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Chain Based Wireless Sensor Network Routing Using Hybrid Optimization (HBO And ACO)

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IJEEE
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In Wireless Sensor Network, due to the energy restriction of each nodes, efficient routing is very important in order to save the energy of the hybrid optimization technique. The results of new protocol i.e. hybrid have been compared with EEPB and IEEPB. Simulation results show that the lifetime of Hybrid is better as compared to EEPB and IEEPB.

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www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 23 IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426 Chain Based Wireless Sensor Network Routing Using Hybrid Optimization (HBO And ACO) Er.Sadhna 1 , Er.Supreet singh2 1 Electronics and Communication Deptt., Swami Parmananad College of Engineering & Tech., Punjab, India 2 Electronics and Communication Deptt., Baba Banda Singh Bahadur College of Engineering, Punjab, India ABSTRACT: In Wireless Sensor Network, due to the energy restriction of each nodes, efficient routing is very important in order to save the energy of the hybrid optimization technique. The results of new protocol i.e. hybrid have been compared with EEPB and IEEPB. Simulation results show that the lifetime of Hybrid is better as compared to EEPB and IEEPB. Throughput has been increased in the Hybrid since in 50% node mobility EEPB give 1854, IEEPB give 1981 and HEEPB gives 2390 . Thus, the proposed protocol is more energy efficient as compared to chain based protocols i.e. EEPB and IEEPB sensor node and to enhance the lifetime of the network. In this dissertation, a new Optimization Tech.i.e. HYBRID(ACO and HBO) with Improved PEGASIS protocol has been designed.A new approach has been used to overcome the problem of PEGASIS by using. KEYWORDS: Wireless sensor network, Energy efficient PIGASIS based,Improved Energy efficient PIGASIS based,Hybrid Energy efficient PIGASIS based,Improved I. WIRELESS SENSOR NETWORKS A Wireless Sensor Network (WSN) consists of a large number of tiny wireless sensor nodes (often referred to as sensor nodes) that are, typically, densely deployed. Ad hoc networks are defined as the category of wireless networks that utilize multi-hop radio relaying since the nodes are dynamically and arbitrarily located. Ad hoc networks are infrastructure independent networks. • Sensor Node: A sensor node is the core component of a WSN. The sensor nodes can take on multiple roles in a network, such as simple sensing; data storage; routing; and data processing. • Clusters: Clusters are the organizational unit for WSNs. Because of the dense nature of these networks it requires the need for them to be broken down into clusters to simplify tasks such a communication [2]. • Cluster heads: Cluster heads are the organization leader of a cluster. They often are required to organize activity in the cluster. These tasks are not limited to data-aggregation and organizing the communication schedule of a cluster [3]. • Base Station: The base station is at the upper level of the hierarchical WSN. It provide the communication link between the sensor network and the end-user. • End User: The data in a sensor network can be used for a wide-range of applications [1]. Therefore, a particular application may make use of the network data over the internet using a PDA or even a desktop computer Fig1. Wireless Network II. ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS A sensor network consists of a large number of small, low- cost devices with sensing processing, and transmitting capabilities. Main goal of the operation is to observe a region and gather and relay information to a sink node or set of sink nodes, called Base Station (BS). Forwarding the data to the BS is possible in two ways: using direct or multihop communication. In the first case every sensor transmits its data directly to the sink; in the second case, the sensors are communicating with the neighbours that forward the information in the direction of the sink [3]. The sensors are usually deployed densely and often on-the- fly. They operate un-tethered and unattended, are limited in power, computational capacities and memory. Because of these constraints the sensor network must have efficient self- organizing capabilities, while optimizing energy consumption. A primary design issue in sensor networks is energy efficiency. The main goal is to prolong the lifetime of the network, which can be defined in several ways [4]: • The time when the first node depletes its battery, • The time until a given percentage of the sensors has enough energy to operate, • The time until a given percentage of the region is covered by alive sensors. III. ROUTING PROTOCOLS IN WSN
