Search And Rescue Research Articles

A Novel LoRa LPWAN-Based Communication Architecture for Search & Rescue Missions

To avoid risking the lives of rescue team personnel in the event of disasters like earthquakes, volcanic eruptions, hurricanes, etc., Search and Rescue (SAR) robots are increasingly incorporated into the operation. One of the major challenges in integrating SAR robots into rescue operations is the potentially severely damaged infrastructure within the disaster site. A functional communication system is critical for exchanging real-time information between the robots and the base station. Given the limited coverage or absence of communication systems in a severely affected disaster site, a novel communication architecture for search & rescue missions based on Long Range (LoRa) Low Power Wide Area Network (LPWAN) and a SAR robot called Rescuer are proposed. Rescuer is a SAR robot that can operate in worst-case disaster sites where all communication infrastructure has been wiped out. It has been tested in a Gazebo simulated environment as well as an actual test setup inside the University of Detroit Mercy’s lab facility and showed great promise. In this test, the Rescuer robot was monitored and controlled from a remote base station.

A node selection algorithm to graph-based multi-waypoint optimization navigation and mapping

Autonomous robot multi-waypoint navigation and mapping have been demanded in many real-world applications found in search and rescue (SAR), environmental exploration, and disaster response. Many solutions to this issue have been discovered via graph-based methods in need of switching the robotos trajectory between the nodes and edges within the graph to create a trajectory for waypoint-to-waypoint navigation. However, studies of how waypoints are locally bridged to nodes or edges on the graphs have not been adequately undertaken. In this paper, an adjacent node selection (ANS) algorithm is developed to implement such a protocol to build up regional path from waypoints to nearest nodes or edges on the graph. We propose this node selection algorithm along with the generalized Voronoi diagram (GVD) and Improved Particle Swarm Optimization (IPSO) algorithm as well as a local navigator to solve the safety-aware concurrent graph-based multi-waypoint navigation and mapping problem. Firstly, GVD is used to form a Voronoi diagram in an obstacle populated environment to construct safety-aware routes. Secondly, the sequence of multiple waypoints is created by the IPSO algorithm to minimize the total travelling cost. Thirdly, while the robot attempts to visit multiple waypoints, it traverses along the edges of the GVD to plan a collision-free trajectory. The regional path from waypoints to the nearest nodes or edges needs to be created to join the trajectory by the proposed ANS algorithm. Finally, a sensor-based histogram local reactive navigator is adopted for moving obstacle avoidance while local maps are constructed as the robot moves. An improved -spline curve-based smooth scheme is adopted that further refines the trajectory and enables the robot to be navigated smoothly. Simulation and comparison studies validate the effectiveness and robustness of the proposed model.

A GRASP-Based Approach for Planning UAV-Assisted Search and Rescue Missions.

Search and Rescue (SAR) missions aim to search and provide first aid to persons in distress or danger. Due to the urgency of these situations, it is important to possess a system able to take fast action and effectively and efficiently utilise the available resources to conduct the mission. In addition, the potential complexity of the search such as the ruggedness of terrain or large size of the search region should be considered. Such issues can be tackled by using Unmanned Aerial Vehicles (UAVs) equipped with optical sensors. This can ensure the efficiency in terms of speed, coverage and flexibility required to conduct this type of time-sensitive missions. This paper centres on designing a fast solution approach for planning UAV-assisted SAR missions. The challenge is to cover an area where targets (people in distress after a hurricane or earthquake, lost vessels in sea, missing persons in mountainous area, etc.) can be potentially found with a variable likelihood. The search area is modelled using a scoring map to support the choice of the search sub-areas, where the scores represent the likelihood of finding a target. The goal of this paper is to propose a heuristic approach to automate the search process using scarce heterogeneous resources in the most efficient manner.

