Computational Intelligence in Wireless Sensor Networks: A Survey

By Raghavendra V. Kulkarni, Anna Förster, Ganesh Kumar Venayagamoorthy. IEEE Communications Surveys and Tutorials, 2011

728 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Ocean Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/aea8fefa503808eef58228627d06513b00bbdad1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Rajkumar Buyya
2. Zesong Fei
3. Bin Li
4. Marimuthu Swami Palaniswami
5. Abdelmadjid Bouabdallah
6. J. Gubbi
7. Lajos Hanzo
8. Slaven Marusic

In Ocean Engineering

1. Marimuthu Swami Palaniswami
2. Rajkumar Buyya
3. J. Gubbi
4. Lajos Hanzo
5. Slaven Marusic

In Water Science and Technology

1. Marimuthu Swami Palaniswami
2. J. Gubbi
3. Slaven Marusic
4. Sudip Misra
5. Narendra Singh Raghuwanshi
6. Tamoghna Ojha

Citations in the corpus

• Data Mining for Internet of Things: A Survey (2014, background): “For the scale of, say, a smart city, decentralization is essential because the data needed to be processed at the same time are way too large.”
• An Information Framework for Creating a Smart City Through Internet of Things (2014, background): “To further make sense of the information and convert it into knowledge, state-of-the-art computational intelligence techniques such as genetic algorithms, evolutionary algorithms , and neural networks are necessary [14].”
• Energy efficiency in wireless sensor networks: A top-down survey (2014, background): “On the other hand, solutions that require less computations and storage are convenient for distributed computations carried out at each node [143].”
• Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities (2017, neutral): “detect data anomalies [35].”
• A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems (2016, background): “highlighted numerous challenges facing each of the MOPs discussed [42].”
• Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges (2015, background): “Using this feature, multiple sensor nodes collaborate among themselves, and collectively take the final decision (Kulkarni et al., 2011; Bhattacharjee et al., 2012; Wu et al., 2012; Yao et al., 2013).”
• Internet of Things (IoT): A vision, architectural elements, and future directions (2012, methodology): “Current state-of-the-art methods use shallow learning methods where pre-defined events and data anomalies are extracted using supervised and unsupervised learning [66].”
• Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications (2014, neutral): “Meanwhile, [16] discusses computational intelligence methods”