International Journal of Electrical & Electronics Engineering 24 www.ijeee-apm.com Energy consumption can be reduced by the use of various techniques like data aggregation, clustering, data-centric methods, etc. The routing protocols can be classified as flat, hierarchical or location-based as follow:  Flat networks: In flat networks, all nodes are equal. Hence each node plays the same role. This network has no logical hierarchy. It uses a flat addressing scheme. Routing Information Protocol (RIP) is an example of a flat routing protocol.  Hierarchical networks: In hierarchical networks, the nodes are partitioned into a number of small groups called clusters. Each cluster has a cluster head (CH) which is the coordinator of other nodes. These CHs perform data aggregation so that energy inefficiency may be reduced. The cluster heads may change. The node which has the highest energy acts as the CH. Hierarchical routing is an efficient way to lower energy consumption within a cluster. It has major advantages of scalability, energy efficiency, efficient bandwidth utilization, reduces channel contention and packet collisions. Low Power Adaptive Clustering Hierarchy (LEACH), Power efficient gathering in sensor information and (PEGASIS), Hybrid Energy-Efficient Distributed Clustering (HEED), etc. are examples of hierarchical networks. PEGASIS Hierarchical-based routing protocols are widely used for their high energy-efficiency and good expandability. The idea of them is to select some nodes in charge of a certain region routing. These chosen nodes have greater responsibility relative to other nodes which leads to the incompletely equal relationship between sensor nodes. It is the typical hierarchical-based routing protocols. As an enhancement algorithm of PEGASIS is a classical chain- based routing protocol. chain based protocol saves significant energy compared with the LEACH protocol by improving the cluster configuration and the delivery method of sensing data. However, the PEGASIS protocol also has many problems requiring solutions. In recent years, researchers have proposed many improved algorithms based on PEGASIS such as PEG-Ant, PDCH and EEPB et al. •When EEPB builds a chain, the threshold adopted is uncertain and complex to determine, which causes the inevitability of LL if valued inappropriately. • When EEPB selects the leader, it ignores the suitable proportion of nodes energy and distance between node and BS which optimizes the leader selection according to various application environments. Based on the above analysis, this paper presents an improved energy-efficient PEGASIS- based routing protocol called IEEPB. IEEPB compares the distance between nodes twice, finds the shortest path to link the two adjacent nodes. This chain-building method is more simplified and effectively avoids the formation of LL between neighbouring nodes. IV.WSN USING HYBRID HBO AND ANT OPTIMIZATION TECHNIQUE A Wireless Sensor Network (WSN) consists of a large number of tiny wireless sensor nodes (often referred to as sensor nodes or, simply, nodes) that are, typically, densely deployed. Energy efficiency is the most required quality in a sensor network where each node consumes some energy with each transmission over the network. Energy efficiency is required to improve the network life. Our proposed work is defined to improve the energy efficiency in Wireless Sensor Networks. The two algorithms from Artificial intelligence will be used in our work. Also the PEGASIS protocol will be enhanced and then implemented in the WSN scenario. In our work, we will take following parameters into consideration: I. Average energy per iteration II. No of alive nodes per iteration V. SIMULATION ENVIRONMENT A 100 node field is used and generated by randomly placing the nodes in a 100 m x 100 m square area. We assume that the area contains homogeneous sensor nodes with a communication range of 45m. The simulation focuses on number of sensor nodes alive, Average Energy of network and cost slot per iterations which are important indicators to measure performance of different algorithms. The simulation parameters used are shown below: Table 1: Simulation Parameters Parameters Values Number of Nodes 100 Area Size 100×100 Base Station (50, 300) Energy Transmitted 50nj/bit Energy Received 100pj/bit/m2 Amp Energy 0.0013pj/bit/m4 V1.SIMULATION RESULTS 0 500 1000 1500 2000 2500 3000 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Number of rounds AverageEnergyperround IEEPB HEEPB EEPB
