Technological advances have made wireless sensor nodes cheap and reliable enough to be brought into various application domains. The limited energy capacity of sensor nodes is the key factor that restricts their lifespan. In this paper, a Predictive Control strategy for Dynamic Power Management of a set of wireless sensor nodes is proposed. The control formulation is based on Model Predictive Control with constraints and binary optimization variables, leading to a Mixed Integer Quadratic Programming problem. The control algorithm proposed guarantees services and performances levels with a minimum number of active nodes and/or a minimum load on such components. The strategy is evaluated on a real testbench with wireless sensor nodes equipped with batteries and harvesting systems. Experimental results show the effectiveness of the control method proposed.

Abstract-Wireless sensor nodes are now cheap and reliable enough to be deployed in different environments. However, their limited energy capacity limits their lifespan. In this paper, a Management strategy at network-level of a set of nodes is implemented, taking into account an estimation of the remaining energy in each sensor node. The control formulation is based on Model Predictive Control with constraints and binary optimization variables, leading to a Mixed Integer Quadratic Programming problem. The estimation of the remaining energy in batteries must be simple enough to be implemented in lowcost, low-power, low-computational-capability sensor nodes.

This paper details how LINC a coordination middleware, can fasten the development of prototypes that integrate several equipment. A case study of rapid prototyping is presented. It illustrates how a smart parking prototype has been built from several independent and autonomous equipment, coming from different vendors. This has been achieved by parallel development thanks to the resource based approach offered by LINC. This paper also describes how LINC helps building rich user interfaces quickly and easily.