Highly reliable communication protocol for WSN-UAV system ...

Wireless Networking for Unmanned Autonomous Vehicles

Highly Reliable Communication Protocol for WSNUAV System Employing TDMA and PFS Scheme Dac-Tu Ho1, IEEE member and Shigeru Shimamoto2, IEEE Member 1

2

Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 162-0044, Japan Graduate School of Global Information and Telecommunication Studies, Waseda University, Tokyo 169-0051, Japan

Abstract - In this paper, a new MAC protocol is proposed for the communication system between a large wireless sensors network (WSN) and a small unmanned aerial vehicle (UAV). In this system, the direct communication between the sensor and the UAV replaces the conventional multi hop communication among the sensor nodes. The key factor of this system is to ensure the concurrent transmission from the sensor to the UAV. The idea is to divide the active sensors into several sub groups which each is assigned with a proper priority via the proposed PFS scheme (Prioritized Frame Selection). For effective reason, TDMA has been validated for those multiple data transmissions in each sub group. However, the number of sensors is unknown which is a challenging of this scheme in the first flight. This problem has been solved by another proposed FRA scheme (Frame based Random Access). The findings of this MAC protocol are the optimal number of sub groups, data packet size, and altitude of the UAV under an acceptable ratio of packet error rate (PER) and the number of sensors in the network. Index Terms - Unmanned Aerial Vehicle; Prioritized Frame Selection; Time Divison Multiple Access; Wireless Sensor Network.

I.

INTRODUCTION

Wireless sensor network (WSN) has become popular in a wide range of applications which significantly benefit the society. In those applications, the sensors are usually deployed under the conditions that they are directly or indirectly connected with a base station which will retrieve the sensing information. However, this condition is not always satisfied or needed. For instance, if WSN is deployed in a remote area, a full connected sensors network is not an easy mission. On this basis, the paper studies a possible MAC protocol for a WSN system that employs an UAV as in [1]. The onboard equipments include a beacon signal generator and a receiver. The former is responsible for activating the sleeping sensors; and the latter is for retrieving the information from the active sensors. WSN-UAV system is able to simply perform in various kinds of applications [2]-[3]. However, there is a major challenge of this system; which stays at finding a suitable MAC protocol that guarantees a low packet loss ratio, low energy consumption, and high frequency of transmitting data [4]. Our prior studies in [5] and [6] have clarified that the existing MAC protocols for a normal WSN system are not the most effective and appropriate for this WSN-UAV system. For example, CSMA/CA is not appropriate for the WSN-UAV system because of two reasons. First, it suffers a larger delay in communication between the sensors and the UAV than that in the direct communication between the sensor and the UAV. This delay becomes more significant under the high densities of the sensors network. It is also the reason of a high PER

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because of the UAV’s movement. The second reason comes from the well-known hidden terminal effect which could be easily happened because the un-connected situation of the sensors. As a result, the system performance is degraded. To the best of our knowledge, there was a study about MAC protocol for WSN-UAV system; however it only provides the priority of data transmission to a limited number of sensors which was supposed to be the best sensors [7]-[9]. The selected sensors were assumed to be with the highest gain of the channel between them and the UAV. This condition clearly leads to the fact that only a small portion of the active sensors is allowed to transmit their data at one time. Consequently, the period of time for all the sensors’ transmission is large. More explanations about this protocol could be reached in [10] and [11]. Following [12], a method of increasing the number of selected sensors was presented; however, the interference is the emerging issue which was not concerned. Our objective is to maximize the number of transmitting sensors each time while the UAV-sensor link is available. To facilitate this purpose, we have introduced a novel algorithm in organizing the transmission priorities of the sub groups of the activated sensors namely PFS. In this scheme, the active sensors are divided into a number of sub groups or priority groups. Each priority group is assigned with an appropriate priority by applying predefined policies. This priority is identical with the order in data transmission of the respective sub group each time. Also, we has introduced a unique MAC protocol, named PFSC-MAC that leverages both the PFS scheme and well-known CDMA access method. This protocol enables the WSN-UAV system to obtain a low rate of PER and high frequency of data transmission by the sensors. In order to acquire these values at the requirements for data transmission cycle, sensor density, and the packet size, the UAV needs to flexibly adjust its altitude, speed, and the number of sub groups. This adjustment is optimally achieved if both the PER and data transmission cycle are minimized. However, in this protocol, it usually takes a significant time to exchange each sensor node’s information with the UAV before its data transmission. This would become unnecessary if the sensor’s information is known by the UAV. Moreover, a code based scheme (CDMA) for both the sensors and UAV would be more complex than the timeslot based scheme (TDMA) that would be evaluated in this paper. Base on the existing issues as mentioned above, we introduce an enhanced version that also leverages both PFS and TDMA scheme, with following contributions: (1) First time apply both PFS and TDMA based schemes for effective transmissions by activated sensors.