The Efficiency of Drones Usage for Safety and Rescue Operations in an Open Area: A Case from Poland

The use of unmanned aerial systems (UAS) is becoming increasingly frequent during search and rescue (SAR) operations conducted to find missing persons. These systems have proven to be particularly useful for operations executed in the wilderness, i.e., in open and mountainous areas. The successful implementation of those systems is possible thanks to the potential offered by unmanned aerial vehicles (UAVs), which help achieve a considerable reduction in operational times and consequently allow a much quicker finding of lost persons. This is crucial to enhance their chances of survival in extreme conditions (withholding hydration, food and medicine, and hypothermia). The paper presents the results of a preliminary assessment of a search and rescue method conducted in an unknown terrain, where groups were coordinated with the use of UAVs and a ground control station (GCS) workstation. The conducted analysis was focused on assessing conditions that would help minimise the time of arrival of the rescue team to the target, which in real conditions could be a missing person identified on aerial images. The results of executed field tests have proven that the time necessary to reach injured persons can be substantially shortened if imaging recorded by UAV is deployed, as it considerably enhances the chance of survival in an emergency situation. The GCS workstation is also one of the crucial components in the search system, which assures image transmission from the UAV to participants of the search operation and radio signal amplification in a difficult terrain. The effectiveness of the search system was tested by comparing the arrival times of teams equipped with GPS and a compass and those not equipped with such equipment. The article also outlined the possibilities of extending the functionality of the search system with the SARUAV module, which was used to find a missing person in Poland.

Collection of Data on Persons Living With Dementia Who Go Missing: First Responder Perspectives

While it is commonly cited that 60% of persons living with dementia (PLWD) wander, it is unclear whether this number reflects global contexts. Population aging has created a pressing need for the development of programs to mitigate the risks of PLWD from getting lost and going missing. Such programs would require a national strategy for the collection and integration of data on missing incidents involving this population. This study is a first step to inform such a strategy. The purposes were to: 1) identify approaches to data collection on missing persons incidents involving PLWD among Canadian police and search and rescue (SAR) organizations; 2) describe the foreseeable challenges associated with developing a national data collection strategy. We used generic qualitative description to generate data with fifteen key informants. Virtual semi-structured interviews were completed and transcribed verbatim. Content analysis and trustworthiness strategies guided analysis and rigor. Our findings indicate that police and SAR organizations collect a multitude of data pertaining to missing incidents involving PLWD. However, there is a lack of standardization in data collection, entry and analysis. Privacy legislation, limited resources, and incompatible data management systems pose challenges to data sharing and interoperability. Underreporting of missing incidents to police results in an underestimation of missing incidents. An intersectoral, uniform approach to data collection would enable the storage, analysis and comparison of national data. Accurate data on critical wandering can inform prevention, search strategies, resource allocation and effectiveness of programs.

The Associations Between Visitation, Social Media Use, and Search and Rescue in United States National Parks.

Search and rescue (SAR) is vital for visitor safety in US national parks, which are popular destinations for tourists. Previous studies have described SAR and seasonal visitation patterns, but not in the context of overall visitation. In addition, studies on the association between SAR and developed park areas remain limited. Concurrently, social media can be valuable for sharing information about conservation awareness and the joy of being outdoors. However, social media can potentially be an avenue for users to share risky and dangerous behaviors performed during attempts to obtain photos and videos. The associations between SAR and social media have not been discussed in existing literature. Variables included recreational visits, developed site stay visits, backcountry visits, SAR incidents, and tweets. Data from 2017 were obtained from National Park Service visitor use statistics, the SAR incident dashboard, and the University of California, Irvine, Cloudberry application. Correlation analysis was performed using nonparametric Kendall rank correlation coefficients. Recreational visits were correlated with SAR incidents (rτ=0.415, P<0.001). Developed site stays were similarly correlated with SAR incidents (rτ=0.447, P<0.001), as were backcountry visits (rτ=0.428, P<0.001). Backcountry visits had a stronger correlation with fatalities (rτ=0.380, P<0.001) compared to developed site stays (rτ=0.304, P<0.001). Tweets were correlated with SAR incidents (rτ=0.468, P<0.001), recreational visits (rτ=0.403, P<0.001), and fatalities (rτ=0.367, P<0.001). Our findings demonstrate associations between national park visitation, SAR incidents, fatalities, and Twitter use and provides a concept framework for future prospective studies to further investigate the relationships between visitation, SAR, and social media.

Excessive Daytime Sleepiness and Associated Factors in Military Search and Rescue Personnel.

