Privacy-by-design task offloading for UAV-mounted cloudlets

Ma, Weibin; Mashayekhy, Lena

doi:10.1145/3318216.3363320

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Many advancements in collaborative perception using infrastructure and vehicle (V2X) communication have been made for disturbed systems. 7,8 Wang et al 9 used local point cloud data from neighboring infrastructures for 3D object detection, leveraging collaborative perception through an encoder-decoder network architecture and an attention-based learnable communication mechanism. This framework demonstrated enhanced detection accuracy on a custom dataset.…”

Section: Collaboration-based Learningmentioning

confidence: 99%

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Collaborative object labeling in IoBT: a distributed approach for enhanced battlefield perception

Sadik,

Mashayekhy

2024

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

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With the proliferation of the Internet of Things (IoT), military science and technology have been reshaped and will continue to evolve to address modern warfare challenges. The Internet of Battlefield Things (IoBT) has emerged as a pivotal research domain for military operations. IoBT utilizes machine intelligence and networked communication technologies, building on IoT foundations to meet modern military warfare demands. These IoBT devices are being relied upon more and more to assist soldiers and are increasingly becoming indispensable tools for supporting soldiers in maintaining clear perception in a complex battlefield environment. Particularly, camera-based IoBT devices need to provide a clear perception of the environment to facilitate agile and precise decisions. When encountered with an object, these devices need to recognize and label that target object (e.g., human, enemy bunker, tanks) and take appropriate actions. However, the restricted angle of view and the computationally intensive nature of image recognition algorithms further limit these resource-bounded devices for timely object labeling. To address this, we propose a distributed approach by utilizing a group of geo-distributed camera-based IoBT devices in the battlefield environment to rapidly and accurately identify and label objects. We propose a Collaborative Object Labeling Approach, called COLA, that accumulates the opinions of several IoBT devices to label each object. Our proposed distributed approach is based on committee formation and reaching accurate verdicts in real-time. We perform extensive experiments to analyze the performance of our proposed algorithm based on several metrics. These experiments demonstrate COLA's potential to significantly enhance operational efficiency in dynamic and distributed battlefield environments.

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Section: Collaboration-based Learningmentioning

confidence: 99%

“…r ic . cos θ ij log(1 + F (q i , q j )) (8) and add it to the priority queue Q (Lines 7-9). This score considers the device's reliability level r ic , its distance to the object F (q i , q j ), and its angle of view θ ij , ensuring that devices with higher priorities are selected for committee formation.…”

Section: Collaborative Labeling Algorithmmentioning

confidence: 99%