# Energy Efficiency for Green Internet of Things (IoT) Networks: A Survey +
## 3 Literature Review
### Taxonomy of Energy Consumption Solutions
#### 3.7.3 Aggregation Methods  
The integration of WSNs and IoT elements is all about connecting devices to the Internet, making them more convenient to use and maximizing their efficiency [149]. Such integration can generate a large volume of data by these smart devices. There is considerable data redundancy in such networks due to dense deployment. Redundant data requires a considerable amount of energy to process and transmit [150]. Since each sensor is provided with only limited power eliminating data redundancy would considerably improve energy consumption overall for IoT networks. Several studies have focused on reducing the number of data volume and data transmissions for sensor networks. A clustering algorithm is introduced in [80, 151] to minimize energy depletion and lengthen the network lifetime. The proposed method partitions the sensing field into some cells and each cell elects single node to act as a cell head for all of them. Thus, one node forwards all data collected to the cell head node, which accepts the data from their associated nodes, eliminates redundant data, and then the remaining data are delivered to the final destination. In [150], an energy-efficient in-network RFID data filtering scheme (EIFS) was proposed. The algorithm divided the sensing field into subgroups and each group has a single CH node. The CH node removes duplicated packets from its associated nodes and forwards the filtered packets to the final destination. In another study [152], the authors suggested a technique that reduced the number of data transmissions, whereby the proposed method controlled the RF-transmit operation. ON/OFF begins only when the data sensed was largely different from the previous state. In other words, the RF did not send data to the ultimate receiver if the current value is approximately same as the last recorded value. Recent studies have confirmed that cloud computing technology offers several advantages to WSNs and IoT in terms of scalability, storage, computing tasks, etc. over the Internet. It can be used to analyze the data gathered and disseminated by sensors and IoT devices [39]. For example, [PERSON], et al. [39] investigated a cloud-based architecture to enable data gathering, processing and monitoring for IoT devices. The proposed system gathers the data from various IoT devices and forwards it to the end-users via cloud infrastructure. This study aimed to provide interoperability and an efficient communication mechanism for IoT devices. The study by [153] implemented a real-time monitoring system for soil nutrient using WSN. In the proposed system, sensors measured the macro-nutrient of soil and transmitted the information to the cloud infrastructure. The user can access this information and monitor the field conditions from anywhere via a website. Several studies have highlighted the need for real-time monitoring that can gather the data from IoT devices based on the message queuing telemetry transport (MQTT) protocol. MQTT is widely applied in IoT due to its low-overhead protocol that emphasizes the bandwidth and processor limitations of the IoT devices. It uses publish/subscribe pattern and translates messages between sensors, devices, servers and applications [24]. MQTT with IoT has been used in many applications such as military, agriculture, retail, healthcare, environmental monitoring, industry, etc. In [154], the authors proposed a network of IoT monitoring devices for fire detection. The proposed system was able to detect and monitor fires and send the information to the concerned people and authorities so that preventive measures could be taken. In [155], the authors used sensors to measure body temperature, pulse rate, body movement of patients and this measured data were uploaded to the MQTT server. The proposed system aimed to help the doctors to monitor patients from any location and at any time. It also helped patients to view and check their health condition remotely. Another study by [156] proposed a web-based interface for controllingand monitoring an arm robot.