International Journal of Computer Applications (0975 – 8887) Volume 27– No.4, August 2011 49 Comparative Study of Radio Models for data Gathering in Wireless Sensor Network Joydeep Banerjee Undergraduate Student Advanced Digital and Embedded Systems Laboratory Jadavpur University Kolkata, India Swarup Kumar Mitra Department of ECE M.C.K.V.I.E, LiLuah, Howrah Kolkata, India Mrinal Kanti naskar Professor Advanced Digital and Embedded Systems Laboratory Jadavpur University Kolkata, India ABSTRACT Wireless Sensor Network (WSN) consists of irreplaceable nodes which are equipped with limited energy resources. Necessity of power consumption becomes a prior importance for various pervasive and ubiquitous applications. For realistic computation of energy in accordance with available motes like Micaz, Telos, Mica2, a discrete radio model exist. In this paper we have incorporated a discrete radio model over popular data gathering algorithm. We have formulated a data sheet, which relates details of power required to transmit data packets over range of distance that includes Lognormal Shadowing model for acquiring the required received signal strength. Range of distance is the key factor for energy consumption, which justifies the reduction of power levels to a limited count. We have disseminated the power transmission into specific band with respect to distance. Our paper shows the variation of node deployment over Network lifetime, which produces a significant alteration from sparse to dense network. We have conducted a comparison of Network lifetime and Mean energy consumption for chain based, shortest-hop and load balanced energy aware routing protocols. We have conducted a comparative study of the proposed method in TinyOS platform while running the simulation in TOSSIM. General Terms Wireless Sensor Network, Power dissipation Model. Keywords Micaz, Discrete Radio Model, Log Normal Shadowing Model, Energy Bands, Network Lifetime, TOSSIM. 1. INTRODUCTION Wireless Sensor Network (WSN) consists of several nodes in count of hundred or thousand operating in remote location and harsh environment. Recent advancements in the field of digital signal processors, short-range radio electronics, microelectromechanical systems (MEMS) based sensor technology and low power RF designs have enabled the development of inexpensive low power sensors with significant computational capability. Applications of sensor networks vary widely from climatic data gathering, seismic and acoustic underwater monitoring to surveillance and national security, military and health care. The major resource constraint is the energy consumption in the network as the sensor nodes being equipped by irreplaceable batteries. A network with even topology is deployed randomly with sensor nodes. The sensor networks are required to transmit gathered data to the base station (BS) or sink. Each node is provided with transmit power control and omni directional antenna and therefore can vary the areas of its coverage Since communication requires significant amount of energy compared to computations, sensor nodes must collaborate in an energy- efficient manner for transmitting and receiving data so that lifetime can be enhanced and also a better “energy versus delay” [1] performance is achieved. The real issue of energy balancing in WSN can be utilized fully through an efficient power control of nodes. In this paper we have implemented different data gathering protocols using discrete power control [2]. We introduce a radio model, as discussed in [2], which dynamically determines which power level setting should be used to transmit between two nodes. Using the power level setting, the cost of transmission is calculated based on the Chipcon CC2420 radio chip [3] specifications to ensure an accurate estimation. The novelty of our paper is that for realistic implementation of routing algorithms we have taken into consideration of seven discrete power levels as in Crossbow Micaz motes [3]. Secondly we assume Log-normal Shadowing path loss model, which detects an occurrence of an event at a particular distance from the node [4], for calculation of received signal strength (RSS). We have performed an analysis of the discrete model and CC2420 data sheet for determination of various power levels in term of distance and calculated the energy required to transmit data packets to a distance. For performing the above analysis we have taken the help of the experimentation and simulations that we made to calculate the discrete power in terms of distance. We have also made a chart for power output of all the available 32