Middle-East Journal of Scientific Research 20 (12): 2566-2570, 2014 ISSN 1990-9233 © IDOSI Publications, 2014 DOI: 10.5829/idosi.mejsr.2014.20.12.626 A Analysis of Flat Routing Protocols in Sensor N/W 1 1 T. Saravanan, 2G. Saritha and 1V. Srinivasan Department of ETC, Bharath University, Chennai, India 2 Sathyabama University, Chennai, India Abstract: Wireless Sensor Networks (WSN) consists of small nodes with sensing, computation and wireless communications capabilities. Each sensor node operates autonomously with no central point of control in the network. Sensor networks are “data centric” and application specific. Thus, energy-efficient routing algorithms suitable to the inherent characteristics of these types of networks are needed. Also, to prolong the lifetime of the sensor nodes and to communicate each other designing efficient routing protocols is critical. Hence a focus is made on routing techniques. The performance comparison of three flat routing protocols, namely flooding, SPIN and MCFA is presented. Parameters like average energy consumption, lifetime of nodes, average time taken to travel from source node to sink node, average number of nodes participated in the network are taken for comparison. Simulation is performed using Matlab.Simulation results show that MCFA performs the best. This analysis reveals the important features that need to be taken into consideration while designing and evaluating new routing protocols. Key words: Sensor Performance Comparison Energy INTRODUCTION Wireless sensor network consist of tens of thousands of extremely small, low power and low cost devices which share wireless channel bandwidth in order to achieve high quality, fault tolerant sensing networks. Sensor nodes are minute. Each node is capable of running a stripped-down version of a modern operating system are normally integrated into each node for monitoring various physical conditions like temperature, pressure, humidity, motion, noise, light etc. Each sensor node operates autonomously with no central point of control in the network and each node bases its decisions on its mission, the information it currently has and its knowledge of its computing, communication and energy resources. So, sensor networks are “data-centric” networks and are application specific. Adjacent nodes may have similar data and there is a need for communication between nodes about the data to be sent. Sensor nodes are limited with respect to energy supply, restricted computational capacity and communication Lifetime Flat bandwidth [1]. Because networked sensors can use up their limited supply of energy simply performing computations and transmitting information in a wireless environment, energy conserving. Due to their limited capabilities, there are a lot of design issues that must be addressed to achieve an effective and efficient operation of wireless sensor networks. Thus, innovative mrouting techniques to eliminate energy inefficiencies that shorten the lifetime of the network and efficient use of the limited bandwidth are highly required. Such constraints combined with a typical deployment of large number of sensor nodes pose many challenges to the design and management of WSNs and necessitate energy-awareness at all layers of the networking protocol stack. At the network layer, it is highly desirable to find methods for energy-efficient route discovery and relaying of data from the sensor nodes to the BS so that the lifetime of the network is maximized. Routing techniques are classified into three categories based on the underlying network structure as flat, hierarchical and location based. Many, routing, power Corresponding Author: T. Saravanan, Department of ETC, Bharath University, Chennai, India. 2566 Middle-East J. Sci. Res., 20 (12): 2566-2570, 2014 Flat Based Routing: In flat-based networks, each node typically plays the same role and sensor nodes collaborate to perform the sensing task. This is also called Multi-hop flat routing. Some of the flat-based routing techniques are [1] Flooding, SPIN, MCFA, Directed Diffusion, Rumor routing, Gradient-based routing, Energyaware routing. Among these protocols Flooding, SPIN, MCFA are taken for comparative analysis Fig 1: The Components of a sensor node management and data dissemination protocols have been designed, where energy awareness is an essential design issue. Sensor nodes form a network by communicating with each other either directly or through other nodes. One or more nodes among them will serve as sink(s) that are capable of communicating with the user either directly or through the existing wired networks. Each node typically consists of the five Components: sensor unit, analog to digital converter (ADC), central processing unit (CPU), power unit and communication unit as shown in Fig.1. They are assigned with different tasks. Sensor Networks are emerging as a new tool for important applications in diverse fields like military surveillance, habitat monitoring, weather, etc. Routing Protocols: In general, routing [1] in WSNs can be divided into flat routing, hierarchical and location-based routing. In flat-based protocols, all nodes are typically assigned equal roles or functionality. Hierarchical protocols aim at clustering the nodes so that cluster heads can do some aggregation and reduction of data in order to save energy. Location-based protocols utilize the position information to relay the data to the desired regions rather than the whole. A routing protocol is considered adaptive if certain system parameters can be controlled in order to adapt to the