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Optimal Radio Resource Allocation for Mobile Task Offloading in Cellular Networks Yang Cao, Tao Jiang, and Chonggang Wang Abstract

The increasing capabilities of mobile devices (MDs) enable users to run desktoplevel applications and tasks anywhere. However, the local execution of resourcedemanding mobile tasks, such as real-time rendering in 3D gaming, may result in poor performance and short battery lifetime on MDs. One solution to the resource scarcity problem on MDs is to offload resource-demanding mobile tasks to surrogates, which are remote servers with stronger capabilities. When multiple MDs in the same cell attempt to offload mobile tasks to surrogates through a cellular network, the communication latencies for data transmissions may be high due to limited radio resources. To improve the offloading performance of mobile tasks, the limited radio resources should be carefully scheduled to reduce the communication latencies of data transmissions. As motivated, we pose the optimal radio resource allocation problem for the mobile task offloading in cellular networks, and solve the problem. Moreover, extensive numerical results show the performance comparison of the optimal radio resource allocation plan and two baseline plans.

M

obile devices (MDs), for example, smart phones and tablets, are becoming the major computing workspaces for individuals thanks to their increasing capabilities. Users expect to run desktop-level applications (e.g. document processing, media playback, and 3D gaming) on MDs anywhere. Specifically, a mobile application can consist of multiple tasks, which are termed mobile tasks. For instance, real-time rendering is a representative mobile task for 3D gaming on the MD. Although the capabilities of MDs keep increasing, executing resource-demanding mobile tasks on MDs would result in poor performance and short battery lifetime. A recent example is that the rendering capability requirement (in pixels per second) of mainstream desktop 3D gaming in 2012 is more than four times the rendering capability of the most powerful MDs in the same year, such as iPhone 5 [1]. One solution to the resource scarcity problem is to enable MDs to offload resource-demanding mobile tasks to surrogates, which are remote servers with stronger capabilities, through wireless networks [2]. As a result, the local resources on MDs can be reserved, and energy consumption can be reduced. Moreover, the emerging cloud computing technique can provide powerful Internet-based surrogates to help MDs [3]. To offload the mobile task from a MD to an Internet-

Yang Cao and Tao Jiang are with the Department of Electronics and Information Engineering, Huazhong University of Science and Technology. Chonggang Wang is with the InterDigital Communications.

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0890-8044/14/$25.00 © 2014 IEEE

based surrogate, related data would be transferred between the MD and the surrogate through multiple layers of wired and wireless networks. For example, state-of-the-art cellular networks, such as long term evolution (LTE) systems [4], could serve as access networks for MDs to effectively offload mobile tasks. The process of mobile task offloading usually has a deadline to make MD users feel like running applications locally on their MDs. However, when multiple MDs in a cell offload mobile tasks, how to complete the offloading process before deadlines becomes a challenging issue. Specifically, the radio resources in a cell for data transmissions could be insufficient when a number of MDs offload mobile tasks, which would result in large communication latencies and degrade the MD user experience on the mobile task offloading significantly. As a result, it is crucial to effectively allocate radio resources to the data transmissions of the mobile task offloading from multiple MDs. Moreover, heterogenous requirements for mobile task offloading can be utilized to improve performance. Here, heterogenous requirements mean that the amount of data to be transferred between the MD and the surrogate, the surrogate response time, and the deadline of the mobile task offloading, could be different for different MDs. As motivated, we pose the radio resource allocation problem for the case that multiple MDs request mobile task offloading in a cell. Specifically, the purpose of radio resource allocation is to minimize the overall execution time for offloaded mobile tasks from multiple MDs, while satisfying the execution deadline for each mobile task. Moreover, the optimal radio resource allocation plan should take into account heterogenous requirements of the mobile task

