Data Aggregation Protocol Research Articles

A data aggregation protocol based on TFHE

Torus Fully Homomorphic Encryption (TFHE) is a probabilistic cryptosytem over the real torus which allows one to operate directly on encrypted data without first decrypting them. We present an aggregation protocol based on a variant of TFHE for computing the sum of sensitive data, working only with the corresponding ciphertexts. Our scheme is an ideal choice for a system of smart meters – electronic devices for measuring energy consumption – that demands consumers' privacy. In contrast to some other solutions, our proposal does not require any communication among smart meters and it is quantum-safe.

DEVELOPMENT OF A METHODOLOGY FOR DATA NORMALISATION AND AGGREGATION TO ENHANCE SECURITY LEVELS IN INTERNET OF THINGS INTERACTIONS

The number of interacting devices is increasing every day, and with this constant innovation, serious security challenges arise. The concept of the Internet of Things is being actively applied in both domestic and industrial settings. Researchers are increasingly highlighting the challenges and importance of network security. Data preprocessing plays an important role in security by transforming the input data corresponding to algorithmic criteria and thereby contributing to the prediction accuracy. The data preprocessing process is determined by many factors, including the processing algorithm, the data, and the application. Moreover, in Internet of Things interactions, data normalisation and aggregation can significantly improve security and reduce the amount of data used further decision making. This paper discusses the challenges of data normalisation and aggregation in the IoT to handle large amounts of data generated by multiple connected IoT devices. A secure data normalisation and aggregation method promotes successful minimised data transfer over the network and provides scalability to meet the increasing demands of IoT deployment. The proposed work presents approaches used in data aggregation protocols that address interference, fault tolerance, security and mobility issues. A local aggregation approach using the run-length encoding algorithm is presented. The proposed technique consists of data acquisition, data preprocessing, data normalisation and data aggregation steps. Data normalisation was performed via the Z-score algorithm, and the LEACH algorithm was used for data aggregation. In the experimental study, the percentage of faulty nodes reached 35%. The performance of the proposed solution was 0.82. The results demonstrate a reduction in resource consumption while maintaining the value and integrity of the data.

Secure Data Aggregation Using Authentication and Authorization for Privacy Preservation in Wireless Sensor Networks.

Existing secure data aggregation protocols are weaker to eliminate data redundancy and protect wireless sensor networks (WSNs). Only some existing approaches have solved this singular issue when aggregating data. However, there is a need for a multi-featured protocol to handle the multiple problems of data aggregation, such as energy efficiency, authentication, authorization, and maintaining the security of the network. Looking at the significant demand for multi-featured data aggregation protocol, we propose secure data aggregation using authentication and authorization (SDAAA) protocol to detect malicious attacks, particularly cyberattacks such as sybil and sinkhole, to extend network performance. These attacks are more complex to address through existing cryptographic protocols. The proposed SDAAA protocol comprises a node authorization algorithm that permits legitimate nodes to communicate within the network. This SDAAA protocol's methods help improve the quality of service (QoS) parameters. Furthermore, we introduce a mathematical model to improve accuracy, energy efficiency, data freshness, authorization, and authentication. Finally, our protocol is tested in an intelligent healthcare WSN patient-monitoring application scenario and verified using an OMNET++ simulator. Based upon the results, we confirm that our proposed SDAAA protocol attains a throughput of 444 kbs, representing a 98% of data/network channel capacity rate; an energy consumption of 2.6 joules, representing 99% network energy efficiency; an effected network of 2.45, representing 99.5% achieved overall performance of the network; and time complexity of 0.08 s, representing 98.5% efficiency of the proposed SDAAA approach. By contrast, contending protocols such as SD, EEHA, HAS, IIF, and RHC have throughput ranges between 415-443, representing 85-90% of the data rate/channel capacity of the network; energy consumption in the range of 3.0-3.6 joules, representing 88-95% energy efficiency of the network; effected network range of 2.98, representing 72-89% improved overall performance of the network; and time complexity in the range of 0.20 s, representing 72-89% efficiency of the proposed SDAAA approach. Therefore, our proposed SDAAA protocol outperforms other known approaches, such as SD, EEHA, HAS, IIF, and RHC, designed for secure data aggregation in a similar environment.

