doi:10.21311/001.39.11.34 An Improved Clustering Routing Algorithm Based on Energy Balance Li Cai and Jianying Su Chongqing City Management College, Chongqing 401331,China Abstract: For network distribution in homogeneity and hot spots problem, the location of the improved algorithm in computing nodes and the clusters of minimum cost point under the premise of communication, more reference points and the mechanism of cluster chain of thought, using the energy consumption of communication and how much to horizontal layered network, network heterogeneous clustering routing is proposed. Simulation experiment shows that the improved algorithm to solve network hot spots, good prolonged the survival time of network, and obtains the better energy efficiency. Key words: Wireless Sensor Networks, Clustering Routing Algorithm, Hierarchical Multi-hop Clustering Routing Algorithm 1. INTRODUCTION Wireless sensor network routing protocol is one of the key technologies of wireless sensor network, is the optimal path to establish the source node to the destination node, the data packets along the optimal path through the network from the source node to the destination node. As the wireless sensor network and other networks, such as limited resources, no center, resulting in other network routing protocols can not be directly applied in the routing protocol, and directly affect the transmission performance of the network and the network life cycle, it must design a new routing protocol to meet the needs of different applications the research on wireless sensor routing protocol, domestic and foreign scholars have made many excellent algorithms, have also done a lot of improvement, but still need to increase its research efforts to adapt to the rapid development of wireless sensor network (Yassein Muneer Bani, Jaradat Zeinab, Hijazi Neveen, Mavromoustakis Constandinos, 2012). In general, the clustering routing algorithm can be divided into 2 categories from the perspective of development and design, one is based on the classic algorithm LEACH on the basis of the development of the two, is completely independent development. No matter what kind of routing algorithm, it is the process of the implementation of the steps, the basic is the cluster head election, the establishment of clusters, inter cluster routing. The 3 steps are complementary and mutually dependent. Scholars at home and abroad have made great efforts to put forward a lot of excellent clustering routing algorithms, which have achieved remarkable results in energy utilization, balanced consumption, and extended network lifetime. In the cluster head selection, LEACH algorithm is the node itself according to the random number and the threshold to decide whether to become a cluster head, and the lack of relevant parameters of the cluster head number and location (Yang Mengning, Yang Dan, Huang Chao,2012). The HEED algorithm uses the method of primary and secondary double parameters established cluster head (Duan Yabo, Song Chengtian, Xu Lixin,2015), the algorithm in the cluster head election when the full use of the remaining energy of the cluster head election by primary and secondary double multi constraint parameters, fast convergence speed, the cluster head in the deployment of regional distribution is reasonable, balanced energy consumption, long life cycle (Wang Honglin, Shen Jun, 2015). (PEGASIS network power-efficient gathering in sensor information systems) algorithm is used to cluster the LEACH thought, set up a chain of clusters (Fu Chunyao, Wei Wei, Wei Ang, 2012) longitudinal. In the first round of communication, only one cluster head exchanges information with the base station, and communicates with the least energy. 2. IMPROVED ALGORITHM In view of the network distribution is not uniform and the "hot spot" problem in the premise of communication cost location and cluster computing nodes. The algorithm of the minimum point, and the mechanism of cluster multi hop chain reference idea, using the energy consumption of network communication for the horizontal stratification, put forward the heterogeneous network cluster routing. 2.1. Algorithm description The N nodes are randomly deployed in an irregular region, and the energy of the nodes is different, the distribution is uniform, and each node is not positioned. The nodes in the network to communicate with the base station energy consumption size number into the width adjustable layer, according to the principle of maximum 279
residual energy of the cluster head is selected, the nodes in the cluster and cluster head using single hop communication mode; cluster head chain formation among cluster heads, in a multi hop manner for information in exchange, eventually forming a heterogeneous cluster size, energy consumption in multi hop wireless sensor network equilibrium. 2.2. Hierarchical network The arrangement of nodes in irregular regions is not readily available. The method of this algorithm is to arrange the coordinates of all possible points in the irregular region. Of course, this is not the same with the use of random functions generated coordinates, if the coordinates of the distance between points is small enough, then it can be considered that the entire deployment of the random selection of points. Firstly, the nodes in the network are layered, and the hierarchical method is based on the base station, which is extended outward. The base station is set to 0 layers, and the layer adjacent to the base station is of the order of 1, which is in turn: 2, 3, 4... The number of nodes in each layer is not known prior to the delamination. The hierarchical goal so that the node in the network layer will be one of the nodes in ID layer, communication layer and base station node with energy consumption is basically the same, the ID value is the only growth, and the level of synchronization, the number of base stations and the smaller the distance closer, the number of size provides direction parameter route selection, can quickly get the minimum energy consumption path. The steps are as follows: (1) The base station issued a hierarchical command Layer (Wdh, P t, L), Wdh for the layer width parameter, P t for the transmit power, L for the current layer (Xu Heli, Mei Qian, Wang Lihong,2013). (2) The node receives the command to calculate Layer (Wdh, P t, L). (3) If Y=P-P R /P T <0, this command source node does not exceed the width of the layer, replace the Layer (Wdh, P t, L) in the parameters, P t is replaced by the transmission power of the layer, L replaced the layer value. The replacement principle does not directly assign the layer value to the layer, and if the current layer ID is greater than the sending node layer ID+1, then it is replaced. If Y=P-P R /P T >0, indicates that the command source node is out of the width of the layer. (4) The hierarchical command of the node to replace the flooding parameters of the surrounding nodes until all nodes determine their own level, the end of the hierarchy. 