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Solar Energy Based Medium Access Control for Out Door Applications of WSN. First A Mrs. Swati V. Sankpal, Second B Dr. Vishram Bapat Abstract— The wireless sensor network (WSN) is an autonomous long-term environment monitoring network. For outdoor applications sensor networks needs battery supply for their operations. Limitation of energy supply is holding up the progress of WSNs towards large scales and true autonomous operations. In many applications it is very difficult or rather impossible to recharge battery. Research is going on to minimize battery consumption and improve the life of networks. Much of the research is based on minimizing duty cycle of Radio which is most power consuming part in WSN. In this paper we are implementing a technique of MAC Protocol based on Solar Energy. The capacitor charges to maximum energy depending on solar intensity. This energy is used for data transmission by sensor node. We have evaluated performance in terms of throughput by analytical and simulation method and energy required for transmission and reception of variable data packet size. Index Terms—Battery Life, MAC, SOLAR, Telos Mote, WSN

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1 INTRODUCTION A wireless sensor network consists of a large number of sensor nodes deployed over a geographical area for monitoring physical phenomena like temperature, humidity, vibrations, seismic events, and so on. Each sensor node itself consists of, a sensing subsystem for data acquisition from the physical surrounding environment, a processing subsystem for local data processing and storage, and a Radio for data transmission to a central collection point called as sink node or base station. In addition, a power source supplies the energy needed by the device to perform the programmed task. This power source often consists of a battery with a limited energy budget. In addition, it could be impossible or inconvenient to recharge the battery, because nodes may be deployed in a hostile or unpractical environment. On the other hand, the sensor network should have a lifetime long enough to fulfill the application requirements. In many cases a lifetime in the order of several months, or even years, may be required. Therefore, the question is how to enhance the network life time? In outdoor applications it is possible to scavenge energy from the external environment (e.g., by using solar cells as power source). However, external power supply sources often exhibit a non-continuous behavior due to environmental conditions. In any case, energy is a very critical resource and must be used very sparingly. Therefore, energy saving is a key issue in the design of systems based on wireless sensor networks. The greediest part of energy is Radio. Today due to advanced technology it is become possible to design low power Radios. But major role of power consumption depends on behavior of MAC. In this paper, we first provide a brief survey of research on energy harvesting wireless sen-

sor networks and MAC protocols. We then define the proposed MAC (SEMAC) powered by solar energy and energy conservation carried by short preamble MAC for outdoor applications of WSN.

2 RELATED WORK

For wireless sensor networks the literature provides a lot of protocols and is divided it into two major categories: Contention Based MAC Protocols. TDMA Based MAC Protocols. Various MAC protocols [3] have been designed for WSNs sleep and wakeup schedules are proposed to reduce energy usage and prolong network lifetime at the expense of longer delays. Since these schemes assume the use of batteries in their scenarios, energy conservation therefore is a key consideration. The most known MAC protocol for wireless networks is IEEE 802.11 which is the standard now for WLAN applications. IEEE 802.11 performs well on terms of latency and throughput but it is very unwanted in terms of energy consumption because of the (idle listening) problem issued with it. Wei et al, presented sensor-MAC (S-MAC), a contention based MAC protocol designed explicitly for wireless sensor networks. While reducing energy consumption is the primary goal in the design, the protocol also has good scalability and collision avoidance capability. It achieves good scalability and collision avoidance by utilizing a combined scheduling and contention scheme. It achieves efficient energy consumption by using a scheme of periodic listen and sleep reduces energy consumption by avoiding idle listening. S-MAC has a problem of latency because of periodic listen and sleep scheme ———————————————— which is fixed depending on the duty cycle. T-MAC uses a F.A. Author is with the Department of Electronics D.Y. Patil college of time-out mechanism to dynamically determine the end of the engineering Kolhapur. active period. If receiver does not receive any message during S.B. Author is the Principal,College of Engineering Miraj Shivaji Univertime out interval, it goes to sleep and if it receives such messity Kolhapur. sage, the time start starts afresh after reception of message. © 2010 JOT http://sites.google.com/site/journaloftelecommunications/

