There exist discreet numbers of clustering algorithms in the literature. Here, we reviewed the appropriate papers which were related to the proposed work.
2.1. Green Computing without Heuristics
Low-energy adaptive clustering hierarchy (LEACH) [34
] was the first hierarchical clustering algorithm in sensor networks. There are two stages per clustering round in LEACH. The first one is related to cluster head election and formation of clusters within the network and the second one deals with data transmission to the cluster head known as the steady-state stage. A probabilistic model is proposed to choose a cluster head in the cluster setup phase; each sensor node has a certain probability of being assigned as cluster head per round. In general, the probability of a sensor node being elected as a cluster head depends upon a predefined threshold value. Every sensor node generates an arbitrary value between 0 and 1. Generated values of each sensor are compared with the threshold value to become cluster head for an ongoing epoch or round when generated value is below the threshold value. Each elected cluster head broadcasts a message using the carrier-sense multiple-access protocol to avoid inter-cluster interference. The strength of the received signal is used by each sensor node to determine which cluster head they want to join. After that, every cluster head gathers information from their member sensor nodes, and applies data aggregation, and then forwards the aggregated packet to the base station [35
]. Thus, LEACH provides equal opportunity for each sensor node to become a cluster head with equal probability. But there are major shortcomings, as it does not take into account the energy consumption of each node, the geographical location of nodes are not included which causes asymmetrical classification of clusters in a network, and it does not use multi-hop nodes for transmission of data. Hybrid energy efficient distributed (HEED) [36
] clustering protocol rectifies the shortcomings of LEACH in terms of uneven formation of clusters by including one extra parameter: the residual energy of nodes with nodes density (i.e., the proximity of neighbors’ sensor nodes) for selection of cluster heads. Node density plays a major role in reducing the intra-cluster communication. Hybrid energy efficient distributed clustering also uses a probabilistic model to elect temporary cluster heads, and every sensor node increases the probability of being a cluster head by twice in between rotations. Hybrid energy efficient distributed clustering also suffers from problems where some of the nodes are exempted from the cluster head selection process, and these nodes resolve this problem by pronouncing themselves as cluster head. In addition, several sensors may be exempted from all clusters or be freely available. Power-efficient gathering in sensor information systems (PEGASIS) [37
] was introduced to save energy by making a chain of sensor nodes; it uses a greedy approach which means every node accepts delivery of data from its closest neighbors, and these acquired data are then transferred to another closest neighbor node. These assembled data keep on moving subsequently between nodes. Data are fused and then transmitted from specified nodes to the base station. The role of the designated node is replaced by another random node. Therefore, all the nodes deplete their energy proportionally or evenly distribute the load among nodes. Further, average energy spent in each cycle is reduced.
2.2. Green Computing Using Fuzzy-Centric Heuristics
Recently, fuzzy logic systems have been applied to elect cluster heads in sensors-enabled IoT networks. Gupta et al. [38
] have proposed to choose a node as cluster head based on energy, density, and centrality of nodes. The main difference between the protocol proposed in Reference [38
] and LEACH clustering is that this information is sent by node to the base station (known as a centralized approach). The base station is solely responsible for the selection of the cluster head. The base station processes these data with the help of a Mamdani-type fuzzy inference system, which gives output as a chance to decide the future of the preferred node if that would be suitable as a cluster head or not. The rest of the operations for a steady-state phase of that kind are similar to LEACH clustering. In Reference [39
], another cluster head selection mechanism (CHEF) is proposed based on residual energy and local distance. Nodes select a cluster head using local information gathered from neighboring nodes, whereas in Reference [38
] the cluster head is elected by the base station. Another improvement over the low-energy adaptive clustering hierarchy (LEACH) protocol is based on fuzzy logic (LEACH-FL) [40
]. This protocol is proposed by Reference [37
], apart from it, LEACH-FL has three distinct fuzzy variables: node density, energy level, and distance to base station. In this mechanism, the base station gathers information from sensor nodes and applies a Mamdani-type fuzzy inference system to figure out whether a node would be interpreted as a cluster head or not. Lee et al. [41
] put forward a clustering head selection algorithm (LEACH-ERE) with the use of energy prediction techniques in accordance with fuzzy logic for homogenous WSNs. All of the above cluster head election mechanisms are based on fuzzy logic, and are intended to equalize the load among sensor nodes, but these are not able to tune the membership function or weight of the fuzzy descriptor to adapt to the environment.
