scholarly journals A novel methodology for optimum energy consumption in wireless sensor networks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohamed Abbas ◽  
Nasser Otayf
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Design Methodology ◽  
Wireless Sensor ◽  
Energy Usage ◽  
Content Type ◽  
Optimum Energy ◽  
The Creation ◽  
Reducing Energy

PurposeThe purpose of this paper is to minimize energy usage by maximizing network life in the creation of applications and protocolsDesign/methodology/approachThis paper presents a novel methodology for optimum energy consumption in wireless sensor networks. The proposed methodology introduces some protocols and logarithms that effectively contributed to reducing energy consumption in these types of networks.FindingsThe results of that comparison showed the ability of those logarithms and protocols to reduce that energy but in varying proportions. It can be concluded that a significant reduction in energy consumption approximately 50% could be obtained by the proposed methodology.Originality/valueHere, a novel methodology for optimum energy consumption in wireless sensor networks has been introduced.

Secured data aggregation in wireless sensor networks

Sensor Review ◽  
2018 ◽  
Vol 38 (3) ◽  
pp. 369-375 ◽  
Author(s):  
Sathya D. ◽  
Ganesh Kumar P.
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Computation Time ◽  
Wireless Sensor ◽  
Content Type ◽  
Homomorphic Cryptosystem ◽  
Secured Data ◽  
Time And Energy

PurposeThis study aims to provide a secured data aggregation with reduced energy consumption in WSN. Data aggregation is the process of reducing communication overhead in wireless sensor networks (WSNs). Presently, securing data aggregation is an important research issue in WSNs due to two facts: sensor nodes deployed in the sensitive and open environment are easily targeted by adversaries, and the leakage of aggregated data causes damage in the networks, and these data cannot be retrieved in a short span of time. Most of the traditional cryptographic algorithms provide security for data aggregation, but they do not reduce energy consumption.Design/methodology/approachNowadays, the homomorphic cryptosystem is used widely to provide security with low energy consumption, as the aggregation is performed on the ciphertext without decryption at the cluster head. In the present paper, the Paillier additive homomorphic cryptosystem and Bonehet al.’s aggregate signature method are used to encrypt and to verify aggregate data at the base station.FindingsThe combination of the two algorithms reduces computation time and energy consumption when compared with the state-of-the-art techniques.Practical implicationsThe secured data aggregation is useful in health-related applications, military applications, etc.Originality/valueThe new combination of encryption and signature methods provides confidentiality and integrity. In addition, it consumes less computation time and energy consumption than existing methods.

A novel double sparse structure dictionary learning–based compressive data-gathering algorithm in wireless sensor networks

Sensor Review ◽  
2021 ◽  
Vol 41 (1) ◽  
pp. 65-73
Author(s):  
Junying Chen ◽  
Zhanshe Guo ◽  
Fuqiang Zhou ◽  
Jiangwen Wan ◽  
Donghao Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Sparse Representation ◽  
Dictionary Learning ◽  
Data Gathering ◽  
Experimental Results ◽  
Wireless Sensor ◽  
Learning Stage ◽  
Content Type

Purpose As the limited energy of wireless sensor networks (WSNs), energy-efficient data-gathering algorithms are required. This paper proposes a compressive data-gathering algorithm based on double sparse structure dictionary learning (DSSDL). The purpose of this paper is to reduce the energy consumption of WSNs. Design/methodology/approach The historical data is used to construct a sparse representation base. In the dictionary-learning stage, the sparse representation matrix is decomposed into the product of double sparse matrices. Then, in the update stage of the dictionary, the sparse representation matrix is orthogonalized and unitized. The finally obtained double sparse structure dictionary is applied to the compressive data gathering in WSNs. Findings The dictionary obtained by the proposed algorithm has better sparse representation ability. The experimental results show that, the sparse representation error can be reduced by at least 3.6% compared with other dictionaries. In addition, the better sparse representation ability makes the WSNs achieve less measurement times under the same accuracy of data gathering, which means more energy saving. According to the results of simulation, the proposed algorithm can reduce the energy consumption by at least 2.7% compared with other compressive data-gathering methods under the same data-gathering accuracy. Originality/value In this paper, the double sparse structure dictionary is introduced into the compressive data-gathering algorithm in WSNs. The experimental results indicate that the proposed algorithm has good performance on energy consumption and sparse representation.

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.

A Clustering Based Data Integration Algorithm in Wireless Sensor Networks

2011 ◽  
Vol 186 ◽  
pp. 552-555
Author(s):  
Jing Huang ◽  
Hai Hua Li ◽  
Rui Yang ◽  
Hai Yan Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Base Station ◽  
Wireless Sensor ◽  
Important Research ◽  
Integration Algorithm ◽  
Clustering Approach ◽  
Static Clustering ◽  
Reducing Energy

Data fusion is an important research issue in wireless sensor networks (WSN). The clustering based approach can reduce the interference among nodes, maintain the balance of energy consumption within WSNs, and therefore prolong the lifetime of WSNs. A clustering-based algorithm called LEACH-EC is presented in the paper. Aiming at solving the problems of the existing algorithms, the LEACH-EC takes the static clustering approach to reduce the energy consumption during clustering stage by first clustering sensors and then selecting the heads of respective clusters. When the heads of clusters send data to the base station, a proposed multi-hop strategy is adopted to further decrease the energy consumption of head sensors. Compared with the existing algorithms, the LEACH-EC has shown a good performance on both extending the lifetime of WSNs as well as reducing energy consumption of sensors.

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