CONSERVATIVE ESTIMATING FUNCTION IN THE NONLINEAR REGRESSION MODEL WITH AGGREGATED DATA

The purpose of this paper is to study the theory of conservative estimating functions in nonlinear regression model with aggregated data. In this model, a quasi-score function with aggregated data is defined. When this function happens to be conservative, it is projection of the true score function onto a class of estimation functions. By constructing, the potential function for the projected score with aggregated data is obtained, which have some properties of log-likelihood function.

Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning

The Internet of things(IoT)is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring,surveillance,and healthcare.To address the limitations imposed by inadequate resources,energy,and network scalability,this type of network relies heavily on data aggregation and clustering algorithms.Although various conventional studies have aimed to enhance the lifespan of a network through robust systems,they do not always provide optimal efficiency for real-time applications.This paper presents an approach based on state-of-the-art machine-learning methods.In this study,we employed a novel approach that combines an extended version of principal component analysis(PCA)and a reinforcement learning algorithm to achieve efficient clustering and data reduction.The primary objectives of this study are to enhance the service life of a network,reduce energy usage,and improve data aggregation efficiency.We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring.Our proposed approach(PQL)was compared to previous studies that utilized adaptive Q-learning(AQL)and regional energy-aware clustering(REAC).Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.

Shoot Biomass Assessments of the Marine Phanerogam Zostera marina for Two Methods of Data Gathering

In order to compare to data gathering methods for shoot biomass assessments of Zostera marina, we compare two allometric models each one representing a data gathering method, one at leaf level and the other in aggregated form. The first allometric model presented leaf dry weight in terms of leaf length as . The second model is expressed as a several-variables version of the allometric Equation (1) dry weight of each leaf in a given shoot can be considered to be a random variable therefore shoot biomass ws can be represented in the form Both models presented similar determination coefficients values of 0.85 and 0.87 respectively. We found no significant differences between parameters α (p = 0.11) and β (p = 0.50) fitted for each model, showing that both equations conduced to the same result. Moreover, both fitted models presented high Concordance Correlation Coefficients of reproducibility () (0.92 and 0.91). We concluded that for shoot weight assessments if larger samples and faster data processing is required then should model of Equation (2) be used. On the other hand, we proposed model of Equation (1) if data at leaf level is required for other endeavors.

An efficient data aggregation scheme with local differential privacy in smart grid

By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the smart grid collect the users' power usage data on a regular basis and upload it to the control center to complete the smart grid data acquisition. The control center can evaluate the supply and demand of the power grid through aggregated data from users and then dynamically adjust the power supply and price, etc. However, since the grid data collected from users may disclose the user's electricity usage habits and daily activities, privacy concern has become a critical issue in smart grid data aggregation. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring a trusted third party.

Data Aggregation Scheduling with Guaranteed Lifetime and Efficient Latency in Wireless Sensor Networks

In scenarios of real-time data collection of long-term deployed Wireless Sensor Networks （WSNs）, low-latency data collection with long net- work lifetime becomes a key issue. In this paper, we present a data aggregation scheduling with guaran- teed lifetime and efficient latency in WSNs. We first Construct a Guaranteed Lifetime Mininmm Ra- dius Data Aggregation Tree （GLMRDAT） which is conducive to reduce scheduling latency while pro- viding a guaranteed network lifetime, and then de-sign a Greedy Scheduling algorithM （GSM） based on finding the nmzximum independent set in conflict graph to schedule he transmission of nodes in the aggregation tree. Finally, simulations show that our proposed approach not only outperfonm the state-of-the-art solutions in terms of schedule latency, but also provides longer and guaranteed network lifetilre.

A Survey on the Privacy-Preserving Data Aggregation in Wireless Sensor Networks

Wireless sensor networks(WSNs)consist of a great deal of sensor nodes with limited power,computation,storage,sensing and communication capabilities.Data aggregation is a very important technique,which is designed to substantially reduce the communication overhead and energy expenditure of sensor node during the process of data collection in a WSNs.However,privacy-preservation is more challenging especially in data aggregation,where the aggregators need to perform some aggregation operations on sensing data it received.We present a state-of-the art survey of privacy-preserving data aggregation in WSNs.At first,we classify the existing privacy-preserving data aggregation schemes into different categories by the core privacy-preserving techniques used in each scheme.And then compare and contrast different algorithms on the basis of performance measures such as the privacy protection ability,communication consumption,power consumption and data accuracy etc.Furthermore,based on the existing work,we also discuss a number of open issues which may intrigue the interest of researchers for future work.

