QoS Analysis of Routing Protocols in Wireless Sensor Networks in the Monitoring of Wind Farms

Felipe D. M. Oliveira Departamento de Ciências da Computação

Universidade do Estado do RN Natal, Brazil

fdenis_natal@ yahoo.com.br

Rodrigo S. Semente , Tálison A. C. Melo,

Andrés O. Salazar Departamento de Controle e Automação

Universidade Federal do RN Natal, Brazil

rsemente@ yahoo.com.br

Abstract—The rising global demand of energy and the

scarcity of fossil fuels can be mentioned as factors that justify the investment in renewable energies sources in the world, such as the wind power. Wind power has tremendous potential to provide renewable energy without reliance on traditional fossil fuel technologies. Conditional monitoring of wind turbines can help to avert unplanned downtime due to component failure, and this can be done via low-cost wireless sensor networks. This paper presents the Quality of Service (QoS) analysis in the monitoring of wind farms by using Wireless Sensor Networks (WSNs) based on the IEEE 802.15.4 standard using hierarchical and flat routing protocols through a simulated environment in the presence and absence of faults in the nodes. The results presented in this paper will assist in choosing the best protocol that guarantees the best response time of the system and consequently, improving the safety margin, preventing accidents that can damage the equipment and cause huge losses.

Keywords—QoS analysis; wireless sensor networks; wind farms; routing protocols

I. INTRODUCTION As a result of the rising environmental concern, many

countries have made efforts to generate electricity from renewable energy sources. Wind power is a renewable source with infinite availability and without harmful emissions and, therefore, its use increases each year. For instance, in Brazil there are about 2,340 wind turbines distributed in 102 wind farms generating 2,731,205 kW of power [1]. Unfortunately, the number of wind power integrated in electricity systems is very small if you compare with conventional thermal, nuclear, and hydropower plants.

Nonetheless, the technological advances in equipment used to obtain the wind power, such as advanced system of transmission, best aerodynamic, control strategies, and operation of wind turbines, have reduced their costs and also allowed to improve the performance and reliability of the equipment.

However, despite these technological equipment involved in wind farms, there are still several problems to be solved. A big problem is related to the prediction of damage. By the fact that wind turbines have frequent damages, another challenge is related to preventive maintenance of wind farms. The wind turbine components (generators, gears, and rotor blades) have historically been error-prone, and it has been registered, on

average, 2 to 3 incidents per year on a single wind turbine, and this represents half of the accidents related to the mechanical components. Whereas the traditional monitoring of existing structures in control of wind turbines involves the human presence with a high cost, the use of a mechanism for automated remote control does not increase the safety margin but the frequency for inspection as well as its cost is dramatically reduced [2]. There are two kinds of communication networks in wind farms: wired and wireless. The Ethernet Standard based on optical fiber is the communication infrastructure for Supervisory Control and Data Acquisition (SCADA) of the system in wind farms while wireless sensor networks are usually used for long-range communication [21]. Thus the use of WSNs in monitoring the turbine health offers several advantages, such as the economy of costs in cabling and the quick installation of the communication infrastructure, allowing taking preventive measures before the critical and catastrophic failures occur.

A wireless sensor network must provide four essential QoS requirements for a reliable implementation in industrial environments [22] - [23]:

Scalability: the number of sensor nodes installed can be very high, especially when wide geographic areas need to be monitored;

Reliability: percentage of data packets delivered to the network coordinator correctly;

Timeless: data packets delivered to the network coordinator within a pre-defined time;

Energy efficiency: it is extremely important because sensor nodes are usually powered by batteries with poor availability of energy.

The state of the art of techniques and applications involving the monitoring of wind farms was discussed in [24]-[28]. A heuristic algorithm of positioning of wireless sensor nodes in the monitoring of wind farms was proposed in [29] to approximate the best position of the nodes that used to connect each turbine of the wind farm. Results obtained by simulations made with this algorithm have proved its viability. System architecture of WSNs for construction and maintenance of offshore wind farms was proposed by [30]. The performance of this WSN in an offshore environment, that is, in harsh environmental conditions, was analyzed. The system proposed

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was based on the technology of radio Ultra Wide Band (UWB) which allowed it to reach longer distances in the offshore environment without damaging the performance of the network. In [31], a WSN was developed within a wind turbine to verify the behavior and response on the control parameters and generator load by using an algorithm of control redefined for this system. The performance of the wind turbines in some failure scenarios was presented in [32]. The authors compared the performance with and without the systems of the communication network of energy of the wind farm by using Ethernet Intelligent Electronic Devices (IED) via LAN.

