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Showing papers on "Data aggregator published in 2021"


Journal ArticleDOI
TL;DR: This paper reviews the literature with specific attention to aspects of wireless networking for the preservation of energy and aggregation of data in IoT-WSN systems.

105 citations


Journal ArticleDOI
TL;DR: This article presents a smart and practical Privacy-preserving Data Aggregation (PDA) scheme with smart pricing and packing method for fog-based smart grids, which achieves diversified tariffs, multifunctional statistics and efficiency.
Abstract: With the increasingly powerful and extensive deployment of edge devices, edge/fog computing enables customers to manage and analyze data locally, and extends computing power and data analysis applications to network edges. Meanwhile, as the next generation of the power grid, the smart grid can achieve the goal of efficiency, economy, security, reliability, use safety and environmental friendliness for the power grid. However, privacy and secure issues in fog-based smart grid communications are challenging. Without proper protection, customers’ privacy will be readily violated. This article presents a smart and practical Privacy-preserving Data Aggregation (PDA) scheme with smart pricing and packing method for fog-based smart grids, which achieves diversified tariffs, multifunctional statistics and efficiency. Especially, we first propose a smart PDA scheme with Smart Pricing (PDA-SP). With PDA-SP, the Control Center (CC) can compute more complex and higher-order aggregation statistics to provide various services, provide diversiform pricing strategies and choose a double-winning strategy. Subsequently, we put forward a practical PDA scheme with Packing Method (PDA-PM), which is able to reduce the size of encrypted data and improve performance in performing various secure computations. Moreover, we extend our original packing method and present a more useful packing method, which can handle general vectors with large entries. The security analysis shows that our proposed scheme is secure against many threats. The performance evaluation reveals that the computation and communication overheads of our proposed scheme are effectively reduced by employing the Somewhat Homomorphic Encryption (SHE), and our packing method can further significantly reduce these overheads.

101 citations


Journal ArticleDOI
TL;DR: A privacy-preserving data aggregation scheme with a flexibility property uses ElGamal Cryptosystem is proposed and is proved to be secure, private, and flexible with the analysis and performance simulation.
Abstract: The development of the Internet of Things (IoT) and 5th generation wireless network (5G) is set to push the smart agriculture to the next level since the massive and real-time data can be collected to monitor the status of crops and livestock, logistics management, and other important information. Recently, COVID-19 has attracted more human attention to food safety, which also has a positive impact on smart agriculture market share. However, the security and privacy concern for smart agriculture has become more prominent. Since smart agriculture implies working with large sets of data, which usually sensitive, some are even confidential, and once leakage it can expose user privacy. Meanwhile, considering the data publishing of smart agriculture helps the public or investors to real-timely anticipate risks and benefits, these data are also a public resource. To balance the data publishing and data privacy, in this article, a privacy-preserving data aggregation scheme with a flexibility property uses ElGamal Cryptosystem is proposed. It is proved to be secure, private, and flexible with the analysis and performance simulation.

65 citations


Journal ArticleDOI
TL;DR: This article designs an improved symmetric homomorphic cryptosystem and a fog-based communication architecture to support delay- or time-sensitive monitoring and other-related applications and demonstrates that the approach enables privacy-assured medical data aggregation.
Abstract: Wearable body area network is a key component of the modern-day e-healthcare system (e.g., telemedicine), particularly as the number and types of wearable medical monitoring systems increase. The importance of such systems is reinforced in the current COVID-19 pandemic. In addition to the need for a secure collection of medical data, there is also a need to process data in real-time. In this article, we design an improved symmetric homomorphic cryptosystem and a fog-based communication architecture to support delay- or time-sensitive monitoring and other-related applications. Specifically, medical data can be analyzed at the fog servers in a secure manner. This will facilitate decision making, for example, allowing relevant stakeholders to detect and respond to emergency situations, based on real-time data analysis. We present two attack games to demonstrate that our approach is secure (i.e., chosen-plaintext attack resilience under the computational Diffie–Hellman assumption), and evaluate the complexity of its computations. A comparative summary of its performance and three other related approaches suggests that our approach enables privacy-assured medical data aggregation, and the simulation experiments using Microsoft Azure further demonstrate the utility of our scheme.

