Abstract
Wireless sensor networks have a wide range of applications, such as wildlife tracking, environmental monitoring and building monitoring. Because WSNs are usually deployed at the places which are not easily accessible, we use wireless network reprogramming to manage the functions of these applications and update the software which is executed in WSNs. The former researches focus on minimizing the completion time of network reprogramming or minimizing the energy consumption of WSNs. This paper proposes the new direction which is that the choice of relay nodes should base on the remnant energy of sensor node during the process of network reprogramming. If we choose the sensor node which is almost out of energy to be our relay node, it may cause the node to out of energy and this situation may affect the normal execution of network reprogramming. Hence, we design a new multihop network reprogramming protocol which maximizes the minimal remnant energy of sensor node. The main problems in the network reprogramming are the issue of message collision and hidden terminal problem. We propose a sender selection mechanism which not only reduces these problems but also maximizes the minimal remnant energy of sensor nodes. The sender selection mechanism can coordinate the sender candidates of neighborhood and choose the best sender which is decided by its receivers and remnant energy to broadcast data. We use pipelining to enable fast data propagation and put the node into sleep mode when sensor node is idle for the purpose of reducing energy consumption. This research also focuses on solving the problems of using delta compression algorithms which are usually used during the process of network reprogramming for the purpose of minimize energy consumption of WSNs. Because former researches ignore the overhead of decompressing script, delta compression based network reprogramming may cause worse energy consumption than without using the algorithm. Our network reprogramming protocol can avoid the occurrence of this situation and minimize the drawback of using delta compression algorithm. We estimate the energy consumption of using delta compression algorithm during the process of network reprogramming and choose the better way which consumes less energy to transmit data.