All the researches previously cited considers a small number of sensor nodes in the monitoring of behavior of wind turbines, and it does not worry about which routing protocol to be used. According to [33], it is hard or impossible to analyze the behavior of located failures by a small amount of sensor nodes. The choice of the best routing protocol affects directly on QoS and fault tolerance aspects when the number of sensor nodes becomes high.

The main contribution of this work is to analyze, by simulation, the routing protocols that guarantee the QoS of WSNs based on the IEEE 802.15.4 standard [3] in wind farms by considering the main network parameters, such as the number of sent packets, packet losses, throughput, end-to-end delay, jitter, and fault tolerance. Regarding the results, it will be possible to determine which protocol is the most appropriate for this application.

II. OVERVIEW OF ROUTING PROTOCOLS AND SYSTEM ARCHITECTURE USED IN THE SIMULATIONS

A wind turbine monitoring system is responsible for monitoring the tower, the blades, the shaft, the gear box, the generator, and the overall conditions in the nacelle. This involves a variety of sensors e.g. accelerometers, rotary encoders, temperature sensors, oil/liquid level sensors, particle counters, voltage, current and humidity sensors [4]. Devices for monitoring and controlling of wind turbines set up on the towers are usually linked to the central management from wind farms by fiber optic connections and this increases the implementation costs in certain areas of ground with rocky outcrops and brings out installation problems in difficult access areas [5]. In this context, the use of WSN, particularly the ones that works on the IEEE 802.15.04 standard, becomes a useful alternative due to the low cost aspects, the low consume of energy, the reliability, the ease of installing, and the reconfiguration [6].

In a certain WSN with a large amount of sensor nodes distributed in turbines from a wind farm, it is need the use of a routing protocol that guarantees aspects of QoS for the satisfactory monitoring the network. The routing protocols can be classified as flat (reactive or proactive), hierarchical, location-based, and hybrid [7].

In reactive routing protocols, the network nodes only start the processes of route discovery at the point that there is any information to be transmitted. An advantage of this strategy is that the routes are adaptable to an environment that can

constantly change in a WSN (by inserting and removing nodes, for example), once that every node may update its routing table when it happens a change of topology and then, determining new routes. This, however, implies constant recalculation during the data transmission and then, resulting in highest latency of network and increasing energy consumption [7]. Some examples of traditional reactive protocols are DSR [9] and AODV [10].

Proactive routing protocols try to keep on the consistency by updating the routing information from every node to all the other nodes from the network. The use of proactive protocols, therefore, is not recommended to all of the applications because a significant part of network bandwidth is exclusively consumed for maintenance of routing tables of network nodes [8]. Examples of traditional proactive protocols are OLSR [11] and DSDV [12].

Normally, when a wireless network grows up (beyond a certain limit), the flat routing protocols become unfeasible due to the link and data processed overhead. A way to solve this problem consists in hierarchical routing. The wireless hierarchical routing is based on the idea of organizing nodes in groups by assigning different characteristics in and out of the group. Both the size of the routing table as the update package size is reduced by inserting the nodes in a single portion of network instead of in it all. Thus the overload of data becomes low. The most common way to implement the hierarchy is by grouping nodes geographically close to each other to form a group (cluster). Every node in the cluster has a leader (cluster head) that communicates itself with the other nodes in the cluster [7]. CGSR [13] and LEACH [14] are examples of traditional hierarchical protocols.

The routing protocols based on geographical location use information about the position of nodes to determine the best route. The position of the node in the network is determined by the GPS (Global Position System). Such protocols can behave as simple or hierarchical if techniques and devices of geolocation had been inserted. The location-based protocols can represent, therefore, a limiting factor to the use in WSNs due to hardware constraints and, specially, the additional energy consumption in sensor nodes [7]. Therefore, location-based protocols can represent a limiting factor to the use in WSNs due to hardware constraints and, mainly, the additional energy consumption in sensor nodes. Due to these factors, such protocols are not used in simulations in this work. Examples of protocols based on location that are worried about the economy of energy: GEAR [18] and GPSR-TPC [19].

Hybrid routing protocols can merge characteristics of flat and hierarchical protocols by using or not geolocation techniques. These protocols were designed to improve the scalability allowing that close nodes work together to form a kind of group and then, reducing the overload of network from the route discovery. It is usually performed by the proactive maintenance of routes among the neighboring nodes and determining routes of distant nodes through the technique of route discovery [7]. Some hybrid routing protocols are DDR [20] and ZRP [15]. ZRP is used by ZIGBEE communication protocol.