58 citations


Journal ArticleDOI
TL;DR: In this proposed methodology, the Integration of Distributed Autonomous Fashion with Fuzzy If-then Rules (IDAF-FIT) algorithm is proposed for clustering, and also the Cluster Head (CH) is elected in the meanwhile and the routing concept is initiated.
Abstract: In recent years, Wireless Sensor Network (WSN) became a key technology for monitoring and tracking applications in a wide application range. Still, an energy-efficient data gathering protocol has become the most challenging issue. This is because each sensor node in the network is equipped with limited energy resources. To achieve better energy efficiency, better network communication, and minimized delay, clustering is introduced. Therefore, the clustering-based techniques for data gathering play a vital role in terms of energy-saving and increasing the lifetime of the network due to cluster head election and data aggregation. In this proposed methodology, the Integration of Distributed Autonomous Fashion with Fuzzy If-then Rules (IDAF-FIT) algorithm is proposed for clustering, and also the Cluster Head (CH) is elected in the meanwhile. After that, to transmit the packet from source to the destination node by choosing an optimal path, the routing concept is initiated. For this purpose, an Adaptive Source Location Privacy Preservation Technique using Randomized Routes (ASLPP-RR) is presented for routing. Also, Secure Data Aggregation based on Principle Component Analysis (SDA-PCA) algorithm is performed with end-to-end confidentiality and integrity. Finally, the security of confidential data is analyzed properly to obtain a better result than the existing approaches. The overall performance of the proposed methodology when compared with existing is expressed in terms of 20% higher packet delivery ratio, 15% lower packet dropping ratio, 18% higher residual energy, 22% higher network lifetime, and 16% lower energy consumption.

55 citations


Journal ArticleDOI
TL;DR: The proposed blockchain and homomorphic encryption-based data aggregation (BHDA) scheme shows a significant improvement in performance and privacy preservation with minimal computation overhead for data aggregation in smart grids.

50 citations


Journal ArticleDOI
TL;DR: This paper categorizes secure data aggregation methods in wireless sensor networks based on network model, network topology, key cryptography technique, encryption method, application, authentication mechanism, and data recovery ability and believes that this taxonomy can help researchers to design secure and efficient data aggregating methods.

47 citations


Journal ArticleDOI
TL;DR: This work integrates edge computing and blockchain to design a three-layer architecture data aggregation scheme for smart grid, which shows great superiority in terms of resisting network attacks, reducing system computation costs and communication overhead compared with existing schemes.
Abstract: Compared with traditional power systems, smart grid is designed to provide effective and secure energy services. Data aggregation is one of the key technologies in wireless sensor networks, which reduces the amount of data transmission between nodes by merging similar data and simplifying redundant data, thus significantly reducing the computation cost and communication overhead of the system. Many data aggregation schemes have been developed for the smart grid in the past years. However, most of the data aggregation schemes ignore the data security and privacy protection issues of the edge layer. To solve these problems, in this article, we propose an edge blockchain assisted lightweight privacy-preserving data aggregation for smart grid, named EBDA. In this work, we integrate edge computing and blockchain to design a three-layer architecture data aggregation scheme for smart grid. This new architecture supports a two-level data aggregation scheme, which is more efficient and secure. Through theoretical analysis and simulations, EBDA shows great superiority in terms of resisting network attacks, reducing system computation costs and communication overhead compared with existing schemes.

44 citations


Journal ArticleDOI
TL;DR: A privacy-preserving and non-interactive data aggregation algorithm, with which local training data from multiple data owners can be aggregated and trained to a global model without disclosing any private information.

