A Routing Algorithm Designed for Wireless Sensor Networks: Balanced Load-Latency Convergecast Tree with Dynamic Modification Sheng-Cong Hu r00631036@ntu.edu.tw Jen-Hou Liu r99631038@ntu.edu.tw Min-Sheng Liao d00631008@ntu.edu.tw Cheng-Long Chuang Intel-NTU Connected Context Computing Center richardchuang@ntu.edu.tw Kun-Yaw Ho Chia-Yi Agriculture Experiment Station Council of Agriculture, Executive Yuan, Taiwan kunyawho@dns.caes.gov.tw Ju-Min Yang Chia-Yi Agriculture Experiment Station Council of Agriculture, Executive Yuan, Taiwan rmyang@dns.caes.gov.tw Joe-Air Jiang* jajiang@ntu.edu.tw ABSTRACT In recent years, a variety of power-efficient sensor nodes with a very small size have been well developed due to the advance of Microelectromechanical Systems (MEMS). WSNs are applied to many areas, such as industrial process control, medical monitoring, and environmental monitoring. A typical wireless sensor network consists of a gateway, a database, and a large number of wireless sensor nodes. In this study, the gateway is used to transmit the data collected by sensor nodes to the database. Then a user can analyze the data drawn from the database. In order to balance the energy consumption for each node, we propose a balanced low-latency convergecast tree routing algorithm with a dynamic modification. This algorithm is a centralized routing algorithm and suitable for various applications. The topology for a sensor node is determined by the gateway, and the node transmits the data according to the topology. In this algorithm, we also take battery voltage and received signal strength indication (RSSI) into consideration when choosing parent nodes to construct the topology. In additon, the algorithm is applied to real world scenarios to verify its performance on the balance of energy consumption among sensor nodes. Keywords: Wireless sensor network, dynamic modification, received signal strength indication, energy consumption
1 INTRODUCTION WSNs face many challenges when they are applied to real environments. For example, tall trees and animal movement may influence the quality of communication. Weather conditions can also influence the strength of wireless signals. In addition, WSNs are usually used to observe environmental parameters for a long time in fields [1-3], so energy consumption management is a very important issue. In wild fields, for example, energy supply is generally limited; it is a difficult task to recharge batteries. Therefore, many studies have proposed different methods to reduce energy consumption, such as designing an energy-efficient routing protocol and Media Access Control (MAC). Some algorithms, such as the balanced load-latency convergecast tree (BLLCT) [4-6], the first order load-balanced algorithm with the static fixing scheme [7] and the load-balanced tree routing algorithm with the dynamic modification [8] can rearrange the loading of first layer nodes, but these algorithms do not take the battery voltage of parent nodes (battery voltage can be viewed as an indicator of a node s residual power) and RSSI into account when they choose the parent node for each node. The selection of parent node will not be practical if both the residual power and RSSI are ignored. Thus, in this study, we propose a routing algorithm to improve the performance and reliability of BLLCT by using both of the indicators. As shown in Figure 1, node 4 may choose node 1 whose residual power is lower as its parent node, since the BLLCT selects parent nodes randomly. However, our proposed method will choose node 2 as the parent node of node 4, because its remaining power is high. After adding residual power of nodes and RSSI as the criteria of parent node selection to the BLLCT, the energy consumption of nodes is expected to become more even. Figure 1: An illustration of using the proposed algorithm. 2 MATERIALS AND METHODS In this study, we use a PC connected with the base node to perform the proposed algorithm. The PC serves as the gateway. The gateway operates as according to the follow rules. First of all, the gateway establishes the links among sensor nodes.
