12 research outputs found
A Community-Based Event Delivery Protocol in Publish/Subscribe Systems for Delay Tolerant Sensor Networks
The basic operation of a Delay Tolerant Sensor Network (DTSN) is to finish pervasive data gathering in networks with intermittent connectivity, while the publish/subscribe (Pub/Sub for short) paradigm is used to deliver events from a source to interested clients in an asynchronous way. Recently, extension of Pub/Sub systems in DTSNs has become a promising research topic. However, due to the unique frequent partitioning characteristic of DTSNs, extension of a Pub/Sub system in a DTSN is a considerably difficult and challenging problem, and there are no good solutions to this problem in published works. To ad apt Pub/Sub systems to DTSNs, we propose CED, a community-based event delivery protocol. In our design, event delivery is based on several unchanged communities, which are formed by sensor nodes in the network according to their connectivity. CED consists of two components: event delivery and queue management. In event delivery, events in a community are delivered to mobile subscribers once a subscriber comes into the community, for improving the data delivery ratio. The queue management employs both the event successful delivery time and the event survival time to decide whether an event should be delivered or dropped for minimizing the transmission overhead. The effectiveness of CED is demonstrated through comprehensive simulation studies
Filter Pruning For CNN With Enhanced Linear Representation Redundancy
Structured network pruning excels non-structured methods because they can
take advantage of the thriving developed parallel computing techniques. In this
paper, we propose a new structured pruning method. Firstly, to create more
structured redundancy, we present a data-driven loss function term calculated
from the correlation coefficient matrix of different feature maps in the same
layer, named CCM-loss. This loss term can encourage the neural network to learn
stronger linear representation relations between feature maps during the
training from the scratch so that more homogenous parts can be removed later in
pruning. CCM-loss provides us with another universal transcendental
mathematical tool besides L*-norm regularization, which concentrates on
generating zeros, to generate more redundancy but for the different genres.
Furthermore, we design a matching channel selection strategy based on principal
components analysis to exploit the maximum potential ability of CCM-loss. In
our new strategy, we mainly focus on the consistency and integrality of the
information flow in the network. Instead of empirically hard-code the retain
ratio for each layer, our channel selection strategy can dynamically adjust
each layer's retain ratio according to the specific circumstance of a
per-trained model to push the prune ratio to the limit. Notably, on the
Cifar-10 dataset, our method brings 93.64% accuracy for pruned VGG-16 with only
1.40M parameters and 49.60M FLOPs, the pruned ratios for parameters and FLOPs
are 90.6% and 84.2%, respectively. For ResNet-50 trained on the ImageNet
dataset, our approach achieves 42.8% and 47.3% storage and computation
reductions, respectively, with an accuracy of 76.23%. Our code is available at
https://github.com/Bojue-Wang/CCM-LRR
A Community-Based Event Delivery Protocol in Publish/Subscribe Systems for Delay Tolerant Sensor Networks
The basic operation of a Delay Tolerant Sensor Network (DTSN) is to finish pervasive data gathering in networks with intermittent connectivity, while the publish/subscribe (Pub/Sub for short) paradigm is used to deliver events from a source to interested clients in an asynchronous way. Recently, extension of Pub/Sub systems in DTSNs has become a promising research topic. However, due to the unique frequent partitioning characteristic of DTSNs, extension of a Pub/Sub system in a DTSN is a considerably difficult and challenging problem, and there are no good solutions to this problem in published works. To ad apt Pub/Sub systems to DTSNs, we propose CED, a community-based event delivery protocol. In our design, event delivery is based on several unchanged communities, which are formed by sensor nodes in the network according to their connectivity. CED consists of two components: event delivery and queue management. In event delivery, events in a community are delivered to mobile subscribers once a subscriber comes into the community, for improving the data delivery ratio. The queue management employs both the event successful delivery time and the event survival time to decide whether an event should be delivered or dropped for minimizing the transmission overhead. The effectiveness of CED is demonstrated through comprehensive simulation studies
Deep Reinforcement Learning–Based Online One-to-Multiple Charging Scheme in Wireless Rechargeable Sensor Network
