Location Privacy In Wireless Sensor Network Using Reciprocal Protocol
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Location Privacy in Wireless Sensor Network Using Reciprocal Protocol
Abstract: Location privacy has been one of the greatest threats in wireless systems. The utilization of k-anonymity in Wireless Sensor Networks (WSNs) protects the location privacy. This characteristic feature enables the server to receive aggregate k-anonymized locations of internally connected sensor nodes. These k-anonymized locations are comprised of, a minimum of k persons. Nevertheless, an intruder model is used to high point the readiness of privacy risks in overlapping aggregate locations because an adversary can deduce the overlapping areas contained in an area with less than k persons whose requirement violates k-anonymity privacy. Hence, a reciprocal protocol (REAL) is used for establishing location privacy in Wireless Sensor Networks (WSNs). The sensor nodes organize a group of non-overlapping areas and k-anonymized locations into sensing areas. To overcome the privacy threats in REAL, a process state transition is designed with a time delay mechanism that provides accuracy of the messages received and a locking mechanism is designed to enhance reciprocity property. The generation of an error-free query reply and securing location privacy and decreasing computational and communication costs is achieved by the experimental analysis of the REAL protocol.
Radio Frequency Identification and IoT Security
This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Workshop on Radio Frequency Identification and IoT Security, RFIDSec 2016, held in Hong Kong, China, in November/December 2016. The 14 revised full papers were carefully reviewed and selected from 30 submissions and are organized in topical sections on protocols; side channel and hardware; cards and tokens; proximity; and communication.
Handbook of Geospatial Artificial Intelligence
This comprehensive handbook covers Geospatial Artificial Intelligence (GeoAI), which is the integration of geospatial studies and AI machine (deep) learning and knowledge graph technologies. It explains key fundamental concepts, methods, models, and technologies of GeoAI, and discusses the recent advances, research tools, and applications that range from environmental observation and social sensing to natural disaster responses. As the first single volume on this fast-emerging domain, Handbook of Geospatial Artificial Intelligence is an excellent resource for educators, students, researchers, and practitioners utilizing GeoAI in fields such as information science, environment and natural resources, geosciences, and geography. Features Provides systematic introductions and discussions of GeoAI theory, methods, technologies, applications, and future perspectives Covers a wide range of GeoAI applications and case studies in practice Offers supplementary materials such as data, programming code, tools, and case studies Discusses the recent developments of GeoAI methods and tools Includes contributions written by top experts in cutting-edge GeoAI topics This book is intended for upper-level undergraduate and graduate students from different disciplines and those taking GIS courses in geography or computer sciences as well as software engineers, geospatial industry engineers, GIS professionals in non-governmental organizations, and federal/state agencies who use GIS and want to learn more about GeoAI advances and applications.