A Privacy-Preserving Data Aggregation Protocol for Internet of Vehicles with Federated Learning

A Privacy-Preserving Data Aggregation Protocol for Internet of Vehicles with Federated Learning

Node utilization index-based data routing and aggregation protocol for energy-efficient wireless sensor networks

Node utilization index-based data routing and aggregation protocol for energy-efficient wireless sensor networks

Quality of Service (QoS) Enhancement of IoT WSNs Using an Efficient Hybrid Protocol for Data Aggregation and Routing

Quality of Service (QoS) Enhancement of IoT WSNs Using an Efficient Hybrid Protocol for Data Aggregation and Routing

DECTBEEP: A Novel Differential Evolution Clustering Tree based Energy-Efficient Protocol for Data Aggregation in Context of WSN

DECTBEEP: A Novel Differential Evolution Clustering Tree based Energy-Efficient Protocol for Data Aggregation in Context of WSN

A Reliable Protocol for Data Aggregation and Optimized Routing in IoT WSNs based on Machine Learning

A Reliable Protocol for Data Aggregation and Optimized Routing in IoT WSNs based on Machine Learning

Privacy-preserving data aggregation scheme against deletion and tampering attacks from aggregators

Because privacy-preserving data aggregation protocols provide data privacy and data compression, they have been extensively studied in smart grids. However, most of the existing data aggregation protocols are based on honest-but-curious aggregators, or adopt computationally intensive asymmetric homomorphic encryption, making these protocols only provably secure in weak security models, or resulting in high computational complexity for terminal users. To address the aforementioned issues, in this work, we propose a novel lightweight privacy-preserving data aggregation scheme against malicious aggregators based on our symmetric homomorphic encryption. The proposed scheme can reduce the computational cost of smart meters while also resisting tampering and deletion attacks from malicious aggregators. One of the highlights of the proposed scheme is its ability to withstand deletion attacks from malicious aggregators, which makes it unnecessary for the malicious aggregator to aggregate the readings of all smart meters instead of only a part of them. Therefore, our scheme is more secure than other schemes. It is the first privacy-preserving data aggregation scheme based on a stronger security model without multiple rounds of interaction. Finally, the detailed security analysis shows that the proposed scheme satisfies desirable security properties. Moreover, experimental results demonstrate that the proposed scheme is superior to the other four schemes in terms of computational complexity and communication overhead.

SCDAP –secured cluster based data aggregation protocol for energy efficient communication in wireless sensor networks

A network of real time devices that can sense and transmit the information from the deployed environment by using multi hop communication is called as Wireless Sensor Network (WSNs). Despite the rapid advancement of WSN, where an increasing number of physical devices so called as sensors nodes are connected with each other, providing the improved security with optimized energy consumption during data transmission, communication and computation remains huge challenge. In wireless sensor networks, numerous sensor nodes are deployed in the physical environment to sense and collect the required information from the given environment. The sensed information is needed to be transmitted from the nodes to the control station in an energy efficient manner. Data aggregation is one kind of techniques which will optimize the energy usage in wireless sensor networks during the data transmission. In data aggregation, the unnecessary data is removed which will significantly reduce energy of the nodes during data transmission. However, collected data during the data aggregation should be completely protected and there are various threats that can be launched by the intruders to carry out unauthorised data access and can cause threat to the integrity of the network. Therefore, ensuring data security during the data aggregation process is very important and essential for the security of the network. In this paper, a Secure Cluster based Data Aggregation Protocol (SCDAP) have been proposed to provide better security through secure authentication and verification process, and to reduce overall energy consumption of the network by implementing secure clustering process to eliminate the redundant data in the network. Moreover, the proposed system is more efficient in generating public and private keys for effective and secure data transmission and verification process. The proposed system is experimentally tested in NS-3 tool and proves that the proposed system reduces high energy consumption, computational and communicational cost, end-to-end delay and improves the packet delivery ratio. Moreover, the proposed system provides better security in the network when compared to other existing systems during the data aggregation.