38 citations


Journal ArticleDOI
TL;DR: In this article, a secure data aggregation scheme has been proposed, which has three phases: intra-cluster data aggregation, inter-clusters data aggregation and data transfer, and a fuzzy scheduling system is designed to adjust the appropriate data transmission rates of the cluster member nodes.
Abstract: A wireless sensor network (WSN) consists of a set of sensor nodes that are widely scattered in inaccessible areas When deployed in large areas, WSNs generate a large volume of the data Therefore, efficient methods are needed to process the data One solution to minimize traffic on large-scale wireless sensor networks is to use data aggregation schemes In this paper, a secure data aggregation method is proposed The proposed secure data aggregation scheme has three phases: intra-cluster data aggregation, inter-cluster data aggregation, and data transfer In the intra-cluster data aggregation phase, a fuzzy scheduling system is designed to adjust the appropriate data transmission rates of the cluster member nodes In the inter-cluster data aggregation phase, an aggregation tree is created between the cluster head nodes The dragonfly algorithm (DA) is used to find the optimal aggregation tree between cluster head nodes In the data transfer phase, the columnar transposition cipher method is used to establish a secure connection between cluster member nodes and their cluster head node Also, a symmetric and lightweight encryption method based on the residue number system (RNS) is utilized to provide secure communications between the cluster head nodes We modify RNS and call it RNS+ Finally, the simulation results of the proposed scheme are compared to three data aggregation methods including Sign-share, Sham-share, and RCDA The results show that the proposed data aggregation scheme outperforms other data aggregation methods in terms of network lifetime, delay and packet delivery rate

37 citations


Journal ArticleDOI
TL;DR: In this paper, a secure data aggregation method based on a combination of star and tree structures is suggested, where the network is geographically divided into four equal parts, and a stable star structure is formed in each part.
Abstract: Wireless sensor networks (WSNs) are composed of several nodes, distributed in a geographical region. Limited energy of nodes is the main challenge of WSNs. Hence, it is required to apply different methods to consume less energy for calculations and communications. One method to reduce energy consumption in WSNs is to reduce the number of packets transmitted in the network. Data aggregation technique can cause a decrease in the number of transmitted packets. In fact, the technique combines related data and prevents sending additional packets. In this paper, a secure data aggregation method based on a combination of star and tree structures is suggested. Here, the network is geographically divided into four equal parts, and a stable star structure is formed in each part. In the secure hybrid structure data aggregation (SHSDA) method, each node is assigned a parent for transmitting data. To improve the security of data, the lightweight symmetric encryption is applied, and a key is distributed between each parent node and its children. The encrypted data is sent from leaf nodes to parent nodes, and gradually reaches the root through a star structure. Then the data is transmitted to the base station using the tree structure. The proposed method has been simulated using NS2. The results reveal that the average energy consumption and data delivery delay of SHSDA are less compared with that of conventional methods. Also, SHSDA method causes a rise in packet delivery rate, throughput, and flexibility.

Journal ArticleDOI
TL;DR: A Delay-optimized Convergence Routing based E-ADA (DOCR-E-ADA) data collection scheme which combines advance notification mechanism and convergence routings and outperforms the existing schemes in terms of network delay and lifetime.
Abstract: The rapid development of Internet of Things (IoT) can promote the establishment of the smart connected world by using numerous devices and sensors. Collecting the data perceived by sensing devices in a fast and energy-saving style is critical for building a stable network in IoT. In this paper, we propose an Early Message Ahead Join Adaptive Data Aggregation (E-ADA) scheme for IoT. Firstly, an advance notification mechanism based on early message is introduced to improve the probability of data aggregation, thus optimizing energy consumption and network lifetime. In this mechanism, the routing of early message is faster than that of the data packet. Nodes with data packet send an early message forward to notice, and other data packets which monitor the early message can wait for that to aggregate under the delay deadline constraints. Secondly, we propose a Delay-optimized Convergence Routing based E-ADA (DOCR-E-ADA) data collection scheme which combines advance notification mechanism and convergence routings. The duty cycle of nodes in convergence routing is adaptively adjusted, which greatly reduces transmission latency. Finally, extensive experimental results demonstrate that our DOCR-E-ADA outperforms the existing schemes in terms of network delay and lifetime.

