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Wireless Sensor Networks (WSN) have recently become one of the major research areas in the wireless communication field and are implemented in a variety of applications. One of these applications that will be tackled in this paper is monitoring electromagnetic (EM) pollution that is mostly caused by a variety of wireless devices that we use in our daily life. This paper presents a generic algorithm that uses a WSN to monitor EM hazardous emissions and reports variation caused by four violators. Additionally it calculates the network’s lifetime and simultaneously studies the effect of random parameters and their distributions on the network. Finally the different combinations of the random parameters and the altered distributions are compared together to achieve the combination that can prolong the network’s lifetime.

Recently Wireless Sensor Networks have been an attractive field, since they could be implemented in a huge number of applications. Different examples of these applications could be alarm systems, office and home automation, traffic control, civil infrastructure, environmental monitoring, personal health and many others [

This paper proposes a WSN-based framework to monitor these emissions and report any security violation; still the aim of this paper is not only to monitor the excessive electromagnetic waves, but also to prolong the WSN network’s lifetime. The framework presented here is more general and more flexible than the one presented in [

The rest of the paper is organized as follows. Section 2 presents some background information regarding the system presented in [

As previously mentioned, there is always the desire of increasing the wireless infrastructure due to the need of a better coverage and higher bandwidth. A simple example of that is the mobile base stations of different service providers, where multiple signals could overlap together causing higher EM exposure [^{2} area as illustrated in ^{2} area; in order to make sure that the whole area is covered and also match the most commonly used applications.

There are 100 narrow band sensors inside this area and 25 sensors are associated to each frequency polluter as in

The sensors in this model could either be active sensors, which sense the violation, or network masters (NM), which gather the data from the sensors and send it to the sink. Having a network master, also known as a Cluster Head (CH) in wireless sensor networks, has been introduced in LEACH and LEACH C [

While the NM selection is occurring, the sensors are continuously monitoring the power levels of their frequency polluters. This is done using the watchdog algorithm, where the sensors only send their data to the NM, when they detect violation. Other than that, when there is no violation, each sensor should send an “alive” data packet, every predefined period, in order for the sink to detect the network’s failure by the death of the first node. The predefined period in this paper is chosen as in [

The event by event algorithm used in [^{2} network area resulting in a total number of four polluters. There was a specified schedule for this system, meaning that the first frequency polluter violates during the last six hours of the day and on the next day the second polluter violates during the last six hours of the second day and so on. This process repeats itself every four days and upon that is the lifetime of the total network measured. Such an event by event algorithm is not very flexible to capture any diverse and dynamic network change. Therefore, in this paper, a new generic framework is developed in order to add flexibility to [

Network size: 100 ´ 100 m^{2}

Number of Sensors (N): 100 Sensors

Initial Energy: 2J

Transmitter/Receiver Electronics (

Transmitter Amplifier (

Path Loss factor (n): 2

Aggregation Energy (

Data packet size sent by active nodes to NM (K): 64 bits

Data packet size sent by the NM to the sink (K1): 512 bits

Data packet size equivalent to sensing power levels (K2): 1 bit

Sink location: field center

Distribution: Homogeneous Density (

The event by event algorithm in [

In the event by event algorithm, the staring time of the violation was at 6pm everyday and it lasted for six hours. This has been modified in the proposed algorithm to be a random starting time between 12 am, which is the beginning of the day, until 6 pm. Since the violation duration is 6 hours, the last starting time has to be 6 pm, so that when the violation starts at that time it will not extend over the following day.

Matlab [

The violation duration for each polluter was six hours as stated before. Using Matlab [

Previously, in the event by event algorithm it was assumed that each violator will violate on a separate day and each will repeat violation every four days. For instance F1 will violate on day one, F2 on day two, F3 on day three, F4 on day four, and the cycle repeats again,F1 on day five and so on. Here, the number of violators per day became unknown. This means that a minimum of one violator and a maximum of four violators can violate on the same day. Looking at the example of having two polluters violating on the same day, these polluters can be F1 and F2, F1 and F3, F1 and F4, F2 and F3, F2 and F4 or F3 and F4. The total number of combinations here will be:

where n is the total number of polluters and r is the number of polluters violating on the same day. Consequently if three violators will violate on the same day then we will have

Changing the above three parameters that were used as fixed assumptions in the algorithm used in [

In order to add more flexibility to the newly proposed framework, it is designed to accommodate the randomness of the three above-mentioned parameters at the same time. Meaning that according to preference, one can choose to make for example the starting time and the violation duration random at the same time, or have all three parameters be random simultaneously. Different combinations can be chosen to simulate real life examples.

Additionally, different distributions will be introduced to the framework in order to expand its flexibility and meet the requirements of different applications. For simplicity the uniform distribution will be used as the default random distribution. However, one can also choose between the Gaussian and the exponential distribution according to the desired scenario. Certainly it is also possible for every parameter to have a different random distribution than the other or to choose the same distribution for all parameters.

In this section, several scenarios are examined to showcase the capabilities of the proposed algorithm. When monitoring EM pollution different situations can occur. Using this framework, it is easier to simulate these situations, since the main aim of proposing this framework is to make [

One of the common scenarios that one can compare to the previous default example is the effect of having four violators breaching at the same time. It is not possible to examine such scenario with the system in [

Next the effect of randomness on lifetime is going to be obtained by removing all fixed assumptions and considering each parameter as a random variable with different distributions.