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(2) Two operation modes have been proposed for effective communications between the UAV and the active sensors. They are the two cases where the sensor node’s information is known and unknown, respectively. (3) FRA is utilized in the case of no knowledge about the sensors want to communicate with the UAV. FRA has shown its advantages comparing to the CSMA scheme. (4) Optimal parameters for the UAV in order to maximize the system performance in the two cases. More specifically, they include the optimal altitude, packet size, and the number of sub groups at different densities of the sensors. The rest of this paper is organized as follows. Section 2 describes the general system architecture of a WSN-UAV system and explains how PFS schemes are applied. Section 3 explains how TDMA is used with PFS scheme. The last two sections analyze the simulation parameter, numerical results, and a brief conclusion, respectively. II.

WSN-UAV SYSTEM AND PFS SCHEMES

B. Prioritized Frame Selection (PFS) Schemes

A. General Model of a WSN-UAV System UAV

Beacon area contain activated sensors (It also contains a number of sub groups)

END

Furthermost sensors

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Beacon signal Activated sensor (ON)

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but further sensor. Only the sensors located inside the beacon signal range are activated and allowed to communicate with the UAV. This group of sensors is divided into several sub groups located inside that range. Each time, only the sensors in a sub group communicate with the UAV; the other sensors listen and wait for their turns. For the system evaluation and comparison, the beacon signal is assumed not to overlap with the passed areas after the UAV changes its direction. It is obvious that the total time for each flight closely relates to the parameters such as the speed, altitude of the UAV, the flare angle of the beacon signal, and of course the sensors’ area. For instance, at the same speed and flare angle, the higher altitude of the UAV, the larger beacon area. As a result, the time for each flight is reduced. Section B will discuss about the active sensors group and its sub groups division. Specifically, it explains the initiation to assign these sub groups with respective priorities.

UAV’s flight path

Fig. 1 Layout of the WSN-UAV system In a WSN-UAV system, the number of land-based sensors could be large and not fully connected with each other. As a result, the multi hop communication does not work even it is not the effective way [13]. In this system, the direct communication between the sensor and the UAV is used. Therefore, it could save energy for the sensors and also reduce the delay for the end to end communication. In other word, the WSN-UAV system has shifted the responsibility of energyconsuming tasks to the UAV’s. In addition, the sensor goes to the sleep mode when the beacon signal is not available or its strength is weak (i.e. weaker than the threshold). For data collecting mission, the active sensors are assumed to periodically transmit the sensing data to the UAV as many as possible during the UAV’s availability. Physical advantage of this WSN-UAV system is the low path loss exponent which is maximal at about 2.25, as according to our previous studies [14]-[18]. Another useful feature of a WSN-UAV system is that the flight route of the UAV could be predefined or arbitrary. We assume that the UAV flies along the sensors area in order to gradually serve the communication for them as shown in Fig. 1. The UAV starts from the leftmost and nearest sensor to the right most sensor then changes its direction and flies back to the other leftmost