An Efficient and Privacy-Preserving Data Aggregation Scheme Supporting Arbitrary Statistical Functions in IoT

The Internet of Things(IoT)has profoundly impacted our lives and has greatly revolutionized our lifestyle.The terminal devices in an IoT data aggregation application sense real-time data for the remote cloud server to achieve intelligent decisions.However,the high frequency of collecting user data will raise people concerns about personal privacy.In recent years,many privacy-preserving data aggregation schemes have been proposed.Unfortunately,most existing schemes cannot support either arbitrary aggregation functions,or dynamic user group management,or fault tolerance.In this paper,we propose an efficient and privacy-preserving data aggregation scheme.In the scheme,we design a lightweight encryption method to protect the user privacy by using a ring topology and a random location sequence.On this basis,the proposed scheme supports not only arbitrary aggregation functions,but also flexible dynamic user management.Furthermore,the scheme achieves faulttolerant capabilities by utilizing a future data buffering mechanism.Security analysis reveals that the scheme can achieve the desired security properties,and experimental evaluation results show the scheme's efficiency in terms of computational and communication overhead.

A Differentially Private Data Aggregation Method Based on Worker Partition and Location Obfuscation for Mobile Crowdsensing

With the popularity of sensor-rich mobile devices,mobile crowdsensing(MCS)has emerged as an effective method for data collection and processing.However,MCS platform usually need workers’precise locations for optimal task execution and collect sensing data from workers,which raises severe concerns of privacy leakage.Trying to preserve workers’location and sensing data from the untrusted MCS platform,a differentially private data aggregation method based on worker partition and location obfuscation(DP-DAWL method)is proposed in the paper.DP-DAWL method firstly use an improved K-means algorithm to divide workers into groups and assign different privacy budget to the group according to group size(the number of workers).Then each worker’s location is obfuscated and his/her sensing data is perturbed by adding Laplace noise before uploading to the platform.In the stage of data aggregation,DP-DAWL method adopts an improved Kalman filter algorithm to filter out the added noise(including both added noise of sensing data and the system noise in the sensing process).Through using optimal estimation of noisy aggregated sensing data,the platform can finally gain better utility of aggregated data while preserving workers’privacy.Extensive experiments on the synthetic datasets demonstrate the effectiveness of the proposed method.

Secure Data Aggregation Approach Based on Monitoring in Wireless Sensor Networks

In order to avoid internal attacks during data aggregation in wireless sensor networks, a grid-based network architecture fit for monitoring is designed and the algorithms for network division, initialization and grid tree construction are presented. The characteristics of on-off attacks are first studied and monitoring mechanisms are then designed for sensor nodes. A Fast Detection and Slow Recovery (FDSR) algorithm is proposed to prevent on-off attacks by observing the behaviors of the nodes and computing reputations. A recovery mechanism is designed to isolate malicious nodes by identifying the new roles of nodes and updating the grid tree. In the experiments, some situations of on-off attacks are simulated and the results are compared with other approaches. The experimental results indicate that our approach can detect malicious nodes effectively and guarantee secure data aggregation with acceptable energy consumption.

A Path-Based Approach for Data Aggregation in Grid-Based Wireless Sensor Networks

Sensor nodes in a wireless sensor network （WSN） are typically powered by batteries, thus the energy is constrained. It is our design goal to efficiently utilize the energy of each sensor node to extend its lifetime, so as to prolong the lifetime of the whole WSN. In this paper, we propose a path-based data aggregation scheme （PBDAS） for grid-based wireless sensor networks. In order to extend the lifetime of a WSN, we construct a grid infrastructure by partitioning the whole sensor field into a grid of cells. Each cell has a head responsible for aggregating its own data with the data sensed by the others in the same cell and then transmitting out. In order to efficiently and rapidly transmit the data to the base station （BS）, we link each cell head to form a chain. Each cell head on the chain takes turn becoming the chain leader responsible for transmitting data to the BS. Aggregated data moves from head to head along the chain, and finally the chain leader transmits to the BS. In PBDAS, only the cell heads need to transmit data toward the BS. Therefore, the data transmissions to the BS substantially decrease. Besides, the cell heads and chain leader are designated in turn according to the energy level so that the energy depletion of nodes is evenly distributed. Simulation results show that the proposed PBDAS extends the lifetime of sensor nodes, so as to make the lifetime of the whole network longer.