A. Protocols Used in Simulations

The ZIGBEE communication protocol [15], used in the IEEE 802.15.4 standard, is one of the most used by industry due to its reliability and robustness. To realize the communication of nodes in a WSN based on ZIGBEE, the ZBR hybrid routing protocol is used. Depending on the size and network topology, three strategies can be used: direct routing of messages from sensor nodes to the sink node through star topology, suitable for small networks (in this case, the ZBR not act. Who performs the communication is IEEE 802.15.4 MAC layer); use an improved version of AODV, called AODVjr [15], to ad-hocs networks whose topology is mesh; hierarchically routing , through HTR protocol [15] for Cluster Tree network topology.

Thus, for large WSNs, such as those used in wind farms, find out which routing protocol that guarantees the best performance related QoS parameters allow direct implementation of this protocol on devices using the IEEE 802.15.4 standard. The protocols to be simulated and compared will be: AODVjr, DSR, LEACH and HTR.

B. Communication Scheme For sensor nodes can communicate with the sink, it is

important to determine if such communication occurs in always-on mode (no hibernation of sensor nodes' transceivers) or in sleep mode (periodic data transmission, interspersed with hibernation of sensor nodes' transceivers, except the sink, which remains permanently awake).

In case of sleep mode communication, it is necessary a methodology that ensures the times of sending sensor data to the sink, providing efficiency and maximizing the energy savings of sensor nodes. It was developed a temporal communication scheme, based on [16], which will be described as follow.

A RFD time diagram is shown in Fig. 1, for simplification purposes, it is considered two sensors. At first, sensor (S1) measures a physical variable (Mx) and transmits the data (Tx) to FFD coordinator (C). In this ideal case, FFD receives (Rx) the right data, with no communications error, and then it begins to sleep for 1s. When sensor (S1) sleeps, sensor (S2) is sleeping also, waking up in a fixed time Δt, which is generated by microcontroller. After wake up, sensor (S2) performs the same procedure that sensor (S1).

C. System Architecture

Fig. 2 shows the WSN system architecture used in simulations is based on the following subsystems according to [17]:

1) Power supply subsystem: in WSNs, sensor nodes are called Reduced Function Device (RFD) and the Sink is called Full Function Device (FFD). Each RFD is powered by the wind turbine where it is coupled. The FFD is powered by the same energy source that powers the RFDs or other permanent power source, such as the power grid, for example.

2) Sensing subsystem: Performs sensor data acquisition. It communicates with computing subsystem.

3) Computing subsystem: Consisted of a flash memory and a PIC microcontroller. It is part of RFD and FFD nodes. Also performs a communication, which allows integration with higher-level services, including access devices for a monitoring system as a supervisory, for example. These supervisory can be accessed by the Internet.

4) Communication subsystem: It is basically composed by a few radio-frequency (RF) modules, according to IEEE 802.15.4 standard. It is part of RFD and FFD nodes.

III. THE SIMULATION RESULTS All simulations were performed on NS-2, using XBeePro

(TM) parameters, based on IEEE 802.15.4 standard, operating on 2.4Ghz frequency, whose outdoor communication has a reach of up to 1,600 m. The data rate is assumed to be 250kbps and the power of transmission is set to 60mW. The simulation scenario consisted of 80 sensor nodes distributed in grid, one for each wind turbine, and one sink, 600m far from the scenario. 10 nodes are spread in each row, 300m distant from each other, making a total of 8 lines nodes. Each line is distant 1.200 m from each other. Each node is composed of temperature, vibration and pressure sensors. The entire message size is assumed to be 70 Bytes. Transmission time is set up to 41 ms and, in sleep mode communications, sleep time is assumed to be 5 s. To improve the accuracy of the

Fig. 1. Packet loss (%) x Distance (m) in 10 rounds

Fig. 2. (a) RFD block diagram. (b) FFD block diagram.

tests, 10 rounds of 1 hour (3600 ms) were performed for each one. Graphics that reflects the mean of 10 rounds, in each test, were plotted to synthesize the data.

A. Packet loss One of the most important metrics in real-time remote

sensing networks is packet loss. In Fig. 3, all of four routing protocols tested, in always on scheme, presented higher packet losses. The AODVjr behaved better than DSDV, due to creation and maintenance of routing tables during the WSN nodes recognition phase. DSDV, on the other hand, has the need to redefine the distance vectors between each node routed more frequently. LEACH and HTR obtained the smallest number of packet losses, in comparison the two previously aforementioned routing protocols (39.42% and 28%, respectively), indicating that the hierarchical routing may work better on similar plants to the one used in this article. One factor that may have influenced the large packet loses in all protocols is the network nodes dispute of communication for gain permission to communicate with the sink.

