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VPEF: A Simple and Effective Incentive Mechanism in Community-Based Autonomous Networks Yufeng Wang, Athanasios V. Vasilakos, and Jianhua Ma, Member, IEEE
Abstract—This paper focuses on incentivizing cooperative behavior in community-based autonomous networking environments (like mobile social networks, etc.), in which through dynamically forming virtual and/or physical communities, users voluntarily participate in and contribute resources (or provide services) to the community while consuming. Specifically, we proposed a simple but effective EGT (Evolutionary Game Theory)based mechanism, VPEF (Voluntary Principle and round-based Entry Fee), to drive the networking environment into cooperative. VPEF builds incentive mechanism as two simple system rules: The first is VP meaning that all behaviors are voluntarily conducted by users: Users voluntarily participate (after paying round-based entry fee), voluntarily contribute resource, and voluntarily punish other defectors (incurring extra cost to those so-called punishers); The second is EF meaning that an arbitrarily small round-based entry fee is set for each user who wants to participate in the community. We presented a generic analytical framework of evolutionary dynamics to model VPEF scheme, and theoretically proved that VPEF scheme’s efficiency loss defined as the ratio of system time, in which no users will provide resource, is 4/(8 + M ). M is the number of users in community-based collaborative system. Finally, the simulated results using content availability as an example verified our theoretical analysis. Index Terms—Community-based autonomous networks, evolutionary game theory, incentive mechanism, voluntary principle, entry fee.
I. I NTRODUCTION
R
ECENTLY, there has been great development of community-based autonomous networks and systems, which are generally composed of a cluster of technologies and principles that allow large or small groups of people-even if they are strangers-to act in a coherent and coordinated fashion. As defined in [1], a network community is a group of people whose communication and collaboration over underlying networking infrastructure strengthens and facilitates their shared identity and goals. The emergence of network communities is a striking example of what might be called grassroots technology development.
Manuscript received February 21, 2014; revised January 12, 2015; accepted January 27, 2015. Date of publication February 2, 2015; date of current version March 17, 2015. This research is sponsored by NSFC Grant 61171092, JiangSu 973 Program BK2011027, JiangSu Educational Bureau Project 14KJA510004, and Prospective Research Project on Future Networks (JiangSu Future Networks Innovation Institute). The associate editor coordinating the review of this paper and approving it for publication was Y. Diao. Y. Wang is with Nanjing University of Posts and Telecommunications, Nanjing 10003, China (e-mail:
[email protected]). A. V. Vasilakos is with Luleå University of Technology, Computer Science department, 971 87 Luleå, Sweden (e-mail:
[email protected]). J. Ma is with Hosei University, Tokyo 102-8160, Japan (e-mail: jianhua@ hosei.ac.jp). Digital Object Identifier 10.1109/TNSM.2015.2397883
Specifically, through dynamically forming virtual and/or physical communities/groups, users can collaboratively contribute to (while consume) services which are composed by the application-specific resources voluntarily provisioned by participants. Actually, there exist several community-based collaborative applications in both academic and industrial fields. For example, given the new emerging technology developments such as Wi-Fi Direct, a Wi-Fi Direct P2P (Peer-to-Peer) group can be easily and dynamically built, which is a set of devices that are connected together via Wi-Fi Direct to form an adhoc network (details of group formation is given in [2]). Based on Wi-Fi Direct specification, [3] outlined a new approach for fully decentralized mobile P2P, in which group formation is explicitly placed in the core layer, and the incentive mechanism/ fairness is located in the service layer (over the core layer). That is, incentive mechanism would be designed as an independent component in the service layer, which makes use of the fundamental functions provided by the core layer, and offers an interface to the various applications on top of it. We argue that most community-based applications are characterized by the feature of “contributing while consuming”: A user is contributing to the system as a whole while consuming resources. That is, by pooling computing resources (storage, CPU cycles, file, user intention, expert knowledge etc.), participants can significantly increase their value thanks to gains from statistical multiplexing and increases in resource diversity. Intrinsically, the locus of control in creation and configuration of resources in community-based services has been shifting to the grassroots: Stemming from people, relying on people and satisfying people. The existence and prosperity of those applications will depend on achieving a critical mass of users, who share their application-specific resources. In the simplest case, each participant acquires and contributes exactly the same amount of resource. Even for this extremely simple case, stimulating participants to voluntarily provide resource is still a very challenging task. Intuitively, each participant would prefer to “free ride” on the contribution of other participants by consuming available resources and services without contributing anything, and thus avoid the corresponding costs, especially in the dynamic environment without any central entity which can effectively enforce monitoring and accounting. For example, freeriding behavior was observed in the traditional P2P applications [3].
