Data Aggregation Mechanism Research Articles

Data Aggregation Model of Enterprise Financial Supervision Based On BP Neural Network

Aiming at the problems of high damping coefficient and long aggregation response time of traditional enterprise financial supervision data aggregation model, an enterprise financial supervision data aggregation model based on BP neural network is designed. In order to effectively gather enterprise financial supervision data, an enterprise model of financial supervision data is proposed based on concurrent workflow. Using the basic financial data, construct the tree workflow, use the financial data summary server tree, construct the multi-level data aggregation mechanism, compress the enterprise financial supervision data based on BP neural network, and realize the model through the tree workflow and multi-level data aggregation mechanism. The experimental results show that the aggregation damping coefficient and response time of the design model are significantly lower than those of the control group, which can solve the problems of high damping coefficient and long aggregation response time of the traditional enterprise financial supervision data aggregation model.

A Novel Data Aggregation Mechanism using Reinforcement Learning for Cluster Heads in Wireless Multimedia Sensor Networks

Wireless multimedia sensor networks (WMSNs) are getting used in numerous applications nowadays. Many robust energy-efficient routing protocols have been proposed to handle multimedia traffic-intensive data like images and videos in WMSNs. It is a common trend in the literature to facilitate a WMSN with numerous sinks allowing cluster heads (CHs) to distribute the collected data to the adjacent sink node for delivery overhead mitigation. Using multiple sink nodes can be expensive and may incur high complexity in routing. There are many single-sink cluster-based routing protocols for WMSNs that lack in introducing optimal path selection among CHs. As a result, they suffer from transmission and queueing delay due to high data volume. To address these two conflicting issues, we propose a data aggregation mechanism based on reinforcement learning (RL) for CHs (RL-CH) in WMSN. The proposed method can be integrated to any of the cluster-based routing protocol for intelligent data transmission to sink node via cooperative CHs. Proposed RL-CH protocol performs better in terms of energy-efficiency, end-to-end delay, packet delivery ratio, and network lifetime. It gains 17.6% decrease in average end-to-end delay and 7.7% increase in PDR along with a network lifetime increased to 3.2% compared to the evolutionary game-based routing protocol which has been used as baseline.

βDSC2DAM: beta-dominating set centered Cluster-Based Data Aggregation mechanism for the Internet of Things

βDSC2DAM: beta-dominating set centered Cluster-Based Data Aggregation mechanism for the Internet of Things

An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain

The Internet of Things (IoT) is getting important and interconnected technologies of the world, consisting of sensor devices. The internet is smoothly changing from an internet of people towards an Internet of Things, which permits various objects to connect to another wirelessly. The energy consumption of the IoT routing protocol can affect the network life span. In addition, the high volume of data produced by IoT will result in transmission collision, security issues, and energy dissipation due to increased data redundancy because tiny sensors are usually hard to recharge after they are deployed. Generally, to save energy, data aggregation reduces data redundancy at each node by turning some nodes into sleep mode and others into wake mode. Therefore, it is important to group the nodes with high data similarity using the fuzzy matrix. Then, the data received from the member nodes at the Cluster Head (CH) are analyzed using a fuzzy similarity matrix for clustering. In the next step, after clustering, some nodes are chosen from all groups as redundant nodes. The sleep scheduling mechanism is then applied to reduce data redundancy, network traffic jamming, and transmission costs. We have proposed an Energy-Efficient Data Aggregation Mechanism (EEDAM) secured by blockchain, which uses a data aggregation mechanism at the cluster level to save energy. As edge computing is used to provide on-demand trusted services to IoT with minimum delay, blockchain is integrated inside a cloud server, so the edge is validated by the blockchain to provide secure services to IoT. Finally, we performed simulations to calculate the performance of the proposed mechanism and compared it with the conventional energy-efficient algorithms. The simulation results show that the proposed structural design can successfully reduce the amount of data, provide proper security to the IoT, and extend the wireless sensor network (WSN).