IEEE Network • September/October 2014


Symbol

Description

offloading. The performance of mobile task offloading in a cell could be improved with the optimal radio resource allocation plan when compared with the plans obtained by two baseline algorithms, that is, the round robin algorithm [5] and the EDF (Earliest Deadline First) algorithm [6]. We could observe that both round robin and EDF may not utilize the complete information of mobile task offloading. In summary, we present the optimal radio resource allocation problem for offloaded mobile tasks from multiple MDs, which is crucial for improving the experience of mobile users. However, it has not been well investigated by existing literature. We first verify the existence of the feasible radio resource allocation plan, then the optimal plan can be obtained through an adjustment method. Extensive numerical results confirm the performance improvement of mobile task offloading with the optimal plan. In the following section we introduce the background of radio resource allocation and mobile task offloading. The mobile task offloading system for cellular networks is then presented. Then we formulate and solve the optimal radio resource allocation problem for the mobile task offloading. In the next section we present numerical results showing the performance comparison of the optimal radio resource allocation plan and two baseline plans. Conclusions and future directions are given in the final section.

Dup

Size (bytes) of data to be uploaded to the surrogate

Ddown

Size (bytes) of data to be downloaded from the surrogate

tup

Length (slots) of the data uploading phase

tsurrogate

Length (slots) of the surrogate response phase

tdown

Length (slots) of the data downloading phase

texecution

Execution time (slots) of an offloaded mobile task

tthreshold

Preset threshold for texecution

trequest

The instant that the offloading request arrives at the scheduler

toffloading

The instant that the offloading frame begins

tdelay

The delay (slots) of the request processing

Q

Offloading request queue

M

Number of offloading requests in a group

Radio Resource Allocation and Mobile Task Offloading

K

Number of slots in an offloading frame

nup

Required number of RBs to upload the offloaded data (uplink RBs)

ndown

Required number of RBs to download the output data (downlink RBs)

Ψ

Set of uplink RB indexes

Φ

Set of downlink RB indexes

Radio Resource Allocation in Cellular Networks Recently, radio resource allocation in cellular networks, especially networks based on OFDMA (Orthogonal FrequencyDivision Multiple Access), has been widely studied. In [7] uplink resource allocation was investigated for OFDMAbased cellular networks to maximize the sum utility of multiple MDs. Radio resource allocation algorithms were developed in [8] to maximize the weighted throughput in uplink LTE systems. H. Zhu et al. [9] proposed a low-complexity chunk-based resource allocation scheme for OFDMAbased cellular networks, which maximizes the network throughput under a total transmit power constraint. In [4] the resource allocation schemes for the downlink LTE systems take into account the quality of service requirements for video delivery. Similar to [4], our study focuses on radio resource allocation in cellular networks for a specific application, that is, mobile task offloading.

Mobile Task Offloading During the past decade some notable scheduling schemes have been proposed to improve the performance of mobile task offloading. In [10] X. Gu et al. proposed a method to reduce the execution time of a mobile task by proper task partition while taking into account the MD memory constraint. In [11] S. Ou et al. evaluated the offloading performance for a MD in mobile environments, with the consideration of unreachable servers due to the mobility of the MD. In [12] a task offloading system was proposed to support an efficient deployment of mobile applications for a MD. K. Yang et al. [13] proposed a routing scheme for a single-MD task offloading system in ad hoc network environments. D. Huang et al. [14] proposed an adaptive offloading algorithm that offloads a mobile task to a dedicated server based on mobile environments. With the adaptive algorithm, more energy can be saved when the execution time constraint is satisfied. By contrast, we study radio resource allocation to the mobile task offloading for multiple MDs in cellular networks.


Table 1. Notations used in this article.

