Constraint-based self-adaptation of wireless sensor networks

Abstract

In recent years, the Wireless Sensor Networks (WSNs) have become a useful mechanism to monitor physical phenomena in environments. The sensors that make part of these long-lived networks have to be reconfigured according to context changes in order to preserve the operation of the network. Such reconfigurations require to consider the distributed nature of the sensor nodes as well as their resource scarceness. Therefore, self-adaptations for WSNs have special requirements comparing with traditional information systems. In particular, the reconfiguration of the WSN requires a trade-off between critical dimensions for this kind of networks and devices, such as resource consumption or reconfiguration cost. Thus, in this paper, we propose to exploit Constraint-Satisfaction Problem (CSP) techniques in order to find a suitable configuration for self-adapting WSNs, modelled using a Dynamic Software Product Line (DSPL), when the context changes. We exploit CSP modeling to find a compromise between contradictory dimensions. To illustrate our approach, we use an Intelligent Transportation System scenario. This case study enables us to show the advantages of obtaining suitable and optimized configurations for self-adapting WSNs.

Hierarchical Control for Self-adaptive IoT Systems: A Constraint Programming-Based Adaptation Approach

“…The objective functions support the selection of the most suitable configuration regarding specific QoS dimensions. More specific for IoT, CP techniques could find a suitable configuration considering the distributed resources impacted by contextual changes [6]. Using this decision method could find a compromise between contradictory systems and context dimensions.…”