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The basic T-MAC scheme suffers from so called early sleep problem which can reduce throughput. Berkeley MAC (BMAC) protocol, which extends previous work, uses a tone to wakeup sleeping neighbors. In B-MAC sensor nodes independently follow a sleeping schedule based on the target duty cycle for the sensor network. Since the sensor nodes operate on independent schedules, B-MAC uses very long preambles for message transmission. The source sensor node transmits a preamble long enough that the destination, which periodically senses the channel, has enough time to wake-up and sense activity. Sensor nodes that sense activity on the channel remain awake to receive the message following the preamble or return to sleep if they do not detect activity on the channel. Before transmitting, sensor nodes delay a random time to prevent synchronization, and sense the channel to prevent corrupting an ongoing transmission. Additionally, the BMAC authors provide a great deal of flexibility through a protocol interface that allows the sensor node to change many operating variables in the protocol, such as delay and bakeoffs values. WiseMAC, which is based on ALOHA, also uses preamble sampling to achieve low power communications in infrastructure sensor networks. WiseMAC uses a similar technique to B-MAC, but the sender learns the schedules of the receiver ‘awake’ periods, and schedules its transmission so as to reduce the length of the extended preamble. In addition, for low traffic loads where the preamble is longer than the data frame, WiseMAC repeats the data frame in place of the extended preamble. While there has been extensive research on wireless sensor networks, those specific to energy harvesting WSNs are just emerging. Solar power [2] is the most common and matured among the different forms of energy harvesting. However, it has the disadvantage of being able to generate energy only when there is sufficient sunlight or artificial light; furthermore, existing systems were not designed for use with low power WSNs, prompting new research efforts. To ensure that energy is not unnecessarily lost during the transfer from the harvester to the wireless sensor, a low-power maximum power point tracker (MPPT) circuit has been proposed to efficiently transfer the harvested solar energy to rechargeable batteries even in non-optimal weather conditions. The Heliomote project focused on developing a plug-and-play solar energy harvesting module for use with Crossbow/Berkeley motes. Another effort conducted empirical and mathematical analysis of two micro-solar power systems and used the results to propose design guidelines for micro-solar power systems for WSNs. Another solution that has been adopted is to make use of sensor nodes that rely on energy harvesting devices for power. Since batteries have limited recharge cycles, super-capacitors [7][10] with unlimited recharge cycles are an attractive option for use in such WSNs to replace batteries because they can operate perpetually without the need for replacement. Some examples of WSN-HEAP have been deployed in test-beds. There are also commercially available sensor nodes which rely on ambient energy harvesting for power. The devices developed by Microstrain harvest and use energy from two sources, viz. solar and mechanical energy.

3 PROPOSED MAC-SEMAC This paper proposes a MAC based on solar energy. Much research on sensor networks have focused on extending the lifetime of sensor networks which are assumed to rely on finite energy sources like batteries for power. In contrast, wireless sensor networks (WSNs) powered by ambient energy harvesting are more useful and economical in the long-term as they can operate for very long periods of time until hardware failure because ambient energy may be harvested from the environment at all times. However, as the rate of charging is usually much lower than the rate of energy consumption for the sensor nodes, SEMAC nodes can only be awake for a short period of time before it needs to shut down in order to recharge. Moreover, the time taken to charge up the sensor is not constant due to environmental factors. Our analysis focuses on the throughput of each sensor node and energy required for sending and receiving variable size packets. The CSMA technique is used for accessing medium [9]. The number of collisions can be reduced by having a backoff scheme. Secondly, by not having time slots, energy required is reduced during the carrier sensing state. This is because once the node senses that the channel is busy, it can go into the charging state to recharge immediately. In this section, we present an unslotted form of the CSMA protocol. There are five states which are, the charging, carrier sensing, receive, idle and transmit states.

a. State transitions for unslotted CSMA Initially, the sensor is uncharged so it would be in the charging state. When the super-capacitor is full, it would go into the carrier sensing state to determine whether the channel is free. If the channel is free, it would transmit the data packet. Then, it would move into the receive state to wait for an acknowledgment (ACK) packet from the sink. After receiving the ACK packet from the sink, it would return to the charging state. If the channel is busy, it would perform a backoff and go back into the charging state. If the super-capacitor is full but the sensor has not reached the end of its backoff period, then it would be in the idle state until the end of the backoff period when it would go into the carrier sensing state. Figure1 shows flow chart for backoff mechanism of unslotted CSMA. Each backoff duration ranges from one unit backoff period to a maximum of 255 unit backoff periods. Each unit backoff period is 320 microseconds which is the duration of a time slot specified in IEEE 802.15.4 standards. In each backoff period, the node would be recharged until the maximum energy is obtained. We implement the unslotted form of CSMA protocol using MATLAB-Tool. We have developed analytical and simulation model for SEMAC, for estimating throughput and energy required for sending and receiving packets. Figure2 illustrates the energy model for a successful data transmission if the channel is free at the first sensing attempt.

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(2) =Energy Required for Transmitting data packet =Energy Required for Receiving data packet

=Throughput

C Simulation details The analytical results are based on optimistic assumptions: no collision, and no noise or interference. These are not the real working situation for a WSN. Therefore, further evaluation should be conducted to estimate the performance of the schemes based on more realistic traffic and channel models. For this purpose, a simulation platform is established using MATLAB. 1. Channel Model The ultimate performance limits of a wireless communication system, as well as the performance of practical systems, are determined by the channel in which it operates. Realistic channel models are thus of utmost importance for system design and testing. The wireless channel, as is well known, often shows unpredictable behavior. Terrestrial communication links (i.e., at the level of the ground, or underground/underwater) are always affected by the presence of elements that interfere with the propagation of electromagnetic waves. In free-space conditions these effects can be neglected and the well-known Friis formula can be used to express the power loss between transmitter and receiver antenna connectors as a function of the distance d and the electromagnetic wavelength λ.

