Secure data aggregation methods and countermeasures against various attacks in wireless sensor networks: A comprehensive review

Yousefpoor, Mohammad Sadegh; Yousefpoor, Efat; Barati, Hamid; Barati, Ali; Movaghar, Ali; Hosseinzadeh, Mehdi

doi:10.1016/j.jnca.2021.103118

Cited by 69 publications

(39 citation statements)

References 74 publications

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“…WSNs have various uses in the IoT and Industrial IoT IIoT, according to M. S. Yousefpoora et al [32] in this study the data aggregation algorithms are well-known in WSNs for their ability to reduce energy consumption. Furthermore, because of their wireless connectivity, these networks are vulnerable to a variety of threats.…”

Section: A Data Aggregation Schemes For Iot Enabled Wsnmentioning

confidence: 99%

Light-Weight Secure Aggregated Data Sharing in IoT-Enabled Wireless Sensor Networks

et al. 2022

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Internet of Things (IoT) is a network of physical objects or things that can communicate and share information. In IoT-enabled Wireless Medical Sensor Network (WMSN), the smart sensing devices share remote patient monitoring data towards central repositories. During medical data aggregation and transmission, security is mandatory to guard against intruders. The main problem in existing base schemes is that complex multiplication operations are used for batch key creation. These schemes are computationally expensive and require huge memory space at the aggregator node (AN). This paper presents a lightweight Secure Aggregation and Transmission Scheme (SATS) for secure and lightweight data computation and transmission. SATS provides a lightweight XOR operation for obtaining batch keys instead of the expensive multiplication operation. Furthermore, the AN Receiving Message Algorithm (ARMA) is presented at the AN to aggregate data generated by sensor nodes. The Receiving Message Extractor (RME) algorithm is presented to decrypt the message and perform batch verification at the Fog-Server. SATS protects against several security threats such as denial of service attacks, the man in the middle attack, and reply attacks. The proposed SATS is simulated by using simulation tool NS 2.35. The results show that SATS provides lightweight data transmission by reducing computation and communication costs.The computation cost of the SATS scheme is 14%, 23% and 59% at AN, and at Fog-Node 6.5%, 21.5% and 51%, and Communication cost 6%, 3%, and 4% at Sensor Node, and at AN 6%, 9%, and 12% better than PPDAS, IDAP, and ASAS respectively. The proposed SATS is compared with relevant schemes and the results show that it provides better storage capacity, computations cost, communications cost, and energy consumption.

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Section: A Data Aggregation Schemes For Iot Enabled Wsnmentioning

confidence: 99%

Light-Weight Secure Aggregated Data Sharing in IoT-Enabled Wireless Sensor Networks

et al. 2022

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“…Today, wireless sensor networks (WSNs) have become an attractive research subject for many researchers in industry and university 1 , 2 . WSNs consist of a high number of sensor nodes distributed in the network environment to monitor the Region of Interest (RoI) 3 , 4 . Initially, these networks were specifically designed for military applications 5 , 6 .…”

Section: Introductionmentioning

confidence: 99%

An energy-aware and Q-learning-based area coverage for oil pipeline monitoring systems using sensors and Internet of Things

Rahmani

Ali

Malik

et al. 2022

Sci Rep

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Pipelines are the safest tools for transporting oil and gas. However, the environmental effects and sabotage of hostile people cause corrosion and decay of pipelines, which bring financial and environmental damages. Today, new technologies such as the Internet of Things (IoT) and wireless sensor networks (WSNs) can provide solutions to monitor and timely detect corrosion of oil pipelines. Coverage is a fundamental challenge in pipeline monitoring systems to timely detect and resolve oil leakage and pipeline corrosion. To ensure appropriate coverage on pipeline monitoring systems, one solution is to design a scheduling mechanism for nodes to reduce energy consumption. In this paper, we propose a reinforcement learning-based area coverage technique called CoWSN to intelligently monitor oil and gas pipelines. In CoWSN, the sensing range of each sensor node is converted to a digital matrix to estimate the overlap of this node with other neighboring nodes. Then, a Q-learning-based scheduling mechanism is designed to determine the activity time of sensor nodes based on their overlapping, energy, and distance to the base station. Finally, CoWSN can predict the death time of sensor nodes and replace them at the right time. This work does not allow to be disrupted the data transmission process between sensor nodes and BS. CoWSN is simulated using NS2. Then, our scheme is compared with three area coverage schemes, including the scheme of Rahmani et al., CCM-RL, and CCA according to several parameters, including the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime. The simulation results show that CoWSN has a better performance than other methods.

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“…Due to this broad scope, these networks have a great ability to build efficient applications with its own characteristics and essentials. 1 Developing efficient algorithms for various contexts is a challenging task. In specific, wireless sensor network (WSN) developers should handle the issues of security, reliability, clustering, localization, aggregation, fault detection, event scheduling and energy-aware routing.…”

Section: Introductionmentioning

confidence: 99%

Similarity‐based clustering and data aggregation with independent component analysis in wireless sensor networks

Shahina

Kumar

2022

Trans Emerging Tel Tech

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Wireless sensor networks are consisted of group of sensor nodes, which are scattered in inaccessible areas with limited resources. Data aggregation and clustering methods minimize sensor network's energy usage by reducing the quantity of data transmission. Machine learning techniques like reinforcement learning, neural networks, and swarm intelligence significantly cut down the transmission of data and make use of network's distributive features. Quantity of aggregated data can be reduced if the measured data are similar. Based on this concept, a similarity based clustering method is proposed in which sensor node with similar data are grouped as a cluster for data aggregation. Then an algorithm is proposed that aggregates the data using independent component analysis which is performed on cluster head sensor nodes. Data aggregation process is executed on clusters which are having higher data similarity. Implementation shows that proposed method is performing better in terms of computational overhead, aggregation ratio, and energy consumption.

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Secure data aggregation methods and countermeasures against various attacks in wireless sensor networks: A comprehensive review

Cited by 69 publications

References 74 publications

Light-Weight Secure Aggregated Data Sharing in IoT-Enabled Wireless Sensor Networks

Light-Weight Secure Aggregated Data Sharing in IoT-Enabled Wireless Sensor Networks

An energy-aware and Q-learning-based area coverage for oil pipeline monitoring systems using sensors and Internet of Things

Similarity‐based clustering and data aggregation with independent component analysis in wireless sensor networks

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