2.3. Determination of the number of cluster heads in a layer Consumption and robust connectivity, energy network is an important index to measure the performance of the network, how much influence the number of cluster heads in each layer and the cluster head position on the index of energy consumption is huge, which played decisive role in all indicators, resulting in the number of energy consumption as the starting point to analyze the in the best cluster head is reasonable. According to (Hu Guanghao, Mao Zhizhong, Yang Fei, 2013; Stephan Olariu, Ivan Stojmenov ie, 2006), the optimal number of cluster heads is calculated. This way the hierarchical algorithm, the distance from the base station closer, to the cluster head number, which is consistent with our analysis, cluster heads closer to the base station for the transmission of information to the energy consumption of more than the inner layer, so that the number of cluster heads, to balance the network energy consumption balance. 2.4. Formation of clusters within a layer Similar to general clustering clustering strategy, the process of this algorithm is the selection of cluster head, cluster, chain path for exchange of information, select the cluster head and the base station, the cluster head replacement form. (1) In the first round of selection of cluster head, in accordance with the principle of giving priority to the residual energy with the best proportion of cluster head middle layer J k jopt, calculate the cluster head election threshold from any node in line with the conditions of the selected as a cluster head. Although the nodes are randomly selected, but to meet the threshold requirements of the residual energy of the node, the threshold value of TH j =k jopt. and k jopt according to the number of the nodes, easily calculate the cluster head number, when a node needs to be cluster head conditions, can be used as cluster head. The first round of the cluster head generated by random way, once the node is selected as the cluster head node to the nodes around at a fixed power generated by cluster command, add information and wait for other nodes. (2) The ordinary nodes around the cluster head node receives clustered commands, storing the received command all the cluster cluster head ID and accept the command of the receiving power of Pr. Generally, the greater the Pr, the stronger the received signal, the closer the sending node is to the node. Since all of the cluster heads send a broadcast command with a fixed power, the receiving power Pr of the node can represent the distance between the cluster head and the node. In the ordinary nodes for a period of time, will choose the Pr value of a cluster into the largest cluster hair commands from the locally stored information, and waits for the cluster head to reply, when receiving a handshake message, the node into the cluster of success. All nodes choose the corresponding cluster head in this way, after a period of time, all the nodes are clustered. Due to the 280
minimum energy consumption in the formation of clusters, so the energy consumption in the cluster node and cluster head communication is the least and the energy of the sensor network is limited by (Chen Hongwei, Zhang Chunhua, Zong Xinlu, Wang Chunzhi; 2013) (3) When selecting cluster head and base station to exchange information in the chain path, the first consideration is the energy consumption problem, to choose the lowest energy consumption path to the base station for information exchange. If the outermost from the beginning of each cluster head to the base station direction has been selected energy expenditure as their next hop, then the chain once formed, an optimal path, established between the cluster head and base station. (4) The method of queue is used to update the cluster head. Collect the common node residual energy and communication in the cluster head nodes and common nodes information, and establish a dynamic queue queue according to the residual energy from large to small queue, replace the new cluster head when the first node from the queue queue as the cluster head, and broadcast the cluster members. The replacement of the cluster head information, member nodes and cluster head shake hands after complete replacement of cluster head. Since the specified cluster head is the most energy in the cluster, it can be used as the cluster head, which will consume the energy of the node and balance the energy consumption of the network. In this way, the algorithm is simple, easy to implement, the cluster is not reconstructed, and the energy is saved. 2.5. Algorithm execution steps The algorithm performs the following steps: (1) Initiating Nodes Sink node sends configuration parameters to the networks. (2) Node layered. (3) Each layer according to the cluster head election threshold of this layer, random election the cluster head. (4) Construction of cluster and cluster chains. (5) When a cluster's life cycle is completed, and less than a preset number of cycle C x,the cluster head select a cluster node to become a cluster head, and the dissolution of the original cluster. (6) When reach cycle number C, repeat steps (3) to (6) process. 3. SIMULATION ANALYSIS The algorithm and LEACH, SEP, HEED algorithm in the NS2 do a simulation analysis for the network life cycle and energy consumption of two aspects of the performance comparison. 