A Mobile Task Offloading System for Cellular Networks As illustrated in Fig. 1, a cell serves multiple MDs in a cellular network. Similar to video content delivery, data transmissions for the mobile task offloading can be treated as an important application in the cellular network [4]. Thus, it is critical to employ features in cellular standards to optimize the performance of the mobile task offloading in the cellular environment. To this end, we assume there is a scheduler at the base station to allocate radio resources to the data transmissions of mobile task offloading. Specifically, we consider the case in which the scheduler manages a spectrum band, which can be divided into multiple orthogonal subchannels in the frequency. Further, the time can be divided into a number of time slots, and a combination of a time slot and a subchannel could be termed a resource block (RB), that is, the minimum unit of the radio resource allocation. Since there is no intra-cell spectrum sharing, one RB can only be allocated to the uplink or downlink data transmissions of one mobile task. With the purpose of shortening the application response time and cutting down the MD energy consumption, we only consider the computing resource-demanding mobile tasks (e.g. real-time rendering) that are suitable for being offloaded to surrogate. Clearly, there are mobile tasks (e.g. I/O interface)

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mobile task offloading. Moreover, the length of an offloading frame equals the overall execution time Core network of all offloaded mobile tasks in a group. Internet In what follows we summarize the performance metrics of mobile task offloading. First, to ensure Task 1 the user experience, the execution time of each MD mobile task is required to be shorter than a preset threshold tthreshold. Mobile task offloading is unsatisSurrogate factory if the actual execution time is longer than Task 2 tthreshold. Moreover, the scheduler expects to minimize the overall execution time of all mobile tasks Base station in a group, that is, the length of the offloading frame, while satisfying the time threshold for exeGateway Group cuting each mobile task. When the overall execuCell tion time is shortened, the length of the offloading frame becomes shorter, thus more mobile tasks can be handled over a subchannel during a period of Figure 1. A system overview of the mobile task offloading. time. Notice that the mobile task can be viewed as a job with a deadline in the processor scheduling problem. However, in the general processor scheduling problem, a job usually has a single phase, while a that require much data transmissions, but few computations, mobile task consists of three phases, and the first phase (data which are not suitable for being offloaded. We assume that uploading) and the last phase (data downloading) are at least the network bandwidth between the base station and each surt surrogate apart. As a result, we propose a specific scheduling rogate is sufficient, thus the communication latency between a MD and a surrogate is dominated by the communication method for mobile task offloading, which is given in the follatency between a MD and the base station. Moreover, for a lowing section. surrogate, the computing capability allocated to one mobile task is limited, thus the surrogate response time is not negligible. Before the task offloading process, the MD should request Radio Resource Allocation for Mobile Task a connection to the surrogate that supports mobile task Offloading offloading. Then each MD sends a mobile task offloading request to the base station (scheduler) over a control chanAs mentioned in the previous section, the scheduler assigns an nel, and each task offloading request contains task metadata, offloading frame to a group of mobile tasks. Denote trequest as that is, the information of task offloading requirements. As the instant the offloading request of a mobile task arrives at illustrated in the top part of Fig. 2, a subchannel can be the scheduler, and denote toffloading as the instant the offloaddivided into offloading frames in time. At the beginning of an ing frame assigned to this mobile task begins. We have tdelay = offloading frame, the scheduler assigns this offloading frame toffloading – trequest as the delay of the request processing. For to a number of mobile tasks (as a group illustrated in Fig. 1), the sake of improving efficiency, t delay is required to be as and decides the RB allocation plan to support the data transmissions of the mobile task offloading from the MDs in Decision making the group. When the decision making at the beginning of an offloadOverall execution time ing frame is completed, the task offloading process of each MD in a group begins. Specifically, the task offloading process of a MD is illustrated in the bottom part of Fig. 2, which conSubchannel sists of three phases: 1) Data uploading: When the mobile task offloading starts, Offloading Offloading ... Offloading ... the MD uploads Dup bytes of related data (e.g. tables or figframe frame frame ures to be added to an online document) to the surrogate via Time the base station over the allocated RBs. Data uploading is completed when the data transmission from the MD to the Downlink RB Released RB Uplink RB surrogate is finished. The length of the phase is tup slots. 2) Surrogate response: In this phase, the uploaded data is Base station processed by the surrogate, then the output data (e.g. online document editing result) is ready to be sent to the MD. The 1) Data uploading length of the phase is tsurrogate slots, which may be affected by the processing capability of the surrogate and the priority of the mobile task. Mobile Surrogate device 3) Data downloading: When the surrogate response phase ends, the data downloading phase begins. Specifically, the surrogate sends D down bytes of output data to the MD via the 2) Surrogate response base station over the allocated RBs. Data downloading is completed when the output data transmission is finished. The 3) Data downloading length of the phase is tdown slots. Obviously, the execution time of an offloaded mobile task Figure 2. An illustration of the process of the mobile task is t execution = t up + t surrogate + t down. Since we are consider a offloading. low-mobility scenario, the MD stays in the same cell during

