Optimization on TEEN routing protocol in cognitive wireless sensor network

Ge, Yanhong; Wang, Shubin; Ma, Jinyu

doi:10.1186/s13638-018-1039-z

Cited by 32 publications

(11 citation statements)

References 6 publications

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“…In certain cases, the secondary users will not receive any throughput. Yanhong Ge et al 32 introduced an Advanced Threshold‐based Energy Efficient model for making the spectrum and routing more stable. It ensured that the communication between the cluster head (CH) and the normal nodes, which minimizes the waiting time and made the routing path more secure.…”

Section: Motivationmentioning

confidence: 99%

Hybrid multi‐objective optimization algorithm using Taylor series model and Spider Monkey Optimization

Menon

Kulkarni

Singh

et al. 2021

Numerical Meth Engineering

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Multi‐objective optimization is used for optimizing a number of objectives simultaneously. Mostly, the optimization algorithms considered the previous iterative position to find the next position updates. The main intention of this research is to design and develop a new model to solve the computational complexity, and the resource allocation problem. Based on this perspective, the Taylor series model and its predictive theory are applied to Spider Monkey Optimization (SMO), and a new optimization, named Taylor‐Spider Monkey Optimization (TaySMO) is developed. The proposed TaySMO computes the updated position of the swarm using the local leader phase and the global leader phase. However, a new position update equation is derived to enhance the searching process of the SMO. Here, multiple objectives such as, throughput, power, and fairness index are considered to solve the resource allocation problem. However, the performance of the proposed algorithm is evaluated using the conventional optimization function in terms of fitness function and convergence criteria as the mean square error (MSE) with the neural network learning is 0.3747, congestion rate of the resource allocation problem is 8.736E‐23, and MSE of the spectrum sensing is 8.74E‐23, respectively.

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Section: Motivationmentioning

confidence: 99%

Hybrid multi‐objective optimization algorithm using Taylor series model and Spider Monkey Optimization

Menon

Kulkarni

Singh

et al. 2021

Numerical Meth Engineering

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“…A high-precision, distributed, low-complexity and fault-tolerant positioning algorithm is needed to obtain the location of all unknown nodes. This can improve the performance of wireless sensor networks and on the other hand, it can reduce costs and benefit its Large-scale application [2] .…”

Section: Composition Of Wireless Sensor Network Nodesmentioning

confidence: 99%

Wireless Sensor Network Location Algorithm Based on Intelligent Estimation

2018

2018 1st International Conference on Engineering, Communication and Computer Sciences (ICECCS 2018)

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Wireless sensor networks are widely used in environment, transportation, military, aviation, medical and health, with many nodes and large network scale. How to use the intelligent estimation optimization algorithm to design a positioning mechanism to meet the requirements of low cost, low energy consumption, low dependence, strong robustness and high adaptability, which is the research and development target of modern large-scale wireless sensor networks.

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“…This algorithm divides nodes in the network into cluster heads and cluster members by clustering, which can fully use network node resources and effectively lengthen the life cycle of the network. In addition, algorithms such as PEGASIS [10] and TEEN [11] employed clustering idea to reduce the number of nodes those directly communicate with base station. By periodically changing cluster head, the energy consumption is balanced among nodes in the network; it is used to prolong the network life cycle.…”

Section: Introductionmentioning

confidence: 99%

Tree-Based Multihop Routing Method for Energy Efficiency of Wireless Sensor Networks

et al. 2021

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In wireless sensor networks (WSNs), due to the limited energy of sensor nodes, how to design efficient hierarchical routing algorithms to balance network resources and extend network life is an important problem to be solved. Aiming at the problems such as random selection of cluster head, redundancy of working node, and construction of cluster head transmission path, which affect network energy consumption, this paper proposes a multihop routing algorithm based on path tree (MHRA-PT) to optimize the network energy. Firstly, some nodes are those close to the base station and have large remaining energy which are selected to construct a cluster head set. Then, after clustering, each cluster is divided into different regions, and in each region, nodes with residual energy greater than the average residual energy of the cluster are selected as a working node. Finally, the cluster heads are sorted according to their distance from base station, and the next hop node is selected for each cluster head in turn until a path tree rooted at base station is formed completely, leading to data transmission from working node to base station. Simulation results show that the proposed algorithm can effectively reduce network energy consumption, balance network resources, and prolong network life cycle.

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Optimization on TEEN routing protocol in cognitive wireless sensor network

Cited by 32 publications

References 6 publications

Hybrid multi‐objective optimization algorithm using Taylor series model and Spider Monkey Optimization

Hybrid multi‐objective optimization algorithm using Taylor series model and Spider Monkey Optimization

Wireless Sensor Network Location Algorithm Based on Intelligent Estimation

Tree-Based Multihop Routing Method for Energy Efficiency of Wireless Sensor Networks

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