## 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 overall decrease in energy consumption and also reduces the interference among sensor nodes due to specific allocation of timeslots for communication purposes [4; 5]. The clustering topology in WSN can effectively optimize the data redundancy by significantly reducing the size of the collected data using data aggregation and data fusion techniques at CH level. The aggregated or fused data can then be forwarded to the Base Station (BS) for further processing and accurate decision making of interested events [4; 5; 6]. The recent literature shows that researchers have proposed a working-sleeping cycle strategy in WSN to save battery power in case the sensor nodes are idle and not performing any of the designated tasks. [PERSON] et al. [7] discussed that the sensor nodes go to sleep to save their battery power and wake up before performing their routine operations. These node scheduling techniques can be categorized as synchronous and asynchronous working-sleeping scheduling. These node scheduling techniques are designed in accordance with the scenario to prolong the network lifetime and improve energy utilization by creating an opportunistic node connection between sensor nodes. According to [8; 9; 10; 11; 12], Opportunistic Routing (OR) is a paradigm for wireless networks that benefits from broadcast characteristics of a wireless medium by selecting multiple sensor nodes as candidate forwarders. In [10; 11; 12], a set of nodes were selected as potential forwarders that transmitted the data packet according to some special criteria after receiving data packet from their neighbors. This set of sensor nodes is called a Candidate Set (CS). The performance of OR significantly depends on several key factors, such as the OR metric, the candidate selection algorithm, and the candidate coordination method. The idea of OR can also be exploited in clustering topology of WSN to extend network lifetime, improve energy utilization, increase the packet delivery ratio (PDR) by adapting asynchronous working-sleeping cycle strategy. Entropy in information theory is employed in various fields involving data analysis. Entropy uses the Probability Distribution Function (PDF) to statistically measure the degree of uncertainty in information sources [13]. The entropy \(H(X)\) of a random variable \(X=\{x_{1},x_{2},\ldots x_{n}\}\) having probability distribution as \(p(X)\) can be given as \(H(X)=-\sum\limits_{x\in X}p(x)log_{2}p(x)\) for \(0\leq H(X)\leq 1\)[14; 15; 16]. As we want to utilize the functionality of information entropy in clustering WSN, it should be kept in focus that CH or BS should not be hesitant or irresolute about any of their decisions regarding cluster formation and data fusion. Keeping in view the opportunistic connection between sensor nodes in heterogeneous clustering, we selected multiple parameters including an asynchronous working-sleeping cycle, status transition frequencies, residual energy, link quality factor in terms of signal-to-noise ratio, distance between sensor node and BS, and number of supported sensor nodes by a potential CH as our attributes of hesitant fuzzy set.