Efficient Packet Scheduling Technique for Data Merging in Wireless Sensor Networks

Wireless Sensor Networks(WSNs) has become a popular research topic due to its resource constraints. Energy consumption and transmission delay is crucial requirement to be handled to enhance the popularity of WSNs. In order to overcome these issues, we have proposed an Efficient Packet Scheduling Technique for Data Merging in WSNs. Packet scheduling is done by using three levels of priority queue and to reduce the transmission delay. Real-time data packets are placed in high priority queue and Non real-time data packets based on local or remote data are placed on other queues. In this paper, we have used Time Division Multiple Access(TDMA) scheme to efficiently determine the priority of the packet at each level and transmit the data packets from lower level to higher level through intermediate nodes. To reduce the number of transmission, efficient data merge technique is used to merge the data packet in intermediate nodes which has same destination node. Data merge utilize the maximum packet size by appending the merged packets with received packets till the maximum packet size or maximum waiting time is reached. Real-time data packets are directly forwarded to the next node without applying data merge. The performance is evaluated under various metrics like packet delivery ratio, packet drop, energy consumption and delay based on changing the number of nodes and transmission rate. Our results show significant reduction in various performance metrics.

Distributed Weighted Data Aggregation Algorithm in End-to-Edge Communication Networks Based on Multi-armed Bandit

As a combination of edge computing and artificial intelligence,edge intelligence has become a promising technique and provided its users with a series of fast,precise,and customized services.In edge intelligence,when learning agents are deployed on the edge side,the data aggregation from the end side to the designated edge devices is an important research topic.Considering the various importance of end devices,this paper studies the weighted data aggregation problem in a single hop end-to-edge communication network.Firstly,to make sure all the end devices with various weights are fairly treated in data aggregation,a distributed end-to-edge cooperative scheme is proposed.Then,to handle the massive contention on the wireless channel caused by end devices,a multi-armed bandit(MAB)algorithm is designed to help the end devices find their most appropriate update rates.Diffe-rent from the traditional data aggregation works,combining the MAB enables our algorithm a higher efficiency in data aggregation.With a theoretical analysis,we show that the efficiency of our algorithm is asymptotically optimal.Comparative experiments with previous works are also conducted to show the strength of our algorithm.

A Trusted NUMFabric Algorithm for Congestion Price Calculation at the Internet-of-Things Datacenter

The important issues of network TCP congestion control are how to compute the link price according to the link status and regulate the data sending rate based on link congestion pricing feedback information.However,it is difficult to predict the congestion state of the link-end accurately at the source.In this paper,we presented an improved NUMFabric algorithm for calculating the overall congestion price.In the proposed scheme,the whole network structure had been obtained by the central control server in the Software Defined Network,and a kind of dual-hierarchy algorithm for calculating overall network congestion price had been demonstrated.In this scheme,the first hierarchy algorithm was set up in a central control server like Opendaylight and the guiding parameter B is obtained based on the intelligent data of global link state information.Based on the historical data,the congestion state of the network and the guiding parameter B is accurately predicted by the machine learning algorithm.The second hierarchy algorithm was installed in the Openflow link and the link price was calculated based on guiding parameter B given by the first algorithm.We evaluate this evolved NUMFabric algorithm in NS3,which demonstrated that the proposed NUMFabric algorithm could efficiently increase the link bandwidth utilization of cloud computing IoT datacenters.

NLDA non-linear regression model for preserving data privacy in wireless sensor networks

Recently,the application of Wireless Sensor Networks(WSNs)has been increasing rapidly.It requires privacy preserving data aggregation protocols to secure the data from compromises.Preserving privacy of the sensor data is a challenging task.This paper presents a non-linear regression-based data aggregation protocol for preserving privacy of the sensor data.The proposed protocol uses non-linear regression functions to represent the sensor data collected from the sensor nodes.Instead of sending the complete data to the cluster head,the sensor nodes only send the coefficients of the non-linear function.This will reduce the communication overhead of the network.The data aggregation is performed on the masked coefficients and the sink node is able to retrieve the approximated results over the aggregated data.The analysis of experiment results shows that the proposed protocol is able to minimize communication overhead,enhance data aggregation accuracy,and preserve data privacy.

Research on stateful public key based secure data aggregation model for wireless sensor networks

Data aggregation technology reduces traffic overhead of wireless sensor network and extends effective working time of the network,yet continued operation of wireless sensor networks increases the probability of aggregation nodes being captured and probability of aggregated data being tampered.Thus it will seriously affect the security performance of the network. For network security issues,a stateful public key based SDAM( secure data aggregation model) is proposed for wireless sensor networks( WSNs),which employs a new stateful public key encryption to provide efficient end-to-end security. Moreover,the security aggregation model will not impose any bound on the aggregation function property,so as to realize the low cost and high security level at the same time.

