Energy Efficiency for Mica Mode to Improve Network Life Time using Greedy Scheduling Algorithm

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1 IJIRST National Conference on Latest Trends in Networking and Cyber Security March 2017 Energy Efficiency for Mica Mode to Improve Network Life Time using Greedy Scheduling Algorithm S. Kannadhasan 1 M. Rajesh Baba 2 1 Research Scholar 2 Engineer 1,2 Department of Electronics and Communication Engineering 1,2 Anna University, Madurai, Tamilnadu, India Abstract The goal of this work is to preserve full coverage-target area while minimizing the number of active nodes. The nodes which are available in the wireless sensor networks consume more energy even when the nodes are not sensing or covering the target area. The nodes which should enter the hibernation state will be determined using the energy greedy scheduling algorithm which has several principles and objectives. Radio energy models are being used to find the energy consumed during access of the nodes at various modes like transmit, receive, idle and sleep mode. When the nodes enters the hibernation state and doesn t sense or cover any target area then there will be an occurrence of blind point and that particular spot where the blind point occurs can be said as an blind spot. The blind point can be removed using the back of mechanism and that s the major part of the work. The issue of energy saving is significant since in a battery-operated wireless node, the battery energy is definite and a node can only transmit a definite number of bits. The maximum number of bits that can be sent is defined by the total battery energy divided by the required energy per bit. Key words: Mica mode, MicaZ mode, User defined Mode I. INTRODUCTION Sensors integrated into systems, instruments and the environment coupled with the adequate delivery of sensed information could provide tremendous benefits to society. A potential benefit includes fewer catastrophic failures, conservation of natural resources, improved emergency response and enhanced homeland security. Wireless sensing networks can eliminate these costs, easing installation and eliminating connectors. The ideal wireless sensor is networked scalable, consumes very little power, software programmable, capable of fast data acquisition, reliable and authentic over the long term, costs little to invest and install, and requires no real maintenance. Selecting the optimum sensors and wireless communications link lacks the knowledge of the utilization and problem interpretation. Battery extension, sensor revise rates and size are all major design considerations. Examples of low data rate sensors include temperature, humidity, and peak strain apprehend without resistance. Examples of high data rate sensors include strain, simulation, and vibration. Recent advances have resulted in the ability to integrate sensors, radio communications, and digital electronics into a specific integrated circuit (IC) package. This capacity of the networks is very low price that sensors are able to communicate with each other using low power wireless data routing periods. A wireless sensor network (WSN) normally consists of a base station that can communicate with a number of wireless sensors via a radio link. II. RELATED WORK Michaela Cardei [1] In this paper we propose an efficient method to extend the sensor network life time by organizing the sensors into a maximal number of set covers that are activated successively only the sensors from ongoing active set are responsive for observing all targets and for supervising all targets and for transmitting the collected data, while all other nodes are in a low-energy sleep mode. In this paper we model the solution as the maximum set covers problem and design two heuristics that efficiently compute the sets, using linear programming and a greedy approach. Chih-fan Hsin and Mingyan Liu[2], This paper investigates the problem of providing network coverage using wireless sensors that operate on low duty cycles (measured by the proportion time of a sensor is deactivate or active), i.e., each sensor varies between active and sleep states to conserve energy with an average sleep period (much) longer than the active period. The dynamic change in topology as a result of such duty-cycling has potentially disrupted effect on the operation and performance of the network. A dispersed probabilistic coverage composition protocol (DPCCP) is recommended in this paper. DPCCP insures that the system disclosure contingency is maintained after turning off lots of irrelevant nodes. We extend LEACH by embedding DPCCP into LEACH seamlessly without any modification of the original workflow. DPCCP can effectively reduce the number active sensor nodes, and LEACHE outperforms LEACH in terms of system lifetime and energy efficiency [4]. The two major issues in a wireless sensor networks are Insurance Power Conservancy IJIRST 2017 Published by IJIRST 5

