Impact of the Structure of the Data Gathering Trees on Node Lifetime and Network Lifetime in Wireless Sensor Networks

2017 ◽  
pp. 184-196
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Data Gathering ◽  
Wireless Sensor ◽  
Node Failure ◽  
Minimum Number ◽  
Bottleneck Link ◽  
Bottleneck Node ◽  
The Impact

We analyze the impact of the structure of the Data Gathering (DG) trees on node lifetime (round of first node failure) and network lifetime (minimum number of rounds by which the network gets either disconnected due to node failures or the fraction of coverage loss reaches a threshold) in wireless sensor networks through extensive simulations. The two categories of DG trees studied are: the Bottleneck Node Weight-Based (BNW-DG) trees and Bottleneck Link Weight-Based (BLW-DG) trees. The BNW-DG trees incur a smaller diameter and a significantly larger fraction of nodes as leaf nodes: thus, protecting a majority of the nodes in the network from simultaneously being exhausted of the energy resources (contributing to a significantly larger network lifetime); nevertheless the nodes that serve as intermediate nodes in the first few instances of the BNW-DG trees are bound to lose their energy more quickly than the other nodes, leading to a smaller node lifetime compared to that of the BLW-DG trees (that incur a larger diameter and a relatively lower fraction of nodes as leaf nodes).

A Benchmarking Algorithm for Maximum Bottleneck Node Trust Score-based Data Gathering Trees in Wireless Sensor Networks

2015 ◽  
Vol 7 (3) ◽  
pp. 13-35
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Gathering ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Intermediate Node ◽  
Root Node ◽  
Link Weight ◽  
Bottleneck Link ◽  
Bottleneck Node

The author proposes a benchmarking algorithm to determine maximum bottleneck node trust score-based data gathering trees (MaxBNT-DG trees) for wireless sensor networks (WSNs) wherein the bottleneck node trust score of a path (minimum trust score for any node on the path, including those of the end nodes) from any node to the root node of the DG tree is the maximum. He compares the performance of the MaxBNT-DG trees with that of the maximum bottleneck link weight-based data gathering trees (MaxBLT-DG trees) for which the bottleneck link trust score (minimum trust score for constituent links) of a path from any node to the root node is the maximum. The author observes the MaxBNT-DG trees to incur a smaller tree diameter, a larger percentage of nodes as leaf nodes and a larger trust score per intermediate node; whereas, the MaxBLT-DG trees incur a lower aggregation delay, indicating a trust-aggregation delay tradeoff in WSNs. The MaxBNT-DG algorithm is also generic and can be extended to any other node criterion like residual energy, wake-up frequency, etc

scholarly journals A Generic Algorithm to Determine Maximum Bottleneck Node Weight-based Data Gathering Trees for Wireless Sensor Networks

2015 ◽  
Vol 7 (3) ◽  
pp. 18 ◽  
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Gathering ◽  
Leaf Node ◽  
Wireless Sensor ◽  
Generic Algorithm ◽  
Intermediate Node ◽  
Root Node ◽  
Bottleneck Node ◽  
Node Weight

We propose a generic algorithm to determine maximum bottleneck node weight-based data gathering (MaxBNW-DG) trees for wireless sensor networks (WSNs) and compare the performance of the MaxBNW-DG trees with those of maximum and minimum link weight-based data gathering trees (MaxLW-DG and MinLW-DG trees). Assuming each node in a WSN graph has a weight, the bottleneck weight for the path from a node u to the root node of the DG tree is the minimum of the node weights on the path (inclusive of the weights of the end nodes). The MaxBNW-DG tree algorithm determines a DG tree such that each node has a path of the largest bottleneck weight to the root node. We observe the MaxBNW-DG trees to incur lower height, larger percentage of nodes as leaf nodes and a larger weight per intermediate node compared to the leaf node; the tradeoff being a larger a network-wide data aggregation delay due to larger number of child nodes per intermediate node. The MaxBNW-DG algorithm could be used to determine DG trees with larger trust score, larger energy (and other such criterion for node weight) per intermediate node compared to the leaf node. 

Lifetime Maximization Routing Based on Genetic Algorithm for Wireless Sensor Networks

2011 ◽  
Vol 230-232 ◽  
pp. 283-287
Author(s):  
You Rong Chen ◽  
Tiao Juan Ren ◽  
Zhang Quan Wang ◽  
Yi Feng Ping
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Data Gathering ◽  
Transmission Probability ◽  
Wireless Sensor ◽  
Total Energy Consumption ◽  
Lifetime Maximization

To prolong network lifetime, lifetime maximization routing based on genetic algorithm (GALMR) for wireless sensor networks is proposed. Energy consumption model and node transmission probability are used to calculate the total energy consumption of nodes in a data gathering cycle. Then, lifetime maximization routing is formulated as maximization optimization problem. The select, crosss, and mutation operations in genetic algorithm are used to find the optimal network lifetime and node transmission probability. Simulation results show that GALMR algorithm are convergence and can prolong network lifetime. Under certain conditions, GALMR outperforms PEDAP-PA, LET, Sum-w and Ratio-w algorithms.

