Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Localization from mere connectivity
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Range-free localization schemes for large scale sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
Distributed localization in wireless sensor networks: a quantitative comparison
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks
Simulating the power consumption of large-scale sensor network applications
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Wireless sensor network localization techniques
Computer Networks: The International Journal of Computer and Telecommunications Networking
Mobility '06 Proceedings of the 3rd international conference on Mobile technology, applications & systems
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
Leveraging power of transmission for range-free localization of tiny sensors
W2GIS'12 Proceedings of the 11th international conference on Web and Wireless Geographical Information Systems
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Localization within a Wireless Sensor Network consists of defining the position of a given set of sensors by satisfying some non-functional requirements such as (1) efficient energy consumption, (2) low communication or computation overhead, (3) no, or limited, use of particular hardware components, (4) fast localization, (5) robustness, and (6) low localization error. Although there are several algorithms and techniques available in literature, localization is viewed as an open issue because none of the current solutions are able to jointly satisfy all the previous requirements. An algorithm called ROCRSSI appears to be a suitable solution; however, it is affected by several inefficiencies that limit its effectiveness in real case scenarios. This paper proposes a refined version of this algorithm, called ROCRSSI++, which resolves such inefficiencies using and storing information gathered by the sensors in a more efficient manner. Several experiments on actual devices have been performed. The results show a reduction of the localization error with respect to the original algorithm. This paper investigates energy consumption and localization time required by the proposed approach.