High-level abstractions in wireless sensor networks: Status, taxonomy, challenges, and future directions

Alajlan, Abrar M.; Elleithy, Khaled

doi:10.1109/aseezone1.2014.6820645

Cited by 2 publications

(3 citation statements)

References 36 publications

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“…Due to the advantages of the algorithmic skeleton, researchers utilize it in different fields of computer engineering science. Some examples are listed in the following, accompanied by a few article samples: Parallel programming for distributed memory environments [16], Mainstream parallel programming [17], Multi-core clusters [18], Evolutionary multi-agent systems in Erlang [19], [20], High-performance computing [21], [22], Heterogeneous CPU-GPU architectures [23]- [25], Using MapReduce model in Big data [26], Presenting fast solver for structured linear systems [27], Evaluating approximate computing and heterogeneity for energy efficiency [28], Replicable parallel branch and bound search [29] and Wireless sensor networks [30], [31].…”

Section: Related Workmentioning

confidence: 99%

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Reusable Algorithmic Skeleton Framework for Clustering Algorithms in Wireless Sensor Network (SCW)

Taheri

Savadi

Abrishami

2023

Preprint

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In Wireless Sensor Networks (WSNs), clustering is often used to improve communication and routing. Therefore, clustering approaches highly attract several researchers since performing clustering saves energy, and energy efficiency is a significant goal in WSN. To beneficially adopt WSN technology, efficient application development is necessary. Therefore, a user-friendly programming abstraction is required to simplify the programming chore without sacrificing efficiency. Using suitable higher-level programming abstraction, it is neither obligatory for a programmer to be an expert in most fields related to WSN nor to be distracted from the application logic by focusing on low-level system issues. To ease the development of new clustering algorithms, a prefabricated algorithmic skeleton, namely SCW, is presented which only requires two functions to be filled in, i.e., to be implemented. The rest of the work (e.g., synchronization, sensing the environment, data aggregation, nodes’ energy calculations, and routing) will be handled by the proposed framework. Hence, SCW has the capability of performing a level of optimization in the background without user interference. By considering software metrics such as Lines of Code (LoC), Halstead metrics, and McCabe complexity while employing the proposed framework, one can implement a WSN clustering algorithm with fewer source lines of code, less programming effort, and difficulty, less time to understand and implement when compared to a built-from-scratch implementation. Although this algorithmic skeleton framework is proposed for implementation, to show its efficiency in this paper, we use the simulation environment.

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Section: Related Workmentioning

confidence: 99%

“…In the Node-dependent approach, a group of nodes can be considered at the same time in one single code. This approach permits the user to express the performance of a distributed system based on the nodes and their states [31], [44].…”

Section: Related Workmentioning

confidence: 99%

Reusable Algorithmic Skeleton Framework for Clustering Algorithms in Wireless Sensor Network (SCW)

Taheri

Savadi

Abrishami

2023

Preprint

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“…Several scalability metrics were used to compare the architectures, such as availability, latency, inter-server communication, resource costs, energy consumption, etc. In addition, several scalability difficulties faced by these categories, such as handling a huge volume of users, network resource management, information dissemination and storage, and energy consumption, have been thoroughly examined [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Twitter Data Analysis Using Hadoop and ‘R’ and Emotional Analysis Using Optimized SVNN

Kumar¹,

Manoj²,

Geetha³

2023

Computer Systems Science and Engineering

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Standalone systems cannot handle the giant traffic loads generated by Twitter due to memory constraints. A parallel computational environment provided by Apache Hadoop can distribute and process the data over different destination systems. In this paper, the Hadoop cluster with four nodes integrated with RHadoop, Flume, and Hive is created to analyze the tweets gathered from the Twitter stream. Twitter stream data is collected relevant to an event/topic like IPL-2015, cricket, Royal Challengers Bangalore, Kohli, Modi, from May 24 to 30, 2016 using Flume. Hive is used as a data warehouse to store the streamed tweets. Twitter analytics like maximum number of tweets by users, the average number of followers, and maximum number of friends are obtained using Hive. The network graph is constructed with the user's unique screen name and mentions using 'R'. A timeline graph of individual users is generated using 'R'. Also, the proposed solution analyses the emotions of cricket fans by classifying their Twitter messages into appropriate emotional categories using the optimized support vector neural network (OSVNN) classification model. To attain better classification accuracy, the performance of SVNN is enhanced using a chimp optimization algorithm (ChOA). Extracting the users' emotions toward an event is beneficial for prediction, but when coupled with visualizations, it becomes more powerful. Bar-chart and wordcloud are generated to visualize the emotional analysis results.

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High-level abstractions in wireless sensor networks: Status, taxonomy, challenges, and future directions

Cited by 2 publications

References 36 publications

Reusable Algorithmic Skeleton Framework for Clustering Algorithms in Wireless Sensor Network (SCW)

Reusable Algorithmic Skeleton Framework for Clustering Algorithms in Wireless Sensor Network (SCW)

Twitter Data Analysis Using Hadoop and ‘R’ and Emotional Analysis Using Optimized SVNN

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