## 2 Previous Work
### Tracking Algorithms  
With respect to the application layer, various approaches have been used in target tracking algorithms. Some are based on different types of measurements, such as the received signal strength (RSS) [5], angle of arrival (AOA) [5, 6], time of arrival (TOA) [7], time difference of arrival (TDOA) [8], extended Kalman filters (EKF) [9], and hybrid approaches [10, 11], to mention a few. Some others are based on the use of binary sensors, which provide only 1-bit information regarding the presence or absence of a target in their detection area [12]. Concerning proposals using a WSN for target tracking purposes, [PERSON] _et al._[13] addressed the topic of detection and tracking of a single target in a WSN with a static sink, using the coordination between routing protocols and location algorithms. They then extended it to multiple target tracking. In [14], the authors analyzed the fundamental performance limits of tracking a target in a two-dimensional field of binary proximity sensors, determining the accuracy with which a target's trajectory can be tracked. In a later work [15] they extended it to a multiple target tracking solution; in their work they do not address communication protocols used by the sensor nodes, focusing solely on the efficacy of collaborative tracking. [PERSON] _et al._ in [16] studied target tracking and classification. They defined the specifications of WSN deployment for target detection, identifying the best types of sensors to be used by these applications in dense network environments, when focusing on human targets or vehicles. [PERSON] and [PERSON] proposed a maximum likelihood acoustic source location estimation method for target localization in [17]. However, since this method uses nonlinear optimization, it is difficult to obtain closed form solutions. [PERSON] _et al._[18] developed an application for control and surveillance in large-scale, real-time WSNs, using a multi-target tracking algorithm, which combined a multisensor fusion method and a Markov chain Monte Carlo Data Association (MCMCDA) algorithm. Their solution is able to automatically start and finish the tracking procedure. The Continuous Object Detection and tracking Algorithm (CODA) was proposed in [19] by [PERSON] _et al._ to detect and track the spread of continuous objects such as wild fires, toxic gases, _etc_. [PERSON] _et al._ introduced a target tracking algorithm based on binary sensors and a static sink. The algorithm utilizes the sensor outputs to estimate individual positions in the path of the target and finds the trajectory that best fits the path points. This estimated trajectory is then used to estimate the current position of the target. [PERSON] _et al._ presented in [11] an approach for target tracking for WSN by combining maximum likelihood estimation and Kalman filtering using the distance measurement. The maximum likelihood estimator is used for prelocalization of the target and measurement conversion. The converted measurement and its associated noise statistics are then used in a standard Kalman filter for recursive update of the target state. Concerning solutions supporting mobile sinks, [PERSON] _et al._[PERSON] _et al._ (2012) proposed a Dynamical Object Tracking (DOT) algorithm, devised to be used by a mobile source (sink) to chase a moving target. The algorithm uses the knowledge of spatial neighborhood defined on a planar graph, where the face neighbors are identified by a Gabriel Graph. When a target is detected, the sensors need to record the target tracks. When the source requires the target location, it sends a query to the node keeping the track information, which replies with the tracking information, until the source catches the target. [PERSON] _et al._[PERSON] _et al._ (2013) proposed a distributed management scheme that uses a set of access points to support transmission from a sensor to the sink.