The Internet of Things (IoT) has a rapid growth in the development of smart and green environments. The IoT-based physical objects are interconnected with sensors, actuators, and data centers to gather, process and store the environmental data. The collected data is further utilized for post analysis and smarter decisions by the Base Station (BS). However, the limited resources of IoT-based sensors in terms of energy, processing, and transmission power place pressure on the development of green societies. Moreover, the IoT-based network also connects with big data using the Internet to manipulate and store a huge amount of data on the cloud. Therefore, along with the decreasing of energy consumption, security, and data integrity is other research concerns. Accordingly, an energy-efficient and big data-based secure framework using the Internet of Things for a green environment is presented. Firstly, the IoT-based sensors are connecting for data gathering and perform data routing using the Dijkstra-based optimal algorithm. Such a proposed algorithm imposes lower overheads and minimizes energy consumption in finding the most reliable and least transmission distance routes. Secondly, it also secures the generated big data from network attackers and maintains the consistency of the green environment. The experimental results reveal the EBDS framework increases the performance of green environment for energy consumption by 16%, packet drop ratio by 33%, network throughput by 13%, end-to-end delay by 15.5%, and route stability by 16% in non-uniform data traffic as compared to other work.

物联网（IoT）在智能和绿色环境的发展中快速增长。基于物联网的物理对象与传感器，执行器和数据中心互连，以收集，处理和存储环境数据。基站（BS）将收集到的数据进一步用于后期分析和更明智的决策。但是，基于物联网的传感器在能源，处理和传输功率方面的资源有限，给绿色社会的发展带来了压力。此外，基于物联网的网络还使用Internet与大数据连接，以在云上操纵和存储大量数据。因此，随着能源消耗的减少，安全性和数据完整性也是其他研究重点。因此，提出了一种使用物联网实现绿色环境的节能，基于大数据的安全框架。首先，基于物联网的传感器正在连接以使用基于Dijkstra的最佳算法进行数据收集和执行数据路由。这样的算法在发现最可靠和最小的传输距离路线时会降低开销，并使能量消耗最小。其次，它还可以保护网络攻击者生成的大数据，并保持绿色环境的一致性。实验结果表明，EBDS框架将绿色环境的能耗提高了16％，丢包率提高了33％，网络吞吐量提高了13％，端到端延迟提高了15.5％，路由稳定性提高了16％与其他工作相比数据流量不一致。