BACKGROUND: Abnormal excessive daytime sleepiness (EDS) has been reported worldwide, but too little is known about EDS and its determinants in Search and Rescue (SAR) populations. We aimed to determine the prevalence of abnormal EDS and contributing factors among Royal Norwegian Air Force (RNoAF) SAR helicopter personnel.METHODS: In this cross-sectional study, a total of N = 175 RNoAF SAR personnel completed an electronic survey including socio-demographic and lifestyle questions. The Epworth Sleepiness Scale (ESS) was used as both a continuous and categorical outcome variable to measure EDS.RESULTS: Abnormal EDS defined by ESS was found in 41% of the participants in this study. We observed no associations between socio-demographic and lifestyle factors and abnormal EDS in this study. DISCUSSION: There is a high prevalence of abnormal EDS in the current RNoAF SAR population. Despite this elevated level of fatigue, we did not find that the socio-demographic and lifestyle factors assessed in this study were associated with abnormal EDS in RNoAF SAR helicopter personnel. Also unusually, the study cohort did not demonstrate higher scores in factors found to change ESS scores in similar study populations (e.g., caffeine use, tobacco use, exercise level). Further research is required to investigate other factors (organizational, operational) that may be associated with abnormal EDS in this and other SAR populations.Akter R, Larose TL, Sandvik J, Fonne V, Meland A, Wagstaff AS. Excessive daytime sleepiness and associated factors in military search and rescue personnel. Aerosp Med Hum Perform. 2021; 92(12):975-979.

APRS Data Receiver Using Raspberry Pi in LAPAN-A2 Satellite

Lapan-A2 Satellite, also known as LAPAN-ORARI, is the second satellite developed by the Indonesian National Institute of Aeronautics and Space (LAPAN) especially by Satellite Technology Center. This satellite was launched in 2015 which one of the payloads is for amateur radio communication such as VR (Voice Repeater) and APRS (Automatic Packet Reporting System). The APRS is a method of transmitting messages, status and positions using certain range of frequency and often used by Search and Rescue (SAR) team. Due to its function, APRS are not only used for disaster mitigation but also transmit various kind of data such as text message and weather information. In order to receive such information, APRS must be equipped with supporting devices. Formerly, APRS utilize terminal node controller and special hardware to decode its information but those technology is quite expensive. To address that challenge, this paper proposed an alternative way to decode the information send both from satellite APRS and terrestrial APRS by using raspberry Pi to replace those high-cost system.

The Internet of Cooperative Agents Architecture (X-IoCA) for Robots, Hybrid Sensor Networks, and MEC Centers in Complex Environments: A Search and Rescue Case Study.

Cloud robotics and advanced communications can foster a step-change in cooperative robots and hybrid wireless sensor networks (H-WSN) for demanding environments (e.g., disaster response, mining, demolition, and nuclear sites) by enabling the timely sharing of data and computational resources between robot and human teams. However, the operational complexity of such multi-agent systems requires defining effective architectures, coping with implementation details, and testing in realistic deployments. This article proposes X-IoCA, an Internet of robotic things (IoRT) and communication architecture consisting of a hybrid and heterogeneous network of wireless transceivers (H2WTN), based on LoRa and BLE technologies, and a robot operating system (ROS) network. The IoRT is connected to a feedback information system (FIS) distributed among multi-access edge computing (MEC) centers. Furthermore, we present SAR-IoCA, an implementation of the architecture for search and rescue (SAR) integrated into a 5G network. The FIS for this application consists of an SAR-FIS (including a path planner for UGVs considering risks detected by a LoRa H-WSN) and an ROS-FIS (for real-time monitoring and processing of information published throughout the ROS network). Moreover, we discuss lessons learned from using SAR-IoCA in a realistic exercise where three UGVs, a UAV, and responders collaborated to rescue victims from a tunnel accessible through rough terrain.

An Information-Motivated Exploration Agent to Locate Stationary Persons with Wireless Transmitters in Unknown Environments.

Unmanned Aerial Vehicles (UAVs) show promise in a variety of applications and recently were explored in the area of Search and Rescue (SAR) for finding victims. In this paper we consider the problem of finding multiple unknown stationary transmitters in a discrete simulated unknown environment, where the goal is to locate all transmitters in as short a time as possible. Existing solutions in the UAV search space typically search for a single target, assume a simple environment, assume target properties are known or have other unrealistic assumptions. We simulate large, complex environments with limited a priori information about the environment and transmitter properties. We propose a Bayesian search algorithm, Information Exploration Behaviour (IEB), that maximizes predicted information gain at each search step, incorporating information from multiple sensors whilst making minimal assumptions about the scenario. This search method is inspired by the information theory concept of empowerment. Our algorithm shows significant speed-up compared to baseline algorithms, being orders of magnitude faster than a random agent and 10 times faster than a lawnmower strategy, even in complex scenarios. The IEB agent is able to make use of received transmitter signals from unknown sources and incorporate both an exploration and search strategy.

