3,194 research outputs found
Latitude, longitude, and beyond:mining mobile objects' behavior
Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity
Layout Sequence Prediction From Noisy Mobile Modality
Trajectory prediction plays a vital role in understanding pedestrian movement
for applications such as autonomous driving and robotics. Current trajectory
prediction models depend on long, complete, and accurately observed sequences
from visual modalities. Nevertheless, real-world situations often involve
obstructed cameras, missed objects, or objects out of sight due to
environmental factors, leading to incomplete or noisy trajectories. To overcome
these limitations, we propose LTrajDiff, a novel approach that treats objects
obstructed or out of sight as equally important as those with fully visible
trajectories. LTrajDiff utilizes sensor data from mobile phones to surmount
out-of-sight constraints, albeit introducing new challenges such as modality
fusion, noisy data, and the absence of spatial layout and object size
information. We employ a denoising diffusion model to predict precise layout
sequences from noisy mobile data using a coarse-to-fine diffusion strategy,
incorporating the RMS, Siamese Masked Encoding Module, and MFM. Our model
predicts layout sequences by implicitly inferring object size and projection
status from a single reference timestamp or significantly obstructed sequences.
Achieving SOTA results in randomly obstructed experiments and extremely short
input experiments, our model illustrates the effectiveness of leveraging noisy
mobile data. In summary, our approach offers a promising solution to the
challenges faced by layout sequence and trajectory prediction models in
real-world settings, paving the way for utilizing sensor data from mobile
phones to accurately predict pedestrian bounding box trajectories. To the best
of our knowledge, this is the first work that addresses severely obstructed and
extremely short layout sequences by combining vision with noisy mobile
modality, making it the pioneering work in the field of layout sequence
trajectory prediction.Comment: In Proceedings of the 31st ACM International Conference on Multimedia
2023 (MM 23
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
Energy sink-holes avoidance method based on fuzzy system in wireless sensor networks
The existence of a mobile sink for gathering data significantly extends wireless sensor networks (WSNs) lifetime. In recent years, a variety of efficient rendezvous points-based sink mobility approaches has been proposed for avoiding the energy sink-holes problem nearby the sink, diminishing buffer overflow of sensors, and reducing the data latency. Nevertheless, lots of research has been carried out to sort out the energy holes problem using controllable-based sink mobility methods. However, further developments can be demonstrated and achieved on such type of mobility management system. In this paper, a well-rounded strategy involving an energy-efficient routing protocol along with a controllable-based sink mobility method is proposed to extirpate the energy sink-holes problem. This paper fused the fuzzy A-star as a routing protocol for mitigating the energy consumption during data forwarding along with a novel sink mobility method which adopted a grid partitioning system and fuzzy system that takes account of the average residual energy, sensors density, average traffic load, and sources angles to detect the optimal next location of the mobile sink. By utilizing diverse performance metrics, the empirical analysis of our proposed work showed an outstanding result as compared with fuzzy A-star protocol in the case of a static sink
Propose effective routing method for mobile sink in wireless sensor network
Wireless sensor network is one of the popular technologies used for maximizing the lifetime of network and to enhance the data collection process and energy efficiency by mobility. So, this work was proposed and focused on sink mobility which plays a key role in data collection process. The main challenge task was to discover the route in the active network. We have proposed an opportunistic algorithm in this paper with mobile sink to discover the ideal path starting the source to destination node. The proposed system has focused on a sensor field to sense and to report on building during fires where the sensors could be destroyed. The proposed system was evaluated through simulation and compared with existing algorithms (Genetic algorithm, multi-layer perceptron neural network). The performance which showed data delivery can be increased by up to 95%
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