Bio-inspired in-network filtering for wireless sensor monitoring systems

In-network filtering schemes can be used for computing type-threshold functions in wireless sensor networks. Instead of relaying all data to a sink node, sensor nodes can filter measurements to provide only the set of data required to compute a given function (e.g., maximum, range). In this context, the network can progressively learn where relevant data are available and use this information to compute the function over time by only querying a subset of nodes. Trails between sink and these nodes can be obtained based on bio-inspired strategies, reducing the energy consumption and prolonging the network lifetime. The adaptive behavior of swarm intelligence allows to overcome a lot of obstacles presented in wireless communication networks. In this work, we evaluate the PhINP (Pheromone-based in Network Processing) mechanism, which drives the filtering process based on the integration of metaheuristic and learning algorithms. MAX function computation in oneand multiple-source environment monitoring is used as a case study. We show by simulation that communication cost can be significantly reduced respect to traditional mechanisms, increasing the network lifetime, while keeping a low computational error. Finally, node density requirements for efficient event detection in real applications are analyzed.