Performance and scalability evaluation of the Castalia wireless sensor network simulator

2010 ◽  
Author(s):  
Dimosthenis Pediaditakis ◽  
Yuri Tselishchev ◽  
Athanassios Boulis
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Network Simulator

scholarly journals Lifetime Responsive Depth Based Routing for Underwater Wireless Sensor Network using Hybrid Soft Computing Technique with Optimal Delay

2020 ◽  
Vol 9 (11) ◽  
pp. 357-364
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Cluster Formation ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Node Mobility ◽  
Network Simulator ◽  
Computing Technique ◽  
Optimal Delay

Underwater wireless sensor network (UWSN) is an emerging technology that has been used for different applications such as warning systems for disasters, monitoring the ecosystem, drilling of the oil, defense surveillance. Although underwater communication may be accomplished using electromagnetic or optical waves, but these methods are not feasible for practical UWSN, due to signal attenuation. UWSN faces several issues like restricted bandwidth, irregular node mobility, increased delay etc. which affects the routing behavior. In this paper, an optimal delay and lifetime aware depth based routing (ODLDR) protocol is proposed. Protocol first introduced the group partitioning algorithm for cluster formation that minimizes the energy consumption of network then an efficient priority based scheduling algorithm is proposed for trust computation, which helps in finalizing the CH and routing path. The ODLDR protocol is tested with the high density nodes in Network Simulator (NS2) tool. The simulation results shows the effectiveness of the ODLDR protocol in terms of energy consumption, packet delivery ratio, positioning accuracy, end-to-end delay, throughput and network lifetime.

scholarly journals Testing and Implementation of Different Wireless Sensor Net-work Algorithms in Agriculture Area

2018 ◽  
Vol 7 (4.20) ◽  
pp. 1
Author(s):  
Jayashree S. Awati ◽  
Dr. M. R. Patil ◽  
Dr. S.. A. Patil
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Wireless Network ◽  
Wireless Sensor ◽  
Network Simulator ◽  
Network Algorithms

The paper demonstrates the Testing and Implementation of Different Wireless Sensor Network Algorithms in Agriculture Area. From this algorithm, it will be finalized that which algorithm is more suitable in agriculture area. Agriculture areas are large areas geographically, so to monitor agriculture parameters the wireless sensor networks are more suitable. Graham, Jevis, LPCN l algorithms explained and one personal algorithm implemented. All the results of algorithms are explained in the paper. The results are obtained using wireless network simulator. 

scholarly journals Bayesian Localized Energy Optimized Sensor Distribution for Efficient Target Tracking

2019 ◽  
Vol 8 (12) ◽  
pp. 4517-4524
Keyword(s):  
Wireless Sensor Network ◽  
Target Tracking ◽  
Sensor Network ◽  
Sensor Node ◽  
Average Energy ◽  
Extended Period ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Network Simulator ◽  
Average Scheme

In wireless sensor network application, the localization of nodes are carried out for extended life time of the node. Many applications in wireless sensor network perform localization of nodes over an extended period of time with energy variance. However, optimal selection algorithm poses new challenges to the overall transmission power levels for target detection, and thus localized energy optimized sensor management strategies are necessary for improving the accuracy of target tracking. In this work, it is proposed to develop a Bayesian Localized Energy Optimized Sensor Distribution (BLEOSD) scheme for efficient target tracking in Wireless Sensor Network. The sensor node localized with Bayesian average scheme thatestimates the sensor node’s energy are optimized as per data transfer capacity verification. The Bayesian average energy level of the sensor network is compared with the energy of each sensor node. The sensor nodes are localized and energy distribution based on the Bayesian energy estimate for efficient target tracking. The sensor node distribution strategy improves the accuracyto identify the targets effectively. Experiments are conducted using simulation of WSN by varying number of nodes, energy levels of the node and target object density using the Network Simulator Tool (NS2) The proposed BLEOSD technique is compared with various recent methods by evaluating accuracy of target tracking, energy consumption rate, localized node density and time for target tracking. The experimental results shows that the performance of BLESOD is more encouraging compared to contemporary methods.

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