Selective reprogramming of WSNs: energetic study and functionality optimization

Abstract

Wireless sensors networks consist of large numbers of small, battery-powered, self-organizing computing motes. Nowadays, these networks are considered ideal candidates for a wide range of applications such as environmental monitoring, military operations and other application fields where it is hard to maintain a continuous presence of human beings. Online remote reprogramming is usually carried out to update the code running on nodes due to factors such as changes in the environment or application. Remote reprogramming might be applied to the whole network or just to a subset of nodes (selective reprogramming), either way it is crucial to provide reliability for such procedure. Therefore, most of the approaches oriented to remote reprogramming resort to flooding the whole network, leading to a major waste of energy in network nodes. When dealing with selective reprogramming, the waste of energy increases steeply even when just a small number of nodes need to get the update messages. These messages may be received and retransmitted from all nodes in the network resulting in a waste of resources. This research identifies multiple scenarios for selective reprogramming and proposes a different energy-aware approach for each one trying to reduce energy consumption in the network by taking advantage of multiple and complementary solutions such as wise routing, clustering and the ability to manage nodes sleeping time instead of using the typical flooding approach. These approaches were tested and compared with typical flooding and Deluge solutions. The results show a significant reduction of the power consumption, thus, making the selective remote reprogramming more energy-efficient.