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Task 1

Round robin

small as possible, and the mobile tasks that are batched to the same group are EDF required to have similar values of tdelay. In Task 2 what follows we give an explicit method to form groups of mobile tasks. Suppose that the scheduler manages an offloading Optimal Task metadata request queue Q. At the beginning of each offloading frame, the scheduler fetches M successive requests that are Uplink RB for task 1 RB allocation plans made around the same time from the head of Q, then the mobile tasks associatUplink RB for task 2 ed with these requests form a group, Execution deadline of task 1 Downlink RB for task 1 which will be served during the offloading frame. Clearly, M ≥ 0, and Q is empty Execution deadline of task 2 Downlink RB for task 2 when M = 0. In this article we focus on RB allocation for a group of mobile tasks Unsatisfactory Surrogate response time during an offloading frame, with the assumption that group formation and Figure 3. An example of RB allocation plans for two offloaded tasks. offloading frame assignment are determined. Each mobile task is associated with a task Obtaining an Optimal Plan metadata {Dup, Ddown, tsurrogate, tthreshold}, which is included in the offloading request. Obviously, the size of the task metadaBefore solving the RB allocation problem, we need to first ta is quite small, thus the resulting overhead to be delivered solve the problem that whether feasible RB allocation plans over the control channel is limited. Considering spatial diverexist or not. The authors in [15] provide a method to search sity, the achievable data rate of uplink or downlink data transfeasible usage time allocation plans for mobile task offloading missions for different MDs over the same RB may be in a time-division multiple access based network, which can be different. Since the scheduler can estimate the average chanutilized to solve our RB allocation problem. First, M mobile nel quality of the link between the base station and each MD tasks in a group can form a number of orders, for example, 1, during an offloading frame, the scheduler can obtain the 2, …, M, and the number of possible orders equals the factorirequired number of RBs to upload the offloaded data, and al of M. Then, the scheduler searches feasible RB allocation the required number of RBs to download the output data for plans for all orders. For an order, successive unused RBs are each MD, which are denoted by nup and ndown, respectively. first allocated to the downlink data transmissions of each mobile task according to the order, from the RB with index Moreover, tthreshold needs to be updated to take into account tthreshold. Then the remaining RBs are allocated to the uplink tdelay. data transmission of each mobile task. For the case that there Optimization Problem is at least one feasible RB allocation plan, the scheduler can obtain the optimal RB allocation plan from available feasible The scheduler allocates RBs for data transmissions of M ≥ 1 RB allocation plans through the adjustment method [15]. The offloaded mobile tasks in a group. We assume that the offloadadjustment method can adjust a feasible RB allocation plan to ing frame contains a number of RBs from RB 1 to RB K, and a candidate optimal plan by searching all RB allocation orders the allocation plan of RB k is denoted by pk. Specifically, |pk| for the uplink data transmissions of M mobile tasks, and the equals the index of a mobile task if the RB is allocated to this number of all possible orders equals the factorial of M. Finalmobile task, where |·| denotes the absolute value. As illusly, the optimal RB allocation plan can be obtained from cantrated in Fig. 2, pk is positive when the RB is used for uplink didate optimal plans. The computational complexity of data transmission (termed an uplink RB), and is negative obtaining the optimal RB allocation plan factorially grows when the RB is used for downlink data transmission (termed with M, which is acceptable when M is not large, for example a downlink RB). pk equals zero when the RB is not allocated when M ≤ 5. to any mobile task, which is termed a released RB. A released Here we use an example to show how the optimal RB alloRB may be allocated to support other services in this cell. cation plan works and the comparison with plans obtained by Given task metadata for all mobile tasks in a group, the two baseline algorithms, namely, the Round Robin Algorithm scheduler decides RB allocation plan pk, k ∈ {1, 2, …, K}, to and the EDF Algorithm. As illustrated in Fig. 1, task 1 and minimize the overall execution time, that is, K. Further, K task 2 are expected to be offloaded from two MDs in a group, equals the maximum value of t execution for a mobile task group. where task 1 has a later execution deadline than that of task 2. Denote Ψ and Φ as the sets of uplink and downlink RB Fig. 3 shows more detailed task metadata of the two mobile indexes for a mobile task, respectively. The largest element tasks. Generally, the round robin algorithm could ensure fairand the smallest element in a set are denoted by sup(·) and ness. As a result it allocates RBs to the data transmissions of inf(·), respectively. We have t up = sup(Ψ), and t down = each mobile task one by one. The EDF algorithm is based on sup(Φ) – inf(Φ) +1. For each mobile task, there are some the deadline-driven rule, which first allocates RBs to the constraints: mobile task with the earliest deadline. We can observe that • t execution ≤ t threshold. when the round robin algorithm is adopted, the RB is allocat• nup and ndown equal the numbers of elements in Ψ and Φ, ed to the data transmissions of two mobile tasks by turn withrespectively. out the awareness of the execution deadlines. Thus, the • t surrogate ≤ inf(Φ) – sup(Ψ) – 1. performance can be poor since both mobile tasks are unsatisClearly, the above RB allocation problem is an integer profactory. When the EDF algorithm is adopted, the RB would gramming problem. The optimal RB allocation plan can be be allocated to the data transmissions of task 2 first since it obtained through the methods proposed in [15], which are has an earlier execution deadline. However, task 1 is unsatisintroduced in the following subsection.


