Integrating Mules Protocol Into Wsn

Published Date: 02 Nov 2017

Abstract-

Although sensor deployment depends on the sensing application, most of the routing and data dissemination protocols for WSNs assume that sensors are very densely deployed in a network. One of the most important features of sensor deployment is network connectivity by which any source sensor is able to communicate directly or indirectly with the sink in order to report its sensed data. To guarantee network connectivity, two different deployment approaches can be used. Firstly, a network can be deployed by using a large number of sensors, yielding a densely connected network in which the source sensors send their sensed data to the sink through multihop communication paths including other intermediate sensors. Secondly, a network can be deployed by using multiple sinks that cover the entire geographical area, where the source sensors communicate directly with the nearest sink to report their sensed data. To address this, a three - tier architecture based on mobile entities, called mobile ubiquitous LAN extensions (MULEs), was proposed to collect sensed data from source sensors in sparse networks.

Index Terms: MULEs, sink node, multihop communication, smartdust.

INTRODUCTION

Foremost , Network can be deployed by using a large number of sensors, yielding a densely connected network in which the source sensors send their sensed data to the sink through multihop communication. Secondly, a network can be deployed by using multiple sinks that cover the entire geographical area, where the source sensors communicate directly with the nearest sink to report their sensed data. While the first approach may not be cost effective to build a dense and fully connected network, the second one is not cost effective either, but reduces the communication cost that would be incurred when the sensors communicate with only one single sink. Both scenarios raise the need for developing an architecture that benefits from the two approaches and can guarantee cost - effective connectivity in a sparse network while reducing the energy consumption of the sensors.

The MULEs architecture has three main components. The bottom layer contains static wireless sensors that are responsible for sensing an environment. The top layer includes WAN connected devices and access points or central repositories for analyzing the sensed data. These access points can be positioned at locations providing network connectivity and power. They communicate with a central data warehouse enabling them to synchronize the collected data, identify redundant data, and acknowledge the receipt of the data sent by the MULEs for reliable data transmission. The middle layer has mobile entities (MULEs) that move in the sensor field and collect sensed data from the source sensors when in proximity to deliver them to those access points when in close range.

These MULEs have the capabilities to communicate with both the access points and the sensors using short range wireless communications. so, the MULE component can be considered as a mobile transport agent that connects heterogeneous nodes, ie, the source sensors and the access points. moreover, the MULEs can communicate with each other, thus forming a multihop MULE network that can be used to reduce the latency between MULEs and those access points. Because of their motion, the MULEs are able to collect and store data from the sensors, and acknowledge them. Which implies that the MULEs are equipped with larger storage capacities compared to the sensors. The MULE architecture helps the sensors to save their energy as much as possible and thus extend their lifetime. Since the sensors directly communicate with the MULEs through short - range paths, they deplete their energy slowly and uniformly. So, the MULE architecture has low sensor energy consumption. However, the MULEs move in the sensor fi eld in a random fashion, which guarantees that all the sensors are equally visited and consume the same amount of energy during their monitoring task.

In addition, the MULE architecture has low infrastructure cost. Because of the direct communication between the source sensors and the MULES, there is no routing overhead that would drain the energy of the sensors. As far as robustness and scalability are concerned, the MULE architecture is fault tolerant and scale well. If a MULE fails, it will not affect any particular sensor because no sensor is dependent on any MULE. However, it will degrade the performance of a sparse network for decreasing its data success rate and increasing its latency. when the number of sensors or the number of MULEs increases, there is no need for any network reconfiguration.

The sensors have to wait until the MULEs come close by to report their sensed data to the MULEs. Therefore, for time - critical applications, the MULE architecture may introduce an undesirable delay in reporting the sensed data of the source sensors and thus may not be practical. One way to solve this problem is to equip the MULEs with an always - on connection so that they act as mobile sinks (i.e., MULEs and access points). Furthermore, when a MULE fails, the corresponding sensed data will never reach the sink.