An Energy-Efficient Mobile Agent-Based Data Aggregation Scheme for Wireless Body Area Networks

Due to the advancement in wireless technology and miniaturization, Wireless Body Area Networks (WBANs) have gained enormous popularity, having various applications, especially in the healthcare sector. WBANs are intrinsically resource-constrained; therefore, they have specific design and development requirements. One such highly desirable requirement is an energy-efficient and reliable Data Aggregation (DA) mechanism for WBANs. The efficient and reliable DA may ultimately push the network to operate without much human intervention and further extend the network lifetime. The conventional client-server DA paradigm becomes unsuitable and inefficient for WBANs when a large amount of data is generated in the network. Similarly, in most of the healthcare applications (patient's critical conditions), it is highly important and required to send data as soon as possible; therefore, reliable data aggregation in WBANs is of great concern. To tackle the shortcomings of the client-server DA paradigm, the Mobile Agent-Based mechanism proved to be a more workable solution. In a Mobile Agent-Based mechanism, a task-specific mobile agent (code) traverses to the intended sources to gather data. These mobile agents travel on a predefined path called itinerary; however, planning a suitable and reliable itinerary for a mobile agent is also a challenging issue in WBANs. This paper presents a new Mobile Agent-Based DA scheme for WBANs, which is energy-efficient and reliable. Firstly, in the proposed scheme, the network is divided into clusters, and cluster-heads are selected. Secondly, a mobile agent is generated from the base station to collect the required data from cluster heads. In the case, if any fault occurs in the existing itinerary, an alternate itinerary is planned in real-time without compromising the network performance. In our simulation-based validation, we have found that the proposed system delivers significantly improved fault-tolerance and reliability with energy-efficiency and extended network lifetime in WBANs.

An Efficient Information Aggregation Scheme in Internet of Things: Multi Agent based Approach

The use of wireless sensor technology in various Internet of Things (IoT) applications is growing rapidly. With the exponential increase of smart devices and their applications, collecting and analyzing data is gradually becoming one of the most difficult tasks. As sensor nodes are powered by batteries, energy efficiency is essential. To that intention, before passing the final data to the central station, a sensor node should reduce redundancies in the received data from neighbor nodes. There will be some redundancy in the data because different sensor nodes typically notice the same phenomenon. Data aggregation is one of the most important approaches for eliminating data redundancy and improving energy efficiency, as well as extending the life time of wireless sensor networks. Furthermore, the effective data aggregation technique might help to reduce network traffic. In this paper we have proposed cluster based data aggregation using intelligent agents. The performance of the proposed scheme is compared with Centralized Data Aggregation (CDA) mechanism in IoT.

Data Aggregation Mechanisms on the Internet of Things: A Systematic Literature Review

Nowadays, the Internet of Things (IoT) has gained considerable attention in different academic and real-world research domains. Due to the data-driven nature of IoT, the data aggregation process in such systems is significantly challenging. The main goal of data aggregation mechanisms is to achieve high Quality of Services (QoS), including optimal data transmission delay, reliability, and energy consumption. Despite various types of available review papers on this particular topic, we feel that the existing literature does not satisfy the requirements of being up to date and comprehensive. This paper presents a systematic literature review for data aggregation mechanisms on IoT that illustrates the critical challenges in the context design issues. Further, the data aggregation mechanisms are divided into two main categories: client-server-based and mobile agent-based ones. The client-server-based data aggregation mechanisms are also presented in three groups: cluster-based, tree-based, and centralized methods. Finally, we outline possible future research trends in data aggregation on IoT.

Marginal and average weight-enabled data aggregation mechanism for the resource-constrained networks

In Wireless Sensor Networks (WSNs), data redundancy is a challenging issue that not only introduces network congestion but also consumes a considerable amount of sensor node resources. Data redundancy occurs due to the spatial and temporal correlation among the data gathered by the neighboring nodes. Data aggregation is a prominent technique that performs in-network filtering of the redundant data and accelerates the knowledge extraction by eliminating the correlated data. However, most of the data aggregation techniques have lower accuracy as they do not cater for erroneous data from faulty nodes and pose an open research challenge. To address this challenge, we have proposed a novel, lightweight, and energy-efficient function-based data aggregation approach for a cluster-based hierarchical WSN. Our proposed approach works at two levels, i.e., at the node level and at the cluster head level. At the node level, the data aggregation is performed using Exponential Moving Average (EMA) and a threshold-based mechanism is adopted to detect any outliers for improving the accuracy of aggregated data. At the cluster head level, we have employed a modified version of Euclidean distance function to provide highly-refined aggregated data to the base station. Our experimental results show that our approach reduces the communication cost, transmission cost, energy consumption at the nodes and cluster heads, and delivers highly-refined and fused data to the base station.

