Optimizing energy consumption in WSN-based IoT using unequal clustering and sleep scheduling methods

An intelligent sleep-awake energy management system for wireless sensor network

In recent times, Wireless Sensor Networks (WSNs) are becoming more and more popular and are making significant advances in wireless communication thanks to low-cost and low-power sensors. However, since WSN nodes are battery-powered, they lose all of their autonomy after a certain time. This energy restriction impacts the network’s lifetime. Clustering can increase the lifetime of a network while also lowering energy use. Clustering will bring several similar sensors to one location for data collection and delivery to the Base Station (BS). The Cluster Head (CH) uses more energy when collecting and transferring data. The life of the WSNs can be extended, and efficient identification of CH can minimize energy consumption. Creating a routing algorithm that considers the key challenges of lowering energy usage and maximizing network lifetime is still challenging. This paper presents an energy-efficient clustering routing protocol based on a hybrid Mayfly-Aquila optimization (MFA-AOA) algorithm for solving these critical issues in WSNs. The Mayfly algorithm is employed to choose an optimal CH from a collection of nodes. The Aquila optimization algorithm identifies and selects the optimum route between CH and BS. The simulation results showed that the proposed methodology achieved better energy consumption by 10.22%, 11.26%, and 14.28%, and normalized energy by 9.56%, 11.78%, and 13.76% than the existing state-of-art approaches.

/pdf/a-hybrid-mayfly-aquila-optimization-algorithm-based-energy-3w1376c0.pdf

A Hybrid Mayfly-Aquila Optimization Algorithm Based Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks

Wireless sensor networks (WSNs) have now become a substantial part of our life as it provides an incredible setup for monitoring environmental conditions such as glaciers melting, earthquakes, climate change, volcanic eruption, agriculture, and other natural calamities. One of the most important challenges in data gathering and examining accurate data is “energy consumption.” However, continuously changing environmental conditions, mobility in devices and extreme resource usage, such as battery capacity in WSNs collectively contribute to a high possibility of redundant data transmission thus making it tremendously hard to achieve a satisfactory network lifetime. Additionally, these environmental monitoring applications periodically senses and aggregate data which usually exhibits high temporal and spatial redundancies. As a result, exploiting these redundancies is essential for both resource utilization, as well as accurate data transmission. Since a huge amount of energy is exhausted in transmitting the redundant data, which has become a bottleneck in scaling such applications. In this context, this article provides a thorough examination of existing energy‐efficient redundant data transmission reduction techniques, as well as their strengths and drawbacks. The idea of redundant data transmission reduction concept is divided into three stages: the sensor nodes (SN) at the first stage, the cluster heads (CHs) at the second stage, and the base station (BS) at the third stage. Furthermore, this survey focuses on the current issues and challenges, as well as the future directions in reducing redundant data transmission for future investigation.

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

Clustering is an effective topology control approach that evenly distributes loads across sensor nodes, enhances network scalability, and increases the lifetime in wireless sensor networks. In this paper, we propose a novel energy-efficient weighted cluster head (CH) selection approach that improves the overall performance of the network and increases energy efficiency. An optimization strategy is proposed that emphasizes adjusting the transmission range with the appropriate node density, which increases energy efficiency for intra- and inter-cluster communications to 86% and 97%, respectively. In addition, the implementation of a quantum search algorithm for choosing the CH is explained. Compared to the classical method such as EECS and HEED, the proposed quantum search algorithm has a quadratic speed-up advantage. The classical search algorithm requires N steps to find a specific element in an array of N elements, but instead of using a classical algorithm, Grover’s quantum search algorithm minimizes the complexity to O (N). In this work, an energy-efficient cluster head selection approach is illustrated through a classical weighted clustering algorithm, and its implementation is also extended through a quantum weighted search algorithm which is demonstrated by the simulation results.