Journal ArticleDOI
TL;DR: This article proposes a privacy-preserving multidimensional data aggregation scheme without trusted authority in smart grid based on the ElGamal homomorphic cryptosystem with distributed decryption, which can resist the coalition attack from the gateway and the control center.
Abstract: Privacy-preserving multidimensional data aggregation is a significant basic building block for protecting the users’ privacy in smart grid, and it can not only expand the applications of data aggregation but also fulfill the demands of the fine-grained analysis of multidimensional data. However, traditional multidimensional data aggregation schemes depend on the trusted authority and cannot resist the coalition attack from the gateway (GW) and the control center (CC), which may cause the users’ fears about privacy violations. Therefore, this article proposes a privacy-preserving multidimensional data aggregation scheme without trusted authority in smart grid based on the ElGamal homomorphic cryptosystem with distributed decryption, which can resist the coalition attack from the GW and the CC. What is more, the proposed scheme does not depend on the trusted authority which is not fully trusted in the real world. The detailed security analysis indicates that our scheme can satisfy the security requirement of smart grid. The performance analysis shows that the proposed scheme achieves the lowest computation and communication costs in data encryption phase and data aggregation phase, thus it is appropriate for many practical applications.

Journal ArticleDOI
TL;DR: An efficient and privacy-preserving data aggregation scheme with authentication for IoT-based healthcare applications (EPPDA) as mentioned in this paper is based to verification and authorization phase to verify the legitimacy of the nodes that need to join the process of aggregation.
Abstract: Nowadays, IoT technology is used in various application domains, including the healthcare, where sensors and IoT enabled medical devices exchange data without human interaction to securely transmit collected sensitive healthcare data towards healthcare professionals to be reviewed and take proper actions if needed. The IoT devices are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range. In healthcare applications, many miniaturized devices are exploited for healthcare data collection and transmission. Thus, there is a need for secure data aggregation while preserving the data integrity and privacy of the patient. For that, the security, privacy, and aggregation of health data are very important aspects to be considered. This paper proposes a novel secure data aggregation scheme called “An Efficient and Privacy-Preserving Data Aggregation Scheme with authentication for IoT-Based Healthcare applications” (EPPDA). EPPDA is based to verification and authorization phase to verify the legitimacy of the nodes that need to join the process of aggregation. EPPDA, also, uses additive homomorphic encryption to protect data privacy and combines it with homomorphic MAC to check the data integrity. The major advantage of homomorphic encryption is allowing complex mathematical operations to be performed on encrypted data without knowing the contents of the original plain data. The proposed system is developed using MySignals HW V2 platform. Security analysis and experimental results show that our proposed scheme guarantees data privacy, messages authenticity, and integrity, with lightweight communication overhead and computation.

Journal ArticleDOI
TL;DR: A novel homomorphic privacy-preserving protocol (called NHP3) for data aggregation that has a low computational cost compared to its rivals and is secure even when the gateway or aggregator turns malicious.
Abstract: Advanced Metering Infrastructure (AMI) facilitates the communication between smart meters and network operators in smart grid. For better demand-response management, smart meters are supposed to send live or sometimes periodic consumption reports. If such reports are intercepted or eavesdropped by a malicious entity, customers’ privacy is compromised, since vital information can be inferred from power consumption data. In this article, we propose a novel homomorphic privacy-preserving protocol (called NHP3) for data aggregation that has a low computational cost compared to its rivals. It is fault-tolerant, supports multi-category aggregation, and can do batch verification at the intermediate aggregator as well as the central system. The proposed protocol is secure even when the gateway or aggregator turns malicious. It does not allow any compromised meter to find other users’ consumption information either. Moreover, in this scheme, the central system cannot infer any usage data even if it is curious and gains access to the data packets sent from meters to the intermediate aggregator. A comprehensive and comparative analysis is carried out at the end of this article which shows the advantages of the proposed scheme in terms of security features and cost.

Journal ArticleDOI
TL;DR: A cluster-based systematic data aggregation model (CSDAM) for real-time data processing that minimizes the consumption of energy and transmission delay effectively thereby increasing the network lifespan.
Abstract: In present decade, wireless sensor networks is applied in a variety of applications such as health monitoring, agriculture, traffic management, security domains, pollution management, and so on. Owing to the node density, the same data are collected by multiple sensors that introduce redundancy, which should be avoided by means of proper data aggregation methodology. With that note, this paper presents a cluster-based systematic data aggregation model (CSDAM) for real-time data processing. First, the network is formed into a cluster with active and sleep state nodes and cluster-head (CH) is selected based on ranking given to sensors with two criteria: existing energy level (EEL) and geographic-location (GL) to base station (BS), [i.e., Rank(EEL,GL)]. Here, the CH is the aggregator. Second, Aggregation is carried out in 3 levels where the data processing of level 3 has been reduced by aggregating the data at level 1 and level 2. If the energy of aggregator goes below the threshold, we choose another aggregator. Third, Real time application should be given more precedence than other applications, so additionally an application type field is added to each sensor node from which the priority of data processing is given first to real time applications. The simulation results show that CSDAM minimizes the consumption of energy and transmission delay effectively, thereby increasing the network lifespan.