current network conditions and available energy levels. Furthermore, these protocols can be classified into multipath-based, querybased, negotiation-based, QoS-based, or coherent-based routing techniques depending on the protocol operation. In addition to the above, another class of routing protocols is called the cooperative routing protocols. In cooperative routing, nodes send data to a central node where data can be aggregated and may be subject to further processing, hence reducing route cost in terms of energy use. Flooding: Akyildiz in [2] proposed an old routing mechanism called flooding that might also be used in sensor networks. In flooding, a node sends out the received data or the management packets to its neighbours by broadcasting, unless a maximum number of hops for that packet are reached or the destination of the packets is arrived. However, there are some deficiencies like implosion, overlap and resource blindness. SPIN: Sensor Protocols for Information via Negotiation: A family of adaptive protocols called Sensor Protocols for Information via Negotiation is] proposed by Heinzelman et.al. in [3] which disseminates all the Information at each node to every node in the network assuming that all nodes in the network are potential base-stations. This enables a user to query any node and get the required information immediately. These protocols make use of the property that nodes in close proximity have similar data and hence there is a need to only distribute the data other nodes do not posses. The SPIN family of protocols uses data negotiation and resource-adaptive algorithms. Nodes running SPIN assign a high-level name to completely describe their collected data and perform metadata negotiations of data through the nodes. SPIN’s meta-data negotiation solves the classic flooding by negotiation and resource adaptation. The SPIN family of protocols is designed based on two basic ideas Sensor nodes operate more efficiently and conserve energy by sending data that describe the sensor data instead of sending all the data. Conventional protocols like flooding or gossipingbased routing protocols waste energy and bandwidth when sending extra and unnecessary copies of data by sensors covering overlapping areas. SPIN implementation is based on the concept of metadata. Meta-data is a description of data. SPIN uses the following messages to communicate between nodes as shown in Fig 2. 2567 Middle-East J. Sci. Res., 20 (12): 2566-2570, 2014 Table 1: Radio Characteristics Initial node energy (Ei) Transmitter Electronics (ETx-elec) Receiver Electronics (ERx-elec) Transmit Amplifier (ETx-amp) Number of bits for data transmission (k) 0.2 Joules 50 nJ/bit 50 nJ/bit 100nJ/bit/m2 2000 Fig 3: Random Topology Fig 2: The SPIN Protocol ADV- this message is used by a node to inform other nodes that it has data to send. Note that actual data is sent only when acknowledged and requested by a node. REQ- this is send by the recipient to the sender node, if the recipient is interested in the actual data. DATA- this is the actual data MCFA: Minimum Cost Forwarding Algorithm: The MCFA algorithm [4] exploits the fact that the direction of routing is always towards the fixed external base-station. This protocol chooses the path, which takes minimum number of hops to reach base station. Each node maintains the least cost estimate from itself to the BS. Source node will forward the message to its neighbour, which is nearest to the base station. This process repeats until the BS is reached. Simulation: Simulation is carried out in Matlab. Matlab is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis and numeric computation. Matlab can be used in a wide range of applications, including signal and image processing, communications, control design, test and measurement, financial modeling and analysis and computational biology. Matlab code can be integrated with other languages and applications. Fig 4: Radio Model The network topology is a random topology as shown in Fig.3 with an area of 50m 50m dimension. Twenty-five nodes are deployed randomly in the network. Sink node (or) Base Station is located far away from the sensor nodes Performance parameters like average energy consumption, lifetime of nodes, average time taken to travel from source node to sink, average number of nodes participated in the network are taken for the performance comparison of routing protocols. Salhieh. and L. Schwiebert [5] discussed a first-order radio model shown in Fig.4 is chosen for the analysis. The transmit and receive power requirements [6] are calculated using the equations (1) and (2) shown in Table 1. Etx (k, d) = ETx-elec (k) + ETx-amp (k, d) (1) Erx (k) = k (ERx-elec) (2) The average energy consumpt ion (Ea) [7] is calculated across the ent ire topology. It measures the average difference between the initial level of energy and the final level of energy that is 2568 Middle-East J. Sci. Res., 20 (12): 2566-2570, 2014 Table 2: Comparison of routing protocols Parameters ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Average energy Consumption by the Lifetime of nodes (number of rounds Average number of nodes participated in Protocol sensor node for one round in Joules completed when first node dies) one transmission on completion of one round MCFA SPIN Flooding 0.25781mJ 0.4699 mJ 26.8 mJ 702 452 3 13 12 5 K=n Ea= (Eik-Efk) K=1 N (3) Ei = Initial energy level of a node Ef = Final Energy level of a node N = Number of nodes in the network This metric is important because the energy level that a network uses is proportional to the network’s