Combined Routing and Node Replacement in Energy-Efficient Underwater Sensor Networks for Seismic Monitoring

Mohapatra, Amar Kumar; Gautam, Natarajan; Gibson, Richard L.

doi:10.1109/joe.2012.2208850

Cited by 57 publications

(35 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to other work reported in the literature, e.g., [10], [11], we assume that each sensor node is equipped by an omnidirectional transceiver and the transmission range is r. The transmission range of the sink node is the same as regular sensor nodes. Two nodes can directly communicate with each other if they are within their communication range, whereas relay nodes must be utilized otherwise.…”

Section: B Topology Of Uwsnsmentioning

confidence: 99%

Balancing between robustness and energy consumption in underwater acoustic sensor networks

Venkatesan

2015

2015 IEEE Wireless Communications and Networking Conference (WCNC)

View full text Add to dashboard Cite

In recent years, underwater acoustic sensor networks (UWSNs) are envisioned for different potential applications, ranging from long-term marine environmental monitoring, industrial instrumentation control, to military surveillance and security. Compared to wireless sensor networks (WSNs), energyefficient data transmission becomes more critical in UWSNs due to non-rechargeable batteries of sensor nodes with limited amount of energies in long-term marine monitoring applications. Besides, in underwater acoustic communications, transmitting and receiving power levels dominate the energy consumption during the data transfer. Data packet retransmission caused by network deployment error increases the energy consumption and reduce the network lifetime. In this paper, we investigate a two-dimensional deployment strategy of UWSNs with a square grid topology. We present a mathematical model to study the deployment error of UWSNs. Based on this model, a parameter, i.e., α, is introduced to balance the network robustness and the energy consumption of sensor nodes. α is defined as the ratio of the transmission range of a sensor node to the distance between two closest adjacent sensor nodes. We report optimal values of α corresponding to this balance for different sizes of UWSNs.

show abstract

Section: B Topology Of Uwsnsmentioning

confidence: 99%

Balancing between robustness and energy consumption in underwater acoustic sensor networks

Venkatesan

2015

2015 IEEE Wireless Communications and Networking Conference (WCNC)

View full text Add to dashboard Cite

show abstract

“…On the other hand, the synchronicity is a way to align the phase of the internal clock of node to conduct a synchronously collaborative action. The application of the synchronicity in WSNs is very useful as a simple sensor network coordinator in sampling the high data rates such as seismic analysis of structure [5], and volcanic monitoring [6]. Moreover, is also used as a scheduling mechanism of the node duty cycles in order to save energy, so that all nodes in a network can wake up at the same time.…”

Section: Introductionmentioning

confidence: 99%

Initial Phase Proximity for Reachback Firefly Synchronicity in WSNs: Node Clustering

Misbahuddin

Sari

2016

INT J COMPUT COMMUN

View full text Add to dashboard Cite

Synchronicity is one of the essential basic services to support the main duties of Wireless Sensor Networks (WSNs). Synchronicity is the ability to arrange simultaneously collective actions in WSNs. A high-rate data sampling to analyze the seismic structure and volcanic monitoring is the important applications requiring a synchronicity. However, most of the existing synchronicity algorithm is still executed in a flat network, so that it requires a long time to achieve a synchronous condition. To increase the convergence rate, the synchronicity can be executed concurrently through a clustering scheme approach. In this work, the such scheme is called as the Node Clustering based on Initial Phase Proximity for Reachback Firefly Synchronicity (NCIPP-RFS). The NCIPP-RFS solves in three steps: (1) constructing the node clustering, (2) intra-cluster synchronicity, and (3) inter-cluster synchronicity. The NCIPP-RFS method is based on the firefly-inspired algorithm. The fireflies are a species in the natural system, which are able to manage their flashing for synchronicity in a distributed manner. The NCIPP-RFS was implemented in NS-3 and evaluated and compared with Reachback Firefly Algorithm (RFA). The simulation results show a significant increase in the convergence rate. The NCIPP-RFS can reach a convergence time shorter than the RFA. In addition, the NCIPP-RFS was compared in the various numbers of clusters, where the least number of clusters can reach the fastest convergence rate. Finally, it can also contribute significantly to the increase of the convergence rate if the number of nodes is greater than or equal to 50 nodes.

show abstract

“…There have been significant recent research efforts focused on the analysis and design of underwater networks [1][2][3][4], including medium access control [5][6][7][8], and routing protocols [9][10][11][12][13]. The study of underwater acoustic networks needs to take into consideration a number of design aspects that arise due to the underwater transmission medium.…”

Section: Introductionmentioning

confidence: 99%

Distortion Performance of Convolutionally Coded Underwater Acoustic Networks

Stefanov¹

2016

Fourth International Conference on Advances in Computing, Electronics and Communication - ACEC 2016

View full text Add to dashboard Cite

Abstract-The paper studies the impact of point-to-point convolutional channel coding on the sustainable number of hops through the network for a maximum allowed route distortion requirement. The network is modeled by a uniform distribution of nodes over a finite area. The node-to-node channel is described by the Ricean fading model, while the interference from other nodes in the network is modeled as Gaussian. Numerical examples are presented to illustrate the results of the analysis.

show abstract

Combined Routing and Node Replacement in Energy-Efficient Underwater Sensor Networks for Seismic Monitoring

Cited by 57 publications

References 25 publications

Balancing between robustness and energy consumption in underwater acoustic sensor networks

Balancing between robustness and energy consumption in underwater acoustic sensor networks

Initial Phase Proximity for Reachback Firefly Synchronicity in WSNs: Node Clustering

Distortion Performance of Convolutionally Coded Underwater Acoustic Networks

Contact Info

Product

Resources

About