scholarly journals Improved Location Estimation in Wireless Sensor Networks Using a Vector-Based Swarm Optimized Connected Dominating Set

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 376 ◽  
Author(s):  
Gulshan Kumar ◽  
Rahul Saha ◽  
Mritunjay Rai ◽  
Reji Thomas ◽  
Tai-Hoon Kim ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Estimation Error ◽  
Dominating Set ◽  
Connected Dominating Set ◽  
Location Estimation ◽  
Wireless Sensor ◽  
Minimum Number ◽  
Anchor Nodes

Location estimation in wireless sensor networks (WSNs) has received tremendous attention in recent times. Improved technology and efficient algorithms systematically empower WSNs with precise location identification. However, while algorithms are efficient in improving the location estimation error, the factor of the network lifetime has not been researched thoroughly. In addition, algorithms are not optimized in balancing the load among nodes, which reduces the overall network lifetime. In this paper, we have proposed an algorithm that balances the load of computation for location estimation among the anchor nodes. We have used vector-based swarm optimization on the connected dominating set (CDS), consisting of anchor nodes for that purpose. In this algorithm, major tasks are performed by the base station with a minimum number of messages exchanged by anchor nodes and unknown nodes. The simulation results showed that the proposed algorithm significantly improves the network lifetime and reduces the location estimation error. Furthermore, the proposed optimized CDS is capable of providing a global optimum solution with a minimum number of iterations.

scholarly journals Maximizing Lifetime of Wireless Sensor Networks with Mobile Sink Nodes

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yourong Chen ◽  
Zhangquan Wang ◽  
Tiaojuan Ren ◽  
Yaolin Liu ◽  
Hexin Lv
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Optimization Model ◽  
Data Gathering ◽  
Mobile Sink ◽  
Path Selection ◽  
Wireless Sensor ◽  
Lifetime Optimization

In order to maximize network lifetime and balance energy consumption when sink nodes can move, maximizing lifetime of wireless sensor networks with mobile sink nodes (MLMS) is researched. The movement path selection method of sink nodes is proposed. Modified subtractive clustering method, k-means method, and nearest neighbor interpolation method are used to obtain the movement paths. The lifetime optimization model is established under flow constraint, energy consumption constraint, link transmission constraint, and other constraints. The model is solved from the perspective of static and mobile data gathering of sink nodes. Subgradient method is used to solve the lifetime optimization model when one sink node stays at one anchor location. Geometric method is used to evaluate the amount of gathering data when sink nodes are moving. Finally, all sensor nodes transmit data according to the optimal data transmission scheme. Sink nodes gather the data along the shortest movement paths. Simulation results show that MLMS can prolong network lifetime, balance node energy consumption, and reduce data gathering latency under appropriate parameters. Under certain conditions, it outperforms Ratio_w, TPGF, RCC, and GRND.

Data Gathering With Minimum Number of Relay Packets in Wireless Sensor Networks

2017 ◽  
Vol 17 (21) ◽  
pp. 7196-7208 ◽  
Author(s):  
Chien-Fu Cheng ◽  
Lung-Hao Li ◽  
Chen-Chuan Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Gathering ◽  
Wireless Sensor ◽  
Minimum Number

scholarly journals Adaptive Dual-Mode Routing-Based Mobile Data Gathering Algorithm in Rechargeable Wireless Sensor Networks for Internet of Things

2020 ◽  
Vol 10 (5) ◽  
pp. 1821 ◽  
Author(s):  
Liangrui Tang ◽  
Haobo Guo ◽  
Runze Wu ◽  
Bing Fan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Network Lifetime ◽  
Data Gathering ◽  
Wireless Sensor ◽  
Dual Mode ◽  
Energy Aware ◽  
Mobile Data ◽  
Mobile Data Gathering

Great improvement recently appeared in terms of efficient service delivery in wireless sensor networks (WSNs) for Internet of things (IoT). The IoT is mainly dependent on optimal routing of energy-aware WSNs for gathering data. In addition, as the wireless charging technology develops in leaps and bounds, the performance of rechargeable wireless sensor networks (RWSNs) is greatly ameliorated. Many researches integrated wireless energy transfer into data gathering to prolong network lifetime. However, the mobile collector cannot visit all nodes under the constraints of charging efficiency and gathering delay. Thus, energy consumption differences caused by different upload distances to collectors impose a great challenge in balancing energy. In this paper, we propose an adaptive dual-mode routing-based mobile data gathering algorithm (ADRMDGA) in RWSNs for IoT. The energy replenishment capability is reasonably allocated to low-energy nodes according to our objective function. Furthermore, the innovative adaptive dual-mode routing allows nodes to choose direct or multi-hop upload modes according to their relative upload distances. The empirical study confirms that ADRMDGA has excellent energy equilibrium and effectively extends the network lifetime.

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