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1.INTRODUCTIONWireless sensor node can monitor various environmental states such as temperature, humidity, brightness, pressure, noise, speed and direction of moving objects in real time1. Different from traditional networks, the data that WSN needs to transmit basically does not involve intensive data such as voice, video, and images, so the demand for bandwidth is not high. But the areas that WSN needs to monitor, such as deserts, ponds, forests, etc., are hard-to-reach areas for humans. However, the nodes of WSNs are usually small in size, and the energy carried by the power supply is very limited2, so our priority is to find an algorithm that can be implemented to effectively reduce battery power consumption3. In order to solve the energy problem faced by wireless sensor networks, the LEACH protocol, which has great research significance in the clustering protocol, is preferred in this paper. However, the LEACH protocol selects CH probabilistically according to the set threshold, so that the energy and position of the selected cluster heads are not necessarily optimal. LEACH itself has many shortcomings. Using the original protocol cannot maximize the energy utilization rate of the sensor, and cannot be directly put into the actual network. It needs to be improved. In response to the above problems, many scholars have proposed a variety of methods to optimize LEACH from different angles. The EEUC algorithm4 adopts a non-uniform clustering method, but it needs to randomly select cluster heads periodically, and the selected cluster heads may not be optimal. The DEEC algorithm proposed in Reference5 adopts a single-hop transmission mode in the communication, so the cluster head has a heavy energy load and will die earlier than ordinary nodes6. The energy balance routing algorithm proposed in Reference7 does not consider the selection of the optimal path8, 9. Reference10 does not consider the residual energy of the cluster head in the non-uniform cluster head election process. The algorithm11 adopts the ZigBee distributed address allocation strategy. The algorithm does not change the CH in real time with the relative distance between the surviving communication nodes in the network. The IMP-LEACH algorithm12 only takes into account the residual energy factor13, 14 when selecting the CH, and does not take into account the distance factor15. Aiming at the problems existing in the current WSNs routing algorithm, this paper proposes an improved routing algorithm EEM-LEACH (An energy efficient multi-hop WSNs clustering optimization algorithm) based on LEACH and multi-hop routing protocol. The algorithm uses the weighting idea to improve the LEACH algorithm, so that the node generates a weight based on energy and density, and selects the cluster head through the probability threshold of this weight, and then generates the entire cluster. The weight comprehensively considers the network environment and its own state of the node. The more neighbor nodes where the node is located, the higher the percentage of members in the cluster, the greater the density, and the greater the probability of becoming the cluster head. At the same time, the greater the residual energy of the node, the greater the weight, and the greater the probability of becoming the cluster head node. 2.IMPROVED ALGORITHM EEM-LEACH2.1Cluster head selection processThe first stage is the establishment of clusters, which is randomly selected in the traditional LEACH protocol. In this way, if a sensor node has less remaining energy or is far away from the sink node, it will still be elected as the cluster head multiple times, and the node will increase energy consumption in a short time and lose its function prematurely. Different from the traditional idea, the algorithm in this paper does not adopt the strategy of randomly selecting cluster heads. During cluster head selection, the remaining energy of nodes in the network needs to be given priority. First, query the remaining energy E(i)res of each node. Then, the average residual energy of the network is estimated according to the network model. The average remaining energy Eavg is then calculated16. The following equation is the calculation equation of the threshold T(n) after the adjustment of the EEM-LEACH algorithm among them, The prerequisite for a node to be elected as the CH is that the random number generated by rand(1,1) is less than the threshold T(n). The above process is repeated until all the cluster heads in the round are selected17. Equation (2) makes it easier to select the node that is closer to the neighbor node, closer to the base station, and higher residual energy as the cluster head. The descriptive pseudocode of the specific CH selection process is as follows: 2.2Node clustering processEach node stores a table of information about the cluster head node, including ID, status, remaining energy, node location information, and the number of neighbor nodes corresponding to the CH. Common nodes join the CH with the smallest C(i), and the cost function C(i) is among them, d(i,CH) is the distance between the normal node and CH, dmax(i, BS) is the maximum distance between the normal node and BS, and |N(CH)| is the number of neighbor nodes joining the CH. E0 represents the initial energy of the node, E(CH) is the current remaining energy of the CH, and wc1, wc2 and wc3 are the weighting coefficients. The goal of constructing the above function is to select