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 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.

Genetic optimization of hybrid clustering algorithm in mobile wireless sensor networks

Sensor Review ◽  
2018 ◽  
Vol 38 (4) ◽  
pp. 526-533 ◽  
Author(s):  
Sangeetha M. ◽  
Sabari A.
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Clustering Algorithm ◽  
Wireless Sensor ◽  
Mobile Wireless ◽  
Content Type ◽  
Mobile Wireless Sensor

Purpose This paper aims to provide a prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). MWSNs have characteristics of dynamic topology due to the factors such as energy consumption and node movement that lead to create a problem in lifetime of the sensor network. Node clustering in wireless sensor networks (WSNs) helps in extending the network life time by reducing the nodes’ communication energy and balancing their remaining energy. It is necessary to have an effective clustering algorithm for adapting the topology changes and improve the network lifetime. Design/methodology/approach This work consists of two centralized dynamic genetic algorithm-constructed algorithms for achieving the objective in MWSNs. The first algorithm is based on improved Unequal Clustering-Genetic Algorithm, and the second algorithm is Hybrid K-means Clustering-Genetic Algorithm. Findings Simulation results show that improved genetic centralized clustering algorithm helps to find the good cluster configuration and number of cluster heads to limit the node energy consumption and enhance network lifetime. Research limitations/implications In this work, each node transmits and receives packets at the same energy level throughout the solution. The proposed approach was implemented in centralized clustering only. Practical implications The main reason for the research efforts and rapid development of MWSNs occupies a broad range of circumstances in military operations. Social implications The research highly gains impacts toward mobile-based applications. Originality/value A new fitness function is proposed to improve the network lifetime, energy consumption and packet transmissions of MWSNs.

scholarly journals proposing a new clustering algorithm to Reduction of Energy Consumption using Fuzzy Logic and Genetic Algorithm for Wireless Sensor Networks.

2016 ◽  
Vol 11 (2) ◽  
pp. 2702-2719
Author(s):  
Sayyed Hedayat Tarighi Nejad ◽  
Reza Alinaghian ◽  
Mehdi Sadeghzadeh
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Sensor Network ◽  
Network Lifetime ◽  
Fuzzy System ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Reduction Of Energy Consumption

The large-scale deployment of wireless sensor networks  and the need for data aggregation necessitate efficient organization of the network topology for the purpose of balancing the load and prolonging the network lifetime. Clustering is one of the important methods for prolonging the network lifetime in wireless sensor networks. It involves grouping of sensor nodes into clusters and electing cluster heads for all the clusters. Clustering has proven to be an effective approach for organizing the network into a connected hierarchy.In this paper, using fuzzy system design and system optimization by genetic algorithm and colony of ants is presented approach to select the best cluster head in sensor networks. Using design and simulation a sensor network has been addressed to evaluation the presented fuzzy system in this paper, and finally the amount of energy consumption using proposed fuzzy system in comparison with LEACH method is calculated in select the cluster head. The result of evaluations is representative of a reduction of energy consumption in the proposed method in comparison with LEATCH method for select the cluster head. The reduction of energy consumption directly is effective on lifetime of wireless sensor network and can cause increase the lifetime these networks.

scholarly journals A Subspace Approach to Sparse Sampling Based Data Gathering in Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 985
Author(s):  
Jingfei He ◽  
Xiaoyue Zhang ◽  
Yatong Zhou ◽  
Miriam Maibvisira
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Time Slot ◽  
Data Gathering ◽  
Sparse Sampling ◽  
Wireless Sensor ◽  
Data Reconstruction ◽  
Sampled Data

Data gathering is an essential concern in Wireless Sensor Networks (WSNs). This paper proposes an efficient data gathering method in clustered WSNs based on sparse sampling to reduce energy consumption and prolong the network lifetime. For data gathering scheme, we propose a method that can collect sparse sampled data in each time slot with a fixed percent of nodes remaining in sleep mode. For data reconstruction, a subspace approach is proposed to enforce an explicit low-rank constraint for data reconstruction from sparse sampled data. Subspace representing spatial distributions of the WSNs data can be estimated from previous reconstructed data. Incorporating total variation constraint, the proposed reconstruction method reconstructs current time slot data efficiently. The results of experiments indicate that the proposed method can reduce the energy consumption and prolong the network lifetime with satisfying recovery accuracy.

