Mobile crowdsourcing framework and application in mining evacuation

The word “crowdsourcing” was first used in 2006 by Jeff Howe, a writer for WIRED, to describe the process by which certain firms built their business models by using the wisdom of crowds. This process involved collaboration, aggregation, teamwork, consensus and creativity (Brabham, 2008). Mobile crowdsourcing (MCS), a novel paradigm that combines crowdsourcing and smartphone-based mobile technologies, offers substantial flexibility because of the rapid development of smartphones with rich built-in sensors (Wang et al., 2017). As a result, novel crowdsourcing applications are made possible by the multisensing capabilities of smartphones, which include geolocation, movement, audio and visual sensors (Chatzimilioudis et al., 2012). Figure 1 Components of each entity in mobile crowdsourcing (Yang et al., 2015). Based on the kinds of events being accessible and tracked, MCS-sensing applications can be roughly divided into two categories: personal and community sensing (Wang et al., 2017). Personal sensing applications monitor phenomena that are exclusive to an individual, whereas community sensing apps monitor widespread phenomena that are difficult for a single person to effectively quantify. The two subtypes of community sensing are participatory and opportunistic. In participatory sensing, the person taking part directly contributes to the sensing action, such as taking photos of certain events. In opportunistic sensing, the user is unaware of any running programs: for example, on smartphones, everything is handled by the crowdsourcing app. Various sectors of study and research have used opportunistic sensing based on crowdsourcing apps on cell phones to give the information needed to solve challenges and complete tough tasks. Mobile crowdsourcing and smartphone sensing have made important contributions to disaster management and evacuation. Making time-sensitive decisions with little information is necessary for real-time catastrophe response, which is an essential paradigm for disaster response (Callaghan, 2016). The possibility of finding solutions in real time is increased by increasing the number of inputs because the knowledge accessible in the early phases of a crisis may be probabilistic. The Chris William Callaghan-developed probabilistic innovation theory, which describes this phenomenon, asserts that there is a probability-based relationship between scientific breakthroughs and the resources used to achieve them, or between any solvable research problem and the volume of inputs used to solve it. However, it is anticipated that this association will likely become more apparent with large levels of problem-solving input. Researchers have developed SensoRecivico, Crowdsafe and Clark, to name a few, as crowdsourcing systems that deliver practical services via smartphone devices, using the built-in sensors in smartphones as crowdsourcing instruments (Felemban et al., 2020). Smartphone-based mobile crowdsourcing and sensing enable the production of large amounts of problem-solving input required to offer immediate solutions to issues pertaining to disaster management and evacuations. Because split-second choices must be made to safeguard the safety of the crew and equipment, mining evacuations are time-sensitive. This article examines the possibility of using mobile crowdsourcing and smartphone sensing to execute real-time evacuations in mining. Mobile crowdsourcing framework and workflow Three primary stakeholders — the crowdsourcer, crowdworkers and a crowdsourcing platform — make up a thorough MCS architecture and procedure. When a task is crowdsourced, the crowdsourcer submits it to a crowdsourcing platform and may choose to score the accuracy of the data the crowdworkers gave (Wang et al., 2017). There are two different approaches for encouraging users to interact with and provide data to a MCS system. These modes are direct and word-of-mouth (WoM). The direct mode uses a centralized type of control, where a crowdsourcing platform uploads a task, breaks it into smaller microtasks, and then organizes crowdworkers to do the tasks sequentially or concurrently. In general, crowdworkers do not directly communicate and work together. Time and location-sensitive tasks may not be handled well by the direct approach (Wang et al., 2017). Figure 2 Mobile crowdsourcing framework (Wang et al., 2017). By assigning team leaders and followers who share similar interests and collaborate more effectively on mobile crowdsourcing tasks, researchers have made progress toward developing frameworks and algorithms that help to optimize mobile crowdsourcing to perform effectively in time-sensitive and locationsensitive tasks (Hamrouni et al., 2019). Mobile crowdsourcing applications in emergency evacuation Over the past 10 years, mobile digital communication devices have replaced analog