A. Problem Statement Lack of cooperation is one of the key problems that confronts the today’s community-based collaborative applications. As
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an incarnation of one of fundamental principles in distributed and autonomous networks (“accommodate participants’ rational behaviors, and design for participants’ choices” proposed by [4] which has received SIGCOMM’s Test of Time award at the 2012 SIGCOMM meeting), appropriate incentive mechanism easy to be enforced should play a crucial role in encouraging cooperation among independent and rational users, and driving the community-based networking environment into cooperative. Roughly speaking, the concept of communities is different from the notion of groups as commonly defined in distributed systems. A community is an open set of entities. Membership is not controlled by the manager of the community (which might be one particular member or the collective of all members), and members can join and/or leave at any time. The following features of community-based autonomous networking environment make the design of effective and easy-to-implement incentive mechanisms challenging: • anonymous: users prefer to be anonymous, and not accountable for their actions. Users can change their identities with near zero cost (cheap pseudonyms). • dynamic: most interactions among peers are one-time. Each peer has no idea about other peers’ behavior history, except their current behaviors. • autonomous: each strategy and action are voluntarily chosen and determined by independent, rational and autonomous peers, and all behaviors are endogenous. Thus, we argue that, ideally, an appropriate incentive scheme in community-based collaborative applications should possess the following features: It should not rely on the existence of central entity for accounting and enforcing punishment, should not require the ability to permanently expel users from the community, and should not rely on tracking the long term behavior of users, which, in turn, could make the incentive mechanism easy to implement and largely immune to problems of false trading and whitewashing. Generally, whitewashing denotes the behavior that a free-rider might choose to whitewash, i.e., leave and rejoin the network with a new identity, to avoid the penalty imposed on a free-rider. In summary, investigating a simple incentive mechanism that can work in dynamic, anonymous and autonomous communitybased environment is the main motivation of this paper. B. Contributions of This Paper Our paper regards, in community-based networks, an incentive mechanism as the specific system rule, aiming to stimulate all or most users to voluntarily participate, contribute resource to collaborative system, and punish misbehavior users, which would naturally lead to the maximization of the sum of all users’ utilities, so-called social welfare. In detail, our contributions are following: • First, we proposed, analyzed and designed an EGTinspired incentive mechanism, VPEF, which fully respects each user’s rationality, and can encourage users to participate in and pool their local resources into a dynamically formed community. In comparison with classical game-
theoretical approaches, VPEF only assumes light cognitive capabilities of users, that is, each user imitates the behavior of other user with better payoff, so-called backwardlooking approach, which, fits well people’s behavior evolution in real life, and can completely characterize the evolutionary dynamics of strategies as a whole. Specifically, VPEF is based on two simple system rules: VP meaning that all behaviors are voluntarily conducted by users, and EF meaning that an arbitrarily small roundbased entry fee is set for each user who wants to participate in the community. Incentive mechanisms built as simple system rules in community-based networking applications have the following advantage: incentive mechanisms (system rules) could be implemented at the community level (independent of the underlying various networking technologies), and these rules would be part of the community doctrine. For example, certain communities could specify how to form virtual/physical community (e.g., define geographical boundaries), ask a minimum amount of resource contribution, or require an invitation from a member, etc. • Second, we provide a generic analytical framework of evolutionary dynamics. And furthermore, based on the generic analytical framework, we theoretically obtained that: As selection intensity is limiting to infinite, the ratio of system time in full punishers can be given as (2 + M)/(8 + M), where M is the number of users in this system. And, the efficiency loss of VPEF scheme is 4/(8 + M). The detailed explanation about the concepts of punishers and efficiency can be found in Appendix I. • In comparison with other incentive mechanisms, the distinguished features of VPEF lie in that: Do not require to permanently expel users from the community, and do not rely on tracking the long term behavior of users. It should be explicitly pointed out that many popular P2P systems (e.g., BitTorrent and