# Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks
## 2 Literature Review  
The authors in [5] propose a clustering-based routing protocol, Low-Energy Adaptive Clustering Hierarchy (LEACH), which distributes the energy consumption among the sensors in the network to enhance the lifetime of the network. The authors in [6] argue that their organization of clusters is triggered by the events of the monitor area, which means that the network operation is event-centric, and the clusters should be organized again according to the features of the event. The authors in [7] propose an energy-efficient distributed network scheme for phenomena detection, distinguishing between static sensors that belong to the original clusterhead and a dynamic set of sensors that belong to the phenomenon according to their readings, where the readings of the sensors are processed before transmission by using DFT for reducing the dimensionality of the data. The authors in [8; 9; 10; 11] use the simple binary sensor for target tracking. The output of each sensor is only one bit, which means that the energy consumption of the binary sensor model is very low, but also this means there is limited information for the base station to localize the target. The authors in [12] propose an approximation scheme, the Data Compressing-based Target Tracking Protocol (DCTTP), for target tracking with low overheads that may be practical in sensor networks. DCTTP [12] uses the distance measurements of sensor nodes to estimate the target location. The active sensors transmit their readings, including the estimated distances between them and the target and sensor ID to their clusterheads. The clusterheads gather the received data and then process the data in a centralized manner. The clusterhead processes the data by bitmap, which is a record indicating if the sensed data from a sensor can be approximated to the average value \(\mu\) of the data gathered by the clusterhead within a given compressing bound (e.g., using the standard deviation \(\sigma\) to be the compressing bound). Therefore, if the value of a sensor reading is larger than the lower bound (\(\mu-\sigma\)) and smaller than the upper bound (\(\mu+\sigma\)), the data will be replaced by \(\mu\), and the bit set to \"0\", which means the data are compressed to \(\mu\). If they are not, set the bit to \"1\", and store the difference between the data and the average value for decompressing to the original data in the base station. After scanning all of the data, there are a binary sequence (_i.e._, data compression) and a decimal sequence (_i.e._, the difference between the data and \(\mu\)) generated by the bitmap in the clusterhead. Then, the clusterhead transmits the sensor ID, average value and sequences of bitmaps to the base station. The base station decompresses the received data and uses them with the position of each active sensor to estimate the location of the target. The positions of active sensors and available information (e.g., distance measurements) form a possible area for the target (shown in Figure 1), which means the target positioning will be influenced by the position of active sensors. The rationale is that if the active sensor is closer to the target, its weighting for estimation will be larger. Then, the result of position estimation will move toward the active sensor. Moreover, the number and the relative positions of the active sensors with respect to the target also influence the result of the position estimation. That is, if the active sensors are dense to a side, the position estimate will be much closer to the active sensors, and it may make the estimate far away from the target. In this work, for the information processing approach, we apply the concept of bitmap in [12] to compress the measurement information. The perspective of the binary sensor network inspires us to fully utilize the useful known information, which is not directly used by [13]. Accordingly, the CTCI method aims to balance the tracking performance in terms of estimation accuracy and energy consumption.