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 25 Table 2 Network life time VII.CONCLUSION & FUTURE SCOPE A new enhanced scheme based on artificial intelligence has been proposed for Wireless Sensor Networks which helps to improve the energy efficiency as well as lifetime of the Wireless sensor network. Energy efficiency is the most required quality in a sensor network where each node consumes some energy with each transmission over the network. Energy efficiency is also required to improve the network life. The results of the proposed scheme are evaluated in MATLAB.The simulation results shows that the proposed scheme that is hybrid Honey bee optimization and ant colony optimization with improved PEGASIS has the better results as compare to previous techniques. In this proposed work chain complexity is reduced by using hybrid optimization technique and is more efficient in energy saving. In future, the work can be extended by reducing the complexity of chain further by optimizing the energy parameter along with the distance parameter or the nutrient function can be changed. REFERENCES [1] Z. M Wang, S.Basagni, E.Melachrinoudis and C.Petrioli, „„Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime‟‟, Proceedings of the 38th Hawaii International Conference on System Sciences, IEEE Computer Society, 2005. [2] E. H. Callaway, Wireless Sensor Networks, Architectures and Protocols, Auerbach Publications, Taylor & Francis Group, Boca Raton, Fla, USA, 2003. [3] Thanos Stathopoulos, R. Kapur, D.Estrin, “Application-Based Collision Avoidance in Wireless Sensor Networks”, Conference of Computer society, July-December 2005. [4] K. Padmanabhan, Dr. P. Kamalakkannan,“ Energy-efficient Dynamic Clustering Protocol for Wireless Sensor Networks”, International Journal of Computer Applications, Vol. 38, Issue. 11, January 2012. [5] S. R. Das, C. E. Perkins, and E. M. Royer, “Ad hoc on-demand distance vector (AODV) routing”, IETF Internet draft, draft-ietf- manet-aodv- 13.txt, Feb 2003. [6] S.K Singh, M. P Singh and D K Singh , “Routing Protocols in Wireless Sensor Networks –A Survey,” International Journal of Computer Science & Engineering Survey (IJCSES) Vol.1, No.2, November 2010. [7] P.Tyagi, R.P Gupta, R.K Gill,” Comparative Analysis of Cluster Based Routing Protocols used in Heterogeneous Wireless Sensor Network”, International Journal of Soft Computing and Engineering (IJSCE), Vol. 1, Issue. 5, November 2011. 0 500 1000 1500 2000 2500 3000 0 10 20 30 40 50 60 70 80 90 100 Number of rounds Numberofalivenodesperround IEEPB HEEPB EEPB Node mortality EEPB IEEPB HEEPB 1% 387 993 2100 50% 1854 1981 2390 100% 1902 2047 2420

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Chain Based Wireless Sensor Network Routing Using Hybrid Optimization (HBO And ACO)

  • 1. www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 23 IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426 Chain Based Wireless Sensor Network Routing Using Hybrid Optimization (HBO And ACO) Er.Sadhna 1 , Er.Supreet singh2 1 Electronics and Communication Deptt., Swami Parmananad College of Engineering & Tech., Punjab, India 2 Electronics and Communication Deptt., Baba Banda Singh Bahadur College of Engineering, Punjab, India ABSTRACT: In Wireless Sensor Network, due to the energy restriction of each nodes, efficient routing is very important in order to save the energy of the hybrid optimization technique. The results of new protocol i.e. hybrid have been compared with EEPB and IEEPB. Simulation results show that the lifetime of Hybrid is better as compared to EEPB and IEEPB. Throughput has been increased in the Hybrid since in 50% node mobility EEPB give 1854, IEEPB give 1981 and HEEPB gives 2390 . Thus, the proposed protocol is more energy efficient as compared to chain based protocols i.e. EEPB and IEEPB sensor node and to enhance the lifetime of the network. In this dissertation, a new Optimization Tech.i.e. HYBRID(ACO and HBO) with Improved PEGASIS protocol has been designed.A new approach has been used to overcome the problem of PEGASIS by using. KEYWORDS: Wireless sensor network, Energy efficient PIGASIS based,Improved Energy efficient PIGASIS based,Hybrid Energy efficient PIGASIS based,Improved I. WIRELESS SENSOR NETWORKS A Wireless Sensor Network (WSN) consists of a large number of tiny wireless sensor nodes (often referred to as sensor nodes) that are, typically, densely deployed. Ad hoc networks are defined as the category of wireless networks that utilize multi-hop radio relaying since the nodes are dynamically and arbitrarily located. Ad hoc networks are infrastructure independent networks. • Sensor Node: A sensor node is the core component of a WSN. The sensor nodes can take on multiple roles in a network, such as