Load Balancing Based Approach To Improve Lifetime Of Wireless Sensor Network

Wajgi, Dipak W.; Thakur, Nileshsingh V.

doi:10.5121/ijwmn.2012.4411

Cited by 31 publications

(9 citation statements)

References 23 publications

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“…In WSNs, there exist some works which propose schemes for optimal load distribution [22]. For example, clustering schemes proposed by Wajgi and Thakur [23] and Yournis and Fahmi [24] ensure distribution of load among the sensor nodes in the network by electing cluster heads and replacing them based on their energy level. However, these schemes are also not suitable for sensor-cloud as it comprises multiple WSNs where each WSN is owned and deployed by different sensor-owners.…”

Section: Related Workmentioning

confidence: 99%

QoS-Aware Dispersed Dynamic Mapping of Virtual Sensors in Sensor-Cloud

Misra

Chakraborty

2021

IEEE Trans. Serv. Comput.

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In this paper, we study the problem of dynamic mapping of virtual sensors in sensor-cloud for provisioning high quality of Sensors-as-a-Service (Se-aaS) in the presence of multiple sensor-owners and heterogeneous sensor nodes. We divide this problem into two subproblems-optimal dispersed node selection and optimal data-rate distribution, and analyze that these problems are NP-complete. Hence, we propose a game theory-based online scheme, named QADMAP, to solve these two problems in polynomial time. For the optimal node selection problem, we design a dynamic coalition-formation game-based online scheme, while maximizing the dispersion index of the selected nodes. On the other hand, we propose an evolutionary game theory-based scheme for distributing the data-rate requirements of the services among the selected nodes, optimally. As per our knowledge, none of the existing works on dynamic mapping of virtual sensors considers the stochastic behavior of sensor-cloud for provisioning Se-aaS. From simulations, we observe that, using QADMAP, the energy consumption of the network reduces by 29.88-31.73%, thereby improving the QoS in terms of service availability by 11% and increasing the profit of the SCSP by 3.63-9.82%, compared to the existing benchmark schemes.

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Section: Related Workmentioning

confidence: 99%

QoS-Aware Dispersed Dynamic Mapping of Virtual Sensors in Sensor-Cloud

Misra

Chakraborty

2021

IEEE Trans. Serv. Comput.

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“…First component (Ki) is the 148 bits generated by ECC,the second component (SNIDi) is the node ID of 13 bits and the third component (dCH,i ) is the 15 bits for encoding the distance between the CHs and node i. The second component, is allocated 13 bits to make the proposed scheme feasible even in case of large-scale networks that might comprise of thousands of nodes as it allows to represent around eight thousand node IDs [18]. The key at node i is set as {Ki, SNIDi, dCH,i}.…”

Section: Key Generationmentioning

confidence: 99%

An Efficient and Secure Malicious Node Detection Model for Wireless Sensor Networks

M.Morsi¹,

Barakat²,

Nashaat³

2020

IJCNC

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In wireless sensor networks (WSNs), there are many factors, such as the reciprocal interference of wireless links, battlefield applications and nodes are exposed to poor physical protection medium. All of these result in the sensor nodes being more exposed to be attacked and compromised. Since sensors are normally energy limited in WSNs, it is constantly basic to conserve the node energy and expand the lifetime of the network. Besides, hierarchical clustering is deemed as one of the numerous ways to deal with the reduction of energy consumption of nodes and increasing the network lifetime in WSNs. This paper conspicuously introduces an efficient and secure malicious node detection model based on a hybrid clustering network for WSNs (ESMCH) which is based on a trusted mobile node. WSNs are still suffering from some attacks like Man-in-the-Middle Attack and Black hole Attack, so by using the ESMCH model, we can avoid these attacks. Finally, simulation results also indicate that the proposed model can increase the network lifetime, and achieve an efficient and secure clustering network.

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“…By considering this limitation, it is necessary to design novel energy aware and fault tolerant routing protocol for heterogeneous sensor network. In [16] a survey on various existing techniques are studied and the research challenges are listed out.…”

Section: Related Workmentioning

confidence: 99%

Distributed Load Balancing Algorithm for Wireless Sensor Network

Navaz¹,

Athinarayanan²,

Sameena³

et al. 2018

IJCT

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A Wireless Sensor Network (WSN) comprises of spatially scattered autonomous sensors to screen physical or natural conditions and to amiably go their information through the system to a Base Station. Grouping is a basic assignment in Wireless Sensor Networks for vitality effectiveness and system quality. Grouping through Central Processing Unit in remote sensor systems is outstanding and being used for quite a while. In this paper, we propose a few procedures that balance the vitality utilization of these hubs and guarantee greatest system lifetime by adjusting the activity stack as similarly as could be expected under the circumstances. Directly grouping through dispersed strategies is being produced for conveying with the issues like system lifetime and vitality. In our work, we connected both concentrated and conveyed kmeans clustering calculation in system test system. K-means is a model based algorithm that surrogates between two noteworthy advances, passing on perceptions to groups and processing cluster focuses until the point when a ceasing standard is satisfied. Improved results are accomplished and related which demonstrate that conveyed clustering is compelling than brought together grouping.

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Load Balancing Based Approach To Improve Lifetime Of Wireless Sensor Network

Abstract: In wireless sensor network, clustering is used as an effective technique to achieve scalability, selforganization, power saving, channel access, routing etc.[3]

Cited by 31 publications

References 23 publications

QoS-Aware Dispersed Dynamic Mapping of Virtual Sensors in Sensor-Cloud

QoS-Aware Dispersed Dynamic Mapping of Virtual Sensors in Sensor-Cloud

An Efficient and Secure Malicious Node Detection Model for Wireless Sensor Networks

Distributed Load Balancing Algorithm for Wireless Sensor Network

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