Abstract
Wireless sensor networks have become integral components of modern and smart environments. The main challenge for such important data-acquisition tools is the limited amount of available energy. In integrated networks in which cloud systems act as a self-regulatory controller, distributing the computational load among available partitions with rich energy will positively influence the lifetime of the whole network. This article investigates the application of a modified version of multinomial logistic regression model that incorporates spatiotemporal aspects of data collected from smart environments. The contribution of this research is to propose an energy-efficient load balancing strategy based on the proposed prediction model for the purpose of enhancing the lifetime of wireless infrastructure. Our proposed algorithm grows linearly in terms of time complexity. Extensive experiments have been performed to measure the prediction error rate and the energy consumption. The results showed that the proposed model significantly reduces the error rate and distinctly maximizes the lifetime of wireless sensor networks.
Highlights
The world is encountering growing urbanization while major cities have become a driver for economic growth
The results indicated that the difference between traditional load distribution and our proposed load balancing strategy is significant in terms of error rate, the difference between real values and predicted ones
We presented an energy-aware prediction model for wireless sensor networks (WSNs) that are connected to a cloud, which increases the heterogeneity of WSNs
Summary
The world is encountering growing urbanization while major cities have become a driver for economic growth. Several existing smart infrastructures have been developed by multiple integrated wireless sensor networks (WSNs), in which sensors are responsible for acquiring data from real-world environments. One critical challenge in this domain is the limited energy that is available for every sensor, where batteries are the main source of power Such problem directly affects the lifetime of such integrated networks and, their sustainability. In this platform, real-time processing is an essential operation in which cloud services respond immediately to requests. The nominal probability will be modeled as follows log bij biJ
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