the best cluster for nodes to join with the lowest energy consumption. The normal node selects the CH that is closer to the node position, closer to the BS, less number of clustering nodes and more residual energy according to equation (3) to apply for joining, and complete the clustering process. This measure is conducive to reliable communication between nodes and energy balance within the cluster. The descriptive pseudocode of the specific node clustering process is as follows: 2.3Inter-cluster communication processThe cluster head node undertakes the functions of data reception and fusion and data packet forwarding of the cluster member nodes, so the energy saving and energy consumption balance of the cluster head node are the key research contents. When the distance is greater than the critical value of the communication radius, the energy consumption will change significantly even if the distance increases a little. The short-distance transmission energy consumption is proportional to the square of the distance, and the energy consumption will not have a great impact with the increase of the distance. Therefore, it is necessary to shorten the transmission distance as much as possible, and divide the long-distance transmission path into multiple shorter short-distance paths for sequential transmission. The EEM-LEACH algorithm adopts a multi-hop transmission method between clusters, in which the relay nodes in the transmission path are selected from the cluster head. Inter-cluster communication transmission rules are as follows (The communication radius is denoted by R): d <= R : Nodes communicate directly with base stations. d > R : Common nodes transmit data to the CH of this cluster. d > R : The CH transmits the data to the relay node. The CH selects the neighbor cluster head node with few cluster nodes, high residual energy, close to the BS and close to the CH as the relay node through the relay forwarding function R(i, j). Therefore, it is necessary to select a node with a larger R(i, j) value as the next forwarding cluster head. The function R(i, j) is expressed by the following equation: where dij in equation (4) is as follows: among them, j is the neighbor cluster head node. m is the number of nodes within the communication radius of i. E(j)res represents the remaining energy of j, and |N(j)| is the number of cluster nodes of j. • The relay node continues to select the transmission path according to the method in the above steps until the data is transmitted to the BS. The descriptive pseudocode of the multi-hop transmission process between clusters of the algorithm is as follows: 3.SIMULATION RESULTS AND ANALYSISThis paper uses MATLAB to simulate the LEACH, DEEC, IMP-LEACH and EEM-LEACH algorithms in two different scenarios. The two scenarios are a small area of 100×100 m2 and a large area of 200×200 m2. The specific simulation parameter settings are shown in Table 1. Table 1.The simulation parameters.
Figures 1 and 2 are the node distribution diagrams of 100 sensor nodes randomly deployed in 100×100 m2 and 200×200 m2 monitoring areas, respectively. 3.1Network lifecycle comparisonIt can be seen from Figures 3 and 4 that as the number of sensor network data collection increases, the number of dead nodes increases, but the number of dead nodes in the EEM-LEACH algorithm is relatively lower than other algorithms under the monitoring area of 100×100 m2 and 200×200 m2,so the algorithm proposed in this paper fully considers the energy balance. 3.2Network residual energy comparisonThe amount of energy remaining in the network determines how long a node can work. When the remaining energy is 0, the network loses its use value. From Figures 5 and 6, it can be concluded that with the increase of the number of sensor network data collection, the average residual energy of the four routing protocols gradually decreases, but the residual energy of the EEM-LEACH algorithm is relatively higher than other algorithms under the monitoring areas of 100×100 m2 and 200×200 m2. 3.3Network throughput comparisonFrom Figures 7 and 8, it can be concluded that with the increase of the number of sensor network data collection, the amount of data received by the base station of the EEM-LEACH algorithm is gradually larger than that of the other three algorithms. It can be seen that the strategy of determining the relay nodes between clusters according to the relay forwarding function is feasible. 4.CONCLUSIONAiming at the problems of uneven cluster head selection and unbalanced energy consumption in traditional clustering algorithms, this paper proposes a multi-hop routing algorithm for WSN based on non-uniform clustering, EEM-LEACH. This algorithm applies a more reasonable cluster head selection mechanism and a more uniform clustering strategy. In this algorithm, the information transfer between each node and base station adopts the hybrid energy-saving matching mode of single-hop or multi-hop between clusters, which makes up for the deficiency of traditional single-hop routing algorithm. According to the “relay forwarding function election method”, the only best forwarding node is finally determined, and the link formed by several forwarding cluster heads becomes the inter-cluster transmission path of this “round”. Finally, the simulation results verify the effectiveness of the EEM-LEACH algorithm in improving the relevant operational indicators of WSN. REFERENCESZhang, D. and Zhang, L.,
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