The Internet of Things (IoT) integrates different advanced technologies in which a wireless sensor network (WSN) with many smart micro-sensor nodes is an important portion of building various IoT applications such as smart agriculture systems, smart healthcare systems, smart home or monitoring environments, etc. However, the limited energy resources of sensors and the harsh properties of the WSN deployment environment make routing a challenging task. To defeat this routing quandary, an energy-efficient routing protocol based on grid cells (EEGT) is proposed in this study to improve the lifespan of WSN-based IoT applications. In EEGT, the whole network region is separated into virtual grid cells (clusters) at which the number of sensor nodes is balanced among cells. Then, a cluster head node (CHN) is chosen according to the residual energy and the distance between the sink and nodes in each cell. Moreover, to determine the paths for data delivery inside the cell with small energy utilization, the Kruskal algorithm is applied to connect nodes in each cell and their CHN into a minimum spanning tree (MST). Further, the ant colony algorithm is also used to find the paths of transmitting data packets from CHNs to the sink (outside cell) to reduce energy utilization. The simulation results show that the performance of EEGT is better than the three existing protocols, which are LEACH-C (low energy adaptive clustering hierarchy), PEGASIS (power-efficient gathering in sensor information systems), and PEGCP (maximizing WSN life using power-efficient grid-chain routing protocol) in terms of improved energy efficiency and extended the lifespan of the network.

4With the growth of wireless sensor network (WSN) technologies, the applications of IoT-based WSNs allow the interconnection of smart objects or sensors through the Internet. However, energy constraint is a major obstacle in WSN, which directly affects the lifespan of the network. Hence, many researchers have focused on how to program routing protocols to maximize energy conservation in WSNs. The clustering mechanism is demonstrated that separating the network into clusters may significantly decrease energy consumption. In this paper, we propose distributed tree-based clustering routing protocol for IoT applications (EE-DTC). In order to enhance efficient energy, EE-DTC chooses cluster head nodes based on the remaining energy, the location, and the density of nodes. In addition, to lengthen the network lifespan, we create multi-hop routes with short communication links intra-clusters by building the minimum spanning tree (MST) using the Kruskal algorithm. Our experiment results exhibit that the performance of EE-DTC overcomes the TBC and LEACH-VA protocols in terms of increasing network lifespan, reducing energy consumption, and improving efficient energy. Index Terms4Wireless sensor networks, routing protocol, energy-efficient, IoT, tree-based clustering.

4How to use efficient energy in wireless sensor networks (WSN) is one of the major challenges due to limited energy batteries and computation capacity. Therefore, in this paper, we propose combining a chain-base routing scheme and data fusion sensor information (CRSDF for short). CRSDF contains two major works: Firstly, the chain-based routing method is applied to connect sensor nodes into a chain in which each node transmits only with the nearest neighbor using the remaining energy and distance of nodes as standard parameters to determine which node will be selected the chain leader, secondly, we fuse and compress one or more data packets to generate a result packet with small size base on the Slepian-Wolf and Dempster-Shafer theory. The simulation results exhibit that the energy efficiency of our proposed protocol can be improved by 40%, 20%, and 15% compared to low-energy adaptive clustering hierarchy (LEACH), power-efficient gathering in sensor information system (PEGASIS), and an improved energy-efficient PEGASIS-Based protocol, respectively. Index Terms4Energy-efficient, routing protocol, chainbased clustering, wireless sensor networks, data fusion.