# Cluster-Based Routing Protocol with Static Hub (CRPSH) for WSN-Assisted IoT Networks
## 2 Literature Review
Researchers have tried to solve this problem by delivering more energy-efficient routing protocols such as a mobile IoT hub, multihop communication, constrained mobility of the IoT hubs, and grid-based routing protocols. [PERSON] et al. [36] propose the GMR multicast routing protocol. It mainly deals with multicast messages produced by the source IoT device. It finds the subset of IoT devices to send messages to all destinations with less bandwidth and utilizes minimal sensor resources such as battery, memory, and CPU usage. Here, the IoT devices know their position, and periodic beacons are used to send their position information to their neighboring IoT devices. The IoT devices are selected by using their position and running GMR for sending the data to destinations. The best subset of IoT devices can reduce the distance to destinations and reduce the overheads. If some neighbors fail to reduce the distance to the destination, then face routing is used to exit local minima until it finds a new IoT device. [PERSON] and [PERSON] proposed the PANEL routing protocol [37], which divides the network area into clusters geographically and deploys the IoT devices randomly. A PANEL elects the aggregator IoT devices in each cluster. Each aggregator IoT device receives queries from the IoT hub. Here, the processing time is divided into epochs, and for each epoch, a different aggregator IoT device is selected for load balancing in the network. PANEL routing may be of two types. Routing within the cluster is called intracluster routing. Here, the message from IoT devices is routed to current or former aggregator IoT devices. Routing among the clusters is called intercluster routing, where the messages come from a distant source. It suffers from IoT device depletion when cluster connections become divided, which causes the election of more than one within-cluster aggregator. [PERSON] and [PERSON] proposed a Grid-based Coordinated Routing protocol in which the total network area is divided into small-sized (user-specified) grids [38]. Each grid has a coordinating IoT device that determines the routing. The source IoT device floods data and queries to the network. The IoT hub responds to these queries by sending information back on the reverse path. This process continues until any coordinating IoT device's energy becomes depleted or the connectivity is lost between the IoT hub and the source due to the network's partition. A new coordinating IoT device is selected from the grid based on its ID. For example, the second-largest ID is selected when the first becomes exhausted. To preserve energy, noncoordinating IoT devices can sleep by shutting their radio signal. [PERSON] et al. proposed a hierarchical Rendezvous Point Multicast (HRPM) protocol that divides a group into smaller groups [39]. A distributed geographic hashing concept is used for building and maintaining a hierarchy with no additional cost. The network field is divided into square cells of equal size until we reach a cell with a manageable number of IoT devices, and each cell is controlled by an Access Point (AP). Two approaches are used to increase the scalability of location-based multicast: distributed mobile geographic hashing and the hierarchical breakdown of large multicast groups. They provide lightweight hierarchical membership management, reducing per-packet encoding overhead without the significant expense of maintaining distributed state at mobile nodes. The members of the multicast tree fix the rendezvous point as a group manager. A virtual source to AP tree is built from the source that transmits data to this (source to AP) tree. An overlay tree consisting of AP members transfers packets to IoT devices in the group, and Face routing handles the holes in HRPM. [PERSON] et al. proposed a Hierarchical Geographic Multicast Routing Protocol (HGMR) that uses both GMR and HRPM for static networks, providing forward efficiency and reduced encoding overhead [40]. In HGMR, the subgroups' hierarchy is designed to reduce encoding overhead.