lifetime [8]. The lower the energy consumption the longer is the network’s lifespan. For Flooding, as seen from the Table.2 the energy consumption is high. This is because a lot of nodes are receiving duplicate messages. In case of SPIN, the energy consumption is at slower rate [9]. On the other hand in MCFA, Energy consumption is very less compared to flooding and SPIN, because it chooses the path, which is having minimum number of hops. Lifetime of a network is calculated by finding the number of rounds for which the first node dies in the network during simulation [10]. As shown in the Table 2 In flooding first node fails in third round itself. SPIN takes 452 rounds for first node failure [11]. But MCFA takes 702 rounds for one node failure. From the results obtained MCFA is having larger lifetime [12]. Average energy consumption by the protocols is shown in Fig.5. In flooding in first node dies in third round as shown in Table.2. So average energy consumption for three rounds is shown [13]. Another important feature of any routing protocol is the time it requires to send a data from the source to the sink [14]. Fig.6 shows the average time taken to travel from source node to sink node [15]. It is clear that the shortest time was achieved with MCFA.The data has reached the sink on the average of 0:07339 seconds. SPIN protocol takes 0:14 seconds while flooding takes 0: 465 seconds for data to reach the sink. It is important to compare the average number of nodes participated in routing messages from source to sink for each of the routing protocols at hand. Since the Fig 5: Average energy consumption Fig 6: Average time taken to travel from source node to sink node lower amount of nodes participating in the routing would mean the lower the energy depletion of the network. In Table.2, it is shown that flooding and SPIN has used many nodes to send data from source to sink, while MCFA used only small number of sensor nodes respectively. Thus MCFA outperforms all routing protocols. RESULTS AND CONCLUSION Routing in sensor networks is a new area of research. We have compared three flat routing protocols namely flooding, SPIN, MCFA.Parameters like average energy 2569 Middle-East J. Sci. Res., 20 (12): 2566-2570, 2014 consumption, network lifetime, simulation time, average number of nodes participated in the network are taken for comparison. Each protocol has both own merits and demerits. MCFA performs the best. The desirable features that to be considered while designing a good energy efficient protocol for Wireless sensor networks are threshold for sensor nodes to transfer sensed data, random path selection, data fusion and low energy consumption. 8. 9. 10. REFERENCES 1. 2. 3. 4. 5. 6. 7. Jamul N. AL-Karaki and E. Kamal, 2004. Routing techniques in Wireless sensor networks: A survey, 11(6), IEEE Wireless Communications- December. Akyildiz, I.F., W.S.Y. Sankarasubramaniam and E. Cayirci, 2002. Wireless sensor networks: a survey in the Elesever Journal, Computer Networks. Heinzelman, W., J. Kulik and H. Balakrishnan, 1999. Adaptive Protocols for Information Dissemination in Wireless Sensor Networks, Proc. 5th ACM/IEEE Mobicom Conference (MobiCom '99), Seattle, WA, pp: 174-85. Ye, F., A. Chen, S. Liu and L. Zhang, 2001. A scalable solution to minimum cost forwarding in large sensor networks, Proc. of the tenth International Conference on Computer Communications and Networks (ICCCN), pp: 304-309. Salhieh, A. and L. Schwiebert, 2004. Power-aware metrics for wireless sensor networks, 26(4), International Journal of Computers and Applications. Heinzelman, W., A. Chandrakasan and H. Balakrishnan, 2000. Energy-Efficient Communication Protocol for Wireless Mi-crosensor Networks, Proc. of the 33rd Hawaii International Conference on System Sciences (HICSS '00). Tatiana Bokareva, Nirupama Bulusu and Sanjay Jha, 2004. A Performance Comparison of Data Dissemination Protocols for Wireless Sensor Networks, Proc. of Globecom Workshops IEEE Communications Society. 11. 12. 13. 14. 15. 2570 Thooyamani, K.P., V. Khanaa and R. Udayakumar, 2013. Detection of Material hardness using tactile sensor, Middle-East Journal of Scientific Research, ISSN: 1990-9233 15(12): 1713-1718. Thooyamani, K.P., V. Khanaa and R. Udayakumar, 2013. Blue tooth broad casting server, Middle-East Journal of Scientific Research, ISSN: 1990-9233, 15(12): 1707-1712. Thooyamani, K.P., V. Khanaa and R. Udayakumar, 2013. A frame work for modelling task coordination in Multi-agent system, Middle-East Journal of Scientific Research, ISSN: 1990-9233, 15(12): 1851-1856. Thooyamani, K.P., V. Khanaa and R. Udayakumar, 2013. An Integrated Agent System for E-mail Coordination using Jade, Indian Journal of Science and Technology, ISSN: 0974-6846, 6(6): 4758-4761. Udayakumar, R., V. Khanna, T. Saravanan and G. Saritha, 2013. Retinal Image Analysis Using Curvelet Transform and Multistructure Elements Morphology by Reconstruction, Middle-East Journal of Scientific Research, ISSN: 1990-9233, 16(12): 1798-1800. Udayakumar, R., V. Khanna, T. Saravanan and G. Saritha, 2013. Cross Layer Optimization for Wireless Network (Wimax), Middle-East Journal of Scientific Research, ISSN: 1990-9233, 16(12): 1786-1789. Saravanan, T. and G. Saritha, 2013. Buck Converter with a Variable Number of Predictive Current Distributing Method, Indian Journal of Science and Technology, ISSN: 0974-6846, 6(5S): 4583-4588. Saravanan, T. and R. Udayakumar, 2013. Comparision of Different Digital Image watemarking techniques, Middle-East Journal of Scientific Research, ISSN: 1990-9233, 15(12): 1684-1690.