Trust model for cluster head validation in underwater wireless sensor networks

2017 ◽  
Vol 34 (3) ◽  
pp. 106-113 ◽  
Author(s):  
Nitin Goyal ◽  
Mayank Dave ◽  
Anil K. Verma
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Cluster Head ◽  
Trust Model ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks

scholarly journals A Collaborative Secure Localization Algorithm Based on Trust Model in Underwater Wireless Sensor Networks

Sensors ◽  
2016 ◽  
Vol 16 (2) ◽  
pp. 229 ◽  
Author(s):  
Guangjie Han ◽  
Li Liu ◽  
Jinfang Jiang ◽  
Lei Shu ◽  
Joel Rodrigues
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Trust Model ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Underwater Wireless Sensor Networks ◽  
Secure Localization

scholarly journals Dynamic Cluster Based Routing Protocol with Sink Mobility Support for Underwater Wireless Sensor Networks

2018 ◽  
Vol 7 (3.16) ◽  
pp. 57
Author(s):  
Anandalatchoumy S ◽  
Sivaradje G
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks ◽  
Mobility Support ◽  
Mobile Sinks ◽  
Dynamic Cluster

Underwater wireless sensor networks are energy resource constrained due to the scarce battery capacity. Energy efficient routing protocol is highly demanded to be developed for such networks. It is indeed a challenging task to design routing protocol that can achieve energy efficiency due to the dynamic and harsh underwater environment. A dynamic cluster based routing protocol coupled with sink mobility support (DCMMS) is proposed. Two schemes are combined together in the protocol. One is the formation of  clusters and two is the mobile sink management. The cluster formation includes cluster head election process and member           association process. Each cluster member sends the sensed data to the cluster head. Multiple mobile sinks are deployed to gather data directly from cluster heads. Finally, mobile sinks send the collected data after proper aggregation to the static sinks located at the surface. Thus, sink mobility and the dynamic clustering technique together help to balance the load among the nodes thereby       minimizing energy consumption to a significant extent and extending the network life span. Analytical simulations are extensively carried out to attest how the proposed protocol (DCMMS) achieves better performance with minimum energy consumption, less end to end delay and higher packet delivery ratio than its counterpart existing protocol (AA-RP).  

Routing issues and challenges in Underwater Wireless Sensor Networks

2018 ◽  
Vol 6 (2) ◽  
pp. 238-241 ◽  
Author(s):  
Pushpender Sarao ◽  
◽  
Kannaiah Chattu ◽  
Ch. Swapna ◽  
◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks

Fault Tolerant Reliable Protocol (FTRP) Performance Evaluation in Wireless Sensor Networks: An Extensitive Study

2019 ◽  
pp. 1-36
Keyword(s):  
Wireless Sensor Networks ◽  
Fault Tolerance ◽  
Sensor Networks ◽  
Fault Tolerant ◽  
Cluster Head ◽  
Biological Data ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Good Performer ◽  
Wide Range

Fault Tolerant Reliable Protocol (FTRP) is proposed as a novel routing protocol designed for Wireless Sensor Networks (WSNs). FTRP offers fault tolerance reliability for packet exchange and support for dynamic network changes. The key concept used is the use of node logical clustering. The protocol delegates the routing ownership to the cluster heads where fault tolerance functionality is implemented. FTRP utilizes cluster head nodes along with cluster head groups to store packets in transient. In addition, FTRP utilizes broadcast, which reduces the message overhead as compared to classical flooding mechanisms. FTRP manipulates Time to Live values for the various routing messages to control message broadcast. FTRP utilizes jitter in messages transmission to reduce the effect of synchronized node states, which in turn reduces collisions. FTRP performance has been extensively through simulations against Ad-hoc On-demand Distance Vector (AODV) and Optimized Link State (OLSR) routing protocols. Packet Delivery Ratio (PDR), Aggregate Throughput and End-to-End delay (E-2-E) had been used as performance metrics. In terms of PDR and aggregate throughput, it is found that FTRP is an excellent performer in all mobility scenarios whether the network is sparse or dense. In stationary scenarios, FTRP performed well in sparse network; however, in dense network FTRP’s performance had degraded yet in an acceptable range. This degradation is attributed to synchronized nodes states. Reliably delivering a message comes to a cost, as in terms of E-2-E. results show that FTRP is considered a good performer in all mobility scenarios where the network is sparse. In sparse stationary scenario, FTRP is considered good performer, however in dense stationary scenarios FTRP’s E-2-E is not acceptable. There are times when receiving a network message is more important than other costs such as energy or delay. That makes FTRP suitable for wide range of WSNs applications, such as military applications by monitoring soldiers’ biological data and supplies while in battlefield and battle damage assessment. FTRP can also be used in health applications in addition to wide range of geo-fencing, environmental monitoring, resource monitoring, production lines monitoring, agriculture and animals tracking. FTRP should be avoided in dense stationary deployments such as, but not limited to, scenarios where high application response is critical and life endangering such as biohazards detection or within intensive care units.

scholarly journals An Uneven Node Self-Deployment Optimization Algorithm for Maximized Coverage and Energy Balance in Underwater Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1368 ◽  
Author(s):  
Luoheng Yan ◽  
Yuyao He ◽  
Zhongmin Huangfu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Balance ◽  
Optimization Algorithm ◽  
Growth Ring ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks ◽  
Connected Network ◽  
Deployment Optimization

The underwater wireless sensor networks (UWSNs) have been applied in lots of fields such as environment monitoring, military surveillance, data collection, etc. Deployment of sensor nodes in 3D UWSNs is a crucial issue, however, it is a challenging problem due to the complex underwater environment. This paper proposes a growth ring style uneven node depth-adjustment self-deployment optimization algorithm (GRSUNDSOA) to improve the coverage and reliability of UWSNs, meanwhile, and to solve the problem of energy holes. In detail, a growth ring style-based scheme is proposed for constructing the connective tree structure of sensor nodes and a global optimal depth-adjustment algorithm with the goal of comprehensive optimization of both maximizing coverage utilization and energy balance is proposed. Initially, the nodes are scattered to the water surface to form a connected network on this 2D plane. Then, starting from sink node, a growth ring style increment strategy is presented to organize the common nodes as tree structures and each root of subtree is determined. Meanwhile, with the goal of global maximizing coverage utilization and energy balance, all nodes depths are computed iteratively. Finally, all the nodes dive to the computed position once and a 3D underwater connected network with non-uniform distribution and balanced energy is constructed. A series of simulation experiments are performed. The simulation results show that the coverage and reliability of UWSN are improved greatly under the condition of full connectivity and energy balance, and the issue of energy hole can be avoided effectively. Therefore, GRSUNDSOA can prolong the lifetime of UWSN significantly.

On Underwater Wireless Sensor Networks Routing Protocols: A Review

2020 ◽  
Vol 20 (18) ◽  
pp. 10371-10386 ◽  
Author(s):  
Hashim Khan ◽  
Syed Ali Hassan ◽  
Haejoon Jung
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocols ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks
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