The elastic optical network (EON) fulfills the upcoming generation network requirements such as high-definition videos, high bandwidth demand services, and ultra-high-definition televisions. The key issues in EON are routing spectrum assignment and spectrum fragmentation for spectrum allocation. The spectrum fragmentation issues are resultant in poor consumption of spectrum resources and an increase in the new connection blocking. A flexible defragmentation algorithm must utilize more spectrum resources with a high transmission rate. This paper presents a new multiconstrained defragmentation algorithm (MCDFA) for elastic optical networks. The MCDFA addressed two key issues: spectrum allocation for new connections and then reconfiguring the existing connections in a nondisruptive manner. The first-last-exact fit spectrum allocation policy assigns the spectrum slots during the new connection request. It splits each light path request by disjoint/ nondisjoint and by efficiently handling the small fragmented slots in spectrum resources. The simulation results are evaluated using standard metrics such as bandwidth blocking probability, bandwidth fragmentation ratio, and spectrum utilization gain. The results also demonstrated that our proposed algorithm generates promised solution to EON’s routing, spectrum assessment, and fragmentation issues.
Internet of Things-based smart healthcare systems have gained attention in recent years for improving healthcare services and reducing data management costs. However, there is a requirement for improving the smart healthcare system in terms of speed, accuracy, and cost. An intelligent and secure edge-computing framework with wearable devices and sensors is proposed for cardiac arrhythmia detection and acute stroke prediction. Latency reduction is highly essential in real-time continuous assessment, and classification accuracy has to be improved for acute stroke prediction. In this paper, preprocessing and deep learning-based assessment is performed in the edge-computing layer, and decisions are communicated instantly to the individuals. In this work, acute stroke prediction is performed by a deep learning model using heart rate variability features and physiological data. Classification accuracy is improved in this approach when compared to other machine learning approaches. Cloud servers are utilized for storing the healthcare data of individuals for further analysis. Analyzed data from these servers are shared with hospitals, healthcare centers, family members, and physicians. The proposed edge computing with wearable sensors approach outperforms existing smart healthcare-based approaches in terms of execution speed, latency time, and power consumption. The deep learning method combined with DWT performs better than other similar approaches in the assessment of cardiac arrhythmia and acute stroke prediction. The proposed classifier achieves a sensitivity of 99.4%, specificity of 99.1%, and accuracy of 99.3% when compared with other similar approaches.
Irrigation of farmlands is a tough task to farmers, and due to electricity issues in villages, the overhead increases. Breaking of bund and overflow of water to other farmlands is a big issue to a farmer in spite of his/her loss due to drowning of crops. India being the economy of farmlands, farmers are contributing 74% to it. Therefore, any problem of farmers is the problem of the country. To protect this, farmers have to watch over tube well and bunds the whole time until the irrigation is done. Due to electricity issues in village areas, farmers many times need to spend their complete night in the farmlands, and especially in winters when it's chilly out there. So, to help farmers in this, the authors propose a device that helps farmers do so in an easy way. The visual surveillance and machine learning will now do this job for the farmer.
The rapid growth of the technologies, high bandwidth applications and cloud data centers consume heavy internet service. So, the consumer of the internet expects a high capacity medium for communication. The Elastic Optical Network (EON) provides a flexible and reliable transmission service for the consumers. The spectrum fragmentation is a key challenge in EON. In simple terms, unaligned Frequency Slots (FSs) in the network are referred to as fragmented spectrum, while in defragmentation, the available FSs need to be rearranged to create room for the new connection requests. The problem in defragmentation occurs due to the lack of a continuous spectrum and it leads to depreciation in spectrum usage and simultaneously increasing the Blocking Probability (BP) which disrupts the majority of the existing connections in the network. Several techniques and approaches were suggested to reduce the possibility of disruption and reconfiguration in the network while defragmenting the frequency slots. This paper proposes a new algorithm to overcome the drawbacks and improvement in the quality of service of the network. The proposed algorithm holds the approach of proactive and reactive along with the meta-heuristic nature-inspired optimization technique called Jellyfish Search Optimization (JSO). The proposed combination, PR-DF-JFSO outperforms well in terms of spectrum utilization, network efficiency, and quality of service offered when compared to the state-of-the-art spectrum defragmentation algorithms according to the results of experiments done using standard quality metrics.
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