100 nodes are randomly deployed in the irregular region, the maximum radius of the region is changed from 200m to 400m in the experiment, and the distribution of nodes in the deployment area is even., Member node initial energy is set to 0 5J,, the packet size is 4000bit. (1)Residual energy analysis The residual energy of cluster head is an important index to measure the network equilibrium consumption and load balance. After a certain number of rounds, the average value is bigger, the energy saving performance of the algorithm is better, and the energy utilization ratio is higher. Figure 1 shows the mean value of the residual energy of the cluster head after each round. In the figure, it can be concluded that in the period after the first round of LEACH residual energy of cluster head nodes is more obvious than other algorithms should be low, this part of the main energy consumption at the cluster stage, shows that consumption of LEACH algorithm in clustering phase energy than ICED algorithm and more. Figure 1. Residual energy comparison 281
With the increase of the number of rounds of work, the average energy was born in 4 kinds of algorithms of cluster head are in decline, decrease in the amplitude of LEACH, this algorithm decreases, ICED second, and the curve on top of the other 3 algorithms, show that the count after each round of the average residual energy than the other 2 algorithms the higher the average residual energy gap is also growing. It shows that this algorithm is more efficient than other algorithms. (2) Analysis of network lifetime Figure 2 is the largest deployment area radius is 100m, compared with the 4 algorithms, namely LEACH, HEED, SEP, the improved algorithm, a comparison of the four algorithms survival time, in the figure, the earliest death node, LEACH algorithm ICED second, the death node number of rounds greatly backward. The survival of network stability is the longest. Figure 2. Life cycle comparison with radius of 100 The first node in the network to the death of the last node death round the middle span of regional comparison, this algorithm across regional minimum node, the time of death is concentrated in a smaller area time, which indicates that the network load balancing, the residual energy of nodes is consistent, high utilization rate of indirect description the energy of the network. When the maximum radius of node deployment area expanded to 200m, the three algorithms appear dead node number of rounds in advance, that with the expansion of the deployment area, the communication cost of nodes in the network are also improved, so the death of nodes than the radius of 100m area is early, stable life period decreased however, stable life period of this algorithm is still the longest, when the deployment area is larger, reflect the more obvious this indicates that the algorithm of the energy utilization rate is relatively high. There was no significant change in the number of deaths experienced by the node and the last node, indicating that the energy consumption of the network does not decrease with the increase of the deployment area. Figure 3 is a graph of the life span of the network stability. The energy consumption increases gradually, which leads to the decline of the life cycle of the network. But the stable life of this algorithm is much better than the other 3 algorithms. Figure 3. Stable life cycle comparison 282
4. CONCLUSION Based on the idea of clustering and multi hop routing mechanism, an improved algorithm is proposed. Firstly, according to the number of nodes to monitor regional horizontal layered communication base station energy consumption, and then through the calculation, the optimum proportion of cluster head in each layer, the use of the ratio as the threshold value to determine the number of cluster head layer, cluster head election, when the energy of the cluster head after falling by the next round of cluster head as the cluster head node according to the general principle of least energy consumption choice of cluster head, own into clusters, forming communication energy consumption at least, and uniform local clusters, and regularly to ensure the reconstruction of network energy cluster head in the network equilibrium. According to the minimum energy consumption, the cluster head forms a cluster head to exchange information with the base station in multi hop mode. The simulation results show that this algorithm in a more balanced energy consumption, long life and stable network, overcomes the problem of hotspot. REFERENCES Chen Hongwei, Zhang Chunhua, Zong Xinlu, Wang. Chunzhi. (2013) LEACH-G: An optimal cluster-heads selection algorithm based on LEACH, Journal of Software, 10, pp. 2660-2667. Duan Yabo, Song Chengtian, Xu Lixin. (2015) Improving on HEED protocol of wireless sensor networks with sleep scheduling algorithm, Journal of Information and Computational Science, 12(13), pp. 5163-5173. Fu Chunyao, Wei Wei, Wei Ang. (2012) Study on an improved algorithm based on LEACH protocol, Information Technology Journal, 11(5), pp.606-615. Hu Guanghao, Mao Zhizhong, Yang Fei. (2013) Hybrid prediction modeling of leaching rate based on selected ensemble algorithm, Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, 34(5), pp.1049-1053. Stephan Olariu, Ivan Stojmenov ie. (2006) Design Guidelines for Maximizing Life time and A vo-iding Energy Holes in Sensor Networks with Uniform Distribution and Uniform Reporting, IEEE Proceedings of INFOCOM, pp.1-12. Wang Honglin, Shen Jun. (2015) Clustering algorithm design optimization and routing protocols based on leach, Journal of Computational Information Systems, 11(12), pp.4343-4352. Xu Heli, Mei Qian, Wang Lihong. (2013) Further analysis and research on LEACH algorithm for wireless sensor networks, Journal of Computational Information Systems, 9(10), pp.3931-3937. Yang Mengning, Yang Dan, Huang Chao. (2012) An improved HEED clustering algorithm for Wireless Sensor Network, Chongqing Daxue Xuebao/Journal of Chongqing University, 35(8), pp. 101-106. Yassein Muneer Bani, Jaradat Zeinab, Hijazi Neveen, Mavromoustakis Constandinos. (2012) New load balancing algorithm for LEACH protocol (F-VCH LEACH), Sensors and Transducers, 145(10), pp. 172-182. 283