An Efficient Certificate-Based Aggregate Signature Scheme With Provable Security for Industrial Internet of Things

In Industrial Internet of Things (IIoT), the certificate-based aggregate signature (CBAS) scheme was used to ensure privacy and security of IIoT, thus, the concrete construction of CBAS scheme without bilinear pairing has proposed, which was employed as an underlying building block to create an efficient data aggregation protocol. In this article, we first point out that the previous CBAS scheme cannot keep its claimed security. Particularly, a malicious KGC can compute a valid forgery signature, because it has the knowledge of the master secret key. After that, we discussed how specific forgery attacks could be made to against the existing CBAS schemes, which demonstrates that the previous related constructions cannot achieve its claimed security. In order to further provide a secure CBAS scheme for IIoT, we suggest a method to improve the CBAS scheme and propose a concrete construction of it. In the random oracle model, based on the hardness of discrete logarithm problem, the security of our new proposal can be proved. Compared with the previous CBAS schemes, our construction has better security, which is efficient with respect to computation and communication.

Cluster Head Selection and Multipath Routing Based Energy Efficient Wireless Sensor Network

The Wireless Sensor Network (WSN) is a network of Sensor Nodes (SN) which adopt radio signals for communication amongst themselves. There is an increase in the prominence of WSN adaptability to emerging applications like the Internet of Things (IoT) and Cyber-Physical Systems (CPS). Data security, detection of faults, management of energy, collection and distribution of data, network protocol, network coverage, mobility of nodes, and network heterogeneity are some of the issues confronted by WSNs. There is not much published information on issues related to node mobility and management of energy at the time of aggregation of data. Towards the goal of boosting the mobility-based WSNs’ network performance and energy, data aggregation protocols such as the presently-used Mobility Low-Energy Adaptive Clustering Hierarchy (LEACH-M) and Energy Efficient Heterogeneous Clustered (EEHC) scheme have been examined in this work. A novel Artificial Bee Colony (ABC) algorithm is proposed in this work for effective election of CHs and multipath routing in WSNs so as to enable effective data transfer to the Base Station (BS) with least energy utilization. There is avoidance of the local optima problem at the time of solution space search in this proposed technique. Experimentations have been conducted on a large WSN network that has issues with mobility of nodes.

SDA-RDOS: A New Secure Data Aggregation Protocol for Wireless Sensor Networks in IoT Resistant to DOS Attacks

In a typical Wireless Sensor Network (WSN), thousands of sensor nodes can be distributed in the environment. Then, each sensor node transmits its detected data to the base station with the help of cooperation. In this type of network, data aggregation protocols are used to increase the network’s lifetime and reduce each sensor node’s communication load and energy consumption. With Data Clustering, the density of data circulating in the network is reduced, thus increasing the network’s life. Energy, delay, and efficiency are essential criteria in Data Clustering; however, security is another crucial aspect to be considered. A comprehensive solution for secure data clustering has yet to be seen when the literature is examined. In the solutions developed, data availability, which means that the WSN is resistant to Denial of Service (DOS) attacks, has been neglected too much, even though confidentiality, integrity, and authentication are met with different algorithms. This study developed a comprehensive, secure clustering protocol by considering all security requirements, especially data availability. The developed protocol uses the blowfish encryption algorithm, EAX mode, and RSA algorithm. The proposed protocol was theoretically analyzed, empirically evaluated, and simulated from many perspectives. Comparisons were made with LSDAR, SUCID, and OOP-MDCRP protocols. As a result of the study, a comprehensive security solution is provided and more successful results were obtained according to Energy Efficiency, Network Lifetime, Average Delay, and Packet delivery ratio criteria.

