Location-aided routing (LAR) in mobile ad hoc networks
MobiCom '98 Proceedings of the 4th annual ACM/IEEE international conference on Mobile computing and networking
Geography-informed energy conservation for Ad Hoc routing
Proceedings of the 7th annual international conference on Mobile computing and networking
Robust Positioning Algorithms for Distributed Ad-Hoc Wireless Sensor Networks
ATEC '02 Proceedings of the General Track of the annual conference on USENIX Annual Technical Conference
Geocasting in Mobile Ad Hoc Networks: Location-Based Multicast Algorithms
WMCSA '99 Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications
Geometric ad-hoc routing: of theory and practice
Proceedings of the twenty-second annual symposium on Principles of distributed computing
Range-free localization schemes for large scale sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
A mobility-based framework for adaptive clustering in wireless ad hoc networks
IEEE Journal on Selected Areas in Communications
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Mobile ad hoc networks (MANET) are dynamic networks formed on-the-fly as mobile nodes move in and out of each others’ transmission ranges. In general, the mobile ad hoc networking model makes no assumption that nodes know their own locations. However, recent research shows that location-awareness can be beneficial to fundamental tasks such as routing and energy-conservation. On the other hand, the cost and limited energy resources associated with common, low-cost mobile nodes prohibits them from carrying relatively expensive and power-hungry location-sensing devices such as GPS. This paper proposes a mechanism that allows non-GPS-equipped nodes in the network to derive their approximated locations from a limited number of GPS-equipped nodes. In our method, all nodes periodically broadcast their estimated location, in term of a compressed particle filter distribution. Non-GPS nodes estimate the distance to their neighbors by measuring the received signal strength of incoming messages. A particle filter is then used to estimate the approximated location from the sequence of distance estimates. Simulation studies show that our solution is capable of producing good estimates equal or better than the existing localization methods such as APS-Euclidean.