Differential Dynamic Traffic Control for IEEE Networks *

Transcription

1 JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 26, (200) Differential Dynamic Traffic Control for IEEE Networks * Department of Electrical and Computer Engineering Korea University Seoul, Korea IEEE provides beacon enabled and non-beacon enabled modes. Beacon enabled mode is generally used for energy efficiency. However, the main problem of beacon enabled mode is the fixed superframe structure that leads to inefficient transmission in dynamic traffic conditions. Moreover, does not provide differential service according to traffic types. In this paper, we propose a Differential Dynamic Traffic Control (DDTC) that integrates the following schemes. Dynamic active period control determines the appropriate active period to be adaptive to traffic status. The dynamic compression control determines appropriate compression waiting time and the compressed packet is transmitted based on two-level queue scheduling. DDTC is verified with simulations and the performance is measured. Keywords: differential service, power consumption, energy efficiency, traffic control, compression control, wireless sensor network, INTRODUCTION Wireless sensor networks (WSNs) are being widely deployed. The main role of WSN is to aggregate data from an area being monitored and to send the data to a specific point known as a sink node. Possible applications of sensor networks are environmental monitoring, surveillance, health-care, home and other commercial products, and so on [, 2]. Some important characteristics of sensor networks are data-centric and application specific [3]. Since sensor nodes often operate in remote locations with limited power, energy is a critical resource and their lifetime is more critical than the network performance, such as data throughput or latency. IEEE [4-6] is a standard for low rate wireless personal area networks (LR-WPAN) and provides physical and media access control (MAC) layers. The IEEE also provides physical and MAC layers for ZigBee [7], which is an open specification for a suite of high level communication protocols using small, low-power radios for WPANs. In this paper, we focus on IEEE and introduce a differential dynamic traffic control (DDTC) scheme to provide energy efficiency and differential service. DDTC is based on beacon enabled mode. The main problem of beacon enabled mode is the fixed superframe structure that provides inefficient transmission in dynamic traffic conditions. Moreover, does not provide differential service according to the traffic type. In this paper, we propose a Differential Dynamic Traffic Control (DDTC) that inte- Received December 24, 2007; revised April 22 & June 2, 2008; accepted August 4, 2008 Communicated by Ten-Hwang Lai. * This research was supported by the MKE (Ministry of Knowledge Economy), Korea, under the 2008 ITRC (Information Technology Research Center) support program supervised by the IITA (Institute of Information Technology Assessment). 255

2 256 grates following schemes. Dynamic active period control determines the appropriate active period to be adaptive traffic status. Dynamic compression control determines appropriate compression waiting time and the packet compression is handled based on two-level queue scheduling. The paper is organized as follows. Section 2 discusses related work and the motivation of this paper. Section 3 presents a mathematical operation analysis of IEEE Section 4 introduces DDTC scheme. In section 5, performance is measured by performing simulations. Finally, conclusions are made in section RELATED WORK IEEE provides the guaranteed time slot (GTS) mechanism for low-latency applications or applications requiring a specific bandwidth. This GTS mechanism has many problems in real environments. GTS will clearly decrease the usable bandwidth for other devices. Simulation results show that GTS is an expensive approach for low data rate applications [8]. Moreover, substantial time synchronization is required since each GTS is assigned to a specific device. To end within the GTS, more delicate hardware is required, which is inappropriate for low cost devices. We consider data compression as an alternative method. In a wireless medium, compressing data before transmitting can reduce the total power consumption of a sensor node because processing data consumes less power than transmitting data. Though the compression schemes have many merits, they generally require more delay, which is improper for priority data. Several compression schemes for WSNs have already been introduced in [9-]. However, it is hard to find compression research activities that integrate quality of service and energy efficiency. Another part we are going to improve in is energy efficient transmission. IEEE provides beacon enabled and non-beacon enabled modes. In beacon enabled mode, a coordinator periodically broadcasts beacons with superframe structure information recorded in the beacon. Other nodes in the network that received the superframe information start to synchronize to the superframe structure suggested by the coordinator. A superframe consists of two parts, an active period and a sleeping period. The lengths of a superframe and the active period are denoted by beacon interval (BI) and superframe duration (SD), which are determined by beacon order (BO) and superframe order (SO), respectively. In non-beacon enabled mode, however, a coordinator broadcasts beacons only when other nodes request beacons for scanning or association purpose. The beacon enabled mode is generally used for power saving, for it saves power consumption by periodically switching between active/sleeping modes. However, a fixed superframe structure cannot handle the traffic optimally and can waste lots of energies. If active period is longer than traffic volume, long idle time will waste large amounts of energy. If the period is too short, many backoff operations and collisions will consume large amounts of energy. Though uses a wireless medium, it is different from other wireless standards by some unique features for WPANs. For example, we can consider the idle sense mechanism [2] which claims that we can obtain the optimal value of the contention window when the cost function is zero. opt opt N e e NP = η( P ) where η = T SLOT /T c ()

3 DIFFERENTIAL DYNAMIC TRAFFIC CONTROL FOR IEEE NETWORKS 257 In this expression, N denotes the number of contending nodes, P e is the attempt probability, T SLOT denotes the slot duration, and T c is the average collision duration. The average collision duration corresponds to the transmission time of a data frame. In this paper, we use an upper value, 33 bytes. We can derive P e by applying parameter values. For example, T SLOT /T c = (250 kbps rate). Finally, the attempt probability P e is decided by the contention window (CW) as shown in [3]: 2 Pe ( CW) = CW +. (2) The resulting contention window sizes for 0, 20, 30, and 40 contending devices are 34, 70, 06, and 42. The results show that the idle sense scheme is not appropriate for since the contention window size is fixed by the superframe duration and long idle waiting times consume substantial power. The traffic adaptive scheme for was introduced in [4], but it is impractical in real networks. In this scheme, the queue slot of the source node that presents the data volume in the queue is used as the traffic information in the coordinator. The coordinator can determine an improper active period since some node may fail in the data transmission and even a single node that has a great deal of data can increase the active period. Though other traffic adaptive MAC protocols such as T-MAC (Timeout MAC) and TRAMA (Traffic-Adaptive Medium Access) have been introduced, the protocols are also improper for real networks due to the complex algorithm and the processing overhead. For examples, an additional control packet or the computations needed to create slot consistency among the nodes. Our approach is different from theirs in that we apply the delivery ratio to measure the traffic status. Even when several packets have failed, the coordinator can measure the traffic reasonably, since every node in the same network will have similar delivery ratio. Besides, we define compression control according to the data type and the traffic conditions. Our compressions control scheme integrates a traffic adaptive MAC scheme to satisfy both energy efficiency and differential service. 3. OPERATION ANALYSIS In this section, we analyze the operation of IEEE The basic power saving technique of is to incorporate a sleeping period in beacon enabled mode. The transmission attempt probability at a discrete time depends on the number of transmissions attempted in a given time period. We show how the transmission attempt probability affects the operation and the power consumption in a fixed active period. The values of the operation variables are described in Tables and Backoff When the active period is given, the transmission attempt probability is the main factor that determines the channel access probability. The probability (P a ) of channel access can be defined as follows.

4 258 a 2( N )( h) e P = ( P ) (3) where P e is the transmission attempt probability of a device for a CCA time, N denotes the total number of sensor devices, and h is the hidden node probability. According to [5], if there are two randomly deployed devices in the range of a coordinator, then the probability that they will have a hidden node problem is about 4%. The CCA analysis is done after performing the backoff operation. We can define an average backoff count (n b ) and an average backoff time (t AB ) as follows. n P P m ip P m m i i b = a( a) + a( a) i= i= (4) BE 2 tab( BE) = tbop (5) 2 where m is the maximum backoff count before declaring a channel access failure, t BOP denotes the backoff period and BE is the variable backoff exponent which determines a specified number of backoff periods to wait before sensing the channel. The total backoff time is obtained by summing the average backoff time and the CCA analysis (t CCA ) time of each attempt. The total backoff time (t B ) and the total backoff energy (E B ) can be modeled as n b B = AB(min( +, )) + ( b b ) i= tab macminbe + nb macmaxbe + nb + tir + tcca t t macminbe i macmaxbe n n (min(, )) ( )( ) (6) E = ( t ( n + )( t + t )) E + ( n + )( t + t ) E (7) B B b IR CCA I b IR CCA CCA where t IR denotes the transient time from idle to receive mode, E I is energy for idle wait and E CCA is energy for CCA. Based on the above equations and information in Tables and 2, we can estimate the backoff time and energy according to the number of contending devices as shown in Figs. and 2. The results show that the energy increases in proportion to the backoff time. Table. Power consumption values. Description MCU Mode Radio Mode Power Energy for Tx (E T ) Active TX 52.2 mw Energy for Rx (E R ) Active RX 56.4 mw Energy for CCA (E CCA ) Active CCA 55.4 mw Energy for Idle Wait (E I ) Active Idle.278 mw Energy for Processing (E P ) Active Idle 2.0 mw Energy for Sleep (E S ) Sleep Sleep 0.06 mw

5 DIFFERENTIAL DYNAMIC TRAFFIC CONTROL FOR IEEE NETWORKS 259 Table 2. Protocol operation variables and values. Transient Time Values Parameters Values Sleep to Idle (t SI ) 0.96 ms BO 5 Idle to Tx (t IT ) 0.86 ms SO 3-5 Idle to Rx (t IR ) 0.86 ms N 20 Rx to Tx (t RT ) 0.22 ms t BOP 0.32 ms Tx to Rx (t TR ) 0.2 ms t CCA 0.2 ms Elapsed Time (msec) Contending Devices = 5 Contending Devices = 0 Contending Devices = 5 Contending Devices = Transmission Attempt Probability Fig.. Backoff time. Power Consumption (mw) Contending Devices = 5 Contending Devices = 0 40 Contending Devices = 5 Contending Devices = Transmission Attempt Probability Fig. 2. Backoff energy. 3.2 Retransmission The retransmission can be performed at maximum amaxframeretries (m_re, 0-7) times. The main reasons of retransmission are hidden node and simultaneous backoff problems. We approximate to the probability (P h ) that two or more devices that are in a hidden node relationship transmit simultaneously and collide as follows: P h Nh i+ Pa i= N = (8) ( h) where N denotes the total number of sensor devices and h is the hidden node probability. Simultaneous backoff is another event that requires retransmission. The probability (P b ) that two or more devices select the same backoff delay can be modeled as n i= i+ Pb = BE 2 (9) where BE is the variable backoff exponent which determines a specified number of backoff periods to wait before sensing the channel. A successful CCA without a hidden node relationship and without a simultaneous backoff are therefore required for a successful transmission. The probability (P s ) of a successful transmission based on the above three factors and the average retransmission count (r) can be modeled as

6 260 P = P ( P )( P ) (0) s a h b r = P ( P ) m_ re+ ip ( P ) m_ re m_ re i i s s s s i= i= () Figs. 3 and 4 show that the retransmission count is inversely proportional to the transmission success probability. Seeing that on the average, retransmission count indicates consumption of multiple transmission time and energy. Therefore, high transmission success ratio is essential for low power consumption. Transmission Success Probability Contending Devices = 5 Contending Devices = 0 Contending Devices = 5 Contending Devices = Transmission Attempt Probability Fig. 3. Transmission success probability. Average Retransmission Count Transmission Attempt Probability Fig. 4. Retransmission count. Contending Devices = 5 Contending Devices = 0 Contending Devices = 5 Contending Devices = OPERATION ANALYSIS In this section, we describe the system model and the algorithm of DDTC scheme. DDTC scheme measures recent traffic status to determine the appropriate superframe duration. DDTC also provides two-level queue scheduling for differential service. 4. System Model We consider a single-hop star topology WSN with a coordinator and N devices. The network works in a beacon enabled mode. DDTC controls the superframe order (SO) that determines the active period between min SO and max SO. We classify the data into two types, priority type and non-priority type. Queue is also classified into two types, transmission queue and compression queue. The transmission queue is a temporary buffer for transmission and the compression queue is used to wait for compression. The length of compression waiting time can be from min w to max w. The compressed data is forwarded to the transmission queue and waits for transmission. Several compression schemes for WSNs have already been introduced in [7-9]. In Fig. 5, DDTC assumes that one of the compression schemes is used for data compression. In DDTC, the following frames are modified. Beacon frame contains the average delivery ratio information its beacon payload. The data frame contains the delivery ratio field in the data payload.

7 DIFFERENTIAL DYNAMIC TRAFFIC CONTROL FOR IEEE NETWORKS 26 Priority Type Transmission Queue Transmit Packet Receive Packet Non-Priority Type Compression Queue Traffic Measuring Module Delivery Ratio Update Compression Waiting Time Update Fig. 5. DDTC system architecture. 4.2 Differential Dynamic Transmission Control (DDTC) Step : Calculate average backoff time. As a method of measuring the traffic, every device calculates the delivery ratio whenever it tries to access the channel. The delivery ratio is delivered to the coordinator through the data frame. This process enables the coordinator to obtain the average delivery ratio of the source devices during the active period. The coordinator calculates the overall average delivery ratio ( s i ) at every active period. s i = τ γ s + ( τ) k k k= i δ k= γ i δ i s (2) where i denodes the ith active period since the initial power-on, δ denotes the number of recent active periods, and τ is a weight factor with respect to time (which is between 0 and ). γ is a value used to differentiate time period. s k is the average delivery ratio for the kth period, which is obtained by the coordinator. Step 2: Determine active period. To determine proper length of active duration, we assume that the coordinator knows b * which is a basis value that assesses the traffic conditions. The coordinator increases or decreases SO proportionally to the traffic by comparing s i with b * and upper/lower thresholds, εh and εl. If s i exceeds the range between b * + εh and b * εl, SO can be changed. At every beacon interval, the coordinator informs devices with s through a beacon frame. Algorithm Dynamic active period control if b * + εh < s and SO < max i SO then SO = SO else if b * εl > s and SO > min i SO then SO = SO + end if i Step 3: Determine compression waiting time. When a device detects or receives a data, the following processes are different according to its data type. If the data is of a priority type, the device forwards the data straight to the transmission queue. Otherwise, it is

8 262 transferred to the compression queue and waits until compression waiting time expires. After the expiration, the maximum number of data that can be packed into a single packet are compressed. Each device calculates the compression waiting time (t w ) as follows: [ x] ± max w ( x > max w) = min w ( x < min w) x otherwise t () t = [ t ( t ) + α(( st ) st ()) + λ( qt () q) + μ( qt () qt ( ))] (4) w w ± where t w (t) is the t w of a device at the tth period. s(t) is the transmission success ratio of the device at the period t, and α denotes a weight value. q is the occupied transmission queue length of the device. λ and μ are positive numbers which reflects the update step size. q * is the desired size of occupied transmission queue which we hope to stabilize the queue length. The compression waiting time is determined by transmission success ratio and transmission queue length. Once the device notices the transmission success ratio has decreased, it will increase t w. The device also increases t w when transmission queue size exceeds the desired level (q * ) or the the queue size increases. Step 4: Compress and forward data. Once the compression waiting time has expired, the device compresses non-priority data in the compression queue and forwards it to the transmission queue. If non-priority data remains in the compression queue, a new timer is initiated. (3) 5. SIMULATION RESULTS AND DISCUSSION 5. Simulation Environments and Scenarios Our simulation is performed under the following assumptions. There are no channel errors and there is no propagation delay. All nodes in a PAN, including the PAN coordinator, are synchronized. Each node has statistically identical traffic sources. The ratio of priority data traffic is about 30% of the total traffic volume. We use a simulation program which is coded in C to evaluate and compare the performance of legacy and the DDTC. The simulation is performed on a star topology which is composed of a coordinator and 20 devices. All devices are located at a distance of 0m from the coordinator. The packets are generated based on a Poisson process and delivered from the device to the coordinator. A Drop-Tail queue is used and the queue length is 50. The sizes of the data frame and the acknowledgement frame are 25 and bytes respectively. At maximum, 0 packets can be compressed into a single packet. There are no retransmissions. The 2.4 GHz frequency band is used and the PAN works in beacon-enabled mode. The simulation time is,000 seconds. The voltage is 3V. To obtain results that are similar to realistic measurements, we refer the measured value of Chipcon CC2420EM/ EB [6] and Microchip PIC8LF8720 [7] nano Watt series. The values of the parameters are shown in Tables and 2. Battery lifetime extension is not set up. We assume that

9 DIFFERENTIAL DYNAMIC TRAFFIC CONTROL FOR IEEE NETWORKS 263 the energy for internal processing consumes 2mW whenever data is received or transmitted. The basis value (b * ) that assesses the traffic conditions is 80 and the upper/lower thresholds are both 0. In Eq. (2), δ is i 0, τ is 0.3 and γ is i 3. The range of compression waiting time (min w, max w ) is 0~03 ms. To simplify the next few pages we will define some abbreviations. In , the cases when SO is 3 and 5 are denoted as SO3 and SO5, while the case when DDTC is applied is denoted as DDTC. 5.2 Performance Measures Fig. 6 shows the delivery ratio which is the number of successful frames in the total number of transmissions. The traffic increase in a fixed active period decreases the delivery ratio due to increased contention among the devices. When traffic increases in the legacy scheme, the results of SO3 shows low and decreasing delivery ratio due to the short active period, and SO5 also shows gradually decreasing delivery ratio. DDTC starts when SO is 3 and increases SO as the traffic increases. DDTC shows lower transmission success ratio than SO5 in the initial phase since the active period control is based on the recent delivery ratio. The period becomes the same as SO5 as traffic increases. However, DDTC shows improved results both in priority and non-priority traffic as the traffic load increases. The gap between priority traffic and non-priority traffic becomes wider due to the compression control. A higher delivery ratio means that transmission occurs with little overheads such as backoff or retransmission, and it is verified both by the throughput in Fig. 7 and by the power consumption in Fig. 8. Delivery Ratio (%) Priority, SO3 Non-Priority, SO3 Priority, SO5 Non-Priority, SO5 Priority, DDTC Non-Priority, DDTC Offered Load (kbps) Fig. 6. Delivery ratio. Throughput (kbps) Priority, SO3 Non-Priority, SO3 Priority, SO5 Non-Priority, SO5 Priority, DDTC Non-Priority, DDTC Offered Load (kbps) Fig. 7. Throughput. Fig. 7 shows the throughput, which is the amount of successfully transmitted data frames during the simulation time. The throughput of SO3 shows that only a small volume of traffic is delivered regardless of traffic conditions due to its short active period. Accordingly, SO3 results in relatively low power consumption in Fig. 5. The throughput of SO5 shows traditional CSMA/CA result. The result does not show a continuous increase when the capacity reaches its limiting line. DDTC shows different results according to the traffic type. The delivery ratio of priority traffic maintains high, so the throughput continuously increases. However, the delivery ratio of non-priority traffic fluctuates

10 264 due to the compression control. Though the throughput of non-priority traffic in DDTC is lower than SO5, the difference can be compensated when the delivered packets are decompressed. Fig. 8 shows total amount of power consumption. SO3 shows the lowest result due to low delivery ratio. The result of SO5 shows, in spite of decreasing delivery ratio, the highest power consumption and a gentle ascent as the traffic increases. It means transmission failures are increasing proportionally according to the traffic load. Dynamic active period control of DDTC results in rapid increase in power consumption during the initial phase. Power consumption increases rapidly due to increased active period. High transmission success ratio of DDTC also results in lower power consumption in compared with SO5. Fig. 9 shows the queueing delay. It shows the time interval from the instant of generating a data frame to the instant of transmitting the frame with CSMA/CA algorithm. The results of SO3 show higher delay than that of SO5. The reason is that the short active period of SO3 results in many backoffs. The only weakness of DDTC is the queueing delay. Although DDTC starts with the same active period size as SO3, the queueing delay differs from SO3 according to the data types. The delay of priority traffic is decreased in a short time, while the delay of non-priority traffic rapidly increases. Power Consumption (mj) Offered Load (kbps) Fig. 8. Power consumption. SO3 SO5 DDTC Queueing Delay (msec) Priority, SO3 Non-Priority, SO3 Priority, SO5 Non-Priority, SO5 Priority, DDTC Non-Priority, DDTC Offered Load (kbps) Fig. 9. Queuing delay Ratio (%), Time (msec) Offered Load (kbps) Delivery Ratio Compression Ratio Compression Waiting Time Fig. 0. Dynamic compression control according to delivery ratio.

11 DIFFERENTIAL DYNAMIC TRAFFIC CONTROL FOR IEEE NETWORKS 265 Fig. 0 shows the reason in detail. The total delivery ratio at the beginning is below 80%. It is recovered over 90% under DDTC s control. DDTC releases the control when the delivery ratio is high. However, the compression waiting time and the compression rate increase as the traffic increases. 6. CONCLUSION IEEE does not accommodate to burst traffic effectively, due to lack of traffic information. It also does not provide differential service for priority data. Although GTS mechanism is provided for applications requiring specific bandwidth or low latency, it is a very expensive approach and applicable only under limited conditions. We proposed a power-saving scheme in by enhancing the transmission efficiency in dynamic traffic conditions. The simulation shows that DDTC provides a higher delivery ratio and differential service according to data types. DDTC consequently shows better performance in power consumption and priority traffic processing. Instead, an increase in queuing delay of non-priority traffic is inevitable and it is the only defect of DDTC. In the future, we will work on simulations with elaborate and diverse scenarios, and will develop DDTC for multihop connections. REFERENCES. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, Wireless sensor networks: a survey, Computer Network, Vol. 38, 2002, pp D. Puccinelli and M. Haenggi, Wireless sensor networks: applications and challenges of ubiquitous sensing, IEEE Circuits and Systems Magazine, Vol. 5, 2005, pp D. Estrin, R. Govindan, J. Heidemann, and S. Kumar, Next century challenges: scalable coordination in sensor networks, in Proceedings of the 5th Annual ACM/IEEE International Conference on Mobile Computing and Networking, 999, pp IEEE Computer Society, IEEE Std , Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (WPANs), LAN/MAN Standards Committee, IEEE Press, N. F. Timmons and W. G. Scanlon, Analysis of the performance of IEEE for medical sensor body area networking, in Proceedings of the st IEEE International Conference on Sensor and Ad Hoc Communications and Networks, 2004, pp J. S. Lee, Performance evaluation of IEEE for low-rate wireless personal area networks, IEEE Transactions on Consumer Electronics, Vol. 52, 2006, pp ZigBee Alliance, ZigBee Specification Document r3, ZigBee Alliance, J. Zheng and M. J. Lee, A comprehensive performance study of IEEE , Sensor Network Operations, IEEE Press, Wiley Interscience, 2004, pp D. Petrovic, R. C. Shah, K. Ramchandran, and J. Rabaey, Data funneling: routing with aggregation and compression for wireless sensor networks, in Proceedings of the st IEEE International Workshop on Sensor Network Protocols and Applications, 2003, pp T. Arici, B. Gedik, Y. Altunbasak, and L. Liu, PINCO: A pipelined in-network

12 266 compression scheme for data collection in wireless sensor networks, in Proceedings of the 2th International Conference on Computer Communications and Networks, 2003, pp S. S. Pradhan, J. Kusuma, and K. Ramchandran, Distributed compression in a dense microsensor network, IEEE Signal Processing Magazine, Vol. 9, 2002, pp M. Heusse, F. Rousseau, R. Guillier, and A. Duda, Idle sense: An optimal access method for high throughput and fairness in rate diverse wireless LANs, in Proceedings of Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, 2005, pp G. Bianchi, Performance analysis of the IEEE 802. distributed coordinated function, IEEE Journal on Selected Areas in Communications, Vol. 8, 2000, pp Y. Kwon and Y. Chae, Traffic adaptive IEEE MAC for wireless sensor networks, Lecture Nodes in Computer Science, Vol. 4096, 2006, pp Y. C. Tseng, S. Y. Ni, and E. Y. Shih, Adaptive approaches to relieving broadcast storms in a wireless multihop mobile ad hoc network, IEEE Transactions on Computers, Vol. 52, 2003, pp Data Sheet for CC GHz IEEE /ZigBee-ready RF Transceiver, http: //focus.ti.com/lit/ds/symlink/cc2420.pdf. 7. Data Sheet for PIC8LF8720 Microcontroller, /en/devicedoc/39609b.pdf. Wook Kim received the B.S. degree in Computer Science from Soonchunhyang University, Korea, in 993 and M.S. degree in Information Communication from Sogang University, Korea, in 200. Currently he is working for Samsung Electronics and towards the Ph.D. degree on Electronic and Computer Engineering in Korea University, Korea. His research interests include the QoS-aware systems and routing protocol in mobile ad hoc/sensor networks and wireless network architecture for future Internet. Sun-Shin An received the B.S. degree from Seoul National University, Korea in 973, and the M.S. degree in Electrical Engineering from KAIST (Korea Advanced Institute of Science and Technology), Korea in 975 and the Ph.D. degree in Electric and Information from ENSEEIHT, France in 979. He joined the faculty of Korea University in 982, where he is currently a Professor of Electronic and Computer Engineering. Prior to joining Korea University, he was Assistant Professor of Electronic Engineering in Ajou University, Suwon, Korea. He was with NIST (National Institute of Standards and Technology) in U.S.A., as a visiting scientist in 99. His research interests include the distributed system, communication networks and protocols, information network, intelligent network, multimedia communication system, wireless sensor network and mobile RFID network.