The UAV is assumed to adjust the transmitting power in order to maintain the lowest and highest levels of the beacon signal at the sensors. The sensors located inside this range (on the ground) are therefore all activated. The lowest level is still high enough for doing communication at the sensors on the boundary of the beacon area (Fig. 1). The highest level could be obtained at the sensors underneath the UAV under the condition of using the same transmitting power as for calculating the lowest level. Base on the actual distances between the UAV and those sensors, UAV can flexibly change the beacon transmitting power. The sub groups of sensors are corresponding to the intermediate levels that gradually increase from the lowest to the highest level. To facilitate this scheme, each sensor must measure the beacon signal receiving power every time then find its own range based on the information from the UAV: the highest-lowest range and the number of sub groups. Rx power level Highest level

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The priorities of sub groups

Fig. 2 The concept of sub groups division in PFS scheme The left part of Fig. 2 shows an example of this PFS scheme in the case 3 sub groups has been requested. In addition, these groups have been further divided into two major groups. These major groups are behind and in front of the UAV respectively which are assigned with lowering priorities. In each major group, the further one is assigned with the higher priority. This rule is opposite in the other major group.

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This group division and priority assignment are to maximize the number of transmitting sensors and minimize the system PER. Following this distribution in the rights to use the channel, in each sub group the belonging sensors will conduct the channel access and data transmission in association with UAV’s support. Regarding to the channel access, the sensors are usually not fully connected; hence, a suitable access method for the unknown sensors information is a need. For the data transmission, TDMA scheme is selected because it is simple for the UAV and the sensors to execute and implement. The more detail information is described in section III. III.

CHANNEL ACCESS AND DATA TRANSMISSION METHODS FOR THE WSN-UAV SYSTEM

A. Channel access 1) Sensor’s position is unknown (FRA-based scheme)

N_TS(FRA)

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In normal case, a random access method is more preferred to use instead of a scheduled one. However, the former one (i.e. CSMA/CA) usually takes time due to the large amount of overheads. This also means it takes time for successfully accessing the channel between the sensor and the UAV. As a result, some sensors may lose its existing connection while waiting for their turns. To minimize the number of packet loss, our proposal is to organize the time for channel access in consecutive time frames. Each time frame contains a specific number of timeslots which each is enough for transmitting the sensor’s information and acquiring an acknowledgment from the UAV. In each frame, a sensor in the sub group randomly selects a timeslot for its chance to negotiate with the UAV. In the successful case, the sensor will stop struggling for channel access and only wait for its transmission turn. In the else case, that sensor keeps accessing the channel in the next time frames until it is successful. In the case of success, it receives a feedback from the UAV which contains the timeslot order that to be used for its data transmission. This process will be initialized by the UAV after all the sensors complete their channel access. Figure 3 shows that FRA scheme is much more efficient than CSMA/CA scheme by comparing the time consumption used by the CSMA/CA and FRA schemes. In addition, the optimal number of timeslots per frame length or the time frame length has also been described.

The number of sensor node simultaneously contend the channel

Fig. 3 Comparison of the CSMA/CA and FRA schemes

2) Sensor’s position is known (position-based scheme) In this case, the UAV has known the sensor’s information. Therefore it may take a short period of time to inform the sensors in each sub group their assigned timeslot orders. Theoretically, the minimal number of timeslots needed for this period is equal to the number of sensors in its sub group. Comparing to the simulated result in FRA case, this period is even much lesser than that value. B. Data transmission After the channel access phase has been described in section A, the UAV will inform the sensors in the sub group to start the data transmission phase. For these simultaneous transmissions, a well-known CDMA scheme has been proposed in [5] and [7]. Different from the prior studies, this paper employs the TDMA based scheme for those sensors to transmit their data to the UAV. It is expected that this scheme brings both the benefits include the simplicity and less packet error rate comparing to the case of using CDMA scheme. Therefore, a lesser energy consumption for data and algorithm processing will be burned on the sensor compared to that in case of using CDMA. The next part will explain about the formula to calculate the PER of the entire system. In general, the systematic packet error rate, PERSYS, is presented by this equation: PERSYS 1 (1 PERMob )(1 PERTran )

