Cluster based data aggregation in underwater acoustic sensor networks

Manjula, R.; Manvi, Sunilkumar S.

doi:10.1109/indcon.2012.6420597

Cited by 12 publications

(11 citation statements)

References 14 publications

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“…In addition, the base station utilizes the distance information to detect attacks. This proposed scheme reduces energy consumption by reducing the redundancy of the transmitted data [19], [20]. The proposed protocol is also focusing on protecting the aggregated data from attacks such as Sybil and sinkhole.…”

Section: B Sinkhole Attacksmentioning

confidence: 99%

Secure and Energy-Efficient Data Aggregation Method Based on an Access Control Model

et al. 2019

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Wireless sensor networks (WSNs) consist of a large number of sensor nodes that are distributed to capture the information about an area of interest. In WSN, many of the secure data aggregation works are conducted without addressing the authentication process. It is challenging to implement authentication while preserving the energy consumption in the network. The previous research that focus on these issues have several limitations, such as sharing the security key and the key length with a base station node, and not much attention is given to enhance the authentication of the Medium Access Control (MAC) server. This makes the data aggregation network are exposed to malicious activities. This paper presents a new protocol to address the security and energy issue in Wireless Sensor Network (WSN). This newly developed protocol is named Secure and Energy-Efficient Data Aggregation (SEEDA), which is the extension of SDAACA protocol. The proposed protocol aims to enhance authentication by generating a random value and random timestamp with a secret key. The base station node will verify the fake aggregated data when the packets are received using the generated key earlier. Furthermore, the attacks are detected and prevented by utilizing secure node authentication, data fragmentation algorithms, fully homomorphic encryption, and access control model. The secure node authentication algorithm prevents attacks from accessing the network. To avoid network delays, the base station node utilizes the distance information between the participating nodes. To ensure the reliability of our proposed method, we simulate two well-known attacks, called Sybil and sinkhole attacks. Several experimental scenarios are conducted to observe their effect. Evaluation metrics such as malicious activity detection rate, energy consumption, end-to-end delay, and resilience time are measured. The performance of the proposed protocol is compared with SDA, SDAT, SDALFA, EESSDA, SDAACA, and EESDA, which is a widely used protocol in the area of secure data aggregation. The simulation results show that the proposed SEEDA method outperforms the existing scheme with 98.84% malicious nodes detection rate, 3.04 joules for energy consumption, the maximum delay of 0.038 seconds, and the resilient time 0.054, 0.075 seconds when 8%,16% of malicious nodes affecting the network.

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Section: B Sinkhole Attacksmentioning

confidence: 99%

Secure and Energy-Efficient Data Aggregation Method Based on an Access Control Model

et al. 2019

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“…Similarly, [8] proposed a cluster-based method for data gathering and compressing, in which the data gathered by the sensors was transmitted to the data processing center through a hierarchy of cluster heads that employed wavelet decomposition to build wavelet coefficients of the data. Similarly, [9] proposed a cluster-based data aggregation technique which involved formation of clusters, election of cluster heads, and data transmission to the sink node for an underwater acoustic sensor network.…”

Section: Related Workmentioning

confidence: 99%

“…Reference [20] presented a centralized algorithm running within the sink of the wireless sensor network to obtain a distributed wavelet basis. On in underwater acoustic sensor networks [2,9]. Compared with a terrestrial wireless network, the deployment of sensor nodes is sparser and there are fewer nodes in a cluster for underwater acoustic sensor network; thus the spatial correlation is weakened.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Distributed Compressed Sensing Data Aggregation for Cluster-Based Underwater Acoustic Sensor Networks

Wang

et al. 2016

International Journal of Distributed Sensor Networks

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Energy-efficient data aggregation is important for underwater acoustic sensor networks due to its energy constrained character. In this paper, we propose a kind of energy-efficient data aggregation scheme to reduce communication cost and to prolong network lifetime based on distributed compressed sensing theory. First, we introduce a distributed compressed sensing model for a clusterbased underwater acoustic sensor network in which spatial and temporal correlations are both considered. Second, two schemes, namely, BUTM-DCS (block upper triangular matrix DCS) and BDM-DCS (block diagonal matrix DCS), are proposed based on the design of observation matrix with strictly restricted isometric property. Both schemes take multihop underwater acoustic communication cost into account. Finally, a distributed compressed sensing reconstruction algorithm, DCS-SOMP (Simultaneous Orthogonal Matching Pursuit for DCS), is adopted to recover raw sensor readings at the fusion center. We performed simulation experiments on both the synthesized and real sensor readings. The results demonstrate that the new data aggregation schemes can reduce energy cost by more than 95 percent compared with conventional data aggregation schemes when the cluster number is 20.

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“…Moreover, the cluster structure is a promising method to reduce network's energy consumption, which has recently received much attention when deploying a network in an aquatic environment. The cluster-based concept divides the network into groups of nodes (or clusters) and defines a mechanism by which all clusters connect to each other [8,[11][12][13][14][15][16][17][18][19][20]. There are various ways of doing research on network clustering, such as how to optimize cluster size [13], how to select a cluster-head [11,14,15], how to communicate among nodes and among clusters [11,13,16,17], and how to aggregate data in the cluster [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…Data aggregation is the technique under which the WSN attempts to collect the sensed data from the sensors and transmit them to the base station (BS) or the sink. The main role of data aggregation is to not only eliminate redundant data received from sensor nodes, but also reduce the number of transmissions to the BS/sink [12, 18]. Aggregation of data using similarity functions can minimize data redundancy and reduce the size of data packets to be sent to the BS/sink [18].…”

Section: Introductionmentioning

confidence: 99%

UWSNs: A Round-Based Clustering Scheme for Data Redundancy Resolve

Tran

2014

International Journal of Distributed Sensor Networks

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The underwater wireless sensor network (UWSN) is a state-of-the-art approach to exploring potential information and resources in the aquatic environment. However, underwater communication has unique features, such as long propagation delay, low bandwidth capacity, high bit error rates, and mobility, memory, and battery limitations. In this paper, we propose a round-based clustering scheme that can overcome the UWSN's confines mainly by resolving the transmission of redundant data in the network-one of the significant factors that reduces network lifetime. Our proposed scheme works in rounds, with each round consisting of four main phases: initialization, cluster-head selection, clustering, and data aggregation. Suitable mechanisms are chosen to apply in each round. By dealing with most of the redundant data, our proposed clustering scheme better reduces network consumption, thus increasing network throughput. Moreover, the minimum percentage of received data at the sink/base station is guaranteed.

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Cluster based data aggregation in underwater acoustic sensor networks

Cited by 12 publications

References 14 publications

Secure and Energy-Efficient Data Aggregation Method Based on an Access Control Model

Secure and Energy-Efficient Data Aggregation Method Based on an Access Control Model

Energy-Efficient Distributed Compressed Sensing Data Aggregation for Cluster-Based Underwater Acoustic Sensor Networks

UWSNs: A Round-Based Clustering Scheme for Data Redundancy Resolve

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