A pairing-free data authentication and aggregation mechanism for Intelligent Healthcare System

The Intelligent Healthcare System (IHS), a prominent medical area underlying the Internet of Things (IoT), might be deployed to gather and analyze health information across a range of sources to deliver improved as well as more economical medical treatment. Managing all the health-related information over the internet becomes a challenging chore as numerous people rely on it implicitly or explicitly. The significant challenges of IHS comprise the privacy and security along with protection of aggregation of patients’ data as sensors obtain and transmit patient’s health information. The few of surveyed existing works based on security and privacy goals for IHS are inefficient due to cumbersome of huge computational cost and non-resilience of different attacks. Therefore, to resolve such challenges: to provide the robustness, preserve security goals and improve the efficiency, we introduced an efficient data authentication and aggregation protocol for IHS without pairing and ID-based cryptography. Provable security has been provided with Random Oracle model (ROM), which proofs the robustness of the presented scheme. Thus, the defined security requirements of our proposed work have been achieved. The performance analysis through MIRACL shows that the scheme presented outperforms the similar prior mechanisms in terms of computational-communicational cost and energy overheads.

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

Conservation of energy has been a major concern for Wireless Sensor Networks (WSNs) and IoT applications. Several strategies were devised, aimed at optimizing energy consumption in these applications, based on: (a) use of low-powered hardware devices, (b) deploying mobile/relay agents for data collection, (c) clustering, and (d) data aggregation. Amongst these, data aggregation is widely acknowledged as an important tool to conserve energy in WSN and IoTs. The paper provides a comprehensive survey of various data aggregation strategies, discusses the efficacy of these strategies in handling issues that are typical to WSN and IoT applications. These issues severely impact the performance metrics such as: energy efficiency, latency, fault-tolerance, network throughput, and network lifetime. Therefore, to optimize the data aggregation approach, an application developer needs to arrive at optimal tradeoffs between these parameters. A major contribution of the paper is to present a holistic review of data aggregation approaches emphasizing the effect of topology, security, mobility, interference, and fault-tolerance in WSN and IoTs. Based on gap areas in literature, we throw open few challenges and present them as “posers”, and put-forth suggestions for further research.

Integrity Protection for Data Aggregation in Smart Grid

To evaluate the working state of the smart grid, users need to count the status of devices collected by multiple smart meters. To avoid network congestion during data collection processes in the smart grid, an aggregation protocol is required to aggregate messages from multiple smart meters into one short message. However, since these messages may contain sensitive information of smart meters, privacy is a fundamental requirement for such a data aggregation protocol. At the same time, to avoid tampering of transmitted messages, data integrity is another important requirement during data aggregation. Currently, the privacy issue has been well addressed by using homomorphic encryption algorithms such as the Paillier cryptographic system, where multiple smart meters use the same encryption key for encrypting different messages. However, the integrity issue is much harder than the privacy issue since multiple smart meters use different private keys for signing different messages. To address the integrity issue, we propose a novel data aggregation protocol for the smart grid, called DASG. In DASG, we shall show how to aggregate signatures from multiple smart meters using the Chinese Remainder theorem and the Lagrange interpolation techniques, respectively. Since these two techniques can aggregate multiple messages into one, DASG is quite light-weight. Therefore, our newly designed data aggregation protocol for the smart grid can achieve both security and efficiency goals in the smart grid environment. Experimental results show that DASG is feasible for real world applications.

An Energy Aware Grid-Based Clustering Power Efficient Data Aggregation Protocol for Wireless Sensor Networks

A wireless sensor network (WSN) is made up of multiple sensors deployed in a specific sensing area to identify the occurrence of events and quickly transmit useful information back to the base station (BS). In WSNs, schemes to reduce energy consumption are an important topic of research. A well-designed data transmission scheme can effectively extend the lifetime of a network. In this paper, we propose an energy aware grid-based clustering power efficient data aggregation protocol (GB-PEDAP) for WSNs. The proposed scheme has a two-layer architecture: the inner layer and the outer layer. The inner layer uses direct transmission to collect the data of the cluster (cell), and the outer layer uses a tree structure transmission to collect the data of the cluster head (cell head). In our simulations, the number of rounds executed by GB-PEDAP was approximately 1.2 rounds of TBEEP, 1.3 rounds of GSTEB, and 1.5 rounds of PEDAP. With the initial energy, 0.25 J, the execution rounds of the first node death for GB-PEDAP, TBEEP, GSTEB, and PEDAP were 751, 572, 486, and 339, respectively. The proposed GB-PEDAP can evenly disperse the energy consumption of sensors to avoid the rapid death of sensors, extending the lifetime of a WSN.

