Sensor network-based home automation systems are familiar over the recent decades. Incorporating the benefits of the sensor network, energy management systems (EMS), is introduced to benefit end-user through periodic information sharing and remote access. WSN opted for energy harvesters to reduce the maintenance costs and maximize the lifetime of network. It is a perfect match for wireless devices and WSNs. Energy management system designed for effective use of harvested energy. Wireless sensor networks (WSN) coupled with EMS and grid-based applications serve as a support for smart home appliances. The integrated system architectures are cost effective and are energy harvesting that is profitable for end-user applications. Identifying optimal devices and defining an energy management policy are a tedious task as the devices are interfaced through different application support. This manuscript proposes a reward-based energy harvesting (REH) approach for identifying reliable devices in order to frame minimal-allocation energy for its operation. The rewards for the devices are estimated through observations carried out using reinforced learning that determines the operation state of the device. The reward function is computed using a variant function evaluated using the enduring energy and storage metrics of a device. Unlike the other learning methods, this approach operates in variable communication interval retaining the reward from the previous history of the devices. With a distributed WSN support and recursive knowledge of the sensor devices, REH is intended to improve the energy conservation rate with lesser retransmissions. The curtailed number of retransmissions minimizes delay with more preferable ideal devices in a home management system. The performance of the proposed REH is evaluated through simulations considering the following metrics: end-to-end delay, energy utilization, packets forwarded, expected TTL and number of retransmissions.

近几十年来，基于传感器网络的家庭自动化系统已为人们所熟悉。结合了传感器网络的优势，引入了能源管理系统（EMS），通过定期的信息共享和远程访问使最终用户受益。WSN选择了能量收集器，以降低维护成本并最大化网络的使用寿命。它是无线设备和WSN的完美匹配。能源管理系统旨在有效利用收集的能源。无线传感器网络（WSN）结合EMS和基于网格的应用程序可作为对智能家电的支持。集成的系统体系结构具有成本效益，并且能量收集对于最终用户的应用程序有利可图。识别最佳设备并定义能源管理策略是一项繁琐的任务，因为这些设备通过不同的应用程序支持进行接口。该手稿提出了一种基于奖励的能量收集（REH）方法，用于识别可靠的设备，以便为其操作分配最小的能量。通过使用确定设备的运行状态的强化学习进行的观察估计设备的报酬。使用使用设备的持久能量和存储指标评估的变体函数来计算奖励函数。与其他学习方法不同，此方法以可变的通信间隔运行，保留了设备以前的历史记录所带来的收益。借助分布式WSN支持和对传感器设备的递归知识，REH旨在通过更少的重发来提高节能率。减少的重传次数可在家庭管理系统中使用更理想的理想设备来最大程度地减少延迟。拟议的REH的性能是通过考虑以下指标的仿真评估的：端到端延迟，能量利用率，转发的数据包，预期的TTL和重传次数。