65,334 research outputs found
Pushing AI to Wireless Network Edge: An Overview on Integrated Sensing, Communication, and Computation towards 6G
Pushing artificial intelligence (AI) from central cloud to network edge has
reached board consensus in both industry and academia for materializing the
vision of artificial intelligence of things (AIoT) in the sixth-generation (6G)
era. This gives rise to an emerging research area known as edge intelligence,
which concerns the distillation of human-like intelligence from the huge amount
of data scattered at wireless network edge. In general, realizing edge
intelligence corresponds to the process of sensing, communication, and
computation, which are coupled ingredients for data generation, exchanging, and
processing, respectively. However, conventional wireless networks design the
sensing, communication, and computation separately in a task-agnostic manner,
which encounters difficulties in accommodating the stringent demands of
ultra-low latency, ultra-high reliability, and high capacity in emerging AI
applications such as auto-driving. This thus prompts a new design paradigm of
seamless integrated sensing, communication, and computation (ISCC) in a
task-oriented manner, which comprehensively accounts for the use of the data in
the downstream AI applications. In view of its growing interest, this article
provides a timely overview of ISCC for edge intelligence by introducing its
basic concept, design challenges, and enabling techniques, surveying the
state-of-the-art development, and shedding light on the road ahead
Predictive intelligence to the edge through approximate collaborative context reasoning
We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference
Sampling-Based Methods for Factored Task and Motion Planning
This paper presents a general-purpose formulation of a large class of
discrete-time planning problems, with hybrid state and control-spaces, as
factored transition systems. Factoring allows state transitions to be described
as the intersection of several constraints each affecting a subset of the state
and control variables. Robotic manipulation problems with many movable objects
involve constraints that only affect several variables at a time and therefore
exhibit large amounts of factoring. We develop a theoretical framework for
solving factored transition systems with sampling-based algorithms. The
framework characterizes conditions on the submanifold in which solutions lie,
leading to a characterization of robust feasibility that incorporates
dimensionality-reducing constraints. It then connects those conditions to
corresponding conditional samplers that can be composed to produce values on
this submanifold. We present two domain-independent, probabilistically complete
planning algorithms that take, as input, a set of conditional samplers. We
demonstrate the empirical efficiency of these algorithms on a set of
challenging task and motion planning problems involving picking, placing, and
pushing
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