4,925 research outputs found
Space-Time Hierarchical-Graph Based Cooperative Localization in Wireless Sensor Networks
It has been shown that cooperative localization is capable of improving both
the positioning accuracy and coverage in scenarios where the global positioning
system (GPS) has a poor performance. However, due to its potentially excessive
computational complexity, at the time of writing the application of cooperative
localization remains limited in practice. In this paper, we address the
efficient cooperative positioning problem in wireless sensor networks. A
space-time hierarchical-graph based scheme exhibiting fast convergence is
proposed for localizing the agent nodes. In contrast to conventional methods,
agent nodes are divided into different layers with the aid of the space-time
hierarchical-model and their positions are estimated gradually. In particular,
an information propagation rule is conceived upon considering the quality of
positional information. According to the rule, the information always
propagates from the upper layers to a certain lower layer and the message
passing process is further optimized at each layer. Hence, the potential error
propagation can be mitigated. Additionally, both position estimation and
position broadcasting are carried out by the sensor nodes. Furthermore, a
sensor activation mechanism is conceived, which is capable of significantly
reducing both the energy consumption and the network traffic overhead incurred
by the localization process. The analytical and numerical results provided
demonstrate the superiority of our space-time hierarchical-graph based
cooperative localization scheme over the benchmarking schemes considered.Comment: 14 pages, 15 figures, 4 tables, accepted to appear on IEEE
Transactions on Signal Processing, Sept. 201
Trustworthy Edge Machine Learning: A Survey
The convergence of Edge Computing (EC) and Machine Learning (ML), known as
Edge Machine Learning (EML), has become a highly regarded research area by
utilizing distributed network resources to perform joint training and inference
in a cooperative manner. However, EML faces various challenges due to resource
constraints, heterogeneous network environments, and diverse service
requirements of different applications, which together affect the
trustworthiness of EML in the eyes of its stakeholders. This survey provides a
comprehensive summary of definitions, attributes, frameworks, techniques, and
solutions for trustworthy EML. Specifically, we first emphasize the importance
of trustworthy EML within the context of Sixth-Generation (6G) networks. We
then discuss the necessity of trustworthiness from the perspective of
challenges encountered during deployment and real-world application scenarios.
Subsequently, we provide a preliminary definition of trustworthy EML and
explore its key attributes. Following this, we introduce fundamental frameworks
and enabling technologies for trustworthy EML systems, and provide an in-depth
literature review of the latest solutions to enhance trustworthiness of EML.
Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table
Learning Optimal Fronthauling and Decentralized Edge Computation in Fog Radio Access Networks
Fog radio access networks (F-RANs), which consist of a cloud and multiple
edge nodes (ENs) connected via fronthaul links, have been regarded as promising
network architectures. The F-RAN entails a joint optimization of cloud and edge
computing as well as fronthaul interactions, which is challenging for
traditional optimization techniques. This paper proposes a Cloud-Enabled
Cooperation-Inspired Learning (CECIL) framework, a structural deep learning
mechanism for handling a generic F-RAN optimization problem. The proposed
solution mimics cloud-aided cooperative optimization policies by including
centralized computing at the cloud, distributed decision at the ENs, and their
uplink-downlink fronthaul interactions. A group of deep neural networks (DNNs)
are employed for characterizing computations of the cloud and ENs. The
forwardpass of the DNNs is carefully designed such that the impacts of the
practical fronthaul links, such as channel noise and signling overheads, can be
included in a training step. As a result, operations of the cloud and ENs can
be jointly trained in an end-to-end manner, whereas their real-time inferences
are carried out in a decentralized manner by means of the fronthaul
coordination. To facilitate fronthaul cooperation among multiple ENs, the
optimal fronthaul multiple access schemes are designed. Training algorithms
robust to practical fronthaul impairments are also presented. Numerical results
validate the effectiveness of the proposed approaches.Comment: to appear in IEEE Transactions on Wireless Communication
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
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