Significant improvements in packet loss can be perceived in sleep mode communications, except for DSDV, which had an improvement of only 15.18% if compared to the simultaneous communication. In all protocols, only HTR maintained an acceptable percentage of loss (2.70%), below the margin of 5% desirable for this application, demonstrating its efficiency in hierarchical networks.

B. Throughput Fig. 4 shows average throughput of existing studied

protocols. In this chart, is noted that all protocols maintained a higher throughput in always-on mode. This is due to the fact that the communication throughput flows continuously instead of sleep mode, where there is an interruption of 1s for each communication's restart. HTR fared better at interspersed communication if compared to other protocols (406.5B/s and 341.9B/s, respectively).

C. End-to-end Delay The average end-to-end delay expressed in the graph of Fig.

5 shows that all protocols remained acceptable delay values, except DSDV, which kept 0.37s in continuous communication and 0.20s sleep mode, confirming that proactive protocols are

not recommended for wireless real-time systems. HTR fared better, both in always-on communication mode (0.02s) and in sleep mode (0.01s).

D. Jitter As time between two consecutive samples can vary, packet

loss can occur. In this sense, Jitter is defined as the difference of time variation between two consecutive samples. This is one way to measure the communication delay between nodes of WSN.

Fig. 6 shows that in all protocols, except DSDV, the average jitter remained at acceptable levels. Again, HTR was the routing protocol that had the best performance in simulated tests, pointing to its efficiency and suitability to the scenario where there are a lot of wind turbines, distributed in wide area.

E. Packet Loss in Presence of Failures One of the characteristics expected for a good routing

protocol is its ability to define new routes when intermediate nodes participating in a communication fail partially or permanently. A way of measuring it is by the percentage of packet losses in a situation of failure.

For taking fault tolerance tests in always-on or sleep transmission mode, failures were included in communications, which are part of the routing and, because of them, the node responsible for routing was disabled after 5 seconds from the beginning of the communication. Each failure takes 10

Fig. 3. Average packet loss in always-on and sleep communication scheme.Comparative between AODVjr, DSDV, LEACH and HTR.

Fig. 4. Average throughput in always-on and sleep communication scheme.Comparative between AODVjr, DSDV, LEACH and HTR.

Fig. 5. Average end-to-end delay in always-on and sleep communication scheme. Comparative between AODVjr, DSDV, LEACH and HTR.

seconds duration. After 10 seconds, the router node is again enabled and then, being able to normally make communication. Each communication takes 20 seconds in total duration.

The Fig. 7 illustrates the failure scenario in always-on mode. All the protocols presented failures varying of 5.06% by AODVjr and 1.06% by HTR. The results showed that all the protocols that carried out their routing by always-on mode has got to find alternative routes – varying little the amount of packet losses – comparing with the scenario without failures. It should be pointed out that the percentages of packet loss obtained by each of all the protocols tested are high, and that is why the use of them was prevented in this application. The DSDV was not present in these tests because it was the protocol that did not carry out its routing in always-on mode.

In sleep mode however, the percentage of packet losses was very high in flat protocols (18.35% by AODVjr and 10.44% by DSDV) comparing with hierarchical protocols, and this indicates that hierarchical protocols get to define alternative routes of routing more rapidly and efficiently between sensor nodes in their own cluster, as can also joining itself with the neighbor cluster. Once more, the HTR protocol has showed itself the most efficient of all of them by maintaining the packet loss 1.42% greater than in mode without failures, versus 3.93% obtained by the LEACH protocol. Fig. 8 shows these results.

IV. CONCLUSIONS This paper presented analysis of four WSN's most

commonly used routing protocols, based on IEEE 802.15.4 standard applied on Wind Farms Monitoring simulation scenario, ensuring the QoS four key requirements, such as packet losses, throughput, end-to-end delay and jitter.

The results of the simulations showed that the hierarchical routing protocols – particularly the HTR used by ZIGBEE and inclusive simulated in presence of failures of the router node – had better performance comparing themselves with flat protocols. This demonstrated that, in WSNs with a big number of nodes, such as in wind farms, the topology by division into clusters allows better message forwarding, ensuring better data communication between the sensor nodes for the sink even when routing failures are present.

For future work, we plan to study the impact of sleep mode to save energy of sensor nodes in dense networks.

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Fig. 6. Average jitter loss in always-on and sleep communication scheme.Comparative between AODVjr, DSDV, LEACH and HTR.

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