Journal ArticleDOI
TL;DR: An auction framework for privacy-preserving data aggregation in mobile crowdsensing, where the platform plays the role as an auctioneer to recruit workers for sensing tasks and can select a subset of workers to minimize the cost of purchasing their private sensing data subject to the accuracy requirement of the aggregated result.
Abstract: We develop an auction framework for privacy-preserving data aggregation in mobile crowdsensing, where the platform plays the role as an auctioneer to recruit workers for sensing tasks. The workers are allowed to report noisy versions of their data for privacy protection; and the platform selects workers by taking into account their sensing capabilities to ensure the accuracy level of the aggregated result. Observe that when moving the control of data privacy from the data aggregator to the workers, the data aggregator has limited market power in the sense that it can only partially control the noise by judiciously choosing a subset of workers based on workers’ privacy preferences. This introduces externalities because the privacy of each worker depends on the total noise in the aggregated result that in turn relies on which workers are selected. Specifically, we first consider a privacy-passive scenario where workers participate if their privacy loss can be adequately compensated by the rewards. We explicitly characterize the externalities and the hidden monotonicity property of the problem, making it possible to design a truthful, individually rational and computationally efficient incentive mechanism. We then extend the results to a privacy-proactive scenario where workers have individual requirements for their perceivable data privacy levels. Our proposed mechanisms for both scenarios can select a subset of workers to (nearly) minimize the cost of purchasing their private sensing data subject to the accuracy requirement of the aggregated result. We validate the proposed scheme through theoretical analysis as well as extensive simulations.

Journal ArticleDOI
TL;DR: In this paper, the authors proposed that IoT-enabled smart grids can achieve more reliable and high-frequency data collection and transmission compared with existing grids, however, this frequent data processing may cause high power consumption.
Abstract: Internet of Things (IoT)-enabled smart grids can achieve more reliable and high-frequency data collection and transmission compared with existing grids. However, this frequent data processing may c...

Journal ArticleDOI
Lin Bai1, Jiexun Liu1, Jiaxing Wang1, Rui Han1, Jinho Choi2 
TL;DR: An aggregators-aided random access scheme for IoV is proposed, where unmanned aerial vehicles (UAVs), as one of the key components in SAGIN, are deployed as data aggregators to help transmissions of VUEs.
Abstract: Recently, the Internet of Vehicles (IoV) has been employed as an enabling technology for smart transportation, which can be further enhanced by integrating space-air-ground integrated networks (SAGIN). Since the data packets of vehicular user equipments (VUEs) are generally short, random access is usually considered for VUEs to connect to the network. However, collisions caused by the multiple VUEs initiating random access simultaneously are inevitable. To relieve the performance degradation by collisions, data aggregation can be carried out in the IoV, where aggregated packets can be relayed to a base station (BS). In this paper, we first propose an aggregators-aided random access (ARA) scheme for the IoV, where unmanned aerial vehicles (UAVs), as one of the key components in SAGIN, are deployed as data aggregators to help transmissions of VUEs. Then, a Semi-Markov chain is used to analyze the average number of aggregated packets and the metric of average data to overhead ratio (ADOR) is presented to evaluate the efficiency of aggregation. Finally, the altitude of UAVs and the duration of data aggregation are optimized to maximize the ADOR. By numerical simulations, the accuracy of the analysis as well as the effectiveness of the proposed scheme are validated.

