On the design of an energy-efficient low-latency integrated protocol for distributed mobile sensor networks

Self organizing, wireless sensors networks are an emergent and challenging technology that is attracting large attention in the sensing and monitoring community. Impressive progress has been done in recent years even if we need to assume that an optimal protocol for every kind of sensor network applications can not exist. As a result it is necessary to optimize the protocol for certain scenarios. In many applications for instance latency is a crucial factor in addition to energy consumption. MERLIN performs its best in such WSNs where there is the need to reduce the latency while ensuring that energy consumption is kept to a minimum. By means of that, the low latency characteristic of MERLIN can be used as a trade off to extend node lifetimes. The performance in terms of energy consumption and latency is optimized by acting on the slot length. MERLIN is designed specifically to integrate routing, MAC and localization protocols together. Furthermore it can support data queries which is a typical application for WSNs. The MERLIN protocol eliminates the necessity to have any explicit handshake mechanism among nodes. Furthermore, the reliability is improved using multiple path message propagation in combination with an overhearing mechanism. The protocol divides the network into subsets where nodes are grouped in time zones. As a result MERLIN also shows a good scalability by utilizing an appropriate scheduling mechanism in combination with a contention period

Fault prediction in aircraft engines using Self-Organizing Maps

Aircraft engines are designed to be used during several tens of years. Their maintenance is a challenging and costly task, for obvious security reasons. The goal is to ensure a proper operation of the engines, in all conditions, with a zero probability of failure, while taking into account aging. The fact that the same engine is sometimes used on several aircrafts has to be taken into account too. The maintenance can be improved if an efficient procedure for the prediction of failures is implemented. The primary source of information on the health of the engines comes from measurement during flights. Several variables such as the core speed, the oil pressure and quantity, the fan speed, etc. are measured, together with environmental variables such as the outside temperature, altitude, aircraft speed, etc. In this paper, we describe the design of a procedure aiming at visualizing successive data measured on aircraft engines. The data are multi-dimensional measurements on the engines, which are projected on a self-organizing map in order to allow us to follow the trajectories of these data over time. The trajectories consist in a succession of points on the map, each of them corresponding to the two-dimensional projection of the multi-dimensional vector of engine measurements. Analyzing the trajectories aims at visualizing any deviation from a normal behavior, making it possible to anticipate an operation failure.Comment: Communication pr\'esent\'ee au 7th International Workshop WSOM 09, St Augustine, Floride, USA, June 200

Learning a World Model and Planning With a

We present a connectionist architecture that can learn a model of the relations between perceptions and actions and use this model for behavior planning. State representations are learned with a growing selforganizing layer which is directly coupled to a perception and a motor layer. Knowledge about possible state transitions is encoded in the lateral connectivity. Motor signals modulate this lateral connectivity and a dynamic field on the layer organizes a planning process. All mechanisms are local and adaptation is based on Hebbian ideas. The model is continuous in the action, perception, and time domain