A Proximity based Retransmission Scheme for Power Line Ad-hoc LAN

Power line as an alternative for data transmission is being explored, and also being used to a certain extent. But from the data transfer point of view, power line, as a channel is highly dynamic and hence not quite suitable. To convert the office or home wiring system to a Local Area Network (LAN), adaptive changes are to be made to the existing protocols. In this paper, a slotted transmission scheme is suggested, in which usable timeslots are found out by physically sensing the media. Common usable timeslots for the sender-receiver pair are used for communication. But these will not ensure safe packet delivery since packets may be corrupted on the way during propagation from sender to receiver. Therefore, we also suggest a proximity based retransmission scheme where each machine in the LAN, buffers good packet and machines close to the receiver retransmit on receiving a NACK.Comment: Already published in IJDP

Distributed Maximum Likelihood Sensor Network Localization

We propose a class of convex relaxations to solve the sensor network localization problem, based on a maximum likelihood (ML) formulation. This class, as well as the tightness of the relaxations, depends on the noise probability density function (PDF) of the collected measurements. We derive a computational efficient edge-based version of this ML convex relaxation class and we design a distributed algorithm that enables the sensor nodes to solve these edge-based convex programs locally by communicating only with their close neighbors. This algorithm relies on the alternating direction method of multipliers (ADMM), it converges to the centralized solution, it can run asynchronously, and it is computation error-resilient. Finally, we compare our proposed distributed scheme with other available methods, both analytically and numerically, and we argue the added value of ADMM, especially for large-scale networks

Distributed on-line multidimensional scaling for self-localization in wireless sensor networks

The present work considers the localization problem in wireless sensor networks formed by fixed nodes. Each node seeks to estimate its own position based on noisy measurements of the relative distance to other nodes. In a centralized batch mode, positions can be retrieved (up to a rigid transformation) by applying Principal Component Analysis (PCA) on a so-called similarity matrix built from the relative distances. In this paper, we propose a distributed on-line algorithm allowing each node to estimate its own position based on limited exchange of information in the network. Our framework encompasses the case of sporadic measurements and random link failures. We prove the consistency of our algorithm in the case of fixed sensors. Finally, we provide numerical and experimental results from both simulated and real data. Simulations issued to real data are conducted on a wireless sensor network testbed.Comment: 32 pages, 5 figures, 1 tabl