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Optimal

EDF

450

Round robin

Optimal EDF Round robin

100 90

400 Overall execution time (ms)

Satisfactory ratio (%)

80 70 60 50 40 30

350

300

250

20 200 10 0 0

Range 1

Range 2 Range 3 Time threshold

Range 4

150

2 tasks

3 tasks

4 tasks

5 tasks

Figure 4. Satisfactory ratio versus time threshold range by using different RB allocation plans.

Figure 5. Overall execution time versus the number of mobile tasks in a group by using different RB allocation plans.

factory. By contrast, the optimal RB allocation plan not only takes into account execution deadlines, but also considers the amount of data and the surrogate response time, which improves the performance of mobile task offloading for multiple MDs.

different RB allocation plans are depicted in Fig. 5. When there are five mobile tasks, the overall execution time with the optimal RB allocation plan is 15.0 percent shorter than the execution time with the plan obtained by the EDF algorithm, and 37.5 percent shorter than that with the plan obtained by the Round Robin Algorithm. In summary, the optimal RB allocation plan offers an upper bound of the performance in the satisfactory ratio and the overall execution time of mobile tasks. Moreover, the EDF algorithm can be a low-complexity solution when the time thresholds are relatively loose.

Numerical Results In this section extensive numerical results show the performance comparison of mobile task offloading in the cellular network, with different RB allocation plans. First, the task metadata for each mobile task in a group is randomly generated. Moreover, we ensure that there is at least one feasible RB allocation plan to satisfy all time thresholds for the execution time of mobile tasks in a group. Next, we compare the performance of the optimal RB allocation plan with the plans obtained by the Round Robin Algorithm and the EDF Algorithm in two scenarios. The estimated number of RBs that are required to complete uplink or downlink data transmission is uniformly generated from 10 to 50. We set the length of a slot to 1 ms, and the estimated time for the surrogate response phase is uniformly generated from 20 ms to 100 ms. Performance metrics are measured and averaged from repetitive experiments with random task metadata. In the first scenario the time threshold of executing mobile tasks are uniformly generated for four increasing ranges: range 1 is from 100 ms to 150 ms; range 2 is from 150 ms to 200 ms; range 3 is from 200 ms to 250 ms; range 4 is from 250 ms to 300 ms. Notice that the three generated mobile tasks in a group have at least one feasible RB allocation plan, while time thresholds are satisfied simultaneously. Obviously, by using the Round Robin Algorithm or the EDF Algorithm, the actual execution time of each mobile task may exceed the time threshold. We concern ourselves with the satisfactory ratio, which is the ratio of the number of satisfactory tasks to the number of all tasks during the period of interest. Fig. 4 shows the satisfactory ratio versus time threshold range by using different RB allocation plans. When the time threshold range becomes looser, the satisfactory ratios obtained by using the plans obtained by the Round Robin Algorithm and the EDF Algorithm increase. By contrast, the satisfactory ratios obtained by using the optimal plan equal 100 percent for all time threshold ranges. In the second scenario, the time thresholds are uniformly generated from 200 ms to 300 ms. The number of mobile tasks in a group varies from two to five. The curves of the overall execution time versus the number of mobile tasks with