simple sensing; data storage; routing; and data processing. • Clusters: Clusters are the organizational unit for WSNs. Because of the dense nature of these networks it requires the need for them to be broken down into clusters to simplify tasks such a communication [2]. • Cluster heads: Cluster heads are the organization leader of a cluster. They often are required to organize activity in the cluster. These tasks are not limited to data-aggregation and organizing the communication schedule of a cluster [3]. • Base Station: The base station is at the upper level of the hierarchical WSN. It provide the communication link between the sensor network and the end-user. • End User: The data in a sensor network can be used for a wide-range of applications [1]. Therefore, a particular application may make use of the network data over the internet using a PDA or even a desktop computer Fig1. Wireless Network II. ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS A sensor network consists of a large number of small, low- cost devices with sensing processing, and transmitting capabilities. Main goal of the operation is to observe a region and gather and relay information to a sink node or set of sink nodes, called Base Station (BS). Forwarding the data to the BS is possible in two ways: using direct or multihop communication. In the first case every sensor transmits its data directly to the sink; in the second case, the sensors are communicating with the neighbours that forward the information in the direction of the sink [3]. The sensors are usually deployed densely and often on-the- fly. They operate un-tethered and unattended, are limited in power, computational capacities and memory. Because of these constraints the sensor network must have efficient self- organizing capabilities, while optimizing energy consumption. A primary design issue in sensor networks is energy efficiency. The main goal is to prolong the lifetime of the network, which can be defined in several ways [4]: • The time when the first node depletes its battery, • The time until a given percentage of the sensors has enough energy to operate, • The time until a given percentage of the region is covered by alive sensors. III. ROUTING PROTOCOLS IN WSN
  • 2. International Journal of Electrical & Electronics Engineering 24 www.ijeee-apm.com Energy consumption can be reduced by the use of various techniques like data aggregation, clustering, data-centric methods, etc. The routing protocols can be classified as flat, hierarchical or location-based as follow:  Flat networks: In flat networks, all nodes are equal. Hence each node plays the same role. This network has no logical hierarchy. It uses a flat addressing scheme. Routing Information Protocol (RIP) is an example of a flat routing protocol.  Hierarchical networks: In hierarchical networks, the nodes are partitioned into a number of small groups called clusters. Each cluster has a cluster head (CH) which is the coordinator of other nodes. These CHs perform data aggregation so that energy inefficiency may be reduced. The cluster heads may change. The node which has the highest energy acts as the CH. Hierarchical routing is an efficient way to lower energy consumption within a cluster. It has major advantages of scalability, energy efficiency, efficient bandwidth utilization, reduces channel contention and packet collisions. Low Power Adaptive Clustering Hierarchy (LEACH), Power efficient gathering in sensor information and (PEGASIS), Hybrid Energy-Efficient Distributed Clustering (HEED), etc. are examples of hierarchical networks. PEGASIS Hierarchical-based routing protocols are widely used for their high energy-efficiency and good expandability. The idea of them is to select some nodes in charge of a certain region routing. These chosen nodes have greater responsibility relative to other nodes which leads to the incompletely equal relationship between sensor nodes. It is the typical hierarchical-based routing protocols. As an enhancement algorithm of PEGASIS is a classical chain- based routing protocol. chain based protocol saves significant energy compared with the LEACH protocol by improving the cluster configuration and the delivery method of sensing data. However, the PEGASIS protocol also has many problems requiring solutions. In recent years, researchers have proposed many improved algorithms based on PEGASIS such as PEG-Ant, PDCH and EEPB et al. •When EEPB builds a chain, the threshold adopted is uncertain and complex to determine, which causes the inevitability of LL if valued inappropriately. • When EEPB selects the leader, it ignores the suitable proportion of nodes energy and distance between node and BS which optimizes