Journal ArticleDOI
TL;DR: The F-LEACH is proposed, a Fuzzy-based data aggregation scheme for IoT-enabled healthcare applications aiming to maximize the network lifetime, and according to the simulation results, the proposed method outperformed similar works by 5–20%.
Abstract: Internet of Things (IoT) is an emerging paradigm that consists of numerous connected and interrelated devices with embedded sensors, exchanging data with each other and central nodes over a wireless network and internet. Recently, due to the crucial importance of human well-being, IoT-enabled healthcare systems have gained significant attention. On the other hand, as IoT networks are large-scaled and battery-powered, developing proper energy and resource management mechanisms for them is inevitable. On account of the large amount of data generated in IoT environments, data aggregation is vital to lower energy consumption and extend network lifespan, and many researchers have endeavored to enhance its efficiency. However, there is no optimized method for the dynamic, complex, and nonlinear nature of healthcare applications. Fuzzy logic could be effective in these scenarios because it can convert qualitative data to quantitative, implement complex nonlinear functions, and present approximate solutions for cases where there is no single optimal answer, and it changes with a slight variation in conditions. This paper proposes the F-LEACH, a Fuzzy-based data aggregation scheme for IoT-enabled healthcare applications aiming to maximize the network lifetime. According to the simulation results, the proposed method outperformed similar works by 5–20%.

Journal ArticleDOI
TL;DR: This work proposes a verifiable, reliable, and privacy-preserving data aggregation scheme for FA-MCS that preserves privacies of both users’ data and aggregation results, enables requester to verify the correctness of aggregation result, and is able to tolerate several bad FNs without affecting the data aggregation result.
Abstract: Fog-assisted mobile crowdsensing (FA-MCS) alleviates challenges with respect to computation, communication, and storage from the traditional model of mobile crowdsensing (MCS) “requester-server-users.” Data aggregation, as a specific MCS task, has attracted a lot of attentions in mining the potential value of the massive crowdsensing data. However, the process of data aggregation in FA-MCS may threaten the privacies of both users’ data and aggregation results. The untrusted server and fog nodes (FNs) may damage the correctness of aggregation results. Moreover, bad FNs, which do not upload data to server or fail to verify successfully, can endanger the reliability of FA-MCS and the accuracy of aggregation results. To tackle these problems, we propose a verifiable, reliable, and privacy-preserving data aggregation scheme for FA-MCS. Specifically, the proposed scheme preserves privacies of both users’ data and aggregation results, enables requester to verify the correctness of aggregation result, and is able to tolerate several bad FNs without affecting the data aggregation result. Through formal security analysis, the proposed scheme is shown to be secure and privacy preserving. Extensive experiments also show the proposed scheme is efficient and reliable.

Journal ArticleDOI
TL;DR: An efficient privacy-preserving data aggregation and dynamic pricing service PADP in V2G IoT is proposed, by designing an identity-based sequential aggregate signed data (SASD) based on factoring and a threshold homomorphic encryption.
Abstract: With the fast development of Internet of Things (IoT) especially for smart grid and electric vehicle (EV) networking, vehicle-to-grid (V2G) communications have been increasingly studied and recognized as one of the most convincing tools for general road transportation, to effectively reduce the oil demands and gas emissions. Unfortunately, a series of security and privacy issues have significantly impeded its wide adoption. The existing work mainly focused on the static environment, which cannot be directly applied to the mobile setting where EVs travel across regions. The dynamic pricing metric in V2G networks depends on the real-time electricity usage aggregation in one region. To address this issue, in this article, an efficient privacy-preserving data aggregation and dynamic pricing service PADP in V2G IoT is proposed, by designing an identity-based sequential aggregate signed data (SASD) based on factoring and a threshold homomorphic encryption. In the proposed threshold homomorphic encryption, a legal ciphertext can be generated if and only if no less than threshold $k$ individual illegal ciphertexts are aggregated. Therefore, the aggregated power consumption data can be successfully decrypted while the individual power consumption privacy of honest EV users can be well protected against even the collusion between a malicious power charging station and compromised EVs. Furthermore, the technique of SASD guarantees entity authentication with a minimized amount of transmitted data. Finally, formal security proof and extensive performance evaluation demonstrate the effectiveness and practicability of our proposed PADP.