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Conclusions and Future Directions In this article we first introduced the background of mobile task offloading, and presented the motivation for considering radio resource allocation for mobile task offloading. Then the optimal radio resource allocation problem was posed and solved. Extensive numerical results showed the upper bound of performances of mobile task offloading by using the optimal radio resource allocation plan. Possible future directions could be: • Optimal radio resource allocation for multiple cells with the consideration of high MD mobility and inter-cell interference. • Optimization of surrogate selection and computing resource allocation in the Internet datacenters. • MD energy consumption minimization by using mobile task offloading.

Acknowledgment This work was supported by the Major State Basic Research Development Program of China (973 Program) with Grant number 2013CB329006, the Joint Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) and Research Grants Council Earmarked Research Grants (RGC ERG) with number 20130142140002, and the project sponsored by SRF for ROCS, SEM, the National & Major Project with Grant 2012ZX030030042.

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[4] H. Luo et al., “Quality-Driven Cross-Layer Optimized Video Delivery over LTE,” IEEE Commun. Mag., vol. 48, no. 2, 2010, pp. 102–09. [5] M. Sakata, S. Noguchi, and J. Oizumi, “An Analysis of the M/G/1 Queue Under Round-Robin Scheduling,” Operations Research , vol. 19, no. 2, 1971, pp. 371–85. [6] N. Doulamis et al., “Fair Scheduling Algorithms in Grids,” IEEE Trans. Parallel Distrib. Syst., vol. 18, no. 11, 2007, pp. 1630–48. [7] J. Huang et al ., “Joint Scheduling and Resource Allocation in Uplink OFDM Systems for Broadband Wireless Access Networks,” IEEE JSAC , vol. 27, no. 2, 2009, pp. 226–34. [8] J. Fan et al., “Joint User Pairing and Resource Allocation for LTE Uplink Transmission,” IEEE Trans. Wireless Commun., vol. 11, no. 8, 2012, pp. 2838–47. [9] H. Zhu and J. Wang, “Chunk-Based Resource Allocation in OFDMA Systems — Part II: Joint Chunk, Power and Bit Allocation,” IEEE Trans. Commun., vol. 60, no. 2, 2012, pp. 499–509. [10] X. Gu et al., “Adaptive Offloading for Pervasive Computing,” IEEE Pervasive Comput., vol. 3, no. 3, 2004, pp. 66–73. [11] S. Ou et al., “Performance Analysis of Fault-Tolerant Offloading Systems for Pervasive Services in Mobile Wireless Environments,” Proc. IEEE ICC, 2008. [12] M. Kristensen, “Scavenger: Transparent Development of Efficient Cyber Foraging Applications,” Proc. IEEE PerCom, 2010. [13] K. Yang, S. Ou, and H. Chen, “On Effective Offloading Services for Resource-Constrained Mobile Devices Running Heavier Mobile Internet Applications,” IEEE Commun. Mag., vol. 46, no. 1, 2008, pp. 56–63. [14] D. Huang, P. Wang, and D. Niyato, “A Dynamic Offloading Algorithm for Mobile Computing,” IEEE Trans. Wireless Commun., vol. 11, no. 6, 2012, pp. 1991–95. [15] Y. Cao et al., “Performance Optimization for Cyber Foraging Network via Dynamic Spectrum Allocation,” Proc. IEEE INFOCOM Cloud Computing Wksp., 2011.

