Accurate Energy-Efficient Localization Algorithm for IoT Sensors

• Published in: ICC 2020 - 2020 IEEE International Conference on Communications (ICC) pp. 1 - 6
• Main Authors: Mehrabi, Mahshid, Taghdiri, Pooria, Latzko, Vincent, Salah, Hani, Fitzek, Frank H. P.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2020
• Subjects: Biological cells; DV-Hop; Evolutionary; Hopsize; Intelligent sensors; Internet of Things; Localization; Mathematical model; Power demand; Shuffled Frog Leaping Algorithm; Wireless sensor networks
• ISSN: 1938-1883
• DOI: 10.1109/ICC40277.2020.9148860

Wireless Sensor Networks (WSNs) applications have attracted attention in Internet of Things (IoT) as a novel networking paradigm consisting of billions of small sensor nodes. These sensors collect environmental information and communicate with each other to provide solutions for real time IoT applications' requirements. Since the majority of applications require location-based services, it is necessary to improve the accuracy of localization algorithms. DV-Hop is one of the most attractive range-free localization algorithms in wireless sensor networks and several works have been undertaken to improve its accuracy, however, since sensor nodes have limited power resources, the energy consumption of nodes should be also considered. In this paper, we propose a method based on DV-Hop to improve both accuracy and power consumption. Each unknown node calculates the Hopsize of each anchor node according to the limited information it has from the network topology; therefore there is no need to broadcast the Hopsize from anchor nodes, and in this way energy can be saved. In the next step, we use Shuffled Frog Leaping Algorithm (SFLA) as an evolutionary algorithm to improve the accuracy of estimated Hopsizes and a hybrid Genetic-PSO algorithm is applied to the third step of DV-Hop to achieve more accurate values for unknown nodes' positions. Simulation results show that our proposed method decreases the localization error significantly by jointly considering the energy consumption of sensors and is overall 44% more accurate than DV-Hop.