Journal ArticleDOI
TL;DR: Detailed security analysis and extensive performance evaluations show that the proposed fog-enabled privacy-preserving secure data aggregation scheme with fault-tolerance is efficient in terms of encryption, aggregation, decryption and communication costs when compared with the three existing state-of-the-art schemes.

Book ChapterDOI
01 Jan 2021
TL;DR: In this paper, various in-network data aggregation algorithms are analyzed in detail and an insight into the techniques utilized is provided, where the prime idea of data aggregation is to gather, combine, and compress the data from various sensor nodes during transmission to the sink node.
Abstract: A sensor network consists of the random deployment of large numbers of tiny sized sensor nodes in a region of interest to detect the physical/environmental events and transmit the relevant data to the sink node through multihop communication. The nodes have many physical constraints like energy, memory, processing power, and hence the result is the limited or insufficient network lifetime of a network. To solve this problem, data gathering in an energy-efficient manner is an important task in the wireless sensor network to enhance network lifetime. Data aggregation is one such energy-efficient data gathering technique that reduces the data traffic and thereby the energy consumption substantially. The prime idea of the data aggregation is to gather, combine, and compress the data from various sensor nodes during transmission to the sink node. Among the available aggregation methods, in-network processing plays a major role to reduce the amount of data to be transmitted in the network. This article analyzes the various in-network data aggregation algorithms in detail and provides an insight into the techniques utilized.

Journal ArticleDOI
15 May 2021
TL;DR: A novel cryptographic accumulator based on the novel authenticated additive homomorphic encryption which can collect and accumulate data from IoT wireless wearable devices and can be used for analysis in an encrypted form so that the information is not revealed.
Abstract: The proliferation of Internet of Things (IoT) as a promising paradigm has contributed enormously to modern technology design. The wireless body sensor network (WBSN) technology is an application of IoT in healthcare, whereas data security and privacy impediments have raised some concerns. The collected data via IoT wireless body sensors is vulnerable to a variety of internal and external attacks. One solution is to encrypt or sign the collected data to provide confidentiality and integrity, but the computational complexity hinders the application in the real IoT-based healthcare devices. Although there have been some attempts to provide secure and efficient IoT schemes, there is a lack of achieving secure data analysis in modern healthcare. The aggregated data statistics about the patient’s medical status is useful to doctors and healthcare providers. However, the dynamic data continually updating over time is challenging. In this article, we present an efficient and provably secure scheme, which is the first step toward secure data analysis for handling the data collection and analysis for IoT wireless body sensors. The main contribution of our work is a novel cryptographic accumulator based on our novel authenticated additive homomorphic encryption which can collect and accumulate data from IoT wireless wearable devices. These encrypted data can be used for analysis in an encrypted form so that the information is not revealed. To validate security and efficiency, we present security analysis and performance evaluations of our proposed scheme for IoT wireless body sensors.

Journal ArticleDOI
TL;DR: This work proposes a learning-based sparse data reconstruction scheme by jointly utilizing CS and deep learning to reduce the volume of data to be transmitted over IoT networks without losing reconstruction accuracy.
Abstract: Due to the booming of various devices in Internet-of-Things (IoT) networks, more data should be transmitted over the networks, which will thereby consume more transmission bandwidth and more transmit power. Compressed data aggregation (CDA) has been proposed as an effective way to reduce the amount of the collected data in IoT networks. Although adopting compressed sensing (CS), CDA can sample the source data efficiently, and sparse data reconstruction is still a big challenge. In this work, inspired by this, we propose a learning-based sparse data reconstruction scheme by jointly utilizing CS and deep learning. Our objective is to reduce the volume of data to be transmitted over IoT networks without losing reconstruction accuracy. A deep CS network is designed by adopting an end-to-end learning method to build a measurement matrix and an efficient and high-accuracy reconstruction network. To show the performance of the proposed scheme, six data sets with different structured sparse models and a real sensor data set are utilized in doing experiments. The performance of the proposed scheme in terms of mean-squared error, peak-signal-noise-ratio, and structural similarity is investigated. The results demonstrate the effectiveness of the proposed scheme in reconstruction accuracy for given compression ratio. The results also show that the proposed scheme is suitable for the process of CDA, thus can effectively reduce the amount of data to be transmitted in IoT networks.