the leader selection according to various application environments. Based on the above analysis, this paper presents an improved energy-efficient PEGASIS- based routing protocol called IEEPB. IEEPB compares the distance between nodes twice, finds the shortest path to link the two adjacent nodes. This chain-building method is more simplified and effectively avoids the formation of LL between neighbouring nodes. IV.WSN USING HYBRID HBO AND ANT OPTIMIZATION TECHNIQUE A Wireless Sensor Network (WSN) consists of a large number of tiny wireless sensor nodes (often referred to as sensor nodes or, simply, nodes) that are, typically, densely deployed. Energy efficiency is the most required quality in a sensor network where each node consumes some energy with each transmission over the network. Energy efficiency is required to improve the network life. Our proposed work is defined to improve the energy efficiency in Wireless Sensor Networks. The two algorithms from Artificial intelligence will be used in our work. Also the PEGASIS protocol will be enhanced and then implemented in the WSN scenario. In our work, we will take following parameters into consideration: I. Average energy per iteration II. No of alive nodes per iteration V. SIMULATION ENVIRONMENT A 100 node field is used and generated by randomly placing the nodes in a 100 m x 100 m square area. We assume that the area contains homogeneous sensor nodes with a communication range of 45m. The simulation focuses on number of sensor nodes alive, Average Energy of network and cost slot per iterations which are important indicators to measure performance of different algorithms. The simulation parameters used are shown below: Table 1: Simulation Parameters Parameters Values Number of Nodes 100 Area Size 100×100 Base Station (50, 300) Energy Transmitted 50nj/bit Energy Received 100pj/bit/m2 Amp Energy 0.0013pj/bit/m4 V1.SIMULATION RESULTS 0 500 1000 1500 2000 2500 3000 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Number of rounds AverageEnergyperround IEEPB HEEPB EEPB
  • 3. www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 25 Table 2 Network life time VII.CONCLUSION & FUTURE SCOPE A new enhanced scheme based on artificial intelligence has been proposed for Wireless Sensor Networks which helps to improve the energy efficiency as well as lifetime of the Wireless sensor network. Energy efficiency is the most required quality in a sensor network where each node consumes some energy with each transmission over the network. Energy efficiency is also required to improve the network life. The results of the proposed scheme are evaluated in MATLAB.The simulation results shows that the proposed scheme that is hybrid Honey bee optimization and ant colony optimization with improved PEGASIS has the better results as compare to previous techniques. In this proposed work chain complexity is reduced by using hybrid optimization technique and is more efficient in energy saving. In future, the work can be extended by reducing the complexity of chain further by optimizing the energy parameter along with the distance parameter or the nutrient function can be changed. REFERENCES [1] Z. M Wang, S.Basagni, E.Melachrinoudis and C.Petrioli, „„Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime‟‟, Proceedings of the 38th Hawaii International Conference on System Sciences, IEEE Computer Society, 2005. [2] E. H. Callaway, Wireless Sensor Networks, Architectures and Protocols, Auerbach Publications, Taylor & Francis Group, Boca Raton, Fla, USA, 2003. [3] Thanos Stathopoulos, R. Kapur, D.Estrin, “Application-Based Collision Avoidance in Wireless Sensor Networks”, Conference of Computer society, July-December 2005. [4] K. Padmanabhan, Dr. P. Kamalakkannan,“ Energy-efficient Dynamic Clustering Protocol for Wireless Sensor Networks”, International Journal of Computer Applications, Vol. 38, Issue. 11, January 2012. [5] S. R. Das, C. E. Perkins, and E. M. Royer, “Ad hoc on-demand distance vector (AODV) routing”, IETF Internet draft, draft-ietf- manet-aodv- 13.txt, Feb 2003. [6] S.K Singh, M. P Singh and D K Singh , “Routing Protocols in Wireless Sensor Networks –A Survey,” International Journal of Computer Science & Engineering Survey (IJCSES) Vol.1, No.2, November 2010. [7] P.Tyagi, R.P Gupta, R.K Gill,” Comparative Analysis of Cluster Based Routing Protocols used in Heterogeneous Wireless Sensor Network”, International Journal of Soft Computing and Engineering (IJSCE), Vol. 1, Issue. 5, November 2011. 0 500 1000 1500 2000 2500 3000 0 10 20 30 40 50 60 70 80 90 100 Number of rounds Numberofalivenodesperround IEEPB HEEPB EEPB Node mortality EEPB IEEPB HEEPB 1% 387 993 2100 50% 1854 1981 2390 100% 1902 2047 2420