Securing the country’s borders against interests hostile to the country and putting in place systems that can interdict such elements is among the principal objectives of border management. Line of actual control (LAC) is one of the key areas of the Indian border, India has been facing intrusions from china on the western sector of the Sino-Indian border. Since last year it has been a focal point to the border management as China pushing into India’s territory in the western sector is a threat to national security and sovereign borders.
To secure the borders, patrolling points (PPs) are identified and marked on the LAC, which is patrolled with a stipulated frequency by the security forces. The frequency of reaching various PPs are given in the annual patrolling programme. Based on the terrain, the ground situation and the location of the LAC, the duration for visiting each PP is specified – it can vary from once a month to twice a year.[i] With the involvement of AI in day to day functions, we can increase the frequency of the visits which can show the heightened presence of Indian forces at the LAC. The technology can help in analyze and respond to the situation faster and better.
Potential of Artificial Intelligence
Automation of tasks that are repetitive and less creative will ease up the burden on the forces and gives leeway to concentrate more on pressing issues. For physical tasks, we can use heterogeneous robotic systems, which represent a capability of networked robotic systems that integrate various unmanned systems, including vehicles of ‘different sizes and abilities for maritime, land and air environments.[ii] Such networked systems have been of interest in various application areas, including border control and counter-terrorism.[iii] Current capabilities are limited to individual systems and limited integration of platform and sensor data. Their unmanned systems require significant human resources to operate. various developers and border security authorities plan to implement a heterogeneous robotic system, The Indian borders with key focus areas like LAC are land borders, India can use Unmanned and automated land and aerial vehicles for better patrolling and continuous surveillance on the movement of the neighbouring countries patrols and troops along the border effectively.
Logistics
Logistics is generally the detailed organization and implementation of a complex operation. AI has proven to assist a lot in logistics, Predictive capabilities are helping demand forecasting of resources, maintenance of vehicles and tools. Border management is a mix of very complex operations. Having AI taking care of exhaustive management of complex things gives leeway to allocate resources to more important tasks. AI can consume a large amount of data in a very short time and it will be able to identify what needs immediate attention. The united states army uses IBM’s artificial intelligence product Watson for its Logistics Support Activity (LOGSA) software. LOGSA Commander Col. John Kuenzli said that “This can be a way to free up our analysts from some of the more technical work, to let the machine do some of that and to leverage the analysts’ professional expertise to look up and look further toward where the Army is going.”[iv] Securing the LAC also deals with a lot of vehicles and electronic equipment which needs constant monitoring regular maintenance. border management includes various complex operations where AI can perform more efficiently than humans.
Video and Image Analytics
Video analytics applies big data to CCTV footage and deciphers trends to automatically raise the alarm. Big data is inextricably linked with artificial intelligence, this helps to solve the shortcomings of CCTV cameras, which rely on human monitoring and are limited to forensic analysis after the fact. AI can use smart sensors and image recognition to improve surveillance activities and object detection across large areas of border crossings to refocus security agents’ efforts on responding to unauthorized movements of the contested country forces and other security breaches instead of conducting ad hoc patrols. Image and video analytics can be used to detect Minute changes in the patrolling patterns of PLA.
Cognitive Insight
Cognitive insight is a powerful analytics tool powered by “deep learning.” These machine learning applications process large amounts of data and interpret their meaning. The cognitive insight will look for patterns in the data to help predict outcomes in specific situations.[v] Integrating previously siloed datasets can reveal connections that a human would not be able to piece together–but AI could.
ML-enabled cognitive insight generally encompasses larger quantities of data and greater detail and quality of insights provided by the model (e.g. accuracy of predictions) in contrast to traditional analytics.[vi] This technology can be used for providing quality assistance for the tasks dealing with big data. it generates models using the data analysis, the models which can be used by humans or AI to predict the future outcome. They construct a representation of the relationships and tease out patterns between all the different features in your dataset that you can apply to similar data you collect in the future, allowing you to make decisions based on those patterns and relationships. It is more abstract than an architectural model, but it is the same idea: a distilled representation of a greater picture.[vii] This technology can help to predict future deployments of the forces of the neighbouring country based on past data.
