# An Information Entropy Based Event Boundary Detection Algorithm in Wireless Sensor Networks
## 2 Related Work  
Several studies have been presented to solve the problem of event boundary detection. A non-ranging event area detection algorithm to predict the event region considered as circular has been introduced [6]. In Reference [7], the authors propose a graph theory based location-free event boundary detection algorithm that can adjust both network parameters and the threshold dynamically. In Reference [8], the authors propose a distributed in-network event boundary detection algorithm using the node's local information to determine spatial and temporal evolution of event boundary. The authors in Reference [9] proposed an algorithm for detecting boundary nodes based on a combination of statistics with model classification and selection. A mathematical statistical method for edge node detection is presented in References [10; 11], where the aim is to decide whether a boundary candidate node is located on the real boundary of an event region through information interaction with its neighbouring nodes.  
In Reference [12], the authors take into account the spatial-temporal correlation of sensors to detect boundary nodes. A decentralized fault-tolerant event region detection method is put forward in Reference [13]. It can accurately identify fault nodes and eliminate the abnormal sensed data to avert false detection. At the experimental level, they tuned every threshold to get higher boundary fitting accuracy. In Reference [14], the authors presented a Secure Event Boundary Detection (SEBD) method utilizing local level information. This approach is highly resilient and provides a good compromise between node detection and random measurement fault. The main principle behind the SEBD approach is that a highly reliable determination rule and a system threshold are set for a node to recognize itself as a boundary node. In Reference [15], the hidden markov random field model and iterative conditions are used to calculate the event coverage in the WSNs but the parameters of the markov random model need to be trained by a large amount of sample data.  
In Reference [16], a distributed and location-free boundary detection (DBD) algorithm for determining the event boundary is proposed in mobile WSNs. DBD only needs the sensed data of single node three-hop neighbours. However, DBD only considers the scenario of a mobile sensor node density less than 0.6, so it is not applicable to WSNs where all sensor nodes move in real time. In Reference [17], the authors utilized the QUDG for event boundary area detection and partition the event boundary area into disjoint dominating sets based on graph theory. Through periodic rotating the disjoint dominating sets of event boundary area in WSNs can realize the lifespan maximization of boundary sensors. Two algorithms are proposed for identifying faulty node and event boundary detection in Reference [18]. The algorithm can achieve accurate boundary node detection for various scalar sensed data values. The boundary width is fixed equal to half the communication radius of sensors. However, this is not suitable for low network densities and scenarios with a small communication radius of nodes because the number of boundary nodes increases as the network density and node radius increase. Then the communication cost of the boundary node reports the message to the base station increases. Conversely, if the number of boundary nodes is too small, it will affect the boundary node to fit the real boundary. To sum up, the existing boundary node detection algorithm is highly dependent on node sensing data and the binary decision result of a neighbour node with the calculation error is used as input data, which results in cumulative error. In addition, due to the high computational complexity and high energy consumption, the methods proposed above are not suitable for event boundary detection in mobile WSNs, such as marine WSNs.