scholarly journals EECDA: Energy Efficient Clustering and Data Aggregation Protocol for Heterogeneous Wireless Sensor Networks

2011 ◽  
Vol 6 (1) ◽  
pp. 113 ◽  
Author(s):  
Dilip Kumar ◽  
Trilok C. Aseri ◽  
R.B. Patel
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Data Aggregation ◽  
Energy Efficient ◽  
Data Gathering ◽  
Wireless Sensor ◽  
Energy Efficient Clustering

In recent years, energy efficiency and data gathering is a major concern in many applications of Wireless Sensor Networks (WSNs). One of the important issues in WSNs is how to save the energy consumption for prolonging the network lifetime. For this purpose, many novel innovative techniques are required to improve the energy efficiency and lifetime of the network. In this paper, we propose a novel Energy Efficient Clustering and Data Aggregation (EECDA) protocol for the heterogeneous WSNs which combines the ideas of energy efficient cluster based routing and data aggregation to achieve a better performance in terms of lifetime and stability. EECDA protocol includes a novel cluster head election technique and a path would be selected with maximum sum of energy residues for data transmission instead of the path with minimum energy consumption. Simulation results show that EECDA balances the energy consumption and prolongs the network lifetime by a factor of 51%, 35% and 10% when compared with Low-Energy Adaptive Clustering Hierarchy (LEACH), Energy Efficient Hierarchical Clustering Algorithm (EEHCA) and Effective Data Gathering Algorithm (EDGA), respectively.

scholarly journals A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2487 ◽  
Author(s):  
Guorui Li ◽  
Haobo Chen ◽  
Sancheng Peng ◽  
Xinguang Li ◽  
Cong Wang ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Collection ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Hilbert Curve ◽  
Total Energy Consumption ◽  
Cluster Data

In recent years, energy-efficient data collection has evolved into the core problem in the resource-constrained Wireless Sensor Networks (WSNs). Different from existing data collection models in WSNs, we propose a collaborative data collection scheme based on optimal clustering to collect the sensed data in an energy-efficient and load-balanced manner. After dividing the data collection process into the intra-cluster data collection step and the inter-cluster data collection step, we model the optimal clustering problem as a separable convex optimization problem and solve it to obtain the analytical solutions of the optimal clustering size and the optimal data transmission radius. Then, we design a Cluster Heads (CHs)-linking algorithm based on the pseudo Hilbert curve to build a CH chain with the goal of collecting the compressed sensed data among CHs in an accumulative way. Furthermore, we also design a distributed cluster-constructing algorithm to construct the clusters around the virtual CHs in a distributed manner. The experimental results show that the proposed method not only reduces the total energy consumption and prolongs the network lifetime, but also effectively balances the distribution of energy consumption among CHs. By comparing it o the existing compression-based and non-compression-based data collection schemes, the average reductions of energy consumption are 17.9% and 67.9%, respectively. Furthermore, the average network lifetime extends no less than 20-times under the same comparison.

QoS Aware Multi-hop Multi-path Routing Approach in Wireless Sensor Networks

2019 ◽  
Vol 9 (1) ◽  
pp. 43-52
Author(s):  
Omkar Singh ◽  
Vinay Rishiwal
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Life Time ◽  
Base Station ◽  
Environmental Data ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
End To End

Background & Objective: Wireless Sensor Network (WSN) consist of huge number of tiny senor nodes. WSN collects environmental data and sends to the base station through multi-hop wireless communication. QoS is the salient aspect in wireless sensor networks that satisfies end-to-end QoS requirement on different parameters such as energy, network lifetime, packets delivery ratio and delay. Among them Energy consumption is the most important and challenging factor in WSN, since the senor nodes are made by battery reserved that tends towards life time of sensor networks. Methods: In this work an Improve-Energy Aware Multi-hop Multi-path Hierarchy (I-EAMMH) QoS based routing approach has been proposed and evaluated that reduces energy consumption and delivers data packets within time by selecting optimum cost path among discovered routes which extends network life time. Results and Conclusion: Simulation has been done in MATLAB on varying number of rounds 400- 2000 to checked the performance of proposed approach. I-EAMMH is compared with existing routing protocols namely EAMMH and LEACH and performs better in terms of end-to-end-delay, packet delivery ratio, as well as reduces the energy consumption 13%-19% and prolongs network lifetime 9%- 14%.

Sign in / Sign up

Export Citation Format

Share Document