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Forest fire prediction constitutes a significant component of forest management. Timely and accurate forest fire prediction will greatly reduce property and natural losses. A quick method to estimate forest fire hazard levels through known climatic conditions could make an effective improvement in forest fire prediction. This paper presents a description and analysis of a forest fire prediction methods based on machine learning, which adopts WSN (Wireless Sensor Networks) technology and perceptron algorithms to provide a reliable and rapid detection of potential forest fire. Weather data are gathered by sensors, and then forwarded to the server, where a fire hazard index can be calculated.

Low and dynamic duty cycles cause that the E2E delay for packet delivery is more critical in energy-harvesting wireless sensor networks (EH-WSNs). The traditional routing protocols are constrained by the in-technology communication paradigm, where Wi-Fi devices can talk to the Wi-Fi devices only, and so on for ZigBee or wireless technology. This is, however, not necessary by recent advances in cross-technology communication (CTC). The CTC enables ZigBee nodes to be coordinated by a Wi-Fi node without any hardware changes or gateway equipment, which sheds the light on more efficient routing protocols design. In this paper, we introduce a new routing protocol based on a CTC technique called RowBee. RowBee takes the advantages of coordination from the Wi-Fi node to assist the ZigBee nodes for establishing routing paths and allows nodes to choose their duty cycles freely with finer duty-cycle granularity. A simple yet effective method is employed so that the ZigBee nodes are coordinately waked up simultaneously according to the beacons broadcasted by the Wi-Fi nodes. We implement RowBee based on a USRP-N210 and MICAz hybrid platform, and the experimental results show that RowBee can reduce the E2E delay greatly. INDEX TERMS Wireless sensor networks, routing protocol, cross-technology communication, energy-harvesting wireless sensor networks. I. INTRODUCTION As an interesting strategy to extend the network lifetime of Wireless Sensor Networks (WSNs), Energy-Harvesting Wireless Sensor Networks (EH-WSNs) are more economical and useful in the long-term as they can operate for very long periods of time (perhaps more than ten years until hardware failure) relying on rechargeable technologies [1], which convert sources such as foot strike [2], body heat [3], finger strokes [4] and solar [5] into electricity. Assuming energy neutral operation [6], a sensor node 1 can operate perpetually The associate editor coordinating the review of this manuscript and approving it for publication was Zhenyu Xiao.

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