The tradeoff between transmission cost and network lifetime of data gathering tree in Wireless Sensor Networks

2013 ◽  
Author(s):  
Xi Chen ◽  
Chen He ◽  
Lingge Jiang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Data Gathering ◽  
Wireless Sensor ◽  
Transmission Cost ◽  
Data Gathering Tree

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 Joint Link Scheduling and Topology Control for Wireless Sensor Networks with SINR Constraints

2010 ◽  
pp. 184-208 ◽  
Author(s):  
Qiang-Sheng Hua ◽  
Francis Lau
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Topology Control ◽  
Data Gathering ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Link Scheduling ◽  
Wireless Links ◽  
And Topology ◽  
Data Gathering Tree

This chapter studies the joint link scheduling and topology control problems in wireless sensor networks. Given arbitrarily located sensor nodes on a plane, the task is to schedule all the wireless links (each representing a wireless transmission) between adjacent sensors using a minimum number of timeslots. There are two requirements for these problems: first, all the links must satisfy a certain property, such as that the wireless links form a data gathering tree towards the sink node; second, all the links simultaneously scheduled in the same timeslot must satisfy the SINR constraints. This chapter focuses on various scheduling algorithms for both arbitrarily constructed link topologies and the data gathering tree topology. We also discuss possible research directions.

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.

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.

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.

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