Situational Awareness
Situation awareness is very crucial in securing borders in times of stand off’s and engagement of forces. situational awareness relies heavily on Intelligence, Surveillance, and Reconnaissance (ISR) operations. ISR operations are used to acquire and process information to support a range of military activities. Unmanned systems used to carry out ISR missions can either be remotely operated or sent on a pre-defined route. Equipping these systems with AI assists defence personnel in threat monitoring, thereby enhancing their situational awareness. Unmanned aerial vehicles (UAVs) – also known as drones – with integrated AI can patrol border areas, identify potential threats, and transmit information about these threats to response teams.[viii]
Small robotic sensors could be used to collect information, and AI-enabled sensors and processing could help make better sense of that information. Deep neural networks already are being used for image classification for drone video feeds as part U.S. Department of Defense artificial intelligence (AI) project, project Maven, to help humans process the large volumes of data being collected. While current AI methods cannot translate this into an understanding of the broader context, AI systems could be used to fuse data from multiple intelligence sources and cue humans to items of interest.[ix]
Challenges
1) Capture and collation of data To make effective use of AI, it is necessary to first establish all the infrastructure to capture the data and interpret it to the AI. The integration of old data into the system for predictive analysis and cognitive assistance is a big issue because most of the data is analogue and it needs to be converted into digital form and structured for the AI to process.
2) AI needs very robust processing capability to perform its duties efficiently as it is dealing with large quantities of stored and live data. India needs to invest in high-end processing power and data banks.
3) To process and transmit this time-sensitive data from the sensors and data banks to the command centres India needs a secure and high-speed network.
4) training the existing technicians to use the new technologies and interfaces
Recommendations
1) Developing a policy to explore and aspects of securing borders with the integration of advanced technologies like AI.
2) India can use heterogeneous robotic systems for surveillance of land and river borders by ALV’s, UAV’s and integrated systems, sharing data in real-time working as eyes and years of one single machine on the LAC.
3) Procuring autonomous and remotely operated ground and aerial vehicles to increase the frequency of patrols. It will show the heightened presence of Indian patrols in the region, Which is important for showing dominance over the region.
4) Working towards developing in-house technologies for virtual assistance in securing the borders like logistics, predictive analysis, and image and video processing.
5) Focusing on establishing and developing institutions that will work on building robust algorithms of AI, according to current and future needs of securing the borders and national security.
Conclusion
As an enabling technology, AI has many uses across a variety of national security settings. India needs a robust border security system to protect the LAC against China, The use of AI in national security-related issues has seen a major boost in recent decades. AI can be used for vastly improving ISR, fast and secure command and control, efficient logistics, swarming unmanned vehicles, predictive analysis, cognitive insight etc. Adopting and following these changes is of paramount importance to India, This is not only for keep pace with changing times but also to leverage the potential offered by this budding technology. Even though AI is very helpful in a lot of aspects, it needs to be operated under human discretion when the tasks are dealing with human lives and the sovereignty of the country to avoid catastrophic level events.
Endnotes
[i]Singh, Sushant, “Patrolling Points: What do these markers on the LAC signify?” The Indian Express July 13 2020, available at https://indianexpress.com/article/explained/explained-what-do-patrolling-points-pps-on-lac-signify-6496840/
[ii]Roborder.eu (homepage). 2020. As of 15 June 2020: available at https://roborder.eu/
[iii]Miskovic, N., S. Bogdan, I. Petrovic & Z. Vukic. 2014. “Cooperative control of heterogeneous robotic systems”. 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). As of 16 June 2020: https://ieeexplore.ieee.org/document/6859711/authors#authors
[iv]Adam Stone, “Army logistics integrating new AI, cloud capabilities” , C4ISRNet, 07 September 2017, available at https://www.c4isrnet.com/home/2017/09/07/army-logistics-integrating-new-ai-cloud-capabilities/
[v]“Three Types of AI and How You Can Use Them in Your Business”, Artificial intelligence, Kambria, 09 August 2019 available at https://kambria.io/blog/three-types-of-ai/
[vi]“Artificial Intelligence-based capabilities for the European border and coast guard final report” Frontex – European Border and Coast Guard Agency March 2021 Page 14 available at https://frontex.europa.eu/assets/Publications/Research/Frontex_AI_Research_Study_2020_final_report.pdf
[vii]“Machine Learning Model”, Resources, Datarobot, available at https://www.datarobot.com/wiki/model/
[viii]Singh, Tejaswi and Guljane, Amit “8 Key Military Applications for Artificial Intelligence in 2018”, Market research.com, 03 October 2018 available at https://blog.marketresearch.com/8-key-military-applications-for-artificial-intelligence-in-2018
[ix]Horowitz, Michael C., et al. “NATIONAL SECURITY-RELATED APPLICATIONS OF ARTIFICIAL INTELLIGENCE”. Center for a New American Security, 2018, pp. 3–13, Artificial Intelligence and International Security, www.jstor.org/stable/resrep20430.3 Accessed 27 July 2021