Proceedings ArticleDOI
29 Mar 2021
TL;DR: In this article, the authors studied the problem of minimum latency scheduling to aggregate and report data to the sink without data collision in multiple-data-type WSNs having unidirectional links, which is shown to be NP-hard.
Abstract: In wireless sensor networks (WSNs), the problem of reporting data to the sink with minimum latency has been widely discussed in many research works. Many studies address on using data aggregation to report the same type of data to the sink without data collision in a short period of time. However, due to the rapid development of sensor technology in recent years, a sensor is allowed to have multiple sensing capabilities, that is, it can generate and collect different types of data. Because different types of data have different meanings and required aggregation functions, only the data that belong to the same type are allowed to be aggregated. In addition, due to the interference of the environment or the noise, the links in the WSNs are often not bidirectional. This motivates us to study the problem of using minimum latency scheduling to aggregate and report data to the sink without data collision in multiple-data-type WSNs having unidirectional links, which is shown to be NP-hard in the paper. In addition, the Relative-Collision-Graph-Based Scheduling Algorithm (RCGBSA) is therefore proposed accordingly. Simulations are conducted to demonstrate the performance of the RCGBSA.

Journal ArticleDOI
TL;DR: This paper proposes a blockchain-assisted massive IoT data collection (MIDC) intelligent framework to support the security, trust and efficiency of massive data collection for large-scale heterogeneous WSNs and designs a series of novel technologies for the framework.
Abstract: Due to the vigorous development of wireless communication technology, massive sensors have been gradually connected to the Internet of Things (IoT) and generate a massive quantity of valuable IoT data from large-scale wireless sensor networks (WSNs) controlled by different owners. Massive IoT data need to be collected and circulated among multiple data owners and data users. However, existing data collection frameworks may cause heavy computational overhead or rely on trusted third parties, since sensors have constrained resources. Consequently, massive IoT data are transformed among different parties, causing severe trust and security issues. In this paper, we propose a blockchain-assisted massive IoT data collection (MIDC) intelligent framework to support the security, trust and efficiency of massive data collection for large-scale heterogeneous WSNs. In particular, we propose a series of novel technologies for the framework. First, we design a Large-Scale Heterogeneous WSNs Collaborative Identity Verification protocol to ensure reliable data sources. Second, we build a Hierarchical Massive Data Aggregation scheme to collect massive IoT data efficiently and securely. Third, we depict a Blockchain-Based Massive IoT Data Management method to construct trust among different parties. Extensive simulation and prototype experimental results prove the effectiveness of our framework.

Journal ArticleDOI
02 Apr 2021-Sensors
TL;DR: In this paper, the authors proposed PrivDA, a privacy-preserving IoT data aggregation scheme based on the blockchain and homomorphic encryption technologies, where each data consumer can create a smart contract and publish both terms of service and requested IoT data, and the smart contract puts together into one group potential data producers that can answer the consumer's request and chooses one aggregator, the role of which is to compute the group requested result using homomorphic computations.
Abstract: Data analytics based on the produced data from the Internet of Things (IoT) devices is expected to improve the individuals' quality of life. However, ensuring security and privacy in the IoT data aggregation process is a non-trivial task. Generally, the IoT data aggregation process is based on centralized servers. Yet, in the case of distributed approaches, it is difficult to coordinate several untrustworthy parties. Fortunately, the blockchain may provide decentralization while overcoming the trust problem. Consequently, blockchain-based IoT data aggregation may become a reasonable choice for the design of a privacy-preserving system. To this end, we propose PrivDA, a Privacy-preserving IoT Data Aggregation scheme based on the blockchain and homomorphic encryption technologies. In the proposed system, each data consumer can create a smart contract and publish both terms of service and requested IoT data. Thus, the smart contract puts together into one group potential data producers that can answer the consumer's request and chooses one aggregator, the role of which is to compute the group requested result using homomorphic computations. Therefore, group-level aggregation obfuscates IoT data, which complicates sensitive information inference from a single IoT device. Finally, we deploy the proposal on a private Ethereum blockchain and give the performance evaluation.