# Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture
## 2 Related Work  
The smart city emerged as a concept with the fast growth of dependable information and communication models and the combination of sensor technologies [24; 25; 26]. Modern fog computing technology develops intelligent networks combining IoT networks and cloud platforms. Fog computing is applied at the network edge, and they perform a significant number of tasks in terms of processing, storage, and communication. Networking devices such as routers, gateways, etc., make up fog computing. Compared to sensor nodes, these devices have more processing, transmission, and storage capabilities [27; 28]. In an IoT system, fog nodes received data from sensors and are further forwarded for high-cost processing with the support of data centers. A smart city promotes sustainability by utilizing various sensors to collect information from the environment while providing improved social facilities, transportation, and accessibility. The gathered information can then be utilized to manage urban infrastructure, including water supply, food services, environmental monitoring, and traffic congestion [29; 30; 31]. Smart agriculture is based on the IoT with future generation networks and is expected to benefit from the intelligently developed methodologies. The objectives of this strategy are to preserve water resources, lessen soil erosion, and improve soil quality [32; 33]. An effective and scalable protocol for the remote monitoring and decision making of farms in rural areas is named the CL-IoT protocol, and it was proposed to focus on the requirements for smart farming applications [34]. To decrease network communication delay, latency, and energy consumption, cross-layer-based clustering, and routing algorithms were developed. The cluster head (CH) selection method based on cross-layers has been proposed as a means of solving the energy efficiency issue for resource constraint networks. Each sensor's physical, medium access control (MAC), and network layer parameters were analyzed and chosen as the optimal CH for effective data transfer. The algorithm with a novel probabilistic decision rule that was inspired by nature is proposed and serves as a fitness function to choose the best path for data transfer. To choose the cluster head, a hybrid artificial neural network and decision tree method were built with the cognitive radio [35]. The base station receives more packets and collects more data from the typical sensor nodes as the residual energy level rises. The on-demand routing protocol is designed to hold data in local storage for retransmission during link failure to achieve reliable data transfer. Performance metrics for the proposed technique include throughput, packet drop rate, packet delivery ratio, normalized overhead, and residual energy. The effectiveness of the proposed strategy was compared to a cluster-based data aggregation scheme. A novel intelligent routing protocol was proposed in [36] to increase the network lifetime and offer energy efficiency in the routing process, which is used to deliver data to the irrigation system. The protocol is known as terrain-based routing using fuzzy rules for precision agriculture and it uses fuzzy rules to provide a revolutionary intelligent energy-efficient routing scheme. The routing decisions were made using the fuzzy inference method described in this work. The equalized cluster head election routing protocol and region-based routing are two routing algorithms that were constructed and compared with the system. The experimental findings demonstrate that the suggested algorithm outperforms the other available algorithms. For an Agriculture Internet of Things (AG-IoT) network, the authors proposed a supervised machine learning multipath and administrative-distance-based load balancing algorithm [37].