Abstract

Monitoring the forest’s weather has been essential to living things over the years. Currently, there is a shortage of information on real-time temporal and spatial environmental conditions of the forest that drive forest health condition. This work focuses on the sensing of humidity and temperature as weather data from the forest. Unlike the traditional systems used to collect weather information, the use of wireless sensor network (WSN) gives real-time data capture from every point of the forest. However, the WSN faces, the number of challenges, including low bandwidth, low power, and short battery lifespan. In this situation, batteries cannot be replaced since nodes are deployed in an inaccessible area. In order to prolong the network lifetime and reduce the network delay, we propose Zone based Clustering (ZbC) scheme and efficient routing to find the best path between source and cluster head. Initially, we deploy sensor nodes in three coronas namely C1, C2 and C3. We place the sink node at the center of the coronas. Based on the center point of the corona, we split each corona into four partitions each with three zones. Our work composed of two phases such as ZbC and Routing. In the first phase, we reduce energy consumption in data aggregation via ZbC scheme. In ZbC scheme, the hybrid Particle Swarm Optimization (PSO) and Affinity Propagation (AP) algorithm are utilized. Network delay is reduced in the second phase using Ant Colony Optimization (ACO) and FireFly Algorithm (FFA). Simulation results confirm that our proposed solution achieves a higher network lifetime up to 30%, reduces delay up to 35% and enhances throughput compared to the existing cooperative Time Division Multiple Access (cTDMA), Dynamic Random Allocation (DRA) and improved Artificial bee colony (iABC) methods.

Keyword

Wireless sensor network, Coronas, Zone based clustering, Environmental monitoring, Routing, Forests.

Cite this article

Sinde R, Kaijage S, Njau K

Refference

[1][1]Thakur A, Prasad D, Verma A. Deployment scheme in wireless sensor network: a review. International Journal of Computer Applications. 2017; 163(5):12-5.

[2][2]Morsy NA, AbdelHay EH, Kishk SS. Proposed energy efficient algorithm for clustering and routing in WSN. Wireless Personal Communications. 2018; 103(3):2575-98.

[3][3]Ahmad M, Ikram AA, Wahid I, Inam M, Ayub N, Ali S. A bio-inspired clustering scheme in wireless sensor networks: BeeWSN. Procedia Computer Science. 2018; 130:206-13.

[4][4]Yuan X, Elhoseny M, El-Minir HK, Riad AM. A genetic algorithm-based, dynamic clustering method towards improved WSN longevity. Journal of Network and Systems Management. 2017; 25(1):21-46.

[5][5]Elhabyan R, Shi W, St-Hilaire M. A pareto optimization-based approach to clustering and routing in wireless sensor networks. Journal of Network and Computer Applications. 2018; 114:57-69.

[6][6]Singh R, Verma AK. Energy efficient cross layer based adaptive threshold routing protocol for WSN. AEU-International Journal of Electronics and Communications. 2017; 72:166-73.

[7][7]Muthukumaran K, Chitra K, Selvakumar C. An energy efficient clustering scheme using multilevel routing for wireless sensor network. Computers & Electrical Engineering. 2018; 69:642-52.

[8][8]Kulkarni N, Prasad NR, Prasad R. Q-MOHRA: QoS assured multi-objective hybrid routing algorithm for heterogeneous WSN. Wireless Personal Communications. 2018; 100(2):255-66.

[9][9]Hong C, Zhang Y, Xiong Z, Xu A, Chen H, Ding W. FADS: Circular/spherical sector based forwarding area division and adaptive forwarding area selection routing protocol in WSNs. Ad Hoc Networks. 2018; 70:121-34.

[10][10]Bayo A, Antolín D, Medrano N, Calvo B, Celma S. Early detection and monitoring of forest fire with a wireless sensor network system. Procedia Engineering. 2010; 5:248-51.

[11][11]Lloret J, Garcia M, Bri D, Sendra S. A wireless sensor network deployment for rural and forest fire detection and verification. Sensors. 2009; 9(11):8722-47.

[12][12]Aslan YE, Korpeoglu I, Ulusoy Ö. A framework for use of wireless sensor networks in forest fire detection and monitoring. Computers, Environment and Urban Systems. 2012; 36(6):614-25.

[13][13]Bouabdellah K, Noureddine H, Larbi S. Using wireless sensor networks for reliable forest fires detection. Procedia Computer Science. 2013; 19:794-801.

[14][14]Hefeeda M, Bagheri M. Forest fire modeling and early detection using wireless sensor networks. Ad Hoc & Sensor Wireless Networks. 2009; 7(3-4):169-224.

[15][15]Gao Z, Huang L. A forest fire monitoring and early warning system based on the technology of multi-sensor and multilevel data fusion. In 2nd international conference on electrical, computer engineering and electronics 2015(pp. 627-32). Atlantis Press.