Wireless rechargeable sensor networks (WRSN) have been emerging as an effective solution to the energy constraint problem of wireless sensor networks (WSN). However, most of the existing charging schemes use Mobile Charging (MC) to charge nodes one-to-one and do not optimize MC scheduling from a more comprehensive perspective, leading to difficulties in meeting the huge energy demand of large-scale WSNs; therefore, one-to-multiple charging which can charge multiple nodes simultaneously may be a more reasonable choice. To achieve timely and efficient energy replenishment for large-scale WSN, we propose an online one-to-multiple charging scheme based on Deep Reinforcement Learning, which utilizes Double Dueling DQN (3DQN) to jointly optimize the scheduling of both the charging sequence of MC and the charging amount of nodes. The scheme cellularizes the whole network based on the effective charging distance of MC and uses 3DQN to determine the optimal charging cell sequence with the objective of minimizing dead nodes and adjusting the charging amount of each cell being recharged according to the nodes’ energy demand in the cell, the network survival time, and MC’s residual energy. To obtain better performance and timeliness to adapt to the varying environments, our scheme further utilizes Dueling DQN to improve the stability of training and uses Double DQN to reduce overestimation. Extensive simulation experiments show that our proposed scheme achieves better charging performance compared with several existing typical works, and it has significant advantages in terms of reducing node dead ratio and charging latency
Spatial and Temporal Correlations-Based Routing Algorithm in Intermittent Connectivity Human Social Network
The social network formed by people is one of the key applications of Delay-Tolerant Network (DTN). Owing to its intermittent connectivity and unique human mobility patterns, how to transmit data in an effective way is a challenging problem for the social network. In this paper, we propose the idea of Trip History Model (THM) which establishes a model on a single person's mobility, and then a Spatial and Temporal Correlations-Based Routing Algorithm (STC) is proposed. In STC, the node delivery probability is calculated according to both a node's current moving prediction and its history record to give guidance for message transmission. Our simulation results show that, compared with LABEL and PROPHET algorithms, STC effectively improves the routing performance of the network
A Low-Cost Vehicle Anti-Theft System Using Obsolete Smartphone
In modern society, vehicle theft has become an increasing problem to the general public. Deploying onboard anti-theft systems could relieve this problem, but it often requires extra investment for vehicle owners. In this paper, we propose the idea of PhoneInside, which does not need a special device but leverages an obsolete smartphone to build a low-cost vehicle anti-theft system. After being fixed in the vehicle body with a car charger, the smartphone can detect vehicle movement and adaptively use GPS, cellular/WiFi localization, and dead reckoning to locate the vehicle during driving. Especially, a novel Velocity-Aware Dead Reckoning (VA-DR) method is presented, which utilizes map knowledge and vehicle’s turns at road curves and intersections to estimate velocity for trajectory computation. Compared to traditional dead reckoning, it reduces accumulated errors and achieves great improvement in localization accuracy. Furthermore, based on the learning of the driving history, our system can establish individual mobility model for a vehicle and distinguish abnormal driving behaviors by a Long Short Term Memory (LSTM) network. With the help of ad hoc authentication, the system can identify vehicle theft and send out timely alarming and tracking messages for rapid recovery. The realistic experiments running on Android smartphones prove that our system can detect vehicle theft effectively and locate a stolen vehicle accurately, with average errors less than the sight range
Deep Learning versus Professional Healthcare Equipment: A Fine-Grained Breathing Rate Monitoring Model
In mHealth field, accurate breathing rate monitoring technique has benefited a broad array of healthcare-related applications. Many approaches try to use smartphone or wearable device with fine-grained monitoring algorithm to accomplish the task, which can only be done by professional medical equipment before. However, such schemes usually result in bad performance in comparison to professional medical equipment. In this paper, we propose DeepFilter, a deep learning-based fine-grained breathing rate monitoring algorithm that works on smartphone and achieves professional-level accuracy. DeepFilter is a bidirectional recurrent neural network (RNN) stacked with convolutional layers and speeded up by batch normalization. Moreover, we collect 16.17 GB breathing sound recording data of 248 hours from 109 and another 10 volunteers to train and test our model, respectively. The results show a reasonably good accuracy of breathing rate monitoring