Figure 2: The flowchart of the gateway operating procedure. Then the node transmits packets including the information of links between the nodes to the gateway. After the gateway ends collecting the packets, it will build a routing topology for the WSN. Finally, each sensor node will transmit its sensing data to the gateway according to the routing topology. The flowchart of the gateway operating procedure is shown in Figure 2. The proposed algorithm is a centralized routing algorithm [9-11], because the gateway is in charge of managing the WSN. 2.1 Received Signal Strength Indicator (RSSI) In this study, the proposed algorithm will choose the node with a larger value of RSSI as the parent node. The larger value of RSSI means a better quality of communication between nodes. According to Friis equation described in equation (1), the received signal strength acquired from the radio frequency transceiver can be formulated as follows r P d 2 PG r tgr (1) 2 2 4 d where P r is the received signal strength, P t is the transmitting power level (dbm), G t is the antenna gain value of the transmitting node (db), G r is the antenna gain value of the receiving node (db), λ is the electromagnetic wavelength (nm) and d is the distance between nodes (m). The signal strength between a node and its parent node will decrease when the transmission distance increases. In the experiment, we use two sensor nodes (one is the receiver, and the other is the sender) to test the relationship between the successful receiving data rate and RSSI. We move the sender 50 cm away from the receiver each round, until the distance between the receiver and the sender is 9.5 m. In each round, the sender will send 200 packets to the receiver. The successful receiving data rate and RSSI are recorded, as shown in Figure 3. Figure 3: The successful data receiving rate and the averaged RSSI.
It can be seen that the stronger RSSI will lead to high successful data receiving rate. However, a special case is found when the successful data receiving rate sharply drops to 83% at the averaged RSSI of -68 (dbm). This may be caused by environmental interference. 2.2 Balanced Low-Latency Convergecast Tree Algorithm with a Dynamic Modification Our proposed algorithm based on the BLLCT algorithm is able to arrange the routing path for each node. The approach can effectively avoid some of the first order nodes run out their energy fast. The flowchart of the proposed algorithm is shown in Figure 4. Figure 4: The flowchart of the proposed algorithm. The process of parent node section includes two parts. In the first part, which child node will be the first one to be connected to its parent nodes in the upper layer is determined using the (step (3) to (6)). Two selection criteria are used in these steps: a node s loading and the number of parent node candidates of the node. First, the node with the heaviest loading will be chosen to be the first one to establish the link with his parent node. If there are several nodes with the same heaviest loading, the number of parent node candidates of the node will be taken into consideration. If the number of parent candidates for some nodes is identical, we will randomly choose one of the nodes as the first one to link to the parent node. In the second part, four selection criteria a node s loading, the number of child nodes which have not connected to any parent nodes, the residual power of the candidate node and the RSSI of the candidate node are used to decide the parent node of a node (step (7) to (12)). First, the parent candidate node with the smallest loading will be chosen as the parent node. If several nodes all have
identical loading, the parent candidate node with the smallest number of child nodes which have not connected to any parent nodes will be chosen as the parent node. If some parent candidates have an identical number of the child nodes, the residual power of the candidate node will be taken into consideration. The highest residual power of the candidate node will be chosen as the parent node. If there are several nodes with identical residual power, the candidate node with the highest RSSI will be chosen as the parent node. If several nodes have identical RSSI, we will randomly choose one of the nodes as the parent node. The whole process will iterate until all nodes are connected to the gateway. 