Predicting drift characteristics of persons-in-the-water in the South China Sea

Maritime accidents occur with alarming frequency. Finding wrecked objects accurately and efficiently is the primary goal of search and rescue (SAR) operations at sea. The objective of this study is to determine the drift characteristics of persons-in-the-water (PIWs) in the South China Sea via field experiments in conjunction with Monte Carlo simulations. A series of maritime drift experiments were carried out in the South China Sea using dummy buoys in vertical and horizontal positions to obtain 589 valid samples. This data was used to establish drift models of the dummies in two positions: the AP98 leeway drift model and the dynamics drift model. Finally, Monte Carlo simulations were used to compare the vertical PIW and horizontal PIW drift models. Results indicate that the drift trajectory of the vertical PIW is to the right of the horizontal PIW; the drift distance of the vertical PIW is smaller than that of the horizontal PIW. Their jibing frequencies are 5.3% per hour (horizontal PIW) and 7.1% per hour (vertical PIW). The dynamic drift model is more accurate than the AP98 leeway drift model for predicting vertical PIW drift trajectory. The two drift models can be used in combination, however, for the horizontal PIW. The results of this work may provide theoretical support for drift prediction of PIWs at sea, which is of great significance for maritime SAR operations in the South China Sea.

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

Recent advances in autonomy of unmanned aerial vehicles (UAVs) have increased their use in remote sensing applications, such as precision agriculture, biosecurity, disaster monitoring, and surveillance. However, onboard UAV cognition capabilities for understanding and interacting in environments with imprecise or partial observations, for objects of interest within complex scenes, are limited, and have not yet been fully investigated. This limitation of onboard decision-making under uncertainty has delegated the motion planning strategy in complex environments to human pilots, which rely on communication subsystems and real-time telemetry from ground control stations. This paper presents a UAV-based autonomous motion planning and object finding system under uncertainty and partial observability in outdoor environments. The proposed system architecture follows a modular design, which allocates most of the computationally intensive tasks to a companion computer onboard the UAV to achieve high-fidelity results in simulated environments. We demonstrate the system with a search and rescue (SAR) case study, where a lost person (victim) in bushland needs to be found using a sub-2 kg quadrotor UAV. The navigation problem is mathematically formulated as a partially observable Markov decision process (POMDP). A motion strategy (or policy) is obtained once a POMDP is solved mid-flight and in real time using augmented belief trees (ABT) and the TAPIR toolkit. The system’s performance was assessed using three flight modes: (1) mission mode, which follows a survey plan and used here as the baseline motion planner; (2) offboard mode, which runs the POMDP-based planner across the flying area; and (3) hybrid mode, which combines mission and offboard modes for improved coverage in outdoor scenarios. Results suggest the increased cognitive power added by the proposed motion planner and flight modes allow UAVs to collect more accurate victim coordinates compared to the baseline planner. Adding the proposed system to UAVs results in improved robustness against potential false positive readings of detected objects caused by data noise, inaccurate detections, and elevated complexity to navigate in time-critical applications, such as SAR.

U.S. Navy Aeromedical Missions from 20162019 with a Focus on En Route Care Provider Type.

