TAK-ML: Applying Machine Learning at the Tactical Edge

Chin, Peter; Do, Emily; Doucette, Cody; Kalashian, Brandon; Last, D.; Lenz, N.; Lu, Edward; Minor, D.; Noyes, Elias; Rock, Colleenn; Soule, Nathaniel; Walczak, Nicholas; Canestrare, Dave

doi:10.1109/milcom52596.2021.9652909

Cited by 3 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…16 Chin et al introduced the TAK-ML (Tactical Assault Kit-Machine Learning) framework that combines Android TAK (ATAK) handheld devices as well as TAK servers to offload the cognitive burden on Soldiers and commanders of the myriad data collection and analysis tasks in tactical environments. 17…”

Section: Edge Computingmentioning

confidence: 99%

Tactical agility for AI-enabled multi-domain operations

Sturzinger¹,

George²,

Satyanarayanan³

2023

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V

View full text Add to dashboard Cite

Commanders must remain agile and adaptive in the future Artificial Intelligence (AI)-enabled multi-domain battlespace, where critical decisions are made at the tactical edge. Over-reliance on static, cloud-centric approaches to Machine Learning Operations (MLOps) compromises such agility and adaptability. These systems must operate in a dynamic threat environment, and learn to detect novel threats during operation. They must be able to perform this learning through the execution of tactical MLOps under austere and degraded conditions, especially limited wireless network bandwidth. In response to these requirements, this paper describes Hawk, a system that leverages edge proximity for rapid and iterative execution of the Observe stage of the Observe-Orient-Decide-Act loop. Central to this architecture is the use of tactical cloudlets. These mini data centers provide cloud-like computing resources without the communication latency to exascale data centers. Hawk enables a human to guide MLOps at low cognitive load, thus enabling an operational objective to be achieved at speed and scale while remaining usable and explainable.

show abstract

Section: Edge Computingmentioning

confidence: 99%

Tactical agility for AI-enabled multi-domain operations

Sturzinger¹,

George²,

Satyanarayanan³

2023

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V

View full text Add to dashboard Cite

show abstract

The Cloud Continuum for Military Deployable Networks: Challenges and Opportunities

Rojas¹,

Lopez-Pajares²,

Alvarez-Horcajo³

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Human-based gait authentication using multi-modal sensors at the tactical edge device: smartphone

Sandrakumar,

Khan

2024

Big Data VI: Learning, Analytics, and Applications

View full text Add to dashboard Cite

The Department of Defense (DoD) including the US Air force have been seeing increased physical security risks, such as gate runners, active shooter situations, and other use cases (such as Drone ISR, Rapid Deployment, Force Protection, and Security Force), requiring increased coordination and data cooperation. Additionally, technology and operational environments are getting more complex, interconnected and diverse. Recently a team of developers developed a plug-in for the Android Team Awareness Kit (ATAK). This plug-in analyzes sensor data from tactical edge device's (e.g., cell phone) onboard accelerometer and gyroscope to determine the movement of a person when they walk. The plug-in also uses machine learning (ML) algorithms to create a model of that person's gait, and then sends pertinent data through the associated human gait model to authenticate a user. The novelty of our effort lies into enhancing this human gait authentication by using different features extracted from spectral information of the accelerometer and gyroscope signals from the smartphone using a public human activity recognition dataset (WISDM) as a proof of concept, marking a previously unexplored approach. By leveraging spectral data, we seek to enhance the accuracy and robustness of authentication systems in military contexts. After the feature extraction is performed, Kernal Discriminant Analysis (KDA) is utilized to reduce the dimensions of the spectral features to 50; after including the non-spectral features, our total number of final features becomes 65. After adding the total features, we perform feature-level fusion utilizing ML algorithms and the performance shows promising for authentication utilizing 51 users. The SVM-rbf classifiers achieved a mean Equal Error Rate (EER) and mean Accuracy (ACC) of 2% and 97.3% , while the GBM classifiers achieved a mean EER and ACC of 0.4% and 99.1%, and the CNN classifiers achieved a mean EER and ACC of 10% and 90.4% respectively.

show abstract

TAK-ML: Applying Machine Learning at the Tactical Edge

Cited by 3 publications

References 6 publications

Tactical agility for AI-enabled multi-domain operations

Tactical agility for AI-enabled multi-domain operations

The Cloud Continuum for Military Deployable Networks: Challenges and Opportunities

Human-based gait authentication using multi-modal sensors at the tactical edge device: smartphone

Contact Info

Product

Resources

About