## 2 Related Works  
There are a large number of researches that have been implemented to reduce the interference in WSNs. In [5; 6], first, the authors show that reducing the transmission range cannot guarantee low network interference; then they propose several algorithms to construct topologies in which the maximum link interference is minimized while the network is still connected. In [9], the authors have discussed different methods which are used to measure network interference and proposed a new topology control algorithm to describe the interference of the entire network. The new algorithm minimizes the network interference and successfully maintains the spanner properties of the original network. The work in [10] studies the interference in WSNs and proposes several algorithms to construct topologies such that the maximum node interference and the average node interference are minimized. Based on the Yao Gabriel Graph, the authors propose the SBYaoGG (Smart Boundary Yao Gabriel Graph) algorithm in [11]. The SBYaoGG algorithm tends to produce as sparse a network as possible to meet the objective of high energy efficiency and low interference. In [12], the authors propose an interference-based topology control algorithm for the delay constrained mobile ad hoc network (ITCD). In this algorithm, the impact of node mobility on ITCD is investigated. Moreover, the transmission delay, the contention delay, and the queuing delay are taken into account to remove the unstable communication links. For reducing the link interference in co-channel wireless ad hoc and sensor networks, in [13], the authors propose a new link interference model. Based on this model, a centralized topology control algorithm is designed to construct an interference-optimal network. The new interference model can also be found in [14]. This interference model can not only reflect the fact that interference occurs when the node is receiving a message, but also demonstrates robustness when adding or removing nodes from the network. In [15], the authors investigate how to minimize the average interference when a node is receiving message. In this paper, the 1-D (1-Dimensional) and 2-D (2-Dimensional) networks are studied; and two exact algorithms are proposed to minimize the average interference of 1-D and 2-D networks, respectively. Based on the PRR-SINR (Packet Reception Ratio versus Signal to Interference Plus Noise Ratio) model, in [16], the authors propose an accuracy-aware algorithm for interference modeling and measurement in WSNs. This accuracy-aware algorithm can characterize the accuracy of the PRR-SINR model and enforce a specified accuracy bound by adaptively controlling the measurement process during the run time. In [17], the authors define an explicit notion for network interference and propose an interference model based on the current network traffic. The work in [18] models the interference with physical signal and noise ratio, and defines the concept of scheduling complexity for the wireless network connectivity. In [19], the authors present a comprehensive survey on the interference models for wireless ad hoc networks. The survey emphasizes the domains of the application and illustrates them with examples. There are also many research papers which are devoted to measuring the interference in WSNs, such as [20; 21; 22; 23]. In [20], the authors take the shadowing and fading effects into account to determine the interference range of the link. The link interference is determined by the interference number which is calculated based on the real coverage region. The interference measurement algorithms also can be found in [21; 22]. In [21], a passive interference measurement approach which is derived from the actual physical model has been presented. In [22], the authors propose SoNIC (Sensor Network Interference Classification) to detect interference in an 802.15.4 sensor network which shares 2.4 GHz with other Bluetooth and Wi-Fi devices.