II. Transmission media for Wireless Sensor Networks

In a multihop sensor network, communicating nodes are linked by a wireless medium. These links can be formed by radio, infrared or optical media. To enable global operation of these networks, the chosen transmission medium must be available worldwide. One option for radio links is the use of industrial, scientific and medical (ISM) bands, which offer license-free communication in most countries. They are listed in Table:1. Some of these frequency bands are already being used for communication in cordless phone systems and wireless local area networks (WLANs). For sensor networks, a small-sized, low-cost, ultralow power transceiver is required. The certain hardware constraints and the trade-off between antenna efficiency and power consumption limit the choice of a carrier frequency for such transceivers to the ultrahigh frequency range. They also propose the use of the 433 MHz ISM band in Europe and the 915 MHz ISM band in North America. The transceiver design issues in these two bands are addressed. The main advantages of using the ISM bands are the free radio, huge spectrum allocation and global availability. They are not bound to a particular standard, thereby giving more freedom for the implementation of power saving strategies in sensor networks. On the other hand, there are various rules and constraints, like power limitations and harmful interference from existing applications. These frequency bands are also referred to as unregulated frequencies.

Much of the current hardware for sensor nodes is based upon RF circuit design. The l AMPS wireless sensor node, uses a Bluetooth-compatible 2.4 GHz transceiver with an integrated frequency synthesizer. The low-power sensor device uses a single channel RF transceiver operating at 916 MHz. The WINS architecture also uses radio links for communication. Another possible mode of internode communication in sensor networks is by infrared. Infrared communication is license-free and robust to interference from electrical devices. Infrared based transceivers are cheaper and easier to build. Many of today’s laptops, PDAs and mobile phones offer an infrared data association interface. The main drawback though, is the requirement of a line of sight between sender and receiver. This makes infrared a reluctant choice for transmission medium in the sensor network scenario.

An interesting development is that of the smartdust mote which is an autonomous sensing, computing and communication system that uses optical medium for transmission. Two transmission schemes, passive transmission using a corner-cube retroreflector(CCR), and active communication using a laser diode and steerable mirrors, are examined. The mote does not require an onboard light source. A configuration of three mirrors (CCR) is used to communicate a digital high or low. The latter uses an onboard laser diode and an active-steered laser communication system to send a tightly collimated light beam toward the intended receiver. The unusual application requirements of sensor networks make the choice of transmission media more challenging. For instance, marine applications may require the use of the aqueous transmission medium. Here, one would like to use long-wavelength radiation that can penetrate the water surface. Inhospitable terrain or battlefield applications might encounter error prone channels and greater interference. Moreover, a sensor antenna might not have the height and radiation power of those in other wireless devices. Hence, the choice of transmission medium must be supported by robust coding and modulation schemes that efficiently model these vastly different channel characteristics.

Table:1 Frequency bands available for ISM applications

Frequency band

Center frequency

6765–6795 kHz

6780 kHz

13,553–13,567 kHz 13

560 kHz

26,957–27,283 kHz

27,120 kHz

40.66–40.70 MHz

40.68 MHz

433.05–434.79 MHz

433.92 MHz

902–928 MHz

915 MHz

2400–2500 MHz

2450 MHz

5725–5875 MHz

5800 MHz

24–24.25 GHz

2 4.125 GHz

61–61.5 GHz

61.25 GHz

122–123 GHz

122.5 GHz

244–246 GHz

245 GHz

III. Factors influencing sensor network design

A sensor network design is influenced by many factors, which include fault tolerance, scalability, production costs, operating environment, sensor network topology, hardware constraints, transmission media and power consumption. These factors are important because they serve as a guideline to design a protocol or an algorithm for sensor networks. In addition, these influencing factors can be used to compare different schemes.

a)Fault tolerance

Some sensor nodes may fail or be blocked due to lack of power, have physical damage or environmental interference. The failure of sensor nodes should not affect the overall task of the sensor network. This is the reliability or fault tolerance issue. Fault tolerance is the ability to sustain sensor network functionalities without any interruption due to sensor node failures. The reliability or fault tolerance of a sensor node is modelled using the Poisson distribution to capture the probability of not having a failure within the time interval (0; t).

