Fault-tolerant Data Aggregation Research Articles

LFTDA: A lightweight and fault-tolerant data aggregation scheme with privacy-enhanced property in fog-assisted smart grid

In recent years, the data aggregation scheme was widely adopted in smart grids (SG) to protect data privacy while ensuring data availability. Meanwhile, asymmetric homomorphic encryption is a popular technique that guarantees data aggregation and accurate computing results. However, it often brings heavy computation overheads and costs, which increases burdens to SG. In order to lighten SG’s burden, in this paper, a lightweight and fault-tolerant data aggregation scheme (LFTDA) with privacy-enhanced property is proposed. Unlike existing schemes, our proposed scheme uses a lightweight symmetric encryption algorithm and a skillful mathematical structure to achieve data aggregation. In addition, in the proposed scheme, smart meters are allowed to dynamically join and exit from the group while guaranteeing the correctness, security, and efficiency of aggregation. More importantly, our LFTDA is fault-tolerant, which means that the data collection from other devices will not be affected even if some of the smart meters or fog nodes do not work. From the view of security, LFTDA can defend against eavesdropping attack and collusion attack. Finally, we evaluate its performance along with other related schemes in terms of aggregation, decryption, and communication costs, which shows that LFTDA is very competitive and hence is suitable for many practical SG applications.

Fault-Tolerant Data Aggregation Scheme Supporting Fine-Grained Linear Operation in Smart Grid

Fault-Tolerant Data Aggregation Scheme Supporting Fine-Grained Linear Operation in Smart Grid

FPDA: Fault-Tolerant and Privacy-Enhanced Data Aggregation Scheme in Fog-Assisted Smart Grid

The data aggregation approach in smart grid (SG) is an effective solution to make data available while keeping privacy preserving at the same time. The fault tolerance means decryption can still be carried out successfully even if some smart meters (SMs) are breaking down. It is a challenging issue to design an efficient, fault-tolerant data aggregation scheme with no help of centralized trusted authority (TA) or key update after each fault recovery. Recently, a fault-tolerant data aggregation scheme FESDA (Saleem <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.,</i> 2020) was presented. However, we identify a serious and inherent vulnerability in its fault tolerance. Specifically, given an equivalent ciphertext derived from each SM’s private key aiming at resiting faults, the control center can abuse it to obtain any SM’s reading. An effective attack is launched with both theoretical proof and experimentative verification. Furthermore, to fix it and solve the challenging issue, we first design an extended Shamir’s threshold secret-sharing scheme (tSSS) with master secret security and reusability, allowing SMs to reconstruct subsequent multiple secrets without leaking their original secret shares. Then, if some SMs fail to submit data successfully, the fog node (FN) starts extra request–response interactivities among itself and a limited number of SMs. Finally, a privacy-enhanced aggregation of normal reports can also be achieved. Extensive experiments demonstrate that the majority of computation costs and communication overload to acquire fault tolerance are offload on FNs, while SMs are computationally economical.

A fault tolerance data aggregation scheme for fog computing

The fog computing makes the cloud-based internet of things to be more suitable for the time and location-sensitive applications. However, it is still facing challenges to balance the usability of data and privacy protection. In the past years, some excellent works have tried to address this concern using the aggregation method. However, the fact that IoT devices at the edge of the network may malfunction is not paid enough attention. In this paper, a fault-tolerant data aggregation scheme for fog computing networks is presented by employing Shamir's secret sharing and ElGamal cryptosystem. The proposed scheme ensures that even though a few IoT devices fail to work, the aggregated value can still be obtained with the number of IoT devices that reach the threshold of collaboration. In addition, security analysis and performance evaluation show the proposed scheme achieves security, privacy, and efficiency.

