Prolongation of network lifetime is one of the most important issues of sensor networks. EEHF (Environmental Energy Harvesting Framework) is a framework that includes a data gathering scheme and estimates the energy gained from the environment by using the periodicity of environmental power. Thus, sensor nodes can use environmental energy efficiently and the network lifetime is extended. However, since the accuracy of the estimation depends on the length of the period, and the data gathering scheme used in EEHF does not take environmental energy into consideration, this method has room for improvement. In this paper, we propose a more accurate and adaptive energy estimation method that does not depend on the length of the period, and a data gathering scheme that is optimized for the environmental energy-based sensor network, in order to prolong the lifetime of environmental energy-based sensor networks.
Index Terms-sensor network, environmental energy, cluster
I. INTRODUCTIONWireless Sensor Networks (WSNs) receives much recent attention [1]. A WSN comprises many sensor nodes that have wireless communication ability and a few base stations that gather sensing data from the sensor nodes. WSNs may have a profound effect on the efficiency of many military and civil applications, including target field imaging, intrusion detection, weather monitoring, etc. However, many issues still exist with respect to the utilization of WSNs. In particular, prolongation of network lifetime is one of the most important issues, since sensor nodes are powered by small-capacity batteries in general.Energy consumption in a WSN is dominated by the radio transmission energy, which is proportional to the 2nd or 4th power of the transmission distance. Therefore, many routing algorithms aiming to reduce the frequency of transmission and/or to shorten the transmission distance have been proposed. In general, routing in WSNs can be classified as either flat-based routing or hierarchical routing, depending on the network structure [2].In flat-based routing, each node typically has the same role and sensor nodes collaborate to perform the sensing task. Due to the large number of such nodes, it is not feasible to assign a global identifier to each node. This has led to datacentric routing, where the base station sends queries to certain regions and waits for data from sensors located in the selected regions. As major examples of flat-based routing, SPIN [3] and Directed Diffusion [4] have been shown to save energy through data negotiation and the elimination of redundant data.On the other hand, hierarchical or cluster-based routing has advantages related to scalability and efficient communications. As such, the concept of hierarchical routing is also utilized to perform energy-efficient routing in WSNs [5], [6]. In hierarchical routing, a small number of higher-energy nodes can be used to process and send information to base stations, while low-energy nodes can be used to perform sensing in the neighborhood of the target. This means that the crea...