An Efficient Approach for Big Data Aggregation Mechanism in Heterogeneous Wireless Connected Sensor Networks

Recently and due to the impressive growth in the amounts of transmitted data over the heterogeneous sensor networks and the emerged related technologies especially the Internet of Things in which the number of the connected devices and the data consumption are remarkably growing, big data has emerged as a widely recognized trend and is increasingly being talked about. The term big data is not only about the volume of data, but also refers to the high speed of transmission and the wide variety of information that is difficult to collect, store and process using the available classical technologies. Although the generated data by the individual sensors may not appear to be significant, all the data generated through the many sensors in the connected sensor networks are able to produce large volumes of data. Big data management imposes additional constraints on the wireless sensor networks and especially on the data aggregation process, which represents one of the essential paradigms in wireless sensor networks. Data aggregation process can represent a solution to the problem of big data by allowing data from different sources to be combined to eliminate the redundant ones and consequently reduce the amounts of data and the consumption of the available resources in the network. The main objective of this work is to propose a new approach for supporting big data in the data aggregation process in heterogeneous wireless sensor networks. The proposed approach aims to reduce the data aggregation cost in terms of energy consumption by balancing the data loads on the heterogeneous nodes. The proposal is improved by integrating the feedback control closed loop to reinforce the balance of the data aggregation load on the nodes, maintaining therefore an optimal delay and aggregation time.

A consensus-based cooperative Spectrum sensing technique for CR-VANET

Internet of Things (IoT) is expected to empower all aspects of the Intelligent Transportation System (ITS), the main goal of which is to improve transportation safety. However, due to high demands by the increasing number of associated vehicles, the allocated bandwidth of ITS is inadequate. Cognitive Radio (CR) technology can be used as a solution for this high demand level. In CR, the pre-allocated spectrum bands are sensed to find the existing holes, caused by the absence of primary users. Cooperative spectrum sensing is an efficient tool for the detection of free spectrum bands that increase the probability of correct detection. In this paper, a distributed cooperative spectrum sensing technique is proposed using the consensus algorithm which is a distributed data aggregation mechanism whereby each vehicle combines the results received from its neighbors’ spectrum sensing. The combined results are repeatedly shared and combined such that all vehicles reach the same results. In vehicular networks, due to the vehicle’s movement, the number of its neighbors changes dynamically. Therefore, considering the vehicle’s mobility is essential in the spectrum sensing process. The consensus algorithm which is a data aggregation method is used to increase the probability of correct detection, and thus to reduce the number of collisions in the spectrum acquisition process. In our method, each vehicle accurately selects a number of its neighbors dynamically, and involves them in the decision-making process. Moreover, separate weights determined based on the entropy of their information are assigned to the sensing results of the selected neighbors. In this way, even if the vehicles are affected by fading or shadowing, they can make more accurate decisions using the sensing results received from other vehicles. The simulation results of the proposed method show that it increases the probability of correctly detecting free spectrum bands as well as convergence speed.

Wavelet-Based Least Common Ancestor Algorithm for Aggregate Query Processing in Energy Aware Wireless Sensor Network

Wireless sensor network (WSN) is developed as a network of sensors, which engage in sensing and transmitting the data to the sink node. The constraints, such as energy, memory, and bandwidth insist the researchers to develop an efficient method for data transmission in WSN. Accordingly, this paper introduces a data aggregation mechanism based on query processing, Wavelet-based Least Common Ancestor-Sliding window (WLCA-SW). The energy-loss and memory-crisis is well addressed using the proposed WLCA-SW through the successive steps of query processing, duplicate detection, data compression using the wavelet transformation, and data aggregation. The proposed WLCA-SWA is developed with the integration of the weighed sliding window and Least Common Ancestor (LCA), which enables the energy-aware aggregate query processing and de-duplication such that the duplicate records are detected potentially prior to the communication of the sensed data to the sink node. It is prominent that the weighed sliding window is the extension of the existing time-based sliding windows. The effectiveness of the proposed aggregate processing approach is evaluated based on the metrics, such as number of alive nodes, data reduction rate, data-loss percentage, and residual energy, which is found to be 33, 85%, 8.222%, and 0.0610 J at the end of 1000 rounds using 150 nodes for analysis. Moreover, the proposed method has the minimum aggregation error of 0.03, when the analysis is performed using 50 nodes.