(1)

where PERMob is PER caused by the connection loss due to the movement of the UAV. PERTra is the PER caused by the packet transmission between the sensor and the UAV. The radio channel in this case (between the sensor and the UAV) is in line of sight (LOS) condition; hence, PERTra majorly depends on data modulation scheme and Rician factor. Assuming QPSK is used for modulating the data bits and K is Rician factor of the channel between the sensor and the UAV, the possibility of the symbol error rate would be as following: Ps

Kg /2 (1 K ) sin 2 ( )d exp( ) (2) 2 2 0 (1 K ) sin g (1 K ) sin g

where is Es/N0 and α=2; g=1 (QPSK based values) [19]. K factor varies at the incident angle constituted by the sensor and the UAV [20]. This angle is estimated if the both position of the sensor and the UAV are known. From the obtained symbol error rate (Ps) in (Eq. 2), PERTra could be derived. As a result, the PERSYS is estimated by applying (Eq. 1) if PERMob is known. The next section IV starts to describe the simulation parameters and simulation results, as much as the data analysis. IV.

SIMULATIONS AND RESULTS

In these simulations which apply all the parameters described in Table 1, there are two categories of system performance valuations. The first one is the case where the UAV first time flies and serves the communication for the land-based wireless sensors network; or in the case the UAV needs to update the sensors’ information after a regular period or on its demand. In the second category, the UAV is assumed to have all the

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sensors’ information such as their position and ID as well. In both the cases, the PFS scheme is always applied for sub groups (priority groups) division; also, TDMA is the multiple access scheme applied for those sensors in each sub group to transmit their sensing data to the UAV. However, the random access method FRA is only used for the first situation. In the later case, FRA is not applied. TABLE I. SIMULATION PARAMETERS Parameter Area of the sensor [m2] # sensors The UAV’s altitude [m] # priority groups

Value 300x3000 500 - 40000 50-200 1-30 60 2 15 10 2 25 5-200 5 20 2 QPSK FRA, Position-based schemes [Fig. 1] FRA or Position based

2) System performance with the position-based scheme Alt=50,N=500 Alt=200,N=4000 Alt=200,N=500 Alt=50,N=4000

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1) System peformance with the FRA scheme Alt=50,N=500 Alt=200,N=500 Alt=50,N=500 Alt=200,N=500

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Percentage of packet error rate

Beacon signal’s flare angle [degree] RF frequency [GHz] SNR [dB] UAV’s speed [m/s] Exponent path loss (UAV-sensor link) Sensor’s transmission bit rate [kbps] Data packet length [byte] Packet for channel access [byte] Sensor’s transmission range, R [m] Data transmission interval [s] Modulation Channel access UAV’s flight route Channel access

and 200 meters) and the number of sensors in the network (from 500 to 4000 sensors). The purpose of this part of data communication is to obtain all the sensors’ information which could be used for the next flights. Therefore, the most important part of data packet is the sensor’s information which is not as large as the normal data sensed by the sensor. Consequence, there is no need to apply large packet sizes for this communication situation. From the results in Fig. 4, at 5byte packet size, the PER of the end to end system is small enough ensuring that the UAV can fully obtain the sensors’ information if applying not lesser than 20 of sub groups in PFS scheme. This result is still maintained even at high altitude such as at 200 meters and with the large number of sensors up to 4000.

