# An Efficient Opportunistic Routing Protocol with Low Latency for Farm Wireless Sensor Networks
## 2 Related Works  
In view of the instability of transmission in the agricultural environment, the research on how to use WSN to effectively transmit agricultural information has attracted many researchers. The energy consumption, transmission delay, and throughput have an important impact on the network lifetime and monitoring quality. Changes in wireless signal strength under different agricultural scenarios were measured by [PERSON] [7] and [PERSON] [8]. The signal attenuation model of the farmland ZigBee network was established by using a particle swarm optimization algorithm, which provided a reference for building a monitoring network [9]. Different transmission routing will produce different signal attenuation and affect the WSN lifetime. There were many wireless sensor routing algorithms for the agricultural environment [4]. A routing protocol based on a frontward communication area for collecting climate information in the agricultural field was proposed by [PERSON] et al. [10]. Based on the communication radius, the candidate set in the forward communication range was determined. Then, the residual energy was used to select a node to reduce the energy consumption and time delay. The energy centroid was calculated, and nodes close to the energy centroid were regarded as cluster heads [11]. This method balanced the energy consumption and prolonged the network lifetime. However, traditional deterministic routing protocols have many limitations in dealing with unstable links [12]. The studies [13; 14; 15] showed that opportunistic routing strategies are used more and more widely. It makes full use of the broadcast characteristics and has an obvious advantage over traditional routing in solving unreliable links. Opportunistic routing protocol no longer fixes the next node, but selects many next hops, thereby improving performance such as network throughput. The commonly used opportunistic routing algorithm calculated the shortest path according to different routing metrics. [PERSON] et al. designed the EXOR algorithm to construct the candidate sets and sort forwarding order [16]. The neighbor nodes whose expected transmission count (ETX) was less than the source node ETX were added to the candidate set. The smaller the ETX was, the higher the forwarding priority was. A single ETX index is not enough to quantify the network performance, so the opportunistic transmission cost function with multiple influence factors such as distance, energy, and transmission delivery rate is established. In [PERSON]'s research, the routing overhead was converted into three parts: the broadcast cost, the forwarding node selection cost, and the forwarding communication cost [17]. Adjustable and non-adjustable transmission energy models were introduced to calculate the routing cost and selected the optimal candidate sets. Many studies select candidate sets by simultaneously measuring the impact of network energy and transmission distance [18; 19; 20]. [PERSON] et al. adopted Bayesian machine learning to model the candidate nodes selection process as a supervised multi-class non-linear separable problem [18]. [PERSON] et al. further proposed the energy threshold, combined with the maximum and minimum communication range to select the relay node with higher residual energy at different distances [19]. Additionally, an energy-saving opportunity routing protocol based on area density, relative distance, and residual energy was studied to balance energy consumption [21; 22; 23]. The references [24] further introduce the transmission probability as one of the indicators for constructing candidate sets. In [PERSON]'s research, the next-hop selection was based on the node's current energy and its predicted message delivery probability [25]. According to the information of transmission probability, energy, and the number of neighbors of neighbor nodes within one hop range, reference [26] updated node weights through reinforcement learning to select candidate sets.