The next one in this series is the cluster head election mechanism using the fuzzy logic (CHEF) protocol [39
] that is almost the same as the Gupta fuzzy protocol. In CHEF, the base station is not responsible for selection of the cluster head; it does not gather any information from sensor nodes. The mechanism for selection of the cluster head is localized (a distributed approach) within a cluster. The setup phase is similar to the setup phase of LEACH. The CHEF protocol uses two fuzzy parameters: residual energy and local distance. The CHEF protocol works in rounds; in each round the sensor nodes select random numbers between 0 and 1, much like LEACH. If the chosen value is less than the threshold value, they calculate their chance using the fuzzy inference system. If the chance value of a tentative node is greater than all other chance values of sensor nodes, than it becomes the cluster head for the current round. It does far better than the Gupta protocol in terms of the number of cluster head selections; the Gupta protocol selects only a single cluster head per network (simulation was done under certain circumstances), although it is claimed that it can be increased, the process of creating more clusters is unclear [38
Another improved version of the LEACH protocol based on fuzzy logic is LEACH-FL [40
]. This protocol coupled with the above Gupta protocol (a centralized approach) has three distinct fuzzy variables: node density, energy level, and distance to base station. In this protocol, the base station gathers information from sensor nodes and applies a Mamdani-type fuzzy inference system to determine whether a node would be interpreted as a cluster head or not. Lee et al. [41
] put forward a clustering head selection algorithm (LEACH-ERE) to predict residual energy in accordance with fuzzy logic for homogenous sensors-enabled IoT. The chance value of a cluster head is determined with the aid of two fuzzy norms, expected residual energy, and residual energy of a node. It is similar to the LEACH protocol where each node makes the decision itself to become a cluster head or not, without the help of the base station (called a distributed or localized approach). The sensor node having both extra residual energy as well as expected residual energy, gains additional benefit in becoming a cluster head. However, LEACH-ERE does not consider the distance between the cluster head and base station, or the node density around the sensor node which can lead to uneven energy consumption over the network.
Recently Nayak and Devulpalli [42
] proposed a new fuzzy-logic-based clustering algorithm where the base station is mobile, and each cluster head does not send aggregated data to the mobile station. There is one super cluster head (SCH) in the network area that gathers the aggregated data from cluster heads and only the SCH dispatches information to the base station. Similar to LEACH, in each round the cluster heads are selected using a probabilistic model. Furthermore, the SCH is elected among cluster heads based on a Mamdani-type fuzzy inference system. According to a distributed approach, each cluster head is determined by its chance value using three fuzzy descriptors: remaining battery power (residual energy), mobility (referring to when the BS changes its position, and then the distance between the SCH and the BS increases or decreases), and centrality (primarily focusing on how central the SCH is to other cluster heads for communication). The chance value is the summation of the centrality mobility and battery power. These fuzzy labels are taken as additives due to the increase or decrease in the mobility and centrality upon the increase or decrease in the mobility of the base station. The chance value that is greater, this cluster head becomes a super cluster head. So, the SCH degrades the transmission taken by nodes, consequently, it reduces the duration of the first node dead over a number of rounds and enhances the network lifetime over LEACH.
In Reference [44
], Abidoye et al. present the significance of the IoT in wireless sensor networks. Energy-efficient models are presented for enabling service-oriented applications in IoT-enabled WSN areas in two stages: in the first stage, the clustering-based model is used for service of the application, and in second phase, an energy-aware model is designed. Basically, those approaches are good, but not good enough for IoT networks, and their performances are poor when considering those networks are static. As the IoT provides dynamic networks, there is a need to improve the algorithms, so we emphasize fuzzy-based techniques with adaptive neural networks, which adapt to the dynamic networks of the IoT as well. In Reference [45
], Yan li et al. proposed an analytic hierarchy process
and fuzzy-based energy management system for industrial equipment management, and showed intensive case studies over IoT networks. In Reference [46
], fuzzy-based vehicular physical systems were analyzed in the Internet of Vehicles (IoV), which uses fuzzy frameworks with the Markov chain to optimize location-oriented channel access delay. Signal-to-inference ratios and channel access delays are used as parameters for channel quality measurement. Hu et al. proposed [47
] another aspect of the IoV which enables communication at the edge with the help of fuzzy logic. The cluster heads or gateways (smart vehicle) are chosen using fuzzy parameter velocity, vehicle neighboring density, and antenna height. The proposed algorithm provides an optimal number of gateways to bridge the licensed sub 6-GHz communication with millimeter wave to enhance network throughput. In Reference [48
], a genetic-based virtualization approach was used to develop a method to overcome the torrent delay and minimize the energy consumption in IoT-enabled sensor networks. In Reference [10
], the proposed algorithm was used for proper deployment of sensor nodes for coverage and connectivity for agricultural purposes. There are two methods for deployment of sensor nodes based on seven metrics that quantified the qualities measurement of sensor nodes. Test-bed-based experiment (INDRIYA) is done for simulation purposes to show the effectiveness of the proposed algorithm.
All of the above protocols deal with fuzzy-logic-based algorithms, but none of them are able to tune the membership function or weight of the fuzzy descriptor. To the best of our knowledge, none of the above are up to the mark for real implementation, where input–output pairs are changing according to the environment. An adaptive artificial neural network is another soft-computing technique where a supervised learning approach is used to adapt to the environment. Therefore, we propose a novel adaptive neuro-fuzzy clustering algorithm (ANFCA) using both fuzzy logic and a neural network to address the problem of leaning rate of membership function, balancing the load, and minimizing the energy consumption to improve the lifetime of the sensor-enabled IoT.