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Fig. 5 PER in case applying a position-based scheme

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Fig. 4 Evaluation of PER in case of applying FRA scheme Several values of data packet sizes are applied for simulating the PER at this condition, ranges from the minimum at 5 bytes to the maximum at 100 bytes. In this case, the UAV has no information about the sensors whose it will serve the data communication. For simplicity, Fig. 4 only plots the two scenarios with packet sizes at 5 and 100 bytes. The minimal value at 5 bytes is assumed to contain the sensor ID and its position information. In the larger values cases, in addition to the sensor’s information, additional data is an option. In each scenario, four curves will express the respective PERs in the extreme conditions of both the UAV’s altitudes (at 50 meters

With the knowledge of the sensors’ information, the UAV does not need to apply FRA scheme in the channel access phase as mentioned above. The UAV only needs to state all the sensors’ information by a broadcast message to the active sensors in each priority group. Therefore, the period of time used for the channel access is shorter than the consumed time in the case of applying FRA scheme. This situation is the basic for an expecting of higher system performance comparing to the case of unknown sensor information. Also, this case, the data transmission is a main; hence, the simulation for 5-byte packet is no need to conduct. Figure 5 shows that, the PER at 25-byte packet is even better than that at 5-byte packet in case with FRA scheme. At large packet size i.e. 100 bytes, the PER is still about less than 1.5%. This rate is going to be decreased by increasing the number of sub groups; however, it is still high i.e. about 8% in the case that each sensor transmits a 200-byte packet every time. In this figure, these specific results are plotted at some certain values of the UAV’s altitudes and the number of sensors in the network; hence, they are not optimal ones yet. The common trend of minimizing PER is to decrease the

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packet size and select small values of both the number of sensors in the network and the UAV’s altitude. In almost of the actual cases, the number of sensors in the network is not frequently changed. Therefore, it is necessary to find the optimal altitude and optimal number of sub groups at a known number of sensors and packet sizes. 3) Optimal values of UAV’s altitude and PER In the Fig. 6 below shows the optimal values of the altitude of the UAV and its achievable packet error rate. From this figure, we can see that at the packet size of 25 bytes, the optimal PER just slightly increases when the number of sensors increases. In order to maintain this optimal PER, it is necessary to change the altitude of the UAV on the variation of the number of sensors. A given example, if there are less than 2000 sensors in the network, the UAV needs to fly at an altitude of larger than 200 meters. However, if this number of sensor is between 2500 and 3500, the UAV needs to lower down its altitude to 150 meters. At the higher number of sensors, i.e. at 4000 sensors, the UAV cannot fly with the altitude of higher than 100 meters if it wants to obtain the best PER. Similarly, from Fig. 6, other analysis could be done for the cases at 100 and 200 bytes per packet. There is a common thing from our simulated results, at the sparse number of sensors in the network, the UAV usually needs to fly at high altitudes to increase beacon area. This is expected to minimize the PER rate as well as the total time for each flight tour. If this number of sensors in the network increases, the UAV needs to reduce the beacon area for ensuring a not so large number of activated sensors communicating with the UAV each time. Furthermore, another factor which is not shown in Fig. 6 is the optimal number of sub groups. Almost of the cases, the system requires about 30 sub groups for achieving such minimal PER.

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Fig. 6 The optimal values of PER and UAV’s altitude

The optimal altitude of the UAV (m)

The optimal packet error rate

Optimal altitude

V.

