Large scale monitoring systems require reliable and efficient in-network information extraction mechanisms able to effectively track events at the field level. The study of consensus algorithms for distributed data processing has gained a lot of interest in the last decade. Average consensus algorithms used for decentralized sensor fusion in wireless sensor networks, iteratively compute the global average value, in a completely distributed manner through local information exchange among neighbors. In the first instance, it is mandatory to pursue the reduction of convergence time, for energetic reasons, but it is also essential to lead the convergence to a reliable value. In this paper we propose a new weighted average consensus algorithm, tailored for event detection where each sensor selects its own weights on the basis of some local information regarding number of direct neighbouring nodes and estimated distances to each neighbour. Various simulations have been implemented and analysed from a comparative standpoint.