## 2 Related works  
### Locating sensors  
It is obvious that position-based routing protocols require that sensor nodes can somewhat know their position. Though the position information can be known by providing sensors with a GPS unit, it is often unfeasible as the GPS is quite expensive and energy consuming. In order to resolve the locating problem, there are two strategies. One strategy is to equip a limited subset of nodes with a GPS and then derive the location of the other ones by means of other techniques. However, commonly available sensor platforms lack the hardware suitable to acquire location information. The other strategy is that the sensors locate its position based on the relative position to anchors. The localization problem has received considerable attention in the past and recently a number of localization systems have been proposed specifically for sensor networks [12, 13]. Localization algorithms can be divided into two categories: distance-based and distance-free. Distance-based localization algorithms require the sensors to contain hardware for measurements. Distance-free localization algorithms do not use radio signal strengths, angle of arrival of signals or distance measurements and special hardware is not needed. About distance-free localization, various techniques have been proposed for measuring the distances and these techniques can be classified into three subclasses: AOA (Angle of Arrival) measurements, time related measurements and RSS (Received Signal Strength) profiling techniques. (1) AOA measurements make use of the receiver antenna's amplitude response or phase response to measure the distance. Beamforming is the usual technology and uses the anisotropy in the reception pattern of an antenna. [PERSON] proposed to use a minimum of two stationary antennas with known, anisotropic antenna patterns to cope with the varying signal strength problem [14]. Phase interferometry typically requires a large receiver antenna (relative to the wavelength of the transmitter signal) or an antenna array based on the phase differences in the arrival of a wave front [15]. In [16], [PERSON] and [PERSON] have used Angle of Arrival of signals to estimate distances. The accuracy of AOA measurements is limited by the directivity of the antenna, shadowing or by multipath reflections. Another class of AOA measurement methods is based on s subspace-based algorithms, which require a multi-array antenna in order to form a correlation matrix using signals received by the array. The most well known methods include MUSIC (multiple signal classification) [17] and ESPRIT (estimation of signal parameters by rotational invariance techniques) [18]. (2) Time related measurement measure the difference between the sending time of a signal at the transmitter and the receiving time of the signal at the receiver[19, 20]. (3) In RSS profiling localization techniques, a large number of sample points are distributed throughout the coverage area of the sensor network and a vector of signal strengths is obtained at each sample point. The collection of all these vectors provides a map of the whole region, which constitutes the RSS model. It is unique with respect to the anchor locations and the environment [21, 22]. Besides the previous locating algorithms, there are also other distance-free locating algorithms. In [23], [PERSON] _et al._ proposed APIT, in which all possible triangles of the seeds are formed, and the location of a node is the center of intersection region of all triangles. In [24], [PERSON] _et al._ proposed the Gradient algorithm, which allow the nodes to find the hop number to all the seeds. At the same time, seeds estimate the average distance per hop and send to the nodes while nodes can use multilateration to find their locations. DV-Hop algorithm [25] uses a different method for estimating the average distance per hop, which is similar to Gradient algorithm.