Approximate data aggregation in sensor equipped IoT networks

Sketch is a compact data structure used to summarize data streams. It is widely used in the measurement of network traffic, and its accuracy is higher than traditional methods. Currently, there are some typical sketches: Count-Min Sketch, CU Sketch, and Count Sketch. According to the characteristics of network traffic, we propose a new sketch framework called Self-Adaption Sketch, which is combined Sketch with Bloom Filter. In the framework, the sketch is created dynamically and the memory space is adjusted timely according to the network traffic by using the concept carrying. Our experiment results showed that the space utilization and accuracy are significantly improved while the throughput of self-adaption sketch is maintained at a relatively good level. K E Y W O R D S traffic measurement, data stream technology, sketch, bloom filter, space utilization 1 INTRODUCTION 1.1 Background and motivation Network measurement and analysis are essential for network management and they help network administrators to understand the status of the network better and make the right decisions. They help to detect anomalies and attacks in the network. To provide a secure and well-performing network for the continually transforming cyberspace, Internet operators need to monitor and analyze the network status in real time. 1 In recent years, network traffic measurement has been widely used in network accounting, traffic engineering, network security, and other fields. However, there are certain difficulties in the measurement, which are mainly due to the continuous and massive network traffic. At present, there are two main types of high-speed network traffic measurement schemes, one is the sampling technology, and the other is the data stream technology. Sampling technology refers to the collection and processing of some representative network traffic. This method introduces the small system load with a relatively large error. Typical sampling methods are group sampling (such as BSS, 2 EBS 3) and stream sampling (such as ANF 4). Collapsed Gibbs sampling is the most commonly used algorithm that samples latent topics for a word occurrence (token) by integrating out the Dirichlet priors. 5 The sampling technique has been widely used, such as quantile calculation, data collection, and top-k query. 6 Data stream technology processes

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“…Therefore, the error of sketches are independent of each other. The error of sketch part in SA Sketch is shown in Equation (6).…”

Section: Ta B L E 3 Comparison Of Different Space and Time Requiremenmentioning

confidence: 99%

“…Collapsed Gibbs sampling is the most commonly used algorithm that samples latent topics for a word occurrence (token) by integrating out the Dirichlet priors 5 . The sampling technique has been widely used, such as quantile calculation, data collection, and top‐k query 6 . Data stream technology processes all network traffic data.…”

Section: Introductionmentioning

confidence: 99%

SA Sketch: A self‐adaption sketch framework for high‐speed network

Zhu

Yuan

Zhang

et al. 2020

Concurrency and Computation

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“…Data aggregation is an essential process in wireless routing for collecting the data from various sources in the network [15]. It aggregates the sensor's data, which removes the data redundancy and The Figure 2 shows the data collection and aggregation process using CS theory.…”

Section: Data Aggregationmentioning

confidence: 99%

“…Also, it can be extended the network lifetime [14]. These techniques are suitable for some applications like temperature monitoring and gas leakage monitoring [15]. The existing data aggregation techniques are clustering and region-based routing to reduce duplicate data transmission across the network [16].In IoT Routing, we need a suitable technique to aggregate the data among the network nodes.…”

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confidence: 99%

Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things

Sankar¹,

Balasubramaniyam²,

Luhach

et al. 2019

Sensors

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Energy conservation is one of the most critical problems in Internet of Things (IoT). It can be achieved in several ways, one of which is to select the optimal route for data transfer. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for IoT. The RPL changes its path frequently while transmitting the data from source to the destination, due to high data traffic in dense networks. Hence, it creates data traffic across the nodes in the networks. To solve this issue, we propose Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) for IoT. It has two processes, namely parent selection and data aggregation. The process of parent selection uses routing metric residual energy (RER) to choose the best possible parent for data transmission. The data aggregation process uses the compressed sensing (CS) theory in the parent node to combine data packets from the child nodes. Finally, the aggregated data transmits from a downward parent to the sink. The sink node collects all the aggregated data and it performs the reconstruction operation to get the original data of the participant node. The simulation is carried out using the Contiki COOJA simulator. EDADA-RPL’s performance is compared to RPL and LA-RPL. The EDADA-RPL offers good performance in terms of network lifetime, delay, and packet delivery ratio.

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“…In recent years, with the development of artificial intelligence and Internet of ings technology [1][2][3][4][5], the concept and construction of smart city are also constantly breaking through. As an important part of smart city field [6,7], object recognition based on computer vision has attracted much attention.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graph Representation Fusion Framework for Fine‐Grained Object Recognition in Smart Cities

Tian

Zhang

et al. 2021

Complexity

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Autonomous object detection powered by cutting-edge artificial intelligent techniques has been an essential component for sustaining complex smart city systems. Fine-grained image classification focuses on recognizing subcategories of specific levels of images. As a result of the high similarity between images in the same category and the high dissimilarity in the same subcategories, it has always been a challenging problem in computer vision. Traditional approaches usually rely on exploring only the visual information in images. Therefore, this paper proposes a novel Knowledge Graph Representation Fusion (KGRF) framework to introduce prior knowledge into fine-grained image classification task. Specifically, the Graph Attention Network (GAT) is employed to learn the knowledge representation from the constructed knowledge graph modeling the categories-subcategories and subcategories-attributes associations. By introducing the Multimodal Compact Bilinear (MCB) module, the framework can fully integrate the knowledge representation and visual features for learning the high-level image features. Extensive experiments on the Caltech-UCSD Birds-200-2011 dataset verify the superiority of our proposed framework over several existing state-of-the-art methods.

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Approximate data aggregation in sensor equipped IoT networks

Cited by 29 publications

References 29 publications

SA Sketch: A self‐adaption sketch framework for high‐speed network

SA Sketch: A self‐adaption sketch framework for high‐speed network

Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things

Knowledge Graph Representation Fusion Framework for Fine‐Grained Object Recognition in Smart Cities

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