Privacy-enhancing data aggregation and data analytics in wireless networks for a large class of distributed queries

Privacy-enhancing techniques and protocols for data aggregation and analytics in wireless networks require the development of novel methods for efficient and privacy-preserving computation of distributed queries with the protection of outcomes from active attackers. Previous approaches to secure privacy-preserving computation of distributed queries incur significant communication overhead and cannot be applied to big data. This paper proposes two solutions to the problem of efficient and privacy-preserving computation of distributed queries with the protection of outcomes from active outsider attackers for a new large class of distributed statistical or numerical queries. This class contains many useful statistics and is larger than other classes considered in the literature previously. We propose two protocols for the Protection of data from Active Attackers (PAA) in the case of distributed privacy-preserving computation: PAA applying Shamir’s Secret Sharing (PAA-SSS) and PAA applying homomorphic encryption (PAA-HE). The PAA-HE protocol combines the use of ElGamal and Paillier encryption schemes in one system. Theoretical analysis and experimental results show that both protocols outperform alternative approaches. PAA-HE provides stronger protection and is more efficient than PAA-SSS.

Delay-aware relay node selection for cluster-based wireless sensor networks

Maximizing the network lifetime through balancing energy and lowering the delay is a primary design goal of wireless sensor networks (WSNs). In WSNs, the sensor nodes task is to acquire the data from the area or point of interest and transmit it to the sink node via relay nodes timely. Relay nodes must dissipate some energy to perform this task. So, the relay nodes and cluster heads face the challenge of energy balancing. Hence, choosing the optimal cluster heads and relay nodes is necessary to balance the energy among the sensor nodes. In this context, this paper presents a novel clustering algorithm to balance energy by considering different metrics of WSNs, including distance, energy, link lifetime, and delay characteristics—using a relay node, etc.In this paper, a Hybrid Heuristic Data Aggregation (HHDAP) Protocol is used by combining particle swarm optimization (PSO) and improved Ant colony optimization (ACO).Through this HHDAP algorithm, the relay nodes are chosen based on a sensor node's distance, energy, link lifetime, and delay metrics. This work is simulated using NS2, and the performance of proposed and existing works are evaluated. The results show the performance improvements of the proposed work over the existing algorithms in terms of different quality of service metrics.

BBNP: A Blockchain-Based Novel Paradigm for Fair and Secure Smart Grid Communications

As the future energy infrastructure, smart grid aims to overcome the disadvantages of traditional power grid, e.g., low efficiency and unstable service. However, the frequent collection and analysis of the user’s electricity data may bring various security and privacy threats. Besides, the traditional centralized data storage model in the smart grid is prone to the single point of failure. To address these challenges, in this article, for fair and secure smart grid communication, a blockchain-based novel paradigm, named BBNP, is proposed. Specifically, based on the pseudorandom function and auxiliary information generation and sharing technology, a lightweight data aggregation protocol is designed first to protect the user’s data privacy and ensure communication confidentiality. Then, a novel efficient authentication mechanism is proposed to generate and share session keys in a noninteractive way, which is leveraged for MAC authentication to achieve data integrity of the transmitted data. After that, based on the subjective logic reputation model, a blockchain node consensus mechanism is studied to efficiently store smart grid big data and effectively solve the single point failure problem. By constructing the long-term reputation model for consensus nodes (CNs) and integrating batch verification technology, the problems of CN fair selection and scalability of large-scale nodes are solved simultaneously. Finally, the performance evaluation indicates that BBNP outperforms the state-of-the-art similar schemes in computing complexity, communication cost, system availability, and fairness of block generation.