3 RESULTS AND DISCUSSION In the experiment, we deployed a WSN with 35 nodes in an area of 3 3 m 2 to verify the performance of the proposed algorithm on load balance. The gateway is located at the center of the field. The sensor node we used is the Octopus II. The approximately communication range of nodes is from 100 cm to 150 cm. Because the RSSI value changes at different time and different positions, we re-build the network topology every 20 rounds. Thus, the communication quality of the WSN will be improved due to the dynamic adjustment of the network topology. Figure 5 shows the battery voltage of each node in the first layer after 100 rounds of experiment. It is obvious that the node with a higher battery voltage will be chosen as the parent node in the next round. On the other hand, the node with a lower battery voltage will put into an idle mode, until its battery voltage is higher than the voltage of other nodes. By applying the proposed algorithm, the energy consumption among sensor nodes may become more evenly. Moreover, this algorithm can avoid the scenario in which some nodes always act as the parent nodes and their power run out faster than other nodes, leading to a high data loss rate. 3.22 3.20 3.18 Voltage of node (V) 3.16 3.14 3.12 3.10 3.08 3.06 3.04 1 2 3 4 5 6 7 8 9 10 Node number Figure 5: Battery voltage of the deployed nodes. 0 round 20 rounds 40 rounds 60 rounds 80 rounds 100 rounds
4 CONCLUSION In this paper, we develop an algorithm based on the BLLCT algorithm to improve the reliability of routing paths in a WSN as well as achieving the load balance among sensor nodes. The main concept is to incorporate the information of battery voltage of sensor nodes and RSSIs to the criteria used to determine the parent node of a node. The experimental result shows the energy of nodes can be consumed evenly through the proposed algorithm, and thus, the network lifetime can be prolonged effectively. We have deployed the WSN to monitor the oriental fruit fly in fruit farm of Chia-Yi agricultural experiment station, and the energy of node be limited. In the future, we will improve the WSN system with our proposed algorithm. ACKNOLEDGEMENT The authors are grateful for the financial support from the National Science Council, the Council of Agriculture of the Executive Yuan, Taiwan, and the President of National Taiwan University under the grants NSC 100-2218-E-002-006, NSC 100-2218-E-002-005, NSC 101-3113- E-002-005, NSC 100-3113-P-002-012, NSC 101-ET-E-002-012-ET, 101AS-7.1.2-BQ-B1, 101AS- 7.1.2-BQ-B2, 101AS-13.3.1-FB-E1, and 10R70606-4. This work was also supported by the National Science Council, and Intel Corporation under Grants NSC 100-2911-I-002-001, and 101R7501. REFERENCES [1] Jiang, J. A., Tseng, C. L., Lu, F.M., Yang, E. C., Wu, Z. S., Chen, C. P., Lin, S. H., Lin, K. C., and Liao, J. S., (2008), A GSM-based remote wireless automatic monitoring system for field information: A case study for ecological monitoring of the oriental fruit fly, Bactrocera dorsalis (Hendel), Computers and electronics in agriculture, vol.62, pp. 243-259. [2] Alippi, C., Camplani, R., Galperti, C., and Roveri, M., (2011), A robust, adaptive, solar-powered WSN framework for aquatic environmental monitoring, IEEE Sensors, vol. 11, no. 1, pp. 45-55. [3] Jiang, J. A., Shieh, J. C., Lai, T. Y., Chuang, C. L., Yang, E. C., Liao, K. C., Tasy, J. R., and Hsu, W. H., (2010), Study of routing path reliability for static outdoor wireless sensor network in ecological monitoring, The 5 th International Symposium on Machinery and Mechatronics for Agricultural and Biosystems Engineering (ISMAB2010), Fukuoka, Japan. [4] Dai, H., and Han, R., (2003), A node-centric load balancing algorithm for wireless sensor networks, in proceedings of IEEE GLOBECOM '03, vol. 1, pp. 548-552. [5] Jiang, J. A., Lai, T. Y., Chen, C. P., Liu, J. H., and Wang, J. Y., A Novel Dynamic Convergecast Tree Generator for WSN-based Environmental Surveillance of Orchid Plantation, (2012), 2012 2 th International Workshop on Embedded Multi-Core computing and Applications (EMCA 2012), Liverpool, UK, June 25-27, 2012. [6] Jiang, J. A., Chung, T. P., Lin, T. S., Zheng, X. Y., and Yen, P. L., (2011), A loading balancing algorithm based on probabilistic multi-tree for wireless sensor networks, 2011 Fifth International Conference on Sensing Technology, pp. 527-532. [7] Jiang, J. A., Chu, Y. J., Tseng, C. P., Liao, K. C., Wu, Y. C., Lu, F. M., Wang Y. C., Tseng C. L., Yang E. C., and Ho, K. Y., (2009), The First Order Load-Balanced Algorithm with Static Fixing Scheme for Centralized WSN System in Outdoor Environmental Monitoring, IEEE Sensors, pp. 1810-1813. [8] Jiang, J. A., Chu, Y. J., Tseng, C. P., Liao, K. C., Wang, Y. C., Tseng, C. L., Yang, E. C., and Ouyang, C. S., (2009), Application of Load-Balanced Tree Routing Algorithm With Dynamic Modification to Centralized Wireless Sensor Networks, IEEE Sensors, pp. 1392-1395. [9] Tseng, C. P., (2008), Study on Application of Balanced Tree Algorithm to Wireless Sensor Network System, Master Thesis. [10] Guo, P., Jiang, T., Zhang, Q., Zhang, K., (2012), Sleep scheduling for critical event monitoring in wireless sensor networks, IEEE Transactions on parallel and distributed systems, vol. 23, no. 2, pp. 345-352.
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