# Open Collaborative Platform for Multi-Drones to Support Search and Rescue Operations
## 2 Related Works  
For locating victims in SAR operations, there are generally two approaches: Search-and-rescue (SAR) dog [9] and life detector/locator [10]. SAR dog is trained to locate the ascent of any human in a specific search area. However, SAR dogs trained for wilderness or tracking simply cannot deal with the complexities of disaster sites. Disaster sites are often surrounded by debris, dust, fire, and smoke. All of which can reduce the performance of SAR dogs. A life detector is another way to support SAR operation. Life detector is based on signs of life such as tapping, scratching, banging, voices, and chest movements by breathing (using ultra-wideband signals). Two of the well-known companies for life detectors, LEADER [11] in California, U.S.A and Geophysical Survey Systems [10] in New Hampshire, U.S.A, estimated that the average searching distance is around 30 m in free space. Therefore, in order to cover a large disaster area, life detectors will need to be carried and operated by search teams to the site if accessible. Thus, the searching area is limited and additional delay is introduced. While multiple detectors can be deployed, the cost (e.g., US $50,000 for an average one) and interference with other devices are some of the considerations for SAR operations. According to Cisco's Annual Internet Report and Visual Networking Index (VNI) [12], global mobile devices will grow from 8.8 billion in 2018 to 13.1 billion by 2023. Over 70 percent of the global population will have mobile connectivity by 2023. Thus, if the search team can find a signal from a mobile phone, they most likely will find a person nearby. In this paper, Wi-Fi signals are used for localization. Notice that there are other signals (i.e., 3G/4G/5G or Bluetooth) from mobile phones that can be used for localization purposes. There are many localization techniques that use Wi-Fi signals which are classified into three types--(a) _Infrastructure-based_; (b) _Fingerprinting-based_; and (c) _Model-based_. _Infrastructure-based_ techniques (e.g., [13; 14; 15; 16; 17]) are relied on installing specific hardware (e.g., infrared beacons or ultrasound) for indoor localization. _Fingerprinting-based_ techniques (e.g., [18; 19; 20; 21; 22; 23; 24; 25]) are pre-surveyed RF signals such as to fingerprint the surrounding signatures at every location to build a fingerprint database. The location is determined by comparing the measured fingerprint (i.e., RF signals) with the database. _Model-based_ techniques (e.g., [26; 27; 28; 29; 30; 31]) are based on the RF propagation model (i.e., long-distance path loss model) to estimate locations according to the measured received signal strength (RSS) values. In the Krypto module, a _Model-based_ technique is used due to no pre-install hardware (i.e., _Infrastructure-based_ approach) or pre-measurement (i.e., _Fingerprinting-based_ approach) is required. In _Model-based_ technique, there are two methods to estimate the distance between a transmitter (\(Tx\)) and a receiver (\(Rx\))--(a) _Time of Flight_ (TOF) (e.g., [32; 33]) and (b) _Received Signal Strength Indicator_ (RSSI) (e.g., [28; 34; 35]). In the Krypto module, passive listening of RSSIs from mobile phones is used for a disaster scenario. A common path loss model, _Log-Distance Path Loss Model_, is used for free space as follows:  
\[p(d)\ =\ p(d_{0})\ +\ 10n\cdot\log\left(\frac{d}{d_{0}}\right)\ +\ X \tag{1}\]  
where \(p(d)\) is the total path loss (i.e., received signal strength) which measures in \(dBm\) at the length of path \(d\), \(n\) is the path loss exponent, \(d_{0}\) is the reference distance, and \(X\) is a normal (or Gaussian) random variable with zero mean. The hardware of the Krypto module consists of off-the-shelf components--Raspberry PI B+, two Wi-Fi cards, GPS Dongle, and Portable Charger (i.e., battery). The source code and executable file can be downloaded and installed on Raspberry PI for anyone who wants to participate in a search mission.