[16][16]Al-Habashneh AA, Ahmed MH, Husain T. Reliability analysis of wireless sensor networks for forest fire detection. In international wireless communications and mobile computing conference 2011 (pp. 1630-5). IEEE.

[17][17]Hefeeda M, Bagheri M. Wireless sensor networks for early detection of forest fires. In international conference on mobile adhoc and sensor systems 2007 (pp. 1-6). IEEE.

[18][18]Liu Y, Gu Y, Chen G, Ji Y, Li J. A novel accurate forest fire detection system using wireless sensor networks. In seventh international conference on mobile Ad-hoc and sensor networks 2011 (pp. 52-9). IEEE.

[19][19]Son B, Her YS, Kim JG. A design and implementation of forest-fires surveillance system based on wireless sensor networks for South Korea mountains. International Journal of Computer Science and Network Security (IJCSNS). 2006; 6(9):124-30.

[20][20]Soliman H, Sudan K, Mishra A. A smart forest-fire early detection sensory system: another approach of utilizing wireless sensor and neural networks. In SENSORS, 2010 (pp. 1900-4). IEEE.

[21][21]Díaz-Ramírez A, Tafoya LA, Atempa JA, Mejía-Alvarez P. Wireless sensor networks and fusion information methods for forest fire detection. Procedia Technology. 2012; 3:69-79.

[22][22]Dehwah AH, Elmetennani S, Claudel C. UD-WCMA: an energy estimation and forecast scheme for solar powered wireless sensor networks. Journal of Network and Computer Applications. 2017; 90:17-25.

[23][23]Dondi D, Bertacchini A, Brunelli D, Larcher L, Benini L. Modeling and optimization of a solar energy harvester system for self-powered wireless sensor networks. IEEE Transactions on Industrial Electronics. 2008; 55(7):2759-66.

[24][24]Horng GJ, Chang TY, Cheng ST. An effective node-selection scheme for the energy efficiency of solar-powered WSNs in a stream environment. Expert Systems with Applications. 2014; 41(7):3143-56.

[25][25]Ibrahim R, Chung TD, Hassan SM, Bingi K, Salahuddin S. Solar energy harvester for industrial wireless sensor nodes. Procedia Computer Science. 2017; 105:111-8.

[26][26]Noh DK, Kang K. Balanced energy allocation scheme for a solar-powered sensor system and its effects on network-wide performance. Journal of Computer and System Sciences. 2011; 77(5):917-32.

[27][27]Yue R, Ying T. A novel water quality monitoring system based on solar power supply & wireless sensor network. Procedia Environmental Sciences. 2012; 12:265-72.

[28][28]Zhu Y, Song J, Dong F. Applications of wireless sensor network in the agriculture environment monitoring. Procedia Engineering. 2011; 16:608-14.

[29][29]Thayananthan V, Alzranhi A. Enhancement of energy conservation technologies in wireless sensor network. Procedia Computer Science. 2014; 34:79-86.

[30][30]Frezzetti A, Manfredi S, Pagano M. A design approach of the solar harvesting control system for wireless sensor node. Control Engineering Practice. 2015; 44:45-54.

[31][31]Baranov A, Spirjakin D, Akbari S, Somov A, Passerone R. POCO:‘Perpetual’operation of CO wireless sensor node with hybrid power supply. Sensors and Actuators A: Physical. 2016; 238:112-21.

[32][32]Kaur T, Kumar D. Particle swarm optimization-based unequal and fault tolerant clustering protocol for wireless sensor networks. IEEE Sensors Journal. 2018; 18(11):4614-22.

[33][33]Fan Z, Jiang J, Weng S, He Z, Liu Z. Adaptive density distribution inspired affinity propagation clustering. Neural Computing and Applications. 2019; 31(1):435-45.

[34][34]Liu X, He D. Ant colony optimization with greedy migration mechanism for node deployment in wireless sensor networks. Journal of Network and Computer Applications. 2014; 39:310-8.

[35][35]Mosavvar I, Ghaffari A. Data aggregation in wireless sensor networks using firefly algorithm. Wireless Personal Communications. 2019; 104(1):307-24.

[36][36]Mann PS, Singh S. Optimal node clustering and scheduling in wireless sensor networks. Wireless Personal Communications. 2018; 100(3):683-708.

[37][37]Lahane SR, Jariwala KN. Network structured based routing techniques in wireless sensor network. In international conference for convergence in technology (I2CT) 2018 (pp. 1-6). IEEE.

[38][38]Bhardwaj R, Kumar D. MOFPL: Multi-objective fractional particle lion algorithm for the energy aware routing in the WSN. Pervasive and Mobile Computing. 2019; 58.