Energy-Efficient Unequal Clustering Algorithm Using Hybridization of Social Spider with Krill Herd in IoT-Assisted Wireless Sensor Networks
At present, the Internet of Things (IoT) is the interconnection of exclusively recognizable embedded computing devices in the available Internet framework. Wireless sensor networks (WSNs) comprise numerous sensor nodes which undergo deployment in various applications such as medicine, smart industry, surveillance, precision agriculture, etc. [1,2]. Besides, WSN offered an essential part in the emergence of technologies, e.g., big data, cloud, and IoT . On the other hand, these have several design limitations because of the high processing and energy limitations. As a result, energy-efficient clustering techniques have emerged. Normally, WSN comprises hundreds to thousands of sensor nodes and base station (BS). The nodes in WSN transmit data to BS independently or might create diverse clusters with cluster heads (CHs) . The data transmission between the nodes and BS takes place in a direct and indirect way . The architecture of clustering is shown in Figure 8.1. The issue in single-hop data transmission is the excessive energy utilization because of the massive transmission range . At the same time, the nodes which are present nearer to the BS expire rapidly in multi-hop data transmission as they transmit every packet of the network to BS. It is called as hot spot issue and several techniques have been presented for mitigating it by the creation of unequal clusters with respect to size .
Clustering as well as unequal clustering models has been employed to achieve energy efficiency in WSN. The clustering process in WSN takes place by merging the sensor nodes to clusters set. The clustering process enables the CHs to gather the data from the cluster members (CM) [5,8]. The utilization of energy by the CHs closer to the BS is higher than the CHs far from the BS. It implies that the CHs closer to BS are far from the CHs located away from the BS due to the presence of intra-cluster data transmission from its CM, data aggregation, and inter-cluster data from other CHs to relay data to BS. It affects the network connectivity, and the clusters closer to BS lead to the coverage issue which is known as hot spot issue.
FIGURE 8.1 Clustering process in wireless sensor network.
Hybridization of SS with Krill Herd
FIGURE 8.2 Unequal clustering process.
The unequal clustering approach is an effective way that deals with the hot spot issue due to the fact that it can be employed to balance the load between the CHs. The aim of unequal clustering technique is identical to the equal clustering one with extra feature of achieving energy efficiency and resolving hot spot issue . The unequal clustering organizes the cluster size based on its distance to BS. The architecture of unequal clustering is shown in Figure 8.2. A cluster with least size implies lower number of CM and lesser intra-cluster data transmission . So, smaller-sized clusters can spend the energy for inter-cluster data transmission and CHs cannot exhaust their energy quickly. In case of long distance to BS, the cluster size will be increased. When the cluster has more number of CMs, large amount of energy will be utilized for intra-cluster data transmission . Since the cluster is located far from the BS, inter-cluster data transmission will be low and there is no requirement to utilize high energy for routing data between clusters.
The unequal clustering model will force every CH to utilize an identical amount of energy; so, the CHs near or far from the BS utilize identical quantity of energy. In addition, the construction of clusters might create a two-level hierarchy with high as well as low levels. The sensing devices will send the data regularly to its respective CH which will perform data aggregation and send it to the BS in a straightforward way or intermittent CHs. In case of a death of a CH or if it moves to other clusters, the reclustering process will take place among the nodes to elect new CHs.
This chapter introduces a new hybridization of Social Spider (SS) with Krill Herd (KH) algorithm named SS-KH for unequal clustering in WSN. In this case, the SS algorithm firstly selects the tentative cluster heads (TCH) and then the KH algorithm is applied to decide the final cluster heads (FCH). The presented SS-KH algorithm successfully selects the CHs in a proficient way. This algorithm undergoes diverse scenarios based on the positions of CHs. Then, a detailed relative examination is made with respect to different measures under several dimensions.
The chapter is organized as: some background information and related studies are given in Sections 8.2 and 8.3, respectively. The presented SS-KH algorithm is demonstrated in Section 8.4 and its results are analyzed in Section 8.5. Conclusions are derived in Section 8.6.