landlines for use in emergency evacuations. More than 88 percent of U.S. citizens own a mobile phone (Oxendine and Waters, 2014), making it possible for first responders and citizens to communicate more effectively and reduce the risk to evacuees during evacuations. More than 40 percent of cell phone owners, according to a 2011 Pew Research Center study, used their phones in an emergency. For directions to their final destination, evacuees typically rely on their past knowledge of the area, a smartphone with aided Global Positioning System (GPS), or an online mapping tool (like Google Maps) that provides the shortest paths to secure locations in the resulting directions. However, these instructions frequently overlook the risk that may be present along the suggested route (Oxendine and Waters, 2014). Despite continued research efforts, 444 fatal landslides that took place worldwide in 2016 resulted in as many as 2,250 deaths. Scientists and professionals are starting to embrace smartphone technology as a tool to better mitigate and manage disasters as a result of the technology’s growing affordability and availability, particularly in developing nations (Choi, Cui and Zhou, 2018). A growing tendency toward modeling information in crisis response can be seen in the use of statistical models to model risk, such as in cases of tornado risk (Callaghan, 2016). The process of modeling various crises and response dimensions depends on the caliber and quantity of data gathered and processed. Since the advent of the personal computer and computer-supported design, computational geometry has focused on locating the nearest neighbors. Using strategies like divideand- conquer and Voronoi diagrambased preprocessing, several methods have been created. Piegl and Tiller (2002) developed a method to find the nearest neighbors in 2-D without preprocessing. This technique can be used in evacuation models to determine where various people are in relation to escape routes in an emergency. A research project called the RESCUER project sought to support emergency and crisis management during massive events like the Soccer World Cup by gathering crowdsourced information, such as photos and videos from eyewitnesses of dangerous events, fusing similar data coming from eyewitnesses, and giving the command center an emergency response toolkit to manage emergencies effectively. By offering crowdsourced maps, the software product Collabmap uses crowdsourcing to assist emergency planners. With the help of existing maps or aerial photos, the crowdworkers identify buildings or routes, add and confirm escape routes that might not be shown on the existing maps, and identify any missing escape routes (Ramchurn, Huynh and Shi, 2013). Mobile crowdsourcing has also been used in evacuation simulations for building fire emergencies (Bajaj and Singh, 2015). In comparison to the manual process of filling out fire-drill reports, it was found that the mobile crowdsourcing drill gave a more precise evacuation time and monitoring of the participation of residents. The results revealed a somewhat significant difference between the manual and crowdsourced data-derived evacuation timings. Additionally, it revealed that almost 50 percent of the residents did not take part in the drill, a fact that the drill reports did not adequately document. Different techniques have been developed by researchers to evaluate the accuracy of sensed data from smartphones. A tree modeling-based data mining technique was put out by Li et al., (2019) to identify abnormalities in crowdsourced network data. Mobile crowdsourcing has become an essential tool in disaster evacuations as a result of this and other scientific advancements. Figure 3 General architecture of mobile crowdsourcing (Yang et al., 2015). Mobile crowdsourcing challenges in indoor conditions Fires, explosions, flooding, collapsed roofs, toxic gases and chemical contamination are some examples of potential risks in underground mining. Over the decades, mining-related incidents have claimed the lives of more than 100,000 miners in the United States alone. The world still has underground mining mishaps every year, and miners lose their lives as a result of being trapped and unable to escape because exit accesses were closed (Qian, Budiman and Shen, 2011). It is challenging to use mobile crowdsourcing for location services inside an underground mine. Devices are located using the GPS in open areas with minimal to no interference between the devices and the GPS satellites in orbit. There are two basic approaches to applying positioning techniques: self-positioning and remote positioning. In self-positioning, the mobile device uses signals transmitted by antennas (terrestrial or satellite) to determine its position. In remote positioning, the location of the mobile device is determined by analyzing the signals that are sent to and received from a group