List of Titles

Marginal and average weight-enabled data aggregation mechanism for the resource-constrained networks

- Jan, Syed, Khan, Rahim, Khan, Fazlullah, Jan, Mian, Balasubramanian, Venki

Authors: Jan, Syed , Khan, Rahim , Khan, Fazlullah , Jan, Mian , Balasubramanian, Venki
Date: 2021
Type: Text , Journal article
Relation: Computer Communications Vol. 174, no. (2021), p. 101-108
Full Text: false
Reviewed:
Description: In Wireless Sensor Networks (WSNs), data redundancy is a challenging issue that not only introduces network congestion but also consumes a considerable amount of sensor node resources. Data redundancy occurs due to the spatial and temporal correlation among the data gathered by the neighboring nodes. Data aggregation is a prominent technique that performs in-network filtering of the redundant data and accelerates the knowledge extraction by eliminating the correlated data. However, most of the data aggregation techniques have lower accuracy as they do not cater for erroneous data from faulty nodes and pose an open research challenge. To address this challenge, we have proposed a novel, lightweight, and energy-efficient function-based data aggregation approach for a cluster-based hierarchical WSN. Our proposed approach works at two levels, i.e., at the node level and at the cluster head level. At the node level, the data aggregation is performed using Exponential Moving Average (EMA) and a threshold-based mechanism is adopted to detect any outliers for improving the accuracy of aggregated data. At the cluster head level, we have employed a modified version of Euclidean distance function to provide highly-refined aggregated data to the base station. Our experimental results show that our approach reduces the communication cost, transmission cost, energy consumption at the nodes and cluster heads, and delivers highly-refined and fused data to the base station. © 2021 Elsevier B.V. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramaniam” is provided in this record**

Coding observer nodes for sybil attacks detection in mobile wireless sensor networks

- Sassani Sarrafpour, Bahman, Alomirah, Alomirah, Pang, Shaoning, Sarrafpour, Soshian

Authors: Sassani Sarrafpour, Bahman , Alomirah, Alomirah , Pang, Shaoning , Sarrafpour, Soshian
Date: 2021
Type: Text , Conference paper
Relation: 19th IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2021, Shenyang, China. 20-22 October 2021, Proceedings - 2021 IEEE 19th International Conference on Embedded and Ubiquitous Computing, EUC 2021 p. 87-94
Full Text: false
Reviewed:
Description: Sybil attack is one of the most common and serious attacks in wireless sensor networks, in which a malicious node illegitimately forges several (fake) identities. These fake copies confuse and collapse the network. Sybil attack causes too many threats to the routing algorithm, data aggregation, fair resource allocation, voting system, and misbehavior detection. In this paper, we propose a new lightweight algorithm for detecting the Sybil attack in mobile wireless sensor networks using observer nodes. Observer nodes are normal, trustful nodes which have been initially programmed to observe the network and report malicious behaviors. An observer node counts the number of times a node has appeared as a common neighbor between itself and its neighbors. After collecting some information about its neighbors, each observer node considers the nodes whose counters are above a threshold as critical, and nodes having all critical nodes in their neighborhood are considered suspicious nodes. The results show that the true detection rate of the proposed algorithm is 98.1%, and its false detection rate is 0.5%, while similar algorithms could not achieve better than 95.4% and 1.2% for these metrics, respectively. In addition, the proposed algorithm outperforms other algorithms in terms of overhead. © 2021 IEEE.

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