Figure 1-Flow chart for backoff mechanism of unslotted CSAM.

Figure 2-Energy model of a successful transmission in unslotted CSMA.

b. Analytical computation detail The definition and default values of parameters used for computation are listed in table (I). For simplicity computation is carried out for star topology and for uplink transmission. Poisson process traffic model is used. In this probability density function is given as, cumulative distribution function is, f t

Pt

1 e

e t

.

t

and

where Gt and Gr represent the transmit and receive antenna gain, respectively. However, in many applications of wireless systems the presence of the elements (the ground, first of all) interfering with the propagation of electromagnetic waves, cannot be neglected. They introduce several effects which make the power loss a random process. The multipath phenomenon introduces either constructive or destructive interference between separate signal paths thus causing additional signal fading. The time coherence of such a process mainly depends on the obstacles, transmitter and receiver speeds. This effect has a small-scale behavior (smallscale fading), that is, the received power level variations happen in a scale in the order of the wavelength λ. The widely used log-normal model will be suitable for outdoor application which is given as

=Charging time for capacitor © 2010 JOT http://sites.google.com/site/journaloftelecommunications/

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where k1=.ά.10/ln 10 and S is a shadowing sample which is assumed to be Gaussian distributed (i.e., log-normal distributed in linear scale), with zero mean and standard deviation σ ( shadowing spread ). The latter usually takes values between 2 and 3 for rural environments (above grass) while indoor it can also take values around 5.

2. RF Transceiver The selection of the RF communication modules used for the wireless data transmission portion of our design was based on a number of different criteria such as range of communication, power consumption, ease of integration, and cost. The wireless transceiver that we chose for our design is cc2420 compatible with IEEE 802.15. The receive, transmit, sleep functionality of the RF transceiver is modeled. 3. MAC layer Minipreamble MAC protocol that is designed to run on top of the 802.15.4 PHY Minipreamble -MAC reduces the power consumption by switching the radio on and off at regular intervals. To send a packet, initially, the sensor is uncharged so it would be in the charging state. When the supercapacitor is full, it would go into the carrier sensing state to determine whether the channel is free. If the channel is free a node broadcasts a train of short strobe packets. The strobe packet train is long enough to allow all nearby devices to be switched on at least once. After receiving a strobe packet, a node turns on its radio in preparation of receiving a full packet. As an optimization for unicast packets, the strobe packets include the address of the receiver of the full packet. When receiving a unicast strobe, a receiver immediately sends a short acknowledgment packet. The sender can then immediately send its full packet. Other nodes that happen to overhear the strobe packets can turn off their radios until the full packet has been transmitted.

T_data T_ack T_ta T_p Data_Rata T_mp T_rts Rc T_c

Rx mode Time required for data packet Timefor acknowledgement turn around time from TX-RX OR RX-TX Preamble period Data rate mini preamble period Time for RTS packet Charging rate Charging time of capacitor

2.4ms 0.256ms 0.01ms 0.128ms 250kbps 0.032ms 0.256ms variable

4 RESULTS Tcycle vs Throughput

Analytical Simulated

0.8

0.7

0.6 Throughput 0.5

0.4

0.3

0.2

4. Traffic model Poisson model is used. In this probability density function is given as,

e

t

f t 1 e

0

0.015

0.02

4

Analytical Simulated

3.5

3

2.5

Ts_rf Psleep Pr_x

Description power consumption of Radio in Tx mode setup time of rf from sleep to awake Power consumption in sleep mode power consumption of Radio in

Value 35mw

2

1.5

0.1ms 17microwatt 38mw

1

0.5 20

packet

Parameter Pt_x

0.025

Energy for receiving

t

TABLE I: PARAMETERS OF THE WSN.

0.01

Figure 3-Throughput Vs Tcycle.

and cumulative distribution function is,

. The definition and default values of parameters used for computation are listed in table (I).

0.005

Tcycle

Expected Energy to receive a packet

Pt

0.1

40

60 80 Payload in bytes

100

Figure 4- Energy for Receiving vs payload

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Energy for sending

4

Analytical Simulated

9.

10.

3

2.5

11. 2

packet

Energy to send a packet

3.5

1.5

12.

1

0.5 20

13. 40

60 Payload in bytes

80

100

14. Figure 5- Energy for Sending Vs payload.

15. 16.

5 CONCLUSION In this paper we have proposed solar energy based MAC protocol. From the results it is seen that it is very energy efficient for outdoor applications. Analytical and simulation results are matched. This MAC can be useful for variable size of data packets. Throughput is also good. We are extending our work for variable duty cycle based on variations of solar energy with respect to change in intensity in day hours and season to season.

6

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