/pdf/applying-quantum-search-algorithm-to-select-energy-efficient-1b0a7119.pdf

Applying Quantum Search Algorithm to Select Energy-Efficient Cluster Heads in Wireless Sensor Networks

A two-step clustering to minimize redundant transmission in wireless sensor network using sleep-awake mechanism


 
 
 
Project allocation is an annual challenge for lecturers and students. The process of allocating project involves matching preferences of students over project and with of staff over the student, and is thus an instance of stable marriage problem from theoretical computer science aspect. The aim is to find a stable allocation of project to students, such that it is impossible to find a project swap that would make all involved parties (both students, both staff) happier. This paper investigated efficacy of stable marriage algorithm and deployed basic Gale Sharply Algorithm into the process of allocating student project. A system was developed using ruby and MySQL to handle the task. The result showed that the algorithm was able to improve the process by enhancing the stability involved.  
  
 
 
 


/pdf/implementation-of-stable-marriage-algorithm-in-student-q15mf58xdq.pdf

Implementation of Stable Marriage Algorithm in Student Project Allocation

Nowadays, people find it easier to express opinions via social media-formally known as Web 2.0. Sentiment analysis is an essential field under natural language processing in Computer Science that deals with analyzing people's opinions on the subject matter and discovering the polarity they contain. These opinions could be processed in collective form (as a document) or segments or units as sentences or phrases. Sentiment analysis can be applied in education, research optimization, politics, business, education, health, science and so on, thus forming massive data that requires efficient tools and techniques for analysis. Furthermore, the standard tools currently used for data collection, such as online surveys, interviews, and student evaluation of teachers, limit respondents in expressing opinions to the researcher's surveys and could not generate huge data as Web 2.0 becomes bigger. Sentiment analysis techniques are classified into three (3): Machine learning algorithms, lexicon and hybrid. This study explores sentiment analysis of Web 2.0 for novice researchers to promote collaboration and suggest the best tools for sentiment data analysis and result efficiency. Studies show that machine learning approaches result in large data sets on document-level sentiment classification. In some studies, hybrid techniques that combine machine learning and lexicon-based performance are better than lexicon. Python and R programming are commonly used tools for sentiment analysis implementation, but SentimentAnalyzer and SentiWordnet are recommended for the novice. 
Keywords:   Sentiment Analysis; Web 2.0; Applications; Tools; Novice

Sentiment Analysis in the Era of Web 2.0: Applications, Implementation Tools and Approaches for the Novice Researcher

Open-source software has been widely developed and adopted for different purposes, including in the educational sector. Many institutions of learning extensively utilize open-source e-learning software to complement their in-class programs, especially during the COVID-19 pandemic. But many institutions of learning in Nigeria are still unable to utilize the opportunity due to a lack of adequate awareness and guidance on how to adopt the software. This study is aimed at educating the Nigerian public on open-source e-learning software and its benefits in education, especially in tertiary institutions amid the COVID-19 pandemic. To better have a wide coverage of respondents in Nigerian tertiary institutions, we conducted an online survey and a paper-based questionnaire. A sample of 500 responses was collected from Nigerians with different high educational levels regarding Open-Source e-learning adoption and awareness. However, only 349 responses were returned. The results of the survey were analyzed using python-based analysis libraries. The findings indicated that a greater percentage of the respondents were aware of what constitutes open-source e-learning but that most institutions in Nigeria have not fully utilized the platform for learning activities. It is implied that a reasonable number of respondents have literacy knowledge of open-source learning and agree that COVID-19 has greatly influenced the use of e-learning software in Nigerian institutions. It is therefore recommended that more work be done to improve the awareness level of various institutions to fully utilize the technology and the benefits it represents. This led to the development of a web-based solution for creating more awareness among the public. This would help close the gap found in this study and affect how open-source e-learning software is used in Nigerian schools.

A Web-based Awareness System for Improving Open-source e-learning Software adoption by Nigerian Higher Institutions of learning

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Papers

A two-step clustering to minimize redundant transmission in wireless sensor network using sleep-awake mechanism

Implementation of Stable Marriage Algorithm in Student Project Allocation

Sentiment Analysis in the Era of Web 2.0: Applications, Implementation Tools and Approaches for the Novice Researcher

A Web-based Awareness System for Improving Open-source e-learning Software adoption by Nigerian Higher Institutions of learning