Injury severity analysis： comparison of multilevel logistic regression models and effects of collision data aggregation

This paper describes an empirical study aiming at identifying the main differences between different logistic regression models and collision data aggregation methods that are commonly applied in road safety literature for modeling collision severity. In particular, the research compares three popular multilevel logistic models （i.e., sequential binary logit models, ordered logit models, and multinomial logit models） as well as three data aggregation methods （i.e., occupant based, vehicle based, and collision based）. Six years of collision data （2001-2006） from 31 highway routes from across the province of Ontario, Canada were used for this analysis. It was found that a multilevel multinomial logit model has the best fit to the data than the other two models while the results obtained from occupant-based data are more reliable than those from vehicle- and collision-based data. More importantly, while generally consistent in terms of factors that were found to be significant between different models and data aggregation methods, the effect size of each factor differ sub- stantially, which could have significant implications forevaluating the effects of different safety-related policies and countermeasures.

PVF-DA: Privacy-Preserving, Verifiable and FaultTolerant Data Aggregation in MEC

As an emergent-architecture, mobile edge computing shifts cloud service to the edge of networks. It can satisfy several desirable characteristics for Io T systems. To reduce communication pressure from Io T devices, data aggregation is a good candidate. However, data processing in MEC may suffer from many challenges, such as unverifiability of aggregated data, privacy-violation and fault-tolerance. To address these challenges, we propose PVF-DA: privacy-preserving, verifiable and fault-tolerant data aggregation in MEC based on aggregator-oblivious encryption and zero-knowledge-proof. The proposed scheme can not only provide privacy protection of the reported data, but also resist the collusion between MEC server and corrupted Io T devices. Furthermore, the proposed scheme has two outstanding features: verifiability and strong fault-tolerance. Verifiability can make Io T device to verify whether the reported sensing data is correctly aggregated. Strong fault-tolerance makes the aggregator to compute an aggregate even if one or several Io Ts fail to report their data. Finally, the detailed security proofs are shown that the proposed scheme can achieve security and privacy-preservation properties in MEC.

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 reliableDAmay ultimately push the network to operate without much human intervention and further extend the network lifetime.The conventional client-serverDAparadigm 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-serverDAparadigm,theMobile Agent-Basedmechanismproved to be a more workable solution.In aMobile Agent-Based mechanism,a taskspecific mobile agent(code)traverses to the intended sources to gather data.Thesemobile agents travel on a predefined path called itinerary;however,planning a suitable and reliable itinerary for a mobile agent is also a challenging issue inWBANs.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.

Green Architecture for Dense Home Area Networks Based on Radio-over-Fiber with Data Aggregation Approach

The high-density population leads to crowded cities. The future city is envisaged to encompass a large-scale network with diverse applications and a massive number of interconnected heterogeneous wireless-enabled devices. Hence, green technology elements are crucial to design sustainable and future-proof network architectures. They are the solutions for spectrum scarcity, high latency, interference, energy efficiency, and scalability that occur in dense and heterogeneous wireless networks especially in the home area network （HAN）. Radio-over-fiber （ROF） is a technology candidate to provide a global view of HAN＇s activities that can be leveraged to allocate orthogonal channel communications for enabling wireless-enabled HAN devices transmission, with considering the clustered-frequency-reuse approach. Our proposed network architecture design is mainly focused on enhancing the network throughput and reducing the average network communications latency by proposing a data aggregation unit （DAU）. The performance shows that with the DAU, the average network communications latency reduces significantly while the network throughput is enhanced, compared with the existing ROF architecture without the DAU.

Comprehensive Analysis of Secure Data Aggregation Scheme for Industrial Wireless Sensor Network

As an Industrial Wireless Sensor Network(IWSN)is usually deployed in a harsh or unattended environment,the privacy security of data aggregation is facing more and more challenges.Currently,the data aggregation protocols mainly focus on improving the efficiency of data transmitting and aggregating,alternately,the aim at enhancing the security of data.The performances of the secure data aggregation protocols are the trade-off of several metrics,which involves the transmission/fusion,the energy efficiency and the security in Wireless Sensor Network(WSN).Unfortunately,there is no paper in systematic analysis about the performance of the secure data aggregation protocols whether in IWSN or in WSN.In consideration of IWSN,we firstly review the security requirements and techniques in WSN data aggregation in this paper.Then,we give a holistic overview of the classical secure data aggregation protocols,which are divided into three categories:hop-by-hop encrypted data aggregation,end-to-end encrypted data aggregation and unencrypted secure data aggregation.Along this way,combining with the characteristics of industrial applications,we analyze the pros and cons of the existing security schemes in each category qualitatively,and realize that the security and the energy efficiency are suitable for IWSN.Finally,we make the conclusion about the techniques and approach in these categories,and highlight the future research directions of privacy preserving data aggregation in IWSN.