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A Network which has wireless sensor nodes animatedly form a communication-less network or provisional network without using existing network infrastructure, which is defined as wireless network [1]. The sensing of information in decisive conditions during the emergency state where the sensor network is deployed is its main importance. The benefit of developing sensor network is increasing for practical information in physical environment in different applications either manually or randomly [2]. In this paper, the authors have implemented and evaluated the performance of LEACH protocol with different network parameters on different topologies based on varying the pause time and keeping the speed constant (node speed), which is small (1000 m. x 1000 m.), large (2000 m. x 1000 m.) and very large (2000 m. x 2000 m.) Important parameters are Packet Delivery Fraction, Average, End to End Delay, Average Throughput, and NRL and Packet loss. Wireless Sensor Network (WSN) consist of independent sensors, communicating with each other to monitor the environment. Sensor nodes are usually attached to microcontroller and are powered by battery. The resource constrained nature of WSN implies various challenges in its design and operations, which degrades its performance. However, the major fact that sensor nodes run out of energy quickly, has been an issue. Many routing, power management, and data dissemination protocols have been specifically designed for WSNs, while energy consumption is an essential design issue, which preserves longevity of the network. Out of these, clustering algorithms have gained more importance, in increasing the life time of the WSN, because of their approach in cluster head selection and data aggregation. This paper elaborately compares essential routing protocols using MATLAB and NS2 tools for several chosen scenarios. The paper concludes by mentioning valuable observations made from the analysis of results about several imperative protocols.
The evolution of wireless communication has enabled the development of an infrastructure consisting of sensing computation and communication units that makes the administrator capable to observe and react to phenomena in a particular environment. The building block of such infrastructure is comprised of hundreds or thousands of small, low cost, multi functional devices which have the ability to sense, compute and communicate using short range transceivers known as sensor nodes. The interconnection of these nodes form a network called Wireless Sensor Network (WSN) [1]. The low cost, ease of deployment, Ad-hoc and multi functional nature have exposed WSNs as an attractive choice for numerous applications. The application domain of WSNs varies from environmental monitoring, to health care applications, military operation, transportation, security applications, weather forecasting, real time tracking, fire detection [1] and so on. By considering its application areas, WSN can be argued as a wireless network. But in reality, these networks are comprised of battery operated tiny nodes with limitations in their computation capabilities, memory, bandwidth and hardware resulting in resource constrained WSN. WSNs have severe resource constraints, asymmetric many to one data flow and unreliable network nodes. Also, there can be a link or node failure that leads to reconfiguration of the network and re-computation of the routing paths, route selection in each communication pattern that results in either network delay by choosing long routes or degraded network lifetime by choosing short routes resulting in depleted batteries [2]. To this end, energy in these sensors is a scarce resource and must be managed in an efficient manner [3]. To improve WSN’s performance, these challenges are the subjects of investigation. Therefore the solutions for such environments should have an efficient routing mechanism to provide low latency, minimum consumption of energy, high network lifetime, reliable, fault tolerant communication and quick reconfiguration. To maintain reliable information delivery, data aggregation and data fusion is necessary for efficient and effective communication between these sensor nodes [4]. Routing protocols have a critical role in most of these activities. Routing in WSNs [9, 10] is a challenging task primarily because of the absence of global addressing schemes; secondly data source from multiple paths to single source, thirdly because of data redundancy and energy and computation constraints of the WSN.
Where, k is the data packet size in bits. The default values of the data and control packet sizes are 2000 bits, and 248 bits respectively.
Energy efficient routing is possible by means of cluster based routing or hierarchical schemes [5], [11]. In Static Clustering protocol, the clusters are chosen a-priori and fixed. Static Clustering includes scheduled data transmissions from the cluster members to the cluster head and data aggregation at this cluster-head [6]. However, the limitation of Static Clustering routing technique is energy consumption due to fixed cluster head node in every round.