The protocols and algorithms may be designed to address the level of fault tolerance required by the sensor networks. If the environment where the sensor nodes are deployed has little interference, then the protocols can be more relaxed. For example, if sensor nodes are being deployed in a house to keep track of humidity and temperature levels, the fault tolerance requirement may be low since this kind of sensor networks is not easily damaged or interfered by environmental noise. On the other hand, if sensor nodes are being deployed in a battlefield for surveillance and detection, then the fault tolerance has to be high because the sensed data are critical and sensor nodes can be destroyed by hostile actions. As a result, the fault tolerance level depends on the application of the sensor networks, and the schemes must be developed with this in mind.

b)Scalability

The number of sensor nodes deployed in studying a phenomenon may be in the order of hundreds or thousands. Depending on the application, the number may reach an extreme value of millions. The new schemes must be able to work with this number of nodes. They must also utilize the high density nature of the sensor networks. The density can range from few sensor nodes to few hundred sensor nodes in a region, which can be less than 10 m in diameter. The number of nodes in a region can be used to indicate the node density. The node density depends on the application in which the sensor nodes are deployed. For machine diagnosis application, the node density is around 300 sensor nodes in region, and the density for the vehicle tracking application is around 10 sensor nodes per region. The density can be as high as 20 sensor nodes/m3. A home may contain around two dozens of home appliances containing sensor nodes, but this number will grow if sensor nodes are embedded into furniture and other miscellaneous items.

For habitat monitoring application, the number of sensor nodes ranges from 25 to 100 per region. The density will be extremely high when a person normally containing hundreds of sensor nodes, which are embedded in eye glasses, clothing, shoes, watch, jewelry, and human body, is sitting inside a stadium watching a basketball, football, or baseball game.

c)Production costs

Since the sensor networks consist of a large number of sensor nodes, the cost of a single node is very important to justify the overall cost of the networks. If the cost of the network is more expensive than deploying traditional sensors, then the sensor network is not cost-justified. As a result, the cost of each sensor node has to be kept low. The state-of-the-art technology allows a Bluetooth radio system to be less than 10$. Also, the price of a PicoNode is targeted to be less than 1$.

The cost of a sensor node should be much less than 1$ in order for the sensor network to be feasible. The cost of a Bluetooth radio, which is known to be a low-cost device, is even 10 times more expensive than the targeted price for a sensor node. The sensor node also has some additional units such as sensing and processing units . It may be equipped with a location finding system, mobilizer, or power generator depending on the applications of the sensor networks. As a result, the cost of a sensor node is a very challenging issue given the amount of functionalities with a price of much less than a dollar.

IV. Simulation

We simulated the proposed MULES in sensor network with the help of the ns-2 network simulator. In our simulation, 500 mobile sensor nodes are placed within a square area of 500 m by 500 m. We use the Random Waypoint Mobility (RWM) model to determine mobile sensor node movement patterns. In particular, to accurately evaluate the performance of the scheme, we use the RWM model with the steady-state distribution provided by the Random Trip Mobility (RTM) model. In the RWM model, each node moves to a randomly chosen location with a randomly selected speed between a predefined minimum and maximum speed. After reaching that location, it stays there for a predefined particular time. After the particular time, it then randomly chooses and moves to another location. This random movement process is repeated throughout the simulation period. In this We use code to generate RWM-based movements model with a steady-state distribution.

V. Results

We show the results using the network simulator, In this paper we show the results for both type of networks that is for the dense and sparse scenarios. To guarantee network connectivity, we simulate the dense scenarios and then sparse scenarios. We practically prove that sparse scenario provide us the best network connectivity when compared to the dense scenario through the simulation. The sparse scenario is also more cost effective then compared to the dense scenario. This is proven using the MULES protocol in Wireless Sensor Networks.

VI. References

[1] Selvarajah, K.; Tully, A.; Blythe, P.T."Integrating smartdust into the embedded middleware in mobility application (EMMA) project" p.p 1-8,year 2008.

[2]Lambor, S.M.; Joshi,S. "Critical hops calculation for energy conservation in a multi-hop Wireless Sensor Network" P.p 1 – 6, Year: 2010 .

[3] Fang-Jing Wu, Chi-Fu Huang, and Yu-Chee Tseng "Data Gathering by Mobile Mules in a Spatially Separated Wireless Sensor Network" IEEE conference , p.p 195,year 2009.

[4] Girban, G.; Popa, M. "A glance on WSN lifetime and relevant factors for energy consumption" IEEE conference , Page(s): 523 – 528, Year: 2010.