Intelligent Wireless Sensor Network Deployment for Smart Communities

Smart Communities demand a huge amount of data to monitor different human activities. Until now, wireless sensor networks (WSNs) have provided these data with a low cost and low intelligence networks. However, the constraints and the required baseline performance of WSNs have increased exponentially, and there is a lack of a global proposal to satisfy the demand of Smart Communities. There are several approaches regarding efficient WSN deployments to maximize network lifetime and performance. The vast majority of those proposals try to separately address several inherent constraints and limitations related to energy and node lifetime. In fact, they typically provide efficient management approaches based on existing solutions, but they do not propose new tools or algorithms to afford them. To tackle this problem in a global manner, we introduce the concept of intelligent WSNs. In order to outfit the WSN with a huge amount of possibilities to route and control the different nodes, our nodes incorporate the possibility of transmitting and receiving signals from/to several nodes jointly through beamforming. In our approach, this latter technique is intelligently applied to allow the deployment of new WSNs to be able to react and to adapt its features depending on the environmental conditions, maintaining QoS and enlarging network lifetime. This is the main goal of our proposal.

[1]  Mohsen Guizani,et al.  Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[2]  Rafael Marcos Luque Baena,et al.  Clustering and Beamforming for Efficient Communication in Wireless Sensor Networks , 2016, Sensors.

[3]  H. T. Mouftah,et al.  Routing protocols for duty cycled wireless sensor networks: A survey , 2012, IEEE Communications Magazine.

[4]  Houbing Song,et al.  Internet of Things and Big Data Analytics for Smart and Connected Communities , 2016, IEEE Access.

[5]  Toufik Ahmed,et al.  On Energy Efficiency in Collaborative Target Tracking in Wireless Sensor Network: A Review , 2013, IEEE Communications Surveys & Tutorials.

[6]  Jaime Lloret,et al.  Power saving and energy optimization techniques for Wireless Sensor Networks , 2011 .

[7]  Halil Yetgin,et al.  A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks , 2017, IEEE Communications Surveys & Tutorials.

[8]  Chee Yen Leow,et al.  Distributed and Collaborative Beamforming in Wireless Sensor Networks: Classifications, Trends, and Research Directions , 2017, IEEE Communications Surveys & Tutorials.

[9]  Mohsen Guizani,et al.  Smart Cities: A Survey on Data Management, Security, and Enabling Technologies , 2017, IEEE Communications Surveys & Tutorials.

[10]  Miguel Garcia,et al.  Saving energy and improving communications using cooperative group-based Wireless Sensor Networks , 2013, Telecommun. Syst..

[11]  Witold Pedrycz,et al.  An Evolutionary Multiobjective Sleep-Scheduling Scheme for Differentiated Coverage in Wireless Sensor Networks , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[12]  Miguel Garcia,et al.  Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper) , 2011, J. Commun..

[13]  Santiago Zazo,et al.  Energy Efficient Collaborative Beamforming in Wireless Sensor Networks , 2014, IEEE Transactions on Signal Processing.

[14]  Francisco Luna,et al.  Distributed Multi-Objective Metaheuristics for Real-World Structural Optimization Problems , 2016, Comput. J..

[15]  Jarrod Trevathan,et al.  Developing low-cost intelligent wireless sensor networks for aquatic environments , 2010, 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[16]  Federico Viani,et al.  Evolutionary Optimization Applied to Wireless Smart Lighting in Energy-Efficient Museums , 2017, IEEE Sensors Journal.