of receivers. The signals coming from or reflecting off the mobile device to be placed are measured by these receivers, which are deployed at one or more places. The position of the mobile device is estimated from geometric relationships using these signal observations to establish the length and direction of individual radio channels (Zeimpekis, Giaglis and Lekakos, 2002). Mobile crowdsourcing has challenges locating devices in enclosed spaces like deep mines because GPS sensing is not possible there. Researchers have developed a variety of methods to help with device location prediction in underground and indoor environments. These methods have been applied in a few indoor evacuation scenarios and offer results on device locations that are reassuringly accurate. There are many methods for measuring signals, but the ones that are most usually applied are the angle of arrival, time of flight, time of arrival, time difference of arrival, received signal intensity indicator and channel state information (Zare et al., 2021). An indoor location technique called received signal intensity (RSS) evaluates the actual signal strength at the receiving end, which is commonly given in decibel milliwatts (dBm) or milliwatts (mW). The distance between a mobile unit (MU) and a reference unit (RU) is roughly determined by this measurement. Localization systems can be categorized according to the signal being detected and the techniques being used to measure it. Both signal strength and geometrical components like angle and distance are included in signal metrics. The time between MU and RU decreases as RSS increases. A signal propagation model called RSSI also referred to as the RSS indicator, is used to calculate the absolute distance using transmission power as a defined reference point. RSSI is a relative RSS measure of arbitrary units described by chip makers. The RSSI and a straightforward path loss propagation model can be used to determine the distance between MU and RU (Zare et al., 2021). Similar technologies are currently used by numerous mines to convey text messages to underground workers. The primary distinction between these three situations is the method of information transmission: cell phones for weather alerts, roadside electronic monitoring for traffic dangers, and personal transponders for mining data. All of these messages have one common goal when used in an emergency or dangerous situation: to guide the miner in making safer decisions (avoid dangers and leave the mine safely) (Ur Rehman et al., 2020). When someone is given a task that requires them to work somewhere other than their usually assigned location, they should be informed of the area’s escape route at the time of the assignment. However, in accordance with regulations, individuals who typically work in many mine sites should get instructions concerning the locations of all escape routes at least once every 12 months. To locate people during an emergency and propose augmented-reality escape routes, RescueMe, an indoor mobile augmentedreality evacuation system, uses sensed data from smartphones that are crowdsourced and collected (Ahn and Han, 2011). The solution uses individualized pedometry to forecast device placement and direct evacuees using augmented reality. Solutions for indoor GPS have been developed for use in open spaces without considerable obstructions. Several pseudolites provide the navigation signal (pseudo-satellites). These gadgets produce a signal for navigation akin to GPS. To build pseudolite-compatible receivers with the fewest modifications possible to current GPS receivers, the signal is created to be comparable to the GPS signal. To navigate, at least four pseudolites must be visible, much like in the GPS, barring the usage of additional tools like altitude helping (Zeimpekis, Giaglis and Lekakos, 2002). Mobile crowdsourcing for mining evacuation History of mining disasters. Over the past century, there have been numerous disasters in the mining industry. In 716 different accidents, 15,183 miners lost their lives in the United States between 1830 and 2000. The Monongah coal mine explosion in West Virginia resulted in the deadliest mining tragedy in American history, which claimed the lives of 362 miners. Methane gas was to blame for the explosion that occurred on Dec. 6, 1907. The noxious vapors caused several rescuers to perish while attempting to free trapped miners. At the Darr Mine in Pennsylvania 239 other miners perished, and 263 people were killed in an explosion at the Stag Canon Mine in New Mexico in 1913. The greatest gold mine accident in U.S. history occurred in 1922 when 47 miners were burnt to death at a gold mine in California. Another 72 people perished in Ohio’s Willow Grove. The standards at Willow Grove were recognized for being a model of mine safety and for preventing more fatalities. Modern mining safety rules in the United States