Biographies Y ANG C AO ([email protected]) is currently an assistant professor in the Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, P. R. China. He received the Ph.D. degree and B.S. degree in information and communication engineering at Huazhong University of Science and Technology, Wuhan, P. R. China in 2014 and 2009, respectively. His research interests include resource allocation for cellular device-to-device communications and smart grids. TAO JIANG [M’06, SM’10] ([email protected]) is currently a full professor in the Department of Electronics and Information Engineering, Huazhong University


of Science and Technology, Wuhan, P. R. China. He received the B.S. and M.S. degrees in applied geophysics from China University of Geosciences, Wuhan, P. R. China, in 1997 and 2000, respectively, and the Ph.D. degree in information and communication engineering from Huazhong University of Science and Technology, Wuhan, P. R. China, in April 2004. From Aug. 2004 to Dec. 2007 he worked at universities that included Brunel University in the U.K. and the University of Michigan in the U.S. He has authored or co-authored over 100 technical papers in major journals and conferences and five books/chapters in the areas of communications. His current research interests include the areas of wireless communications and corresponding signal processing, especially for cognitive wireless access, vehicular technology, OFDM, UWB, and MIMO, cooperative networks, smart grid, and wireless sensor networks. He served or is serving as a symposium technical program committee member of many major IEEE conferences, including INFOCOM, ICC, and GLOBECOM. He was invited to serve as TPC Symposium Chair for the IEEE GLOBECOM’13 and IEEE WCNC’13, and as a general co-chair for the workshop of M2M Communications and Networking in conjunction with IEEE INFOCOM’11. He served or is serving as associate editor of technical journals in communications, including in IEEE Communications Surveys and Tutorials and IEEE Transactions on Vehicular Technology, and others. He served as guest editor of IEEE Communications Surveys and Tutorials for the special issue on Energy and Smart Grid. He is a recipient of the Best Paper Awards at IEEE CHINACOM’09 and WCSP’09. He is member of IEEE Communication Society, IEEE Vehicular Technology Society, IEEE Broadcasting Society, IEEE Signal Processing Society, and IEEE Circuits and Systems Society. CHONGGANG WANG [SM’09] ([email protected]) received the Ph.D. degree from Beijing University of Posts and Telecommunications (BUPT), China in 2002. He is a senior research staff with InterDigital Communications, focusing on Machine-to-Machine (M2M) communications and Internet of Things (IoT) R&D activities, including technology development and standardization. Before joining InterDigital in 2009 he had conducted various research with NEC Laboratories America, AT&T Labs Research, the University of Arkansas, and Hong Kong University of Science and Technology. He (co-)authored more than 100 journal/conference articles and book chapters. He is on the editorial board of several journals, including IEEE Communications Magazine and IEEE Transactions on Network and Service Management. He was/is co-organizing several special issues for IEEE Network, IEEE Communications Magazine, and IEEE Communications Surveys and Tutorials, among others. He received the Outstanding Leadership Award from IEEE GLOBECOM 2010 and InterDigital’s 2012 Innovation Award. He is the vice-chair of the IEEE ComSoc Multimedia Technical Committee (MMTC).

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