There are three main parameters in the algorithm in [

The first parameter, which is the starting time, will be examined separately. The starting time will be a random variable with uniform distribution from (1 - 19), which is equivalent to 1 am until 6 pm. The limit is at 6pm because the maximum duration violation (six hours) should not spill into the next day. The other two parameters will remain the same as the default case; the number of violators per day will be one starting with F1, F2 etc. and repeating every four days. Also the violation duration will remain six hours. This will result in a lifetime of 162,304 cycles, which has increased the default lifetime by only eight cycles. Since the 0.0049% increase of lifetime is very small, this shows that changing the starting time parameter to a random variable will result in an insignificant increase of the network’s the lifetime.

Since it was shown in the previous section that the starting time, as a random variable, does not have a significant effect on lifetime, it will remain as a fixed variable. Furthermore, the other two parameters the duration and the number of polluters per day, will be investigated further to identify the most effective variable on lifetime. A simple experiment will be simulated, where in scenario (a) it will be assumed that the number of polluters per day is fixed in each case, having F1 fixed everyday and hence there are four cases to simulate all possible situations. Meanwhile, the duration is a random variable uniformly distributed between (1,4). Only four hours are considered here in order to have a fair comparison between both parameters. The results of this experiment are shown in

The results show that changing the number of polluters from 1 - 4 does not show a significant change on lifetime. The lifetime only differs by 0.01% to 2.2%. The same experiment will be repeated, but this time the duration will be fixed, while the number of polluters will be a random variable uniformly distributed form (1,4). This will yield to the following results in

No. of Polluters Fixed vs. Duration Random | ||
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Duration | ||

Cases | No. of Polluters per Day | U (1,4) |

A | F1 | 162.470 |

B | F1, F2 | 160.973 |

C | F1, F2, F3 | 160.373 |

D | F1, F2, F3, F4 | 158.849 |

Duration Fixed vs. No. of Polluters Random | ||
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Duration | ||

Cases | Duration per Cycle | U (1,4) |

A | 1 | 181.799 |

B | 2 | 168.117 |

C | 3 | 150.278 |

D | 4 | 150.242 |

Scenario (b) shows that the duration variable has a huge effect on the lifetime. When 1-4 polluters violate for only one hour per day, the default lifetime will increase by 12.02%. Also when comparing the different cases A, B, C, and D, one can obtain a significant change in lifetime between 7.53% and 17.36%. Hence, the duration parameter has the most notable effect on lifetime.

Through the previous experiments, the duration parameter has proven to yield a significant effect on lifetime when it is changed. Therefore, different distributions will be investigated next with the duration parameter as a random variable. Thus, the same scenario (b) will be implemented again but using Gaussian and exponential distributions, in addition to the existing uniform distribution. The results of this attempt will be as shown in

In order to have a fair comparison, the same mean is used in all three distributions. The Gaussian distribution shows a better outcome than the uniform distribution, while the exponential distribution yields a higher lifetime compared to the Gaussian and the uniform distribution. Comparing the exponential distribution to the uniform distribution, it has increased the lifetime by a factor of 3.97% to 5.57%. Nevertheless this increase is not very significant and this is due to the low range of samples, since the random variable only varies between 1 and 4. Therefore, a wider range will be investigated next.

In this example, case D in

It is clear that the results follow the same trend like scenario (c). The uniform distribution has the least lifetime, while the exponential distribution has the highest lifetime. The Gaussian distribution lies between both of them, but is closer to the uniform distribution lifetime. The difference between the uniform and Gaussian distribution is about 3.4%, while the difference between the uniform and exponential distribution is 13.08%. This is more than a double increase compared to scenario (c). The reason for that is that the wide range of random variable has revealed the real effect of the random distribution on the duration parameter.

Different Distribution for Duration Random Variable | ||||
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Duration | ||||

Cases | No. of Polluters per Day | U (1,4) | N(2,0.5) | Exp(2) |

A | F1 | 162.396 | 167.245 | 171.968 |

B | F1, F2 | 160.973 | 167.297 | 169.991 |

C | F1, F2, F3 | 160.373 | 163.749 | 166.271 |

D | F1, F2, F3, F4 | 158.849 | 162.344 | 165.410 |

Different Distribution for Duration Random Variable | ||||
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Duration | ||||

Cases | No. of Polluters per Day | U (1,23) | N (11, 0.6) | Exp (11) |

D | F1, F2, F3, F4 | 92,111 | 95,278 | 105,972 |

Wireless Sensor Networks are used in several applications that involve monitoring, controlling and tracking. In this paper a wireless sensor network-based framework was developed to monitor the EM emissions transmitted from frequency polluters. The framework developed here is more flexible and generic compared to previous systems in the literature; since all main parameters are treated as random variables. The main parameters are the starting time of the violation, the duration of the violation and at last the number of polluters violating per day.

To illustrate the use of the proposed framework, several case studies were investigated. It was identified through several simulations that the duration parameter is the most affective parameter and its variation can increase the network’s lifetime between 7.53% and 17.36%. Moreover, the change of random distributions used for the parameter’s random variables was investigated further through the examined scenarios. The outcome was that using the exponential distribution compared to the uniform distribution could prolong the network’s lifetime by 13.08%. Finally, it is very important to note that using a wider range for each random variable will always yield better results, when comparing parameters together.

Sara Nouh,Nada Elgaml,Nora Ali,Ahmed Khattab,Ramez Daoud,Hassanein Amer, (2016) Generalized Electromagnetic Pollution Monitoring Using WSN. Wireless Sensor Network,08,85-92. doi: 10.4236/wsn.2016.86009