## 2 Related Work
Three mobile strategies are discussed based on: (i) event, (ii) residual energy and (iii) combination of both (i) and (ii) (i.e., hybrid mobility). In this hybrid strategy, the moving decision is made based on the event and the residual energy. The LEACH protocol and most of its successors use the _radio energy dissipation model_. This model calculates the power consumption and attributes energy loss due to channel transmission. The energy consumption on the wireless sensor node depends on the distance of the BS and on the number of transmit and receive operations. The main idea behind many algorithms is to reduce the distances and frequency of communications between the sensor and BS. The batteries of sensor nodes do not recharge after node deployment. Therefore, we need alternative energy sources, such as solar power. Many proposed solutions [25; 26; 27] include solar-powered sensors because a solar-powered sensor is more likely to be elected as a CH. The problem with solar power is that this energy source is not permanent. For example, a solar-powered node is elected as CH at the beginning of the round, and after that, it has no energy to carry out its tasks until the end of the round. From another perspective, WSNs can be studied as bio-inspired systems. Some characteristics of a WSN can be improved, if sensors are organized based on the knowledge of animal \"social\" behavior [28; 29]. In order to consider a WSN as a bio-inspired system, it is often useful to observe the behavior of birds in the flock [30; 31; 32]. [PERSON] et al. in [33] presented an algorithm that is based on the flock optimization algorithm. Their algorithm takes into account two factors for the CH selection: (i) the minimum distance between the CHs and the cluster members, and (ii) the residual energy of the nodes. In [34], a WSN using multi-hop routing for communication is considered, and an algorithm, based on Particle Swarm Optimization (PSO), has been implemented for finding the optimal location of the sink. The algorithm presented in [31] suggests a way to establish a minimum distance between CH and its cluster members. This algorithm is based on the migration behavior of a flock of birds in the process of searching for food. Most of the proposed clustering algorithms overburden the CH during cluster formation. To overcome this problem, many researchers have come up with the idea of fuzzy logic (FL), which is applied in WSN for decision making. These algorithms focus on the efficiency of CH, which could be adaptive, flexible, and intelligent enough to distribute the load among the sensor nodes that can enhance the network lifetime. But unfortunately, most of the algorithms use type-1 FL (T1 FL) model. In addition, many clustering algorithms have not considered the expected residual energy, which is the predicated remaining energy for being selected as a CH and running a round. [PERSON] and [PERSON] [35] proposed a fuzzy-logic-based clustering approach (namely LEACH-ERE) with an extension to the energy predication to prolong the lifetime of WSNs by evenly distributing the workload. The LEACH-ERE algorithm prolongs the lifetime of the WSN by evenly distributing the workload. It selects the CHs considering expected residual energy of the sensor nodes. LEACH-ERE is more efficient than other distributed algorithms, such as LEACH and CHEF [36] and can be applied to large-scale WSNs. [PERSON] and [PERSON] [37] proposed an interesting fuzzy logic-based clustering algorithm for WSNs. In their approach, a super-CH (SCH) is elected among the CHs who can only send the information to the mobile BS by choosing suitable fuzzy descriptors, such as remaining battery power, mobility of BS, and centrality of the clusters. A fuzzy inference engine (Mamdani's rule) is used to elect the chance to be the SCH. Their protocol performs better than the LEACH protocol in terms of the first node dies, half node alive, better stability, and better lifetime. In particular, their protocol is more stable and have 20% longer lifetime compared to LEACH.