Forest fires have been disastrous to civilizations since time immemorial causing damage to life and property. The human civilization has lived with a pressing need to devise a method to quickly detect and safely transmit fire alerts to minimize losses. This work presents a novel framework for Blockchain and EdgeDrone based Secured Data Delivery for Forest Fire Surveillance (BESDDFFS). A detailed three-layer edge computing architecture is proposed consisting of IoT Layer (wireless network Drones deployed in the forest), the Edge Layer (a Local Processing and Routing Station located at the edge of the IoT network) and a Cloud Server communicating continuously with the Edge Layer. The integration of an edge computing paradigm within a Blockchain setting thereby attaining enhanced levels of security and performance is a defining attribute of this work. Extensive experimentation has been undertaken at software and hardware experimental setups to gauge the efficacy of the proposed architecture. An energy-efficient prospective Leader selection algorithm is proposed as energy conservation is a crucial issue in a resource-constrained wildfire IoT environment. Moreover, an optimal and dynamic drone trajectory algorithm is also proposed to minimize energy consumption. The proposed BESFFSS architecture is compared with baseline algorithms of state-of-the-art approaches. A detailed analysis of QoS parameters reveals that the proposed system achieves up to 66% lesser delay, 36% greater throughput and a 12% greater ratio of successfully delivered packets. The proposed Leader selection (PRELS) algorithm achieves 0.5% lesser energy consumption per node and requires 8.6% lesser time to perform a fair selection of a Leader in a zone.

自古以来，森林火灾就对文明造成了灾难性的后果，对生命和财产造成了损害。人类文明迫切需要设计一种方法来快速检测和安全传输火灾警报，以最大限度地减少损失。这项工作为基于区块链和 EdgeDrone 的森林火灾监控安全数据交付 (BESDDFFS) 提出了一个新框架。提出了一个详细的三层边缘计算架构，包括物联网层（部署在森林中的无线网络无人机）、边缘层（位于物联网网络边缘的本地处理和路由站）和与云服务器持续通信的云服务器。边缘层。将边缘计算范式集成到区块链设置中从而获得更高级别的安全性和性能是这项工作的一个定义属性。已经在软件和硬件实验设置中进行了广泛的实验，以衡量所提出架构的功效。由于节能是资源受限的野火物联网环境中的一个关键问题，因此提出了一种节能的前瞻性领导者选择算法。此外，还提出了一种最优的动态无人机轨迹算法，以最大限度地减少能耗。将提出的 BESFFSS 架构与最先进方法的基线算法进行比较。对 QoS 参数的详细分析表明，所提议的系统实现了高达 66% 的延迟减少、36% 的吞吐量和 12% 的成功交付数据包比率。提议的领导者选择 (PRELS) 算法使每个节点的能耗降低了 0.5%，并且需要 8 个。