sensors Article Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks Junaid Anees 1,2 , Hao-Chun Zhang 1, * , Sobia Baig 3 , Bachirou Guene Lougou 1 and Thomas Gasim Robert Bona 4 1 School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China; j.anees@hit.edu.cn (J.A.); 15bf02043@hit.edu.cn (B.G.L.) 2 Satellite Control Facility (SCF-L) directorate, SE&T wing, Space & Upper Atmosphere Research Commission, Lahore 54000, Pakistan 3 Department of Electrical and Computer Engineering, Lahore Campus, COMSATS University Islamabad (CUI), Lahore 54000, Pakistan; drsobia@cuilahore.edu.pk 4 School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China; thomasrobert@stu.hit.edu.cn * Correspondence: hczhang@hit.edu.cn; Tel.: +86-451-8641-2328 Received: 13 January 2020; Accepted: 5 February 2020; Published: 8 February 2020   Abstract: Limited energy resources of sensor nodes in Wireless Sensor Networks (WSNs) make energy consumption the most significant problem in practice. This paper proposes a novel, dynamic, self-organizing Hesitant Fuzzy Entropy-based Opportunistic Clustering and data fusion Scheme (HFECS) in order to overcome the energy consumption and network lifetime bottlenecks. The asynchronous working-sleeping cycle of sensor nodes could be exploited to make an opportunistic connection between sensor nodes in heterogeneous clustering. HFECS incorporates two levels of hierarchy in the network and energy heterogeneity is characterized using three levels of energy in sensor nodes. HFECS gathers local sensory data from sensor nodes and utilizes multi-attribute decision modeling and the entropy weight coefficient method for cluster formation and the cluster head election procedure. After cluster formation, HFECS uses the same techniques for performing data fusion at the first hierarchical level to reduce the redundant information flow from the first-second hierarchical levels, which can lead to an improvement in energy consumption, better utilization of bandwidth and extension of network lifetime. Our simulation results reveal that HFECS outperforms the existing benchmark schemes of heterogeneous clustering for larger network sizes in terms of half-life period, stability period, average residual energy, network lifetime, and packet delivery ratio. Keywords: hesitant fuzzy entropy; heterogeneous clustering; wireless sensor networks; opportunistic routing; multi-attribute decision modeling; data fusion 1. Introduction In WSNs, the wireless sensors are spatially distributed autonomous devices responsible for sensing the change in the required physical phenomena of their surrounding environment using a small microprocessor, a few transducers, a radio transceiver and a low-power battery [1,2]. These wireless sensor devices collaborate with each other for data sensing, data collection and aggregation purposes [2,3]. In order to reduce the communication overhead and to allocate resources to sensor nodes effectively, we need a topology architecture in which sensor nodes are organized in clusters. Each cluster includes one Cluster Head (CH) and several Cluster Members (CM) [4]. The multi-hop routing used in clustering topology to forward the sensed data from source to destination results in the Sensors 2020, 20, 913; doi:10.3390/s20030913 www.mdpi.com/journal/sensors