A Byzantine Sensing Network Based on Majority-Consensus Data Aggregation Mechanism.

In the current Internet of Things era, digital devices form complex interconnections. The statuses of objects of interest are monitored using sensors, and distributed wireless sensor networks are formed from numerous sensor nodes. Many Byzantine fault tolerance mechanisms in wireless sensor networks (WSNs) were proposed from Byzantine agreement which even with a few faulty nodes in a sensor network, most healthy nodes can reach a consensus, perform data transmission tasks, and maintain network operation. In this study, this mechanism was utilized together with the majority function technique; in particular, the proposed method uses original sensor signals to define a threshold to assert a binary value of one or zero, thereby performing data judgment and aggregation. This approach reduces node energy consumption and enables the nodes to quickly reach a consensus. Moreover, the operating performance of the network can be maintained even when problems such as node failure and faults occur within the fault tolerance range. Compared with existing algorithms, the proposed data aggregation mechanism exhibits a better network life cycle and can effectively extend the flexibility of network operations.

Intelligent Data Aggregation Framework for Resource Constrained Remote Internet of Things Applications

Internet of Things (IoT) is a technology that can connect everything to the Internet. IoT can be used in a wide range of applications which includes remote applications like Underwater networks. Remote applications involve the deployment of several low-power, low-cost interconnected sensor nodes in the specific region. With a massive amount of devices connected to the IoT and the considerable amount of data associated with it, there remain concerns about data management. Also, the amount of data generated in an extensive IoT-based remote sensing network is usually enormous for the servers to process, and many times data generated are redundant. Hence there is a need for designing a framework that addresses both aggregations of data and security-related issues at various aggregation points. In this paper, we are proposing an intelligent data aggregation mechanism for IoT-based remote sensing networks. This method avoids redundant data transmission by adapting spatial aggregation techniques. The proposed method was tested through simulations, and the results prove the efficiency of the proposed work.

IM-LDP: Incentive Mechanism for Mobile Crowd-Sensing Based on Local Differential Privacy

In recent years, the rapid development of embedded technology has given rise to mobile crowd sensing (MCS) systems to outsource sensing tasks to the public crowd equipped with various mobile devices. Sensing data often involves the workers’ privacy, but overprotection of workers’ data leads to the decrease of the data accuracy. Therefore, a crucial issue in such systems is how to balance workers’ data privacy and data aggregation accuracy. The local differential privacy guarantees the data privacy by returning the privacy budget to workers. However, existing works only considered the workers’ reputation as the weight of the aggregation result, but did not correlate with the rewards that workers deserve, which restrained workers’ incentive of participation. Different from these works, by quantifying workers’ reputation, we propose IM-LDP, an incentive mechanism for MCS based on local differential privacy, which includes four mechanisms of incentive, reputation, data perturbation and data aggregation. Specifically, incentive mechanisms are able to select workers who can provide more accurate data and compensate themselves for their privacy costs. The reputation mechanism quantifies the workers’ reputation to improve their payments. The data perturbation mechanism ensures the tradeoff between the data privacy and aggregation accuracy, and the data aggregation mechanism generates highly accurate aggregation results. We evaluate the proposed IM-LDP through theoretical analysis and extensive experiments.

A NOVEL FRAMEWORK FOR PRIVACY-PRESERVING USING DATA AGGREGATION IN INTERNET OF THINGS (IOT)

Today with the development of Internet of Things (IoT) technology its security and privacy becomes very much essential because this technology provide the facility for accessing the devices or resources from anywhere and anytime. For providing the security and privacy to IoT various technology has been developed but in this we propose data aggregation mechanism in which it uses one-time pad and symmetric key to make our data secure and preserve the privacy of it. This mechanism takes less computation time, improve processing speed and also require less cost in implementation. The implementation of proposed approach is done in widely used Python technology which produce better results. In last the proposed mechanism provide more security and preserve our data.

