6,497 research outputs found
Leveraging intelligence from network CDR data for interference aware energy consumption minimization
Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better Qo
Geo-Adaptive Deep Spatio-Temporal predictive modeling for human mobility
Deep learning approaches for spatio-temporal prediction problems such as
crowd-flow prediction assumes data to be of fixed and regular shaped tensor and
face challenges of handling irregular, sparse data tensor. This poses
limitations in use-case scenarios such as predicting visit counts of
individuals' for a given spatial area at a particular temporal resolution using
raster/image format representation of the geographical region, since the
movement patterns of an individual can be largely restricted and localized to a
certain part of the raster. Additionally, current deep-learning approaches for
solving such problem doesn't account for the geographical awareness of a region
while modelling the spatio-temporal movement patterns of an individual. To
address these limitations, there is a need to develop a novel strategy and
modeling approach that can handle both sparse, irregular data while
incorporating geo-awareness in the model. In this paper, we make use of
quadtree as the data structure for representing the image and introduce a novel
geo-aware enabled deep learning layer, GA-ConvLSTM that performs the
convolution operation based on a novel geo-aware module based on quadtree data
structure for incorporating spatial dependencies while maintaining the
recurrent mechanism for accounting for temporal dependencies. We present this
approach in the context of the problem of predicting spatial behaviors of an
individual (e.g., frequent visits to specific locations) through deep-learning
based predictive model, GADST-Predict. Experimental results on two GPS based
trace data shows that the proposed method is effective in handling frequency
visits over different use-cases with considerable high accuracy
Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
With the rapid development of the Intelligent Transportation System (ITS),
accurate traffic forecasting has emerged as a critical challenge. The key
bottleneck lies in capturing the intricate spatio-temporal traffic patterns. In
recent years, numerous neural networks with complicated architectures have been
proposed to address this issue. However, the advancements in network
architectures have encountered diminishing performance gains. In this study, we
present a novel component called spatio-temporal adaptive embedding that can
yield outstanding results with vanilla transformers. Our proposed
Spatio-Temporal Adaptive Embedding transformer (STAEformer) achieves
state-of-the-art performance on five real-world traffic forecasting datasets.
Further experiments demonstrate that spatio-temporal adaptive embedding plays a
crucial role in traffic forecasting by effectively capturing intrinsic
spatio-temporal relations and chronological information in traffic time series.Comment: Accepted as CIKM2023 Short Pape
Predictive Duty Cycle Adaptation for Wireless Camera Networks
Wireless sensor networks (WSN) typically employ dynamic duty cycle schemes to efficiently handle different patterns of communication traffic in the network. However, existing duty cycling approaches are not suitable for event-driven WSN, in particular, camera-based networks designed to track humans and objects. A characteristic feature of such networks is the spatially-correlated bursty traffic that occurs in the vicinity of potentially highly mobile objects. In this paper, we propose a concept of indirect sensing in the MAC layer of a wireless camera network and an active duty cycle adaptation scheme based on Kalman filter that continuously predicts and updates the location of the object that triggers bursty communication traffic in the network. This prediction allows the camera nodes to alter their communication protocol parameters prior to the actual increase in the communication traffic. Our simulations demonstrate that our active adaptation strategy outperforms TMAC not only in terms of energy efficiency and communication latency, but also in terms of TIBPEA, a QoS metric for event-driven WSN
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