BACKGROUND: En route care (ERC) is a military aeromedical mission designed to transport a patient to a higher level of care. With the exception of one manual, there are no other formal Navy ERC guidelines, leaving the service to provide such missions ad hoc. Based on the authors review of available literature, it seems no prior research has been done on Navy rescue swimmers performing ERC, though many search and rescue (SAR) missions take place without designated medical personnel. This study specifically examines the type of provider involved in Navy ERC missions and the types of cases involved with the purpose of influencing Navy policy.METHODS: A cross-sectional study examining 829 air evacuations performed by Navy SAR flight crews from 2016 to 2019 was analyzed.RESULTS: Of 829 cases reviewed, patients were more likely to be active-duty personnel (51%) than civilian (47%), and there were 2.5 times more male than female patients. There were more trauma (54%) than medical (43%) patients, with Basic Life Support (BLS) level care (60%) delivered twice as often as Advanced Life Support (ALS) (28%). Search and Rescue Medical Technicians (SMTs) and rescue swimmers provided 83% of ERC, with rescue swimmers supporting 33% of all ERC missions alone.DISCUSSION: The results of this study are in contrast to previous ERC studies, in which rescue swimmer-only transports were excluded from the data. The results raise the question, do rescue swimmers need to be trained to a higher level of care?Henry SM, Stanfield MM. U.S. Navy aeromedical missions from 20162019 with a focus on en route care provider type. Aerosp Med Hum Perform. 2021; 92(11):873-879.

Coverage path planning for maritime search and rescue using reinforcement learning

In maritime search and rescue (SAR), the planning of the search path will directly affect the efficiency of searching for people overboard in the search area. However, traditional SAR decision-making schemes often adopt a fixed search path planning mode, but the limits are poor flexibility, low efficiency, and insufficient intelligence. This paper plans a search path with the shortest time-consuming and priority coverage of high-probability areas, considering complete coverage of maritime SAR areas and avoiding maritime obstacles. Firstly, a maritime SAR environment model is built using marine environmental field data and electronic charts. Secondly, an autonomous coverage path planning model for maritime SAR is proposed based on reinforcement learning, in which a reward function with multiple constraints is designed to guide the navigation action of the vessel agent. In the iterative training process of the path planning model, the random action selection probability is dynamically adjusted by the nonlinear action selection policy to ensure the stable convergence of the model. Finally, the experimental verification is conducted in different small-scale maritime SAR simulation scenarios. The results indicate that the search path can cover the high-probability areas preferentially with lower repeated coverage and shorter path length compared with other path planning algorithms.

“A Great Investment in Our Communities”: Strengthening Nunavut’s Whole-of-Society Search and Rescue Capabilities

Community-based organizations along with territorial, provincial, and federal agencies are responsible for search and rescue (SAR) in the Canadian Arctic. In delivering response capabilities at all hours of the day and for 365 days a year, the community-based organizations face a wide array of challenges. Using the data collected through the Kitikmeot Search and Rescue Project and the Kitikmeot Roundtable on SAR, coupled with academic and non-government organization literature, this article explores the major challenges facing community SAR organizations in Nunavut and builds a case for how targeted investment can best bolster community-based capabilities. We suggest novel, practical, and holistic solutions that have been proposed by or co-devised with community partners, are rooted in the unique context of Nunavut’s communities, and are reflective of a community resilience-building approach. One set of recommendations involves strengthening current programming, including the expansion of Nunavut Emergency Management’s inReach program, continued support for the enlargement of the CCGA, streamlining the process to activate Canadian Ranger patrols, and encouraging greater cooperation in the provision of training by federal and territorial agencies. We also propose new approaches, including a whole-of-society preventative SAR program centred on educational and youth programming, the adoption of a SAR equipment usage rate model, and the launch of a Community Public Safety Officer program in Nunavut. Finally, to justify greater investment at the community level, we argue that policymakers must change how they conceptualize community-based SAR capabilities in Nunavut. An effective SAR system is about more than the ability to respond to emergency events. It is a critical enabler to broader objectives and goals prioritized in the Arctic and Northern Policy Framework and other federal, territorial, and Inuit strategies.

'Sea of blood': the intended and unintended effects of criminalising humanitarian volunteers assisting migrants in distress at sea.

This paper investigates the impact of the recent criminalisation of humanitarian actors engaged in the search for and rescue of migrants in distress in the Mediterranean Sea, focusing on the impact on the motivation and engagement of humanitarian volunteers in Greece. It argues that criminalisation is aimed at reducing search and rescue (SAR) activities and thus removing perceived 'pull factors' for migrants. The paper locates this phenomenon within the broader trend of policing and punishing those who assist migrants in order to deter them and prevent others from engaging in such endeavours. It finds that efforts to criminalise can have the unintended effects of encouraging and mobilising volunteers, as well as generating public attention and support for migrants. However, the negative consequences of criminalisation are far-reaching, including contributing to a high mortality rate among those crossing the Mediterranean without SAR capabilities and the heightened risk of violence against migrants and those who help them.

