

In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained