Energy Adaptive MAC Protocol for Wireless Sensor Networks with RF Energy Transfer

Transcription

1 Energy Adaptive MAC Protocol for Wireless Sensor Networks with RF Energy Transfer Jaeho Kim U-embedded Convergence Research Center, Korea Electronics Technology Institute, Gyeonggi-do, Korea Jaeho Kim, Jang-Won Lee Dept. of Electrical & Electronic Eng., Yonsei University, Seoul, Korea {jaehokim, Abstract Recently, various energy harvesting techniques from ambient environments were proposed as alternative methods for powering sensor nodes, which convert the ambient energy from environments into electricity to power sensor nodes. However, those techniques are not applicable to the wireless sensor networks (WSNs) in the environment with no ambient energy source. To overcome this problem, an RF energy transfer method was proposed to power wireless sensor nodes. However, the RF energy transfer method also has a problem of unfairness among sensor nodes due to the significant difference between their energy harvesting rates according to their positions. In this paper, we propose a medium access control (MAC) protocol for WSNs based on RF energy transfer. The proposed MAC protocol adaptively manages the duty cycle of sensor nodes according to their the amount of harvested energy as well as the contention time of sensor nodes considering fairness among them. Through simulations, we show that our protocol can achieve a high degree of fairness, while maintaining duty cycle of sensor nodes appropriately according to the amount of their harvested energy. Index Terms wireless energy transfer, energy harvesting, wireless sensor network, medium access control I. INTRODUCTION Wireless sensor networking is emerging technology with wide range of potential applications, e.g. environment monitoring, smart spaces, medical systems, and military applications. Nodes in a wireless sensor network (WSN) are typically battery-powered. However, in many cases, it is difficult to replace or recharge the exhausted battery. This is a reason why the primary objective in WSN design is maximizing node and network lifetime, leaving the other performance metrics as secondary objectives. Hence, most of research on the WSN has focused on extending network lifetime by minimizing energy usage. Recently, alternative methods for powering sensor nodes, which convert the ambient energy from environments into electricity to power sensor nodes, are being actively studied [1], [2]. This has motivated the search for alternative sources of energy to power nodes in the WSN, especially for applications that require sensors to be installed for long durations or embedded into structures where battery replacement is impractical. A variety of techniques are available for energy harvesting in the WSN, including solar and wind powers, thermoelectricity, and vibrational excitations. However, energy harvested from ambient environments is unreliable and uncontrollable, since environmental energy is typically spacedependent and time-varying. In addition, energy harvesting techniques are not applicable to the WSN in the environment with no ambient energy source. To overcome these problems, RF energy transfer methods are proposed to power wireless sensor nodes [3] []. The RF energy transfer system consists of an energy emitting node and energy harvesting nodes located within the tens of meters. The RF energy transfer system has good characteristics for the WSN in terms of reliability, controllability, and predictability, and also has the advantage of being able to supply energy to many sensor nodes simultaneously. Hence, the characteristics of the RF energy transfer system is suitable for the WSN with many nodes and low power consumption. However we need a practical MAC protocol to efficiently utilize a small amount of energy harvested from RF energy transfer and it should be designed considering the differences between RF energy transfer system and ambient energy harvesting system as follows: The amount of energy harvested in the RF energy transfer system is stable over time. The amount of energy harvested by each of energy harvesting nodes is significantly different, highly depending on the distance between the energy emitting node and the energy harvesting node in the RF energy transfer system. To the best of our knowledge, almost all existing research on MAC protocols for the WSN mainly considered the batterypowered sensor network, and the MAC protocol for WSN with RF energy transfer, which is referred to as the RET- WSN in this paper, has not been actively studied. The MAC protocols for the battery-powered WSN can be classified into two categories: synchronous and asynchronous protocols. Synchronous approaches such as S-MAC [6], T-MAC [7], and beacon enabled mode of IEEE [8] synchronize neighboring nodes in order to align their active or sleeping periods. This approach greatly reduces idle listening time, but it is impractical in the RET-WSN, since energy harvesting nodes are difficult to maintain synchronization due to difference in the amount of energy harvested among nodes and a wake-up timer does not work properly when energy is exhausted. B-MAC [9], X-MAC [1], and RI-MAC [11], /11/$ IEEE 89 ICUFN 211

2 which are typical asynchronous MAC schemes to support low power consumption, require extra overheads in the sender side such as long preamble or extra beacon transmission. MAC protocols for the WSN powered by ambient energy harvesting have been studied in [2], [12] [14]. In [2], [12], [13], the authors presented adaptive duty-cycling mechanisms to dynamically adapt to unstable energy harvesting from ambient environments such as solar and wind. However, adapting to unstable energy harvesting is not a key issue in the RET-WSN, since the amount of energy harvested in the RF energy transfer system is stable over time. In [14], the authors proposed CSMA-based and polling-based protocols for the WSN powered by ambient energy harvesting (WSN-HEAP) to improve the network throughput. However, the protocols were designed for sensor nodes with unstable energy source. In addition, they did not consider the unfairness among energy harvesting nodes due to the significant difference between energy harvesting rates that depend on the distance between the energy emitting node and the energy harvesting nodes in the RET-WSN. Recently effective schemes for RF energy harvesting and the WSN with RF energy harvesting have been studied [3], [1] [17]. In [1] and [16], the effective RF energy harvesting technologies are proposed for low power consuming devices. Powercast developed the first practical RF energy harvesters and transmitters for the WSN which are now commercially available [3]. In [17] the authors developed the WSN powered by harvested energy from TV radio to show the effectiveness of RF energy harvesting in the WSN. In this paper, we propose a MAC protocol with energy adaptive duty cycle and energy adaptive contention window for the RET-WSN, called energy adaptive MAC (EA-MAC), and analyze its performance. The rest of this paper is organized as follows. In Section II, we introduce the architecture of the RET-WSN. In Section III, we describe our EA-MAC protocol for the RET-WSN and the simulation results are presented in Section IV. A summary of the contributions and some concluding remarks are given in Section V. II. RET-WSN ARCHITECTURE In this section, we introduce the system architecture of the RET-WSN considered in this paper. For the RET-WSN, we consider a star topology that consists of a single master node which has the capability of gathering data and slave nodes which are able to sense data and transmit it to the master node. The master node operates using main power and emits RF energy to power slave nodes and the slave nodes harvest energy transferred by the master node, as in Fig. 1. The master node always stays awake to receive the data from the slave nodes and the slave nodes go to sleep and active states back and forth, depending on the level of their remaining energy. In the active state, a slave node contends for the channel and transmits its data, if it acquires the channel. In the sleep state, a slave node completely turns off its radio and processor to save energy except for an interrupt routine to wake up. We Fig. 1. Fig. 2. RF energy transfer model for the RET-WSN. System architecture of the RET-WSN. assume that a slave node can transmit at most one packet in an active state and a packet has a fixed length. Fig. 1 shows energy flow for the RET-WSN. Here, P tx is the power level transmitted from the master node to slave nodes and P in,i is the power level received by slave node i. Given P tx, P in,i can be determined by the Friis transmission equation [18] under idealized conditions as ( ) 2 λ P in,i = ep tx G t G r, (1) 4πR i where e is the energy harvesting efficiency of slave nodes, G t and G r are the antenna gains of transmitting and receiving antenna, respectively, λ is the wavelength, and R i is the distance between the master node and slave node i. Slave nodes harvest RF energy with their antenna and convert it to the appropriate DC voltage with their rectifier. To provide appropriate power for transmitting data, slave nodes store energy into their energy storage such as a capacitor. Fig. 2 shows the system architecture of the RET-WSN. III. EA-MAC Note that the amount of energy harvested by slave nodes in the RET-WSN is significantly different, depending on their distance to the master node, as previously mentioned. If this issue is not treated appropriately, it may cause the unfairness among slave nodes. Since a slave node that is far away from the master node will have lower energy harvesting level than a slave node that is close to the master node, the former should have longer sleep time to save energy more than the latter. Hence, we need to investigate a new duty-cycle management scheme that adapts to energy harvesting and consumption conditions and a method of achieving fairness among slave nodes according to their energy harvesting condition. To this 9

3 Fig. 3. State transition diagram for EA-MAC. end, in this paper, we propose a MAC protocol with energyadaptive duty cycle and energy-adaptive contention for the RET-WSN. A. Energy adaptive duty cycle management As mentioned earlier, the master node always stays awake to receive the data from slave nodes and slave nodes go to sleep and active states back and forth, depending on the level of their remaining energy. In the energy adaptive duty cycle management mechanism of EA-MAC, to manage the duty cycle of each slave node according to its harvested energy level, the remaining energy level in its energy storage is used. Initially, the slave node in the sleep state goes into the active state to access the channel, i.e., the contention state, when its harvested energy level reaches δ, which is an energy level threshold to transit from sleep state to active state. The value of δ is determined such that the slave node has enough energy to transmit one packet. If the slave node successfully gets the channel, it goes into the transmit state and transmits its data packet. After packet transmission completes, the slave node goes back to the sleep state. If the slave node fails to get the channel in the contention state, it goes back to the sleep state immediately. Once the slave node goes into the sleep state, it remains in the sleep state until its energy level reaches the threshold δ again. In this way, the slave node repeats transitions between sleep and active states according to its energy harvesting condition. Fig. 3 depicts the state transition diagram for EA-MAC. B. Energy adaptive contention algorithm EA-MAC uses the CSMA/CA algorithm with the energy adaptive contention algorithm based on the unslotted CSMA/CA algorithm in IEEE [8]. The main distinguishing feature of our EA-MAC is that it has the energy adaptive contention algorithm in which the backoff time of each slave node is controlled by its energy harvesting rates. Each slave node i has three variables: NB i, BE i, ω i for the CSMA/CA algorithm of EA-MAC. First, NB i is the number of clear channel assessment (CCA) performed in the CSMA/CA algorithm so far. Second, BE i is the backoff exponent, which is related to the maximum number of backoff slots during which slave node i must wait before attempting to assess the channel. Lastly, ω i is the weight factor. The weight factor is used to compensate the unfairness among slave nodes due to the significant difference between their energy harvesting rates and is calculated considering energy harvesting rate of the corresponding slave node and the average energy harvesting rate of all slave nodes in the network. Fig. 4. CSMA/CA algorithm in EA-MAC. The CSMA/CA algorithm works as follows. First, NB i and BE i are initialized to and minbe, respectively, where minbe is the minimum value of the backoff exponent. The slave node i waits for a random number of backoff slots 1 in the range to ω i 2 BE 1 and then performs a CCA in order to check whether the channel is busy or not. If the channel is assessed to be busy, it increases both NB i and BE i by one. If the value of NB i is less than or equal to maxcsmabackoffs 2, it must return to the backoff procedure for another random backoff, otherwise it declares the failure of channel access and terminates the CSMA/CA algorithm, and then it goes into the sleep state. If the channel is assessed to be idle during the CCA period, it goes into the transmit state and transmits its data packet. Fig. 4 shows the procedure of the CSMA/CA algorithm in EA-MAC. IV. PERFORMANCE ANALYSIS In this section, we evaluate the performance of our EA- MAC in the OPNET simulator [19]. The parameters for the RF energy transfer system, which are based on Powercast s TX911 Powercaster and P211 Powerharvester [3], are summarized in Table I. In addition, Table II summarizes the key parameters that we use for the EA-MAC in each sensor node. Most of these parameters are from IEEE standard [8] and the data sheet of Texas Instruments CC243 [2]. 1 A backoff slot is the time period of a unit slot used for the backoff procedure. 2 maxcsmabackoffs is the maximum number of performing CCA before declare failure of channel access. 91

4 TABLE I PARAMETERS FOR RF ENERGY TRANSFER SYSTEM Parameter Value Unit Frequency (C/λ) 91 MHz Energy harvesting efficiency (e).8 Transmitter antenna linear gain (G t) 1 dbi Receiver antenna linear gain (G r) 6 dbi TABLE II PARAMETERS FOR EA-MAC throughput (Kbps) meter Parameter Value Unit Frequency 2.41 GHz Data rate 2 Kbps A frame size 128 bits Power consumption of contention state 31 ma Power consumption of transmit state 29 ma Power consumption of sleep state. µa Operating voltage 3. V minbe 3 maxcsmabackoffs 4 A backoff slot.32 msec Active threshold (δ) 3 mj throughput (Kbps) meter Fig. 7. fairness index Fig. 6. The throughput of EA-MAC/EAC. EA MAC EA MAC/EAC.3 Comparison of Fairness indices in EA-MAC and EA-MAC/EAC. Fig.. The throughput of EA-MAC. In our simulation, we deploy one master node and 9 slave nodes, each of which is located at the place from s to 1 meters away from the master node with the interval of one meter. We compare the throughput of slave nodes with varying transmitted power for RF energy transfer from the master node. We also compare the performance of the EA- MAC with energy adaptive contention algorithm and that of the EA-MAC without it. We call the former as EA-MAC/EAC and the latter as EA-MAC. Fig. shows the throughput of EA-MAC. From the results, we observe that a slave node that is close to the master node achieves much higher throughput than a slave node that is far away from the master node, since each slave node adaptively controls the duty cycle according to the amount of energy harvested by it. Fig. 6 shows the throughput of EA-MAC/EAC. In EA- MAC/EAC, slave nodes use the weight factor to compensate the unfairness due to the significant difference between energy harvesting rates of the slave nodes. The weight factor, ω i, of slave node i is calculated by the ratio of its harvested energy to the average harvested energy of all slave nodes in the network. The average harvested energy can be informed to each slave nodes by the master node. From Fig. and Fig. 6, we can observe that when the energy adaptive contention algorithm is applied, the throughput of the slave nodes having relatively lower energy harvesting rate is increased, while the throughput of the slave nodes having relatively higher energy harvesting rate is decreased, which implies that we can improve the degree of fairness among slave nodes by using the energy adaptive contention algorithm. Especially, the throughput of the slave node at 1 meters is increased about 24 %, while the throughput of the slave node at s is decreased about 7 %. To show the degree of fairness among slave nodes more clearly, in Fig. 7 we provide Jain s fairness index [21], which is widely used to measure the degree of fairness in many resource allocation schemes in communication networks and is given by I = ( n i=1 S i) 2 n n, (2) i=1 S2 i where n is the number of slave nodes, S i is the throughput of slave node i. The fairness index I has a value between zero and one and in general, as the degree of fairness increases, the value of I also increases. As the figure shows, EA- MAC/EAC achieves a higher degree of fairness than EA-MAC, as expected. The improvement of the degree of fairness in EA- MAC/EAC comes from the adaptive control of the contention time from the energy adaptive contention algorithm. To show 92

5 8 x contention time (Sec) meter network throughput (Kbps) EA MAC EA MAC/EAC 1 Fig. 8. The contention time of EA-MAC. Fig. 1. MAC/EAC. Comparison of network throughputs in EA-MAC and EA- contention time (Sec) 8 x meter Fig. 9. The contention time of EA-MAC/EAC. fairness among slave nodes. Our simulation results showed that EA-MAC with the energy adaptive contention algorithm can significantly improve the degree of fairness compared with EA-MAC without the energy adaptive contention algorithm. VI. ACKNOWLEDGMENTS This work was financially supported by the grant from the strategic technology development program (Project No ) of the Ministry of Knowledge Economy (MKE) of Korea and by the KCC(Korea Communications