are mainly ascribed to an explosion at the Farmington Mine in West Virginia in 1968 that killed 78 people. In 2001, 13 workers died in two gas explosions within the Brookwood Mine in Alabama. The first explosion happened after a boulder landed on a battery charger, and the second one happened because methane gas accumulated after the initial explosion. In January 2006, a mine fire at the Alma No. 1 Mine in West Virginia resulted in the deaths of two miners. There were 29 miners working underground at the time the fire started at the longwall section belt take up. The longwall team was able to exit the section in fresh air thanks to the ventilation system and a set of cut-through entries that drew air into the longwall panel from the mains. Another group that was working close to the fire started to leave; however, this crew’s mantrip was abandoned halfway out of the mine due to excessive smoke. Two miners from this crew perished after becoming separated from the others for an unclear reason. In 2007, at the Crandell Canyon Mine in Utah, six miners and three rescuers perished. In June 2020, rain began to saturate the earth in the northern sector of the Wai Khar openpit jade mine in the district of Hpakant, resulting in what is regarded as Myanmar’s biggest mining disaster. At least 172 jade miners were eventually buried and killed on July 2 when a significant amount of quarry slope debris toppled into a flooded open pit. Mobile crowdsourcing challenges and potential solutions Challenges. Privacy. According to a recent survey, a majority of U.S. citizens support the gathering of location information in times of disaster. On how to use the location data, however, there was debate (Oxendine and Waters, 2014). A significant issue in MCS is how to safeguard a mobile user’s location privacy in location-based services. The potential for violating user privacy by recording and disclosing sensitive information (intentionally or unintentionally) is one of the main issues with autonomous sensing (Wang et al., 2017). Within an MCS framework, data and tasks may reveal personal information. The following is a succinct summary of the privacy threats from data: Privacy of sensed data — Sensitive information about participants, including identities, location data and biometric data, may be included in the sensed data. Participants’ personal information, like their home and business addresses, daily schedules and other details, may be revealed if their location is made public.Output privacy — The outcomes of crowdsourced projects may be private or sensitive. It is possible that end users do not want the service provider or administrator to discover sensitive data in the output results (Yang et al., 2015). The crowdsourced task can be categorized under task privacy of end users and task privacy of participants and may also reveal certain private information of both end users and crowdworkers. The following is a summary of the privacy threats from tasks: End-user task privacy — Task content may give the service provider access to sensitive information about end users.Participants’ task privacy — Tasks may reveal sensitive information about crowdworkers. When a crowdworker participates in tasks by asking for information that might be used to indirectly interpret location data, location information may be disclosed (Yang et al., 2015). Reliability threats. The reliability of crowdsourced data is also extremely important for any MCS-based model: Reliability of sensed data — Incorrect or erroneous data may be reported by crowdworkers with malicious intent; this is known as a “pollution attack.” Crowdworkers have the ability to maliciously configure mobile devices to jam particular data.Reliability of output — It is challenging to spot dishonest computer users who might produce false findings. When a mathematical model or algorithm is used to interpret data and produce an output, the output’s dependability is proportional to the algorithm’s correctness and dependability.Reliability of transmission — The service provider may get the crowdsourced data via networks like 3G/4G, Wi-Fi and so on. Due to human involvement in data transmission, data may be altered by intermediary devices (Yang et al., 2015). Other factors. Due to the influence of devices and the environment, crowdsourced data may be noisy and erroneous when collected infrequently over time (Li et al., 2019). Continuously sending sensed data from a smartphone to a query processor may drain the battery, lengthen perceived delays for users and deteriorate network performance (Chatzimilioudis et al., 2012). Potential solutions. Privacy-preserving. To lessen the likelihood that one user will reveal their real location, several academics have advocated location privacy preservation by the insertion of noise into the reallocation of data (Chi et al., 2017) and introduced a location privacy-preserving technique