To overcome this issue, Low Energy Adaptive Clustering Hierarchy (LEACH) was proposed [7] It is a protocol based on clustering hierarchy architecture. In the LEACH algorithm, the nodes are self organized into different clusters, by electing Cluster Header (CH) nodes. At the end of each round, each node that is not a cluster head selects the closest cluster head and joins that cluster to transmit data. The cluster heads aggregate and compress the data and forward it to the base station, thus it extends the lifetime of major nodes.
In this algorithm, the energy consumption will distribute almost uniformly among all nodes and the non-head nodes turn off as much as possible. LEACH assumes that all nodes are in wireless transmission range of the base station which is not the case in many sensor deployments. In each round, LEACH has cluster heads comprising 5% of the total nodes. It uses Time Division Multiple Access (TDMA) as a scheduling mechanism which makes it prone to long delays when applied to large sensor networks. Figure 1 shows the communications in LEACH protocol with WSN.
Figure 1. Operation and Design Architecture of Leach in WSN
In this section, the authors present the simulation results of LEACH, followed by a comparison among the different techniques.
Ns2, MATLAB and MATLAB simulators are used for simulating LEACH routing protocol that is used as experiment platform. MATLAB is a full fledged simulator for WSNs. It is very powerful in simulating a range of small to large scale WSNs based on a simple and complete Graphical User Interface (GUI). MATLAB's GUI allows users to design various simulation scenarios and display the simulation results graphically in many formats [6].
To evaluate the performance of the hierarchal routing protocol LEACH, the simulation consists of 80 homogeneous nodes with initial energy of 0.5 Joule, scattered randomly within a 40x40 m sensor field as shown in Figure 2. The Base Station is (BS) located at (25,150) m, so it is at least 110 m far from the closest packets and 248 bit control packets. The energy consumption due to communication is calculated using the first order energy model.
Figure 2. Simulated WSN with node distribution, n=80 With MATLAB
The authors assume that each sensor node generates one data packet per time unit to be transmitted to the BS. For simplicity, the authors refer to each time unit as a round. During the simulation process, only a set of sources will be selected randomly at each round to send their data to the BS. Transmission and sensing range are 15 m and 8 m respectively.
Following are the Physical parameters taken for LEACH
Two scenarios are used to measure the performance, which are as follows:
These two scenarios are used according to the Loss of network connectivity. MATLAB reports the network’s life time at the round when a sensor node is isolated (all its neighbors run out of energy), i.e. the network is not fully connected. In the first scenario, the authors ran the simulations to determine the number of rounds of communication when the network loses its connectivity using LEACH, with each node having the same initial energy level and transmission range which are 0.5 Joule and 15m respectively. The simulations show the following results:
The lifetimes of the network are 1017 rounds for LEACH. Figure 3 shows the network life time. The latest total network energy is 2.854 for LEACH, since it has lost connectivity. Figure 4 shows the total network energy versus time for LEACH routing algorithms.
Figure 3. Network life Time
Figure 4. Total Network Energy for LEACH
The authors have compared their simulation with other already available simulations and found that VGA protocol approximately achieves 3 times the number of rounds compared to PEGASIS. It approximately achieves 11x the number of rounds compared to LEACH. While PEGASIS approximately performs 3 times the number of rounds compared to LEACH, the lifetimes of the network are 8124, 2285, and 741 rounds for VGA, PEGASIS, and LEACH respectively. Figures 3 and 4 show the network lifetime and energy level of network respectively. Figure 5 shows the life time of Leach with other two protocols.
Figure 5. Network Life Time of VGA, PEGASIS and LEACH Routing Protocols [8].
In this paper, the authors have presented a simulation model for LEACH, hierarchical routing protocol. MATLAB simulator is used to compare the performance of the three routing protocols. Numbers of the CH (Cluster Header %) also affects the routing of LEACH in network. The 5 % CH of the total nodes of the network is appreciated for better result and greater network life for the network. The simulation results suggest that the LEACH protocol can effectively solve the problems such as the uncertainty of the number of the CH, lack of consideration of the energy state of the nodes in the construction stage of dynamic cluster.
In future, the factors in hierarchical routing protocol which affect the cluster building, communication of Cluster Heads and data fusion of clusters will be one of the research directions which can be more helpful to enhance network lifetime of the WSN.