CONCLUSION

This protocol has provided a reliable communication between a wide area WSN and the moving UAV. The protocol has shown its strength and simplicity for a WSN-UAV system. The optimal altitudes for the UAV, number of sub groups, and suitable data packet size at varying number of sensors in the network, has been also analyzed via simulations. Those optimal values have been found on the case of ensuring a low enough PER that is acceptable in both the conditions of known and unknown sensor’s information. REFERENCES [1] M. Lucchi, A. Giorgetti, M.Z. Win, and M. Chiani “Using A UAV to Collect Data From Low-Power Wireless Sensors,” Proc. XIX Congresso Nazionale AIDAA, Vol. 86, pp. 141-150, Forli, Italy, Sep. 2007. [2] A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson, “Wireless Sensor Networks for Habitat Monitoring,” Proc. of Workshop on Wireless Sensor Networks and Applications, pp. 88-97, Atlanta, Sep. 2002. [3] R. Jafari, A. Encarnacao, A. Zahoory, F. Dabiri, H. Noshadi, and M. Sarrafzadeh, “Wireless Sensor Networks for Health Monitoring,” Proc. Mobile and Ubiquitous Systems: Networking and Services , pp. 479-481, Jul. 2005. [4] I. Demirkol, C. Ersoy, and F. Alagoz, “MAC Protocols for Wireless Sensor Networks,” IEEE Commun. Mag., Vol. 1, pp. 115-121, Apr. 2006. [5] T.D Ho, J. Park, and S. Shimamoto “Novel Multiple Access Scheme for Wireless Sensor Network Employing UAV,” Proc. of IEEE/AIAA 29th DASC, Salt Lake, UT, Oct. 2010. [6] T.D Ho, J. Park, and S. Shimamoto “Power and Performance Tradeoff of MAC Protocol for Wireless Sensor Network Employing Unmanned Aerial Vehicle,” Accepted by IEEE International Conference on Advanced Technology for Communication, Ho Chi Minh, Vietnam, Oct. 2010. [7] P. Venkitasubramaniam, S. Adrieddy, and L. Tong, “Sensor Network With Mobile Access: Optimal Random Access and Coding,” IEEE J. Sel. Areas Commun., Vol. 22, No.6, Aug. 2004 [8] G. Mergen, Q. Zhao, and L. Tong, “Sensor Network With Mobile Access: Energy and Capacity Consideration,” IEEE Trans. Commun., Vol. 54, No.11, Nov. 2006 [9] L. Tong, Q. Zhao and S. Adireddy, “Sensor Networks with Mobile Agents,” Proc. MILCOM, pp. 688-693, Oct. 2003. [10] P. Venkitasubramaniam, S. Adireddy, and L. Tong, “Opportunistic ALOHA and Cross Layer Design For Sensor Networks,” Proc. MILCOM, pp. 705-710, Oct. 2003. [11] Q. Zhao and L. Tong, “Quality-Of-Service Specific Information Retrieval For Densely Deployed Sensor Network,” Proc. MILCOM, pp. 591596, Oct. 2003. [12] Q. Zhao and L. Tong, “Distributed Opportunistic Transmission for Wireless Sensor Networks,” Proc. IEEE ICASSP 2004, pp.833-836. [13] K. Sohrabi, B. Manriquez, and G. J. Pottie “Near Ground Wideband Channel Measurement,” Proc. IEEE Veh. Tech. Conf., pp. 571-574, Jul. 1999. [14] T.D. Ho, and S. Shimamoto “A Proposal of Wide-Band Air-to-Ground Communication at Airports Employing 5-GHz Band,” Proc. IEEE WCNC, pp. 1777-1782, Budapest, Hungary, Apr. 2009. [15] T.D. Ho, and S. Shimamoto “A proposal of a Wide-Band for Air Traffic Control Communications” Proc. IEEE WCNC, pp. 1950-1955, Las Vegas, Nevada, Apr. 2008. [16] T.D Ho, J. Park, S. Shimamoto, and J. Kitarori “Oceanic Air Traffic Control Based on Space-Time Division Multiple Access,” Proc. IEEE/AIAA 28th DASC, 7.D.2-1 - 7.D.2-13, Orlando, FL, Oct. 2009. [17] T.D. Ho, and S. Shimamoto “A proposal of a Wide-Band for Air Traffic Control Communications” Proc. IEEE WCNC, pp. 1950-1955, Las Vegas, Nevada, Apr. 2008. [18] T.D Ho, J. Park, S. Shimamoto, and J. Kitarori “Oceanic Air Traffic Control Based on Space-Time Division Multiple Access,” Proc. IEEE/AIAA 28th DASC, 7.D.2-1 - 7.D.2-13, Orlando, FL, Oct. 2009. [19] A. Goldsmith, Wireless communication, New York: Cambridge University Press, 2005. [20] Iskandar and S. Shimamoto, "The channel characterization for mobile communication employing stratospheric platform," Proc. IEEE 12th International Conference on Telecommunications System, July 2004.

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