TY - GEN
T1 - An evolutionary graph-based approach for managing self-organized IoT networks
AU - Haddad, Yara Mahfood
AU - Ali, Hesham H.
PY - 2017/11/21
Y1 - 2017/11/21
N2 - Wireless sensor networks (WSNs) are one of the most rapidly developing information technologies and promise to have a variety of applications in Next Generation Networks (NGNs) including the IoT. In this paper, the focus will be on developing new methods for efficiently managing such large-scale networks composed of homogeneous wireless sensors/devices in urban environments such as homes, hospitals, stores and industrial compounds. Heterogeneous networks were proposed in a comparison with the homogeneous ones. The efficiency of these networks will depend on several optimization parameters such as the redundancy, as well as the percentages of coverage and energy saved. We tested the algorithm using different densities of sensors in the network and different values of tuning parameters for the optimization parameters. Obtained results show that our proposed algorithm performs better than the other greedy algorithm. Moreover, networks with more sensors maintain more redundancy and better percentage of coverage. However, it wastes more energy. The same method will be used for heterogeneous wireless sensors networks where devices have different characteristics and the network acts more efficient.
AB - Wireless sensor networks (WSNs) are one of the most rapidly developing information technologies and promise to have a variety of applications in Next Generation Networks (NGNs) including the IoT. In this paper, the focus will be on developing new methods for efficiently managing such large-scale networks composed of homogeneous wireless sensors/devices in urban environments such as homes, hospitals, stores and industrial compounds. Heterogeneous networks were proposed in a comparison with the homogeneous ones. The efficiency of these networks will depend on several optimization parameters such as the redundancy, as well as the percentages of coverage and energy saved. We tested the algorithm using different densities of sensors in the network and different values of tuning parameters for the optimization parameters. Obtained results show that our proposed algorithm performs better than the other greedy algorithm. Moreover, networks with more sensors maintain more redundancy and better percentage of coverage. However, it wastes more energy. The same method will be used for heterogeneous wireless sensors networks where devices have different characteristics and the network acts more efficient.
KW - Heterogeneous
KW - Homogeneous
KW - IoT
KW - Self-organized networks
KW - Wireless sensor network
UR - http://www.scopus.com/inward/record.url?scp=85047969755&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047969755&partnerID=8YFLogxK
U2 - 10.1145/3132062.3132068
DO - 10.1145/3132062.3132068
M3 - Conference contribution
AN - SCOPUS:85047969755
T3 - MobiWac 2017 - Proceedings of the 15th ACM International Symposium on Mobility Management and Wireless Access, Co-located with MSWiM 2017
SP - 113
EP - 120
BT - MobiWac 2017 - Proceedings of the 15th ACM International Symposium on Mobility Management and Wireless Access, Co-located with MSWiM 2017
PB - Association for Computing Machinery, Inc
T2 - 15th ACM International Symposium on Mobility Management and Wireless Access, MobiWac 2017
Y2 - 21 November 2017 through 25 November 2017
ER -