Commission), Korea, under the R&D program supervised by the KCA(Korea Communications Agency) (KCA-211-( )). the effectiveness of the energy adaptive contention algorithm, we compare the contention times of slave nodes in EA-MAC and EA-MAC/EAC in Fig. 8 and Fig. 9, respectively. As shown in Fig. 8, in EA-MAC, all slave nodes have almost the same contention time, since in EA-MAC, all slave nodes use the same equation to calculate the backoff time. However, as shown in Fig. in EA-MAC/EAC, as a slave node has a lower energy harvesting rate, it has a shorter contention time, since it can have a shorter backoff time according to its weight factor. A shorter contention time of a slave node with a lower energy harvesting rate can helps to improve its throughput, which results in the improvement of the degree of fairness in EA-MAC/EAC. Finally, in Fig. 1, we compare network throughput achieved by EA-MAC/EAC with that achieved by EA-MAC. As the figure shows, the network throughput of EA-MAC/EAC is lower than that of EA-MAC. This implies that the improvement of the degree of fairness in EA-MAC/EAC is achieved at the price of the decrement of the network throughput. V. CONCLUSION In this paper, we proposed a MAC protocol with energy adaptive duty cycle and energy adaptive contention window that can be used in wireless sensor networks based on RF energy transfer. The proposed MAC protocol adaptively manages not only the duty cycle of the slave node according to its harvested energy level but also the contention time of the slave node by its energy harvesting rate to achieve the REFERENCES [1] S. Sudevalayam and P. Kulkarni, Energy harvesting sensor nodes: Survey and implications, IEEE Communications Surveys & Tutorials, vol. PP, no. 99, pp. 1 19, 21. [2] W. Seah, Z. Eu, and H. Tan, Wireless sensor networks powered by ambient energy harvesting (WSN-HEAP)-Survey and challenges, in Wireless VITAE 29, May 29, pp. 1. [3] Powercast Corporation, TX911 User s Manual & P211 s Datasheet. available online at: [4] R. Selvakumaran, W. Liu, B. Soong, L. Ming, and Y. L. Sum, Design of low power Rectenna for wireless power transfer, in IEEE TENCON 29, Jan. 29, pp. 1. [] Z. Sim, R. Shuttleworth, M. Alexander, and B. Grieve, Compact Patch Antenna Design for Outdoor RF Energy Harvesting in Wireless Sensor Networks, Progress In Electromagnetics Research, vol. 1, pp , 21. [6] W. Ye, J. Heidemann, and D. Estrin, An energy-efficient MAC protocol for wireless sensor networks, in IEEE INFOCOM 22, vol. 3, Jun. 22, pp [7] T. Van Dam and K. Langendoen, An adaptive energy-efficient MAC protocol for wireless sensor networks, in ACM SenSys 23, Nov. 23, pp [8] Part 1.4: Wireless Medium Access Control (MAC) and Physical Layer (Phy) Specifications for Low-Rate Wireless Personal Area Networks (LR- WPANS). IEEE Std, 26. [9] J. Polastre, J. Hill, and D. Culler, Versatile low power media access for wireless sensor networks, in ACM SenSys 24, Nov. 24, pp. 3. [1] M. Buettner, G. Yee, E. Anderson, and R. Han, X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks, in ACM SenSys 26, Oct. 26, pp [11] Y. Sun, O. Gurewitz, and D. Johnson, RI-MAC: a receiver-initiated asynchronous duty cycle MAC protocol for dynamic traffic loads in wireless sensor networks, in ACM SenSys 28, Nov. 28, pp [12] J. Hsu, S. Zahedi, A. Kansal, M. Srivastava, and V. Raghunathan, Adaptive duty cycling for energy harvesting systems, in ACM LSLPED 26, Oct. 26, pp

6 [13] C. Vigorito, D. Ganesan, and A. Barto, Adaptive control of duty cycling in energy-harvesting wireless sensor networks, in IEEE SECON 27, Jun. 27, pp [14] Z. Eu, W. Seah, and H. Tan, A study of MAC schemes for wireless sensor networks powered by ambient energy harvesting, in WICON 28, Nov. 28, p. 78. [1] H. Yan, J. Montero, A. Akhnoukh, L. de Vreede, and J. Burghartz, An integration scheme for RF power harvesting, in SAFE 2, Nov. 2, pp [16] T. Ungan and L. Reindl, Harvesting low ambient RF-sources for autonomous measurement systems, in IEEE IMTC 28, May 28, pp [17] H. Nishimoto, Y. Kawahara, and T. Asami, Prototype implementation of wireless sensor network using TV broadcast RF energy harvesting, in ACM Ubicomp 21, Sep. 21, pp [18] T. Rappaport, Wireless communications. Prentice Hall PTR, 22. [19] OPNET modeler & wireless suit. available online at: rd/modeler wireless.html. [2] B. Selvig, Measuring power consumption with CC243 & Z-Stack, Application Note AN3, Texas Instruments. [21] M. Dianati, X. Shen, and S. Naik, A new fairness index for radio resource allocation in wireless networks, in IEEE WCNC 2, vol. 2, Mar. 2, pp