called CKD that combines k-anonymity with differential privacypreserving to stop the leakage of mobile users’ location information. In addition, the Stackelberg game is used to resolve the trade off between privacy protection and service quality. Private LTE services. The long-term evolution package for the fourth generation of mobile broadband technology is LTE, also referred to as 4G LTE. It has a range of techniques for load balancing, congestion control and traffic priority and can operate in a range of frequency ranges. Comparing 4G LTE to earlier 3G standards, the former offers higher peak-bit rates and significantly lower latency (Mueller et al., 2019). As the internet of things (IoT) and 5G (the fifth generation of mobile broadband technologies) gain popularity, private LTE is emerging to provide flexible and easily adjustable wireless coverage for a range of applications and sectors. By 2025, the private LTE/5G market is expected to treble, according to mobile industry analysts. For a localized region, such as a mine or a stadium, private LTE establishes an LTE service using nearby cellular towers and cell sites. Multiple Wi-Fi connection points would have been needed to cover the same region as LTE. Users choose private LTE because of its capacity, coverage, security and user control over public LTE and Wi-Fi networks. In areas with no existing infrastructure, private LTE networks can act as a cost-effective standalone network or as a supplement to existing networks to ease congestion and increase security (“Private LTE,” n.d.). Currently, LTE coverage is often good in openpit mines all around the world. The widespread adoption of 5G technology is anticipated to bring about a revolutionary improvement soon. The installation of private LTE networks at many openpit mines will help change these regulations to the benefit of the firms, even if many companies have restrictions on the use of any sort of smartphone at the mine site. Companies are carefully considering private LTE as an essential infrastructure for their operation since it offers a range of operational applications for mines at extremely low operating costs and great security compared to alternatives like Wi-Fi (Mueller et al., 2019). Rapidly expanding private LTE networks are being installed in surface and underground mines. The advantages include greatly lowering the number of fixed Wi-Fi access points to just a few LTE radio sites while also enhancing security, overall network capacity and system performance. As 5G gradually enters the mainstream, 5G elements are now being integrated into LTE development, which is still primarily a 4G technology. Miners in Voisey’s Bay, Canada will use mobile phones rather than radios since LTE is taking the place of leaky feeders as the main form of communication. In some Sudbury, Canada mines, LTE is being installed in addition to the already-installed Wi-Fi and leaky feeding infrastructure. Private LTE networks deliver excellent performance because they give users comprehensive traffic control. In contrast to Wi-Fi, which operates on unlicensed channels, the cellular LTE network has strict entry requirements and guards against outside user interference and traffic. High-level security layers are linked to private LTE networks. Users who want to require both SIM card and PIN verification to access the network can enable two-factor authentication. Dual SIM functionality allows for reasonably smooth coverage as enabled smartphones switch between public and private LTE networks (“Private LTE,” n.d.). Other solutions. Instead of a sophisticated user interface, developing simple and effective user interfaces for crowdsourcing mobile applications enhances user involvement. Users are more likely to participate in crowdsourcing assignments when given moral, financial and competitive incentives (Ramchurn, Huynh and Shi, 2013). Conclusion A mobile crowdsourcing architecture can considerably improve emergency evacuation in the mining industry. This approach will assist in preventing indecision and poor decisions during evacuation and ensure accurate decision-making in emergency situations. It will be possible to complete emergency evacuations in the shortest amount of time possible while saving lives and safeguarding equipment if the shortest evacuation routes are provided, taking into account the individual conditions of miners that may be sensed from their smart mobile devices as well as potentially hazardous conditions along evacuation routes. Researchers’ understanding of what has been accomplished thus far and the future course of action will be aided by solutions to the numerous MCS framework difficulties. (References available from the authors.)

Mobile crowdsourcing framework and application in mining evacuation

Authored by:
Frimpong Kwaku Asare, Peter Chidi Augustine, Javad Sattarvand

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