2 Sensors are furnished with defined battery endurance and steadily cover the target area. A major performance to utilize the power to ascend organized protocol through which some nodes stay effective whereas the others enter hibernate state so as to sustain their power [5]. This study presents an authentic algorithm for self-organized node to conclude which one has to shift to the hibernate state. The uniqueness is to take into account the pausing power at every node in the decision of turning off unnecessary nodes. Consequently, the node with a less remaining power has preference over its neighbors to enter hibernate state. The opinion is based on a regional area awareness that diminish the algorithm aerial. To authenticate and calculate the recommended algorithm, reproductions have been organized and have shown it can contribute to extend the network lifetime. Exceptionally, it aims at deceived some deficiency of the current works [6]. Mainly, it aims at achieving balanced energy depletion among nodes exchanging a minimum amount of information for the nodes organizing, while conserving the full insurance of the destination area using minimum active nodes [7]. III. PROPOSED SYSTEM The controversy of power saving is important since in a battery-operated wireless node, the battery power is finite and a node can only transmit a finite number of bits. The ultimate number of bits that can be sent is characterized by the total battery power divided by the sufficient power per bit. Most of the originate research in the area of power strained communication has concentrated on transmission schemes to decrease the transmission power per bit. In this part, we present a generic radio energy model which is derived to estimate the consumed energy for reception and transmission. In a wireless radio transceiver, energy is dissipated in active mode when the radio transmits or receives a packet, in sleep or idle modes of the transceivers, and for the transition among states. A. Radio Specific Energy Models The model reads the energy consumption specifications of the radio where the specifications are defined by the configuration parameters which are the power supply voltage of the radio, electrical current load consumed in transmit, receive, idle and sleep modes. MicaZ radio energy model Mica mode radio models User defined radio model B. MicaZ Radio Energy Model The MicaZ radio energy is a radio-specific energy model which is pre-configured with the specification of power consumption of MicaZ motes (embedded sensor nodes). From the radio interface data sheets provided by the vendors of the wireless interfaces, we have stored the specifications of several commonly used wireless interfaces such as given the name of vendor as configuration parameter, the energy model specifications are loaded for that wireless interface. C. Mica Motes Radio Energy Model The Mica Motes Radio Energy model is a radio-specific energy model which is pre-configured with the specification of power consumption of Mica motes (embedded sensor nodes). D. User-defined Radio Energy Model The User-defined energy model is a configurable model that allows the user to specify the energy consumption parameters of the radio in different power modes. The configurable parameters include the power supply voltage of the radio s hardware, electrical current load consumed in transmit, receive, idle and sleep modes. E. Assumptions and Limitations Large amount of energy is consumed for transmission, reception, idle and sleep modes. F. Radio Energy Models The generic model takes into account the variable and continuous transmission power. Transmission power is constant during the simulation run. Fig. 1: Depicts the components of radio model which consume energy at the receiver and transmitter. 6

3 The energy consumption E required to send k bits consists of three components: E=P on.t on+p sp.t sp+p tr.t tr+p idle.t idle=(p t+p co).t on+p sp.t sp+p tr.t tr+p idle.t idle [1] Where Pon, Psp, Pidle and Ptr are power consumption values for the active mode, the sleep mode, idle mode and the transient mode, respectively. Similarly, T represent the time duration that the transceiver stays at each state. The active mode power Pon comprises the transmission signal power Pt and the circuit power consumption Pco in the whole signal path. Specifically, Pco consists of the mixer power consumption Pmix, the frequency synthesizer power consumption Psyn, the LNA power consumption PLNA, the active. IV. RESULTS AND DISCUSSION Fig. 2: Mica Mode Fig. 3: Mica Transmit Mode Fig. 4: Mica Receive Mode 7

4 Fig. 5: MicaZ Mode Fig. 6: MicaZ Transmit Mode Fig. 7: MicaZ Receive Mode 8

5 Fig. 8: User Defined Mode Fig. 9: User Defined Transmit Mode Fig. 10: User Defined Receive Mode V. CONCLUSION Wireless Sensor Network has the challenge of reduced life time if the energy consumption of nodes is higher. Simulation results show that the proposed technique has superior throughput with condensed packet drop and also a lesser amount of energy consumption which results in increase of network life time. 9

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