10,260 research outputs found
Graph-based Decentralized Task Allocation for Multi-Robot Target Localization
We introduce a new approach to address the task allocation problem in a
system of heterogeneous robots comprising of Unmanned Ground Vehicles (UGVs)
and Unmanned Aerial Vehicles (UAVs). The proposed model, \texttt{\method}, or
\textbf{G}raph \textbf{A}ttention \textbf{T}ask \textbf{A}llocato\textbf{R}
aggregates information from neighbors in the multi-robot system, with the aim
of achieving joint optimality in the target localization efficiency.Being
decentralized, our method is highly robust and adaptable to situations where
collaborators may change over time, ensuring the continuity of the mission. We
also proposed heterogeneity-aware preprocessing to let all the different types
of robots collaborate with a uniform model.The experimental results demonstrate
the effectiveness and scalability of the proposed approach in a range of
simulated scenarios. The model can allocate targets' positions close to the
expert algorithm's result, with a median spatial gap less than a unit length.
This approach can be used in multi-robot systems deployed in search and rescue
missions, environmental monitoring, and disaster response
Survey on Congestion Detection and Control in Connected Vehicles
The dynamic nature of vehicular ad hoc network (VANET) induced by frequent
topology changes and node mobility, imposes critical challenges for vehicular
communications. Aggravated by the high volume of information dissemination
among vehicles over limited bandwidth, the topological dynamics of VANET causes
congestion in the communication channel, which is the primary cause of problems
such as message drop, delay, and degraded quality of service. To mitigate these
problems, congestion detection, and control techniques are needed to be
incorporated in a vehicular network. Congestion control approaches can be
either open-loop or closed loop based on pre-congestion or post congestion
strategies. We present a general architecture of vehicular communication in
urban and highway environment as well as a state-of-the-art survey of recent
congestion detection and control techniques. We also identify the drawbacks of
existing approaches and classify them according to different hierarchical
schemes. Through an extensive literature review, we recommend solution
approaches and future directions for handling congestion in vehicular
communications
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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