Double-Blockchain Assisted Secure and Anonymous Data Aggregation for Fog-Enabled Smart Grid

As a future energy system, the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable services. However, this efficient and reliable service relies on collecting and analyzing users’ electricity consumption data frequently, which induces various security and privacy threats. To address these challenges, we propose a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named DA-SADA. Specifically, we design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain, which provides strong support for achieving efficient and secure data collection in smart grids. Subsequently, we develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption, batch aggregation signature and anonymous authentication. In particular, the system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data aggregation. Finally, the superiority of the proposed scheme is illustrated by a series of security and computation cost analyses.

Privacy-Functionality Trade-Off: A Privacy-Preserving Multi-Channel Smart Metering System

While smart meters can provide households with more autonomy regarding their energy consumption, they can also be a significant intrusion into the household’s privacy. There is abundant research implementing protection methods for different aspects (e.g., noise-adding and data aggregation, data down-sampling); while the private data are protected as sensitive information is hidden, some of the compulsory functions such as Time-of-use (TOU) billing or value-added services are sacrificed. Moreover, some methods, such as rechargeable batteries and homomorphic encryption, require an expensive energy storage system or central processor with high computation ability, which is unrealistic for mass roll-out. In this paper, we propose a privacy-preserving smart metering system which is a combination of existing data aggregation and data down-sampling mechanisms. The system takes an angle based on the ethical concerns about privacy and it implements a hybrid privacy-utility trade-off strategy, without sacrificing functionality. In the proposed system, the smart meter plays the role of assistant processor rather than information sender/receiver, and it enables three communication channels to transmit different temporal resolution data to protect privacy and allow freedom of choice: high frequency feed-level/substation-level data are adopted for grid operation and management purposes, low frequency household-level data are used for billing, and a privacy-preserving valued-add service channel to provide third party (TP) services. In the end of the paper, the privacy performance is evaluated to examine whether the proposed system satisfies the privacy and functionality requirements.

IoTP an Efficient Privacy Preserving Scheme for Internet of Things Environment

Many emerging fields are adopting Internet of Things technologies to incorporate smartness in respective areas. Several IoT based application area produces large volumes of real time data. Data aggregated through sensor nodes may contain highly sensitive information. An effective and successful IoT system must protect sensitive data from revealing to unauthorized persons. In this article, the authors present an efficient privacy-preserving mechanism called Internet of Things privacy (IoTp). The research simulates and analyzes the effectiveness of the proposed data aggregation and data access mechanism for a typical IoT system. Proposed IoTp scheme ensures privacy at data collection, data store and data access phases of the IoT system. The authors have compared proposed work with existing model. Results show that IoTp scheme is efficient and lightweight mechanism for data collection and data access. It is suitable for the resource constrained IoT ecosystems.

An optimal network coding based backpressure routing approach for massive IoT network

In order to mitigate the power consumption issue for the sensor’s nodes, an efficient power optimized routing protocol is needed. Therefore, this paper proposes a network coding backpressure routing (NCBPR) routing scheme for a large-scale Internet of Things (IoT) networks, which exploits the backpressure algorithm in order to divert packets flow. In the network, the packets are flowing from the highly congested node to low congested node, which helps to balance the load and optimized the fair use of the battery power of all the participating nodes. It divides the network into the small clusters, where the selection of clusters head depends upon an additional parameter of battery power apart from other optimum path parameters. It also employs an efficient data aggregation mechanism, which improves the throughput of the network by eliminating redundant packets. The network has been designed by considering 300 nodes in a network and the results have been drawn in terms of network throughput, packet delivery ratio and energy consumption. The results are presented in comparison with conventional well-known information-fusion-based role assignment (InFRA) and data routing for in-network aggregation (DRINA) routing schemes. The results prove that the proposed NCBPR scheme delivers significant improvement, such as throughput which increased by 21.38 and 12.13%, packet delivery ratio improved by 24.73 and 11.38%, and sensors node’s energy consumption is decreased by 61.46 and 44.35% as compared to conventional InFRA and DRINA schemes, respectively.

Data Aggregation Mechanisms in the Internet of Things: A Study, Qualitative and Quantitative Analysis

Data Aggregation Mechanisms in the Internet of Things: A Study, Qualitative and Quantitative Analysis