A fault tolerance data aggregation scheme for fog computing

The fog computing makes the cloud-based internet of things to be more suitable for the time and location-sensitive applications. However, it is still facing challenges to balance the usability of data and privacy protection. In the past years, some excellent works have tried to address this concern using the aggregation method. However, the fact that IoT devices at the edge of the network may malfunction is not paid enough attention. In this paper, a fault-tolerant data aggregation scheme for fog computing networks is presented by employing Shamir's secret sharing and ElGamal cryptosystem. The proposed scheme ensures that even though a few IoT devices fail to work, the aggregated value can still be obtained with the number of IoT devices that reach the threshold of collaboration. In addition, security analysis and performance evaluation show the proposed scheme achieves security, privacy, and efficiency.

Component Based Self-Healing Approach for Fault-Tolerant Data Aggregation in WSN

Node faults in WSNs are often attributed to battery energy drain outs. While data aggregation is widely acknowledged as an energy conservation tool; the aggregator nodes are susceptible to battery energy drain out faults. When an aggregator/ intermediary node in a data aggregation tree fails, the child nodes of the faulty node get detached from the aggregation tree and form disconnected nodes, termed as Affected Nodes (ANs) and/or subtrees. To tolerate such faults, we use the concepts of component-based graph theory and consider the subtrees as components that need to be connected back to the root-component tree. Based on this, we develop two component based self-healing fault-tolerant algorithms SCR and SCR-DTRA, that exploit the inherent redundancy in WSNs to determine alternate paths from the affected components to the root node. Unlike most other approaches, the hallmark of the component-based algorithms is that they preserve precedence relations in the data aggregation hierarchy. The results of our simulations show that the two algorithms succeed in the recovery of more than 90% of ANs and subtrees affected due to faults. In terms of longevity, the two algorithms, last more than twice the number of rounds compared to other algorithms used for comparison.

An Empirical Cluster Head Selection and Data Aggregation Scheme for a Fault-Tolerant Sensor Network

In order to improve the sensor network, the nodes resources should be used in a well-organized way. The new cluster-based routing protocols and data aggregation approach have helped to increase the lifespan of the network. The methods of data aggregation eliminate the network's redundant data packets, which extends the lifetime of the network. A fault tolerant cluster head selection and data aggregation scheme (FT-CHSDA) that performs node clustering and data aggregation in the network is demonstrated in this study. The suggested method uses the energy level of the node to pick the most energy-efficient node as the head of the cluster and executes data aggregation to reduce redundant data packets. In addition, the use of a concept called backup node in a cluster has implemented a novel method to make the network accessible and run without any interruption. In the NS2 simulator, the simulation of the proposed scheme (FT-CHSDA) is being discussed. Using different performance metrics to assess its effectiveness, the proposed scheme (FT-CHSDA) is contrasted with existing proto-cols.

A cluster based fault tolerant data aggregation technique using data caching Based on WSN for Internet of Things

A cluster based fault tolerant data aggregation technique using data caching Based on WSN for Internet of Things

PVF-DA: Privacy-preserving, verifiable and fault-tolerant 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 IoT systems. To reduce communication pressure from IoT 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 IoT devices. Furthermore, the proposed scheme has two outstanding features: verifiability and strong fault-tolerance. Verifiability can make IoT 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 IoTs 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 Routing and Fault Tolerant Data Aggregation (EERFTDA) algorithm for wireless sensor networks

An Energy Efficient Routing and Fault Tolerant Data Aggregation (EERFTDA) algorithm for wireless sensor networks

