241,250 research outputs found
AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems
The evolution towards 6G architecture promises a transformative shift in
communication networks, with artificial intelligence (AI) playing a pivotal
role. This paper delves deep into the seamless integration of Large Language
Models (LLMs) and Generalized Pretrained Transformers (GPT) within 6G systems.
Their ability to grasp intent, strategize, and execute intricate commands will
be pivotal in redefining network functionalities and interactions. Central to
this is the AI Interconnect framework, intricately woven to facilitate
AI-centric operations within the network. Building on the continuously evolving
current state-of-the-art, we present a new architectural perspective for the
upcoming generation of mobile networks. Here, LLMs and GPTs will
collaboratively take center stage alongside traditional pre-generative AI and
machine learning (ML) algorithms. This union promises a novel confluence of the
old and new, melding tried-and-tested methods with transformative AI
technologies. Along with providing a conceptual overview of this evolution, we
delve into the nuances of practical applications arising from such an
integration. Through this paper, we envisage a symbiotic integration where AI
becomes the cornerstone of the next-generation communication paradigm, offering
insights into the structural and functional facets of an AI-native 6G network
LAMBO: Large Language Model Empowered Edge Intelligence
Next-generation edge intelligence is anticipated to bring huge benefits to
various applications, e.g., offloading systems. However, traditional deep
offloading architectures face several issues, including heterogeneous
constraints, partial perception, uncertain generalization, and lack of
tractability. In this context, the integration of offloading with large
language models (LLMs) presents numerous advantages. Therefore, we propose an
LLM-Based Offloading (LAMBO) framework for mobile edge computing (MEC), which
comprises four components: (i) Input embedding (IE), which is used to represent
the information of the offloading system with constraints and prompts through
learnable vectors with high quality; (ii) Asymmetric encoderdecoder (AED)
model, which is a decision-making module with a deep encoder and a shallow
decoder. It can achieve high performance based on multi-head self-attention
schemes; (iii) Actor-critic reinforcement learning (ACRL) module, which is
employed to pre-train the whole AED for different optimization tasks under
corresponding prompts; and (iv) Active learning from expert feedback (ALEF),
which can be used to finetune the decoder part of the AED while adapting to
dynamic environmental changes. Our simulation results corroborate the
advantages of the proposed LAMBO framework.Comment: To be submitted for possible journal publicatio
Developing an Efficient DMCIS with Next-Generation Wireless Networks
The impact of extreme events across the globe is extraordinary which
continues to handicap the advancement of the struggling developing societies
and threatens most of the industrialized countries in the globe. Various fields
of Information and Communication Technology have widely been used for efficient
disaster management; but only to a limited extent though, there is a tremendous
potential for increasing efficiency and effectiveness in coping with disasters
with the utilization of emerging wireless network technologies. Early warning,
response to the particular situation and proper recovery are among the main
focuses of an efficient disaster management system today. Considering these
aspects, in this paper we propose a framework for developing an efficient
Disaster Management Communications and Information System (DMCIS) which is
basically benefited by the exploitation of the emerging wireless network
technologies combined with other networking and data processing technologies.Comment: 6 page
A Secure Lightweight Approach of Node Membership Verification in Dense HDSN
In this paper, we consider a particular type of deployment scenario of a
distributed sensor network (DSN), where sensors of different types and
categories are densely deployed in the same target area. In this network, the
sensors are associated with different groups, based on their functional types
and after deployment they collaborate with one another in the same group for
doing any assigned task for that particular group. We term this sort of DSN as
a heterogeneous distributed sensor network (HDSN). Considering this scenario,
we propose a secure membership verification mechanism using one-way accumulator
(OWA) which ensures that, before collaborating for a particular task, any pair
of nodes in the same deployment group can verify each other-s legitimacy of
membership. Our scheme also supports addition and deletion of members (nodes)
in a particular group in the HDSN. Our analysis shows that, the proposed scheme
could work well in conjunction with other security mechanisms for sensor
networks and is very effective to resist any adversary-s attempt to be included
in a legitimate group in the network.Comment: 6 page
A Process Framework for Semantics-aware Tourism Information Systems
The growing sophistication of user requirements in tourism due to the advent of new technologies such as the Semantic Web and mobile computing has imposed new possibilities for improved intelligence in Tourism Information Systems (TIS). Traditional software engineering and web engineering approaches cannot suffice, hence the need to find new product development approaches that would sufficiently enable the next generation of TIS. The next generation of TIS are expected among other things to: enable
semantics-based information processing, exhibit natural language capabilities, facilitate inter-organization exchange of information in a seamless way, and
evolve proactively in tandem with dynamic user requirements. In this paper, a product development approach called Product Line for Ontology-based Semantics-Aware Tourism Information Systems (PLOSATIS) which is a novel
hybridization of software product line engineering, and Semantic Web engineering concepts is proposed. PLOSATIS is presented as potentially effective, predictable and amenable to software process improvement initiatives
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