An AUV Path Planner for Large-scale Search and Rescue based on A* Algorithm

For large-scale search and rescue (SAR) tasks that require complete coverage of the workspace, it is important to increase the efficiency and obtained sensor data quality. A novel path planner named SAR-A* to this problem is introduced, which takes into account the sensor performance and practical prior information. Firstly, the workspace is decomposed into plenty of hexagonal cells which are treated as waypoints for A* algorithm. Target present probability is then modeled to Gaussian distribution and the performance of the side-scan sonar (SSS) is evaluated. The proposed path planner is validated in a complex terrain scenario which proves that the SAR-A* path planner can increase confidence in locating the target quickly, and is suitable for the large-scale SAR.

Distributed coverage optimisation for a fleet of unmanned maritime systems

&lt;p&gt;Unmanned maritime systems (UMS) can provide important benefits for maritime law enforcement agencies for tasks such as area surveillance and patrolling, especially when they are able to work together as one coordinated system. In this context, this paper proposes a methodology that optimises the coverage of a fleet of UMS, thereby maximising the opportunities for identifying threats. Unlike traditional approaches to maritime coverage optimisation, which are also used, for example, in search and rescue operations when searching for victims at sea, this approach takes into consideration the limited seaworthiness of small UMS, compared with traditional large ships, by incorporating the danger level into the design of the optimiser.&lt;/p&gt;

Unmanned aerial vehicle‐enabled layered architecture based solution for disaster management

AbstractUnmanned aerial vehicle (UAV) has become a priority in the industrial and academic research community for the last few years. With the fast development of wireless networking technologies, it is widely used in many applications such as surveillance, search and rescue (SAR) operation, disaster management, remote monitoring, and so forth. Emergency communication for victims and first responders is one of the critical requirements during natural and human‐made disasters. Airborne Base Stations (BS) or UAV‐mounted BS has been used to provide high‐speed internet services and rescue responses in natural disasters and emergencies in recent years. Most of the previous research on UAV‐mounted BS (UAVmBS) only considers outdoor users. It assumes their coordinate position in a two‐dimensional (2D) plane, taking only Euclidean pathloss‐based distance into account. Though, in man‐made or urban natural disaster situations such as floods, cyclones, building fires, and so forth, users are stuck in single or multistory high‐rise buildings situated in a three‐dimensional (3D) plane. In such cases, it is possible to interact and rescue reliably if pathloss features are known a priori, both for UAVmBS to users (downlink) and vice versa (uplink). As pathloss in the above situations is a mixture of outdoor and indoor elements, adopting a compatible model can be challenging for communications. This article proposes a layered architecture and studies the path loss models best suited for the disaster management system (DMS), which considers both indoor and outdoor parameters. Further, the main goal is to effectively implement a layered architecture employing UAVmBS in urban disasters for better capacity and coverage improvement. Simulation outputs display the progress in transmission delays, throughput, and effective packet delivery for disaster management.

Multiagent Q-learning based UAV trajectory planning for effective situational awareness

In the event of a natural disaster, arrival time of the search and rescue (SAR) teams to the affected areas is of vital importance to save the life of the victims. In particular, when an earthquake occurs in a geographically large area, reconnaissance of the debris within a short-time is critical for conducting successful SAR missions. An effective and quick situational awareness in postdisaster scenarios can be provided via the help of unmanned aerial vehicles (UAVs). However, off-the-shelf UAVs suffer from the limited communication range as well as the limited airborne duration due to battery constraints. If telecommunication infrastructure is destroyed in such a disaster, maximum coverage to be monitored by a ground station (GS) using UAVs is limited to a single UAV?s wireless coverage regardless of how many UAVs are deployed. Additionally, performing a blind search within the affected area could induce significant delays in SAR missions and thus leading to inefficient use of the limited battery energy. To address these issues, we develop a multiagent Q-learning based trajectory planning algorithm that maintains all-time connectivity towards the GS in a multihop manner and enables UAVs to observe as many critical areas (highly populated areas) as possible. The comprehensive experimental results demonstrate that the proposed multiagent Q-learning algorithm is capable of attaining UAV trajectories that can cover significantly larger portions of the critical areas summing up to 43% than that of the existing algorithms, such as the extended versions of Monte Carlo, greedy and random algorithms.