An ANFIS estimator based data aggregation scheme for fault tolerant Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are used widely in many mission critical applications like battlefield surveillance, environmental monitoring, forest fire monitoring etc. A lot of research is being done to reduce the energy consumption, enhance the network lifetime and fault tolerance capability of WSNs. This paper proposes an ANFIS estimator based data aggregation scheme called Neuro-Fuzzy Optimization Model (NFOM) for the design of fault-tolerant WSNs. The proposed scheme employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) estimator for intra-cluster and inter-cluster fault detection in WSNs. The Cluster Head (CH) acts as the intra-cluster fault detection and data aggregation manager. It identifies the faulty Non-Cluster Head (NCH) nodes in a cluster by the application of the proposed ANFIS estimator. The CH then aggregates data from only the normal NCHs in that cluster and forwards it to the high-energy gateway nodes. The gateway nodes act as the inter-cluster fault detection and data aggregation manager. They pro-actively identify the faulty CHs by the application of the proposed ANFIS estimator and perform inter-cluster fault tolerant data aggregation. The simulation results confirm that the proposed NFOM data aggregation scheme can significantly improve the network performance as compared to other existing schemes with respect to different performance metrics.

A lightweight data aggregation scheme achieving privacy preservation and data integrity with differential privacy and fault tolerance

To design an efficient and secure data aggregation scheme fitting real applications has been pursued by research communities for a long time. In this paper, we propose a novel secure data aggregation scheme to simultaneously achieve privacy preservation and data integrity with differential privacy and fault tolerance. Specifically, by introducing some auxiliary ciphertext subtly, a novel distributed solution for fault tolerant data aggregation is put forward to be able to aggregate the functioning smart meter measurements flexibly and efficiently for any rational number of malfunctioning smart meters with discretional long failure period. The proposed scheme also achieves a good tradeoff of accuracy and security of differential privacy for arbitrary number of malfunctioning smart meters. In the proposed scheme, a novel efficient authentication mechanism is also proposed to generate and share session keys in a noninteractive way, which is leveraged for AES encryption to achieve source authentication and data integrity of the transmitted data. Furthermore, through decentralizing the computational overhead and the authority of the hub-like entity of the gateway, the security of our proposed scheme is enhanced and the efficiency is improved significantly. Finally, extensive performance evaluations are conducted to illustrate that the proposed data aggregation scheme outperforms the state-of-the-art similar schemes in terms of computation complexity, communication cost, robustness of fault tolerance, and utility of differential privacy.

Fault Tolerant Data Aggregation Scheduling with Local Information in Wireless Sensor Networks

We study the problem of efficient data aggregation in unreliable wireless sensor networks by designing a fault tolerant data aggregation protocol. A fault tolerant data aggregation protocol consists of two parts: basic aggregation scheduling and amendment strategies. On default, data is aggregated according to the basic aggregation scheduling strategy. The amendment strategy will start automatically when a middle sensor node is out of service. We focus our attention on the amendment strategies and assume that the network adopts a connected dominating set (CDS) based aggregation scheduling as its basic aggregation scheduling strategy. The amendment scheme includes localized aggregation tree repairing algorithms and distributed rescheduling algorithms. The former are used to find a new aggregation tree for every child of the corrupted node, whereas the latter are used to achieve interference free data aggregation scheduling after the amendment. These amendment strategies impact only a very limited number of nodes near the corrupted node and the amendment process is transparent to all the other nodes. Theoretical analyses and simulations show that the scheme greatly improves the efficiency of the data aggregation operation by reducing both message and time costs compared to rebuilding the aggregation tree and rescheduling the entire network.

Fault-tolerant aggregator election and data aggregation in wireless sensor networks

This paper presents three algorithms for aggregator election and data aggregation in wireless sensor networks where sensors can crash and recover. The network is divided in several regions. The algorithms ensure the election of a common data aggregator sensor within each region, in charge of the collection of local data and the election of a unique super-aggregator sensor, in charge of the collection of global data, among all the aggregators. Both elections are achieved by implementing the Omega failure detector, which provides a self-organising and fault-tolerant leader election service. Each algorithm is based on a different connectivity assumption. The first algorithm assumes that every pair of sensors in a region can communicate directly. The second algorithm only requires some correct sensor to communicate directly with the rest of sensors. Finally, the third algorithm only requires the existence of a multi-hop bidirectional path from some correct sensor to the rest of sensors. We also introduce a battery depletion threshold to enhance the quality of service of the wireless sensor network.