Edonkey) implement a Titfor-Tat-like reciprocative incentive scheme without relying on past transactions of peers, but on a direct exchange of resources. Basically, interactions in Tit-for-Tat-like applications were always pairwise: Peers reciprocate uploading to peers which are uploading to them. However, instead of pairwise interactions, due to the feature of “contributing while consuming” in our community-based applications, each user would enjoy service/resource provided by others in whole community (the similar concept has been used in [5]). The paper is organized as follows. Several rule-based incentive mechanisms are briefly introduced in Section II, including fixed-contribution, altruism, and BT-like mechanisms, etc. Section III offers a schematic framework of communitybased collaborative applications with built-in a (VPEF). And then, a general theoretical model is given to characterize the evolutionary dynamics of our proposed incentive mechanism, and one theorem is obtained to characterize the stability and convergence of VPEF scheme. Section IV provides a simulated scenario of the proposed VPEF scheme, using content availability as an example. Specifically, theoretical values and simulation results show that VPEF mechanism can drive the system into the state of almost full punishers, even facing wide
WANG et al.: VPEF: A SIMPLE AND EFFECTIVE INCENTIVE MECHANISM IN AUTONOMOUS NETWORKS
parameter ranges. Section V discusses several issues about VPEF mechanism. Finally, we conclude this paper. II. R ELATED W ORK To seek relatively easy-to-implement approach, many existing schemes avoided using prices and payments as candidates for incentive mechanisms (e.g., free markets, commodity markets and auctions, etc.). Actually, a large part of researches (and practices) on P2P incentives mechanisms have considered the design and deployment of simple rules based on fixed contributions, reciprocity or altruism, etc. Here, we briefly introduce and compare them with our scheme. Recently, in economic field, efficient provision of excludable public goods was deeply investigated by [6], and the following conclusion was inferred: It is possible to approximate the optimal provision mechanism with a scheme that provides a fixed quantity of the goods and charges fixed user fees for access. Through considering content availability as a special kind of excludable public goods, the above result was applied into shared infrastructures [7], which showed that, for a large class of utility functions, a simple fixed-contribution scheme is asymptotically optimal as the number of participants grows to infinity. The fixed contribution scheme simply demands that each peer contributes a uniform minimum number of files to join the network, which can be calculated by an optimization algorithm, given the distribution of peers’ private types. The enforcement of the above fixed-contribution based incentive scheme was investigated in [8], which does not require the use of system memory, but relies only on the time peers are consuming resources to ensure that they contribute adequately. The scheme has two advantages: simplicity and no micro-payment based price involved. Those advantages are very attractive in a large and decentralized system, in which implementing a currency may be difficult. But, the weakpoints are also obvious: First, to calculate the fixed-contribution level, the authors assumed that the distribution of peers’ private types is known to system designer; second, the scheme cannot fully characterize the evolutionary dynamics of various strategies, because it did not consider the strategy mutation of users. Here, strategy mutation means that some users may occasionally did not follow perfect rationality, and arbitrarily changes their strategies due to curiosity or mistake. Our work differs from the fixedcontribution schemes in following aspects: First, in existing schemes, the fixed-contribution level is specific, and has to be calculated based on the global information. In our paper, the round-based entry fee can be set as an arbitrarily small value without knowing global information at all; Second, our scheme can fully characterize the dynamics of users’ behaviors. Finally in [7], the resource is strictly non-rivalrous in demandthat is, one agent’s consumption of the goods does not diminish the amount of the goods left for other agents, actually the resources mostly involved in community-based applications are rivalrous: A participant consuming storage space or CPU cycles in community reduces the goods’ utility to other participants. Briefly, the rivalry of resource provision in community-based collaborative applications is more challenging than the case of non-rivalry.
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A model of the interaction of rational peers in a P2P network was developed in [9], which has its heart in altruism, an intrinsic parameter reflecting peers’ inherent willingness to contribute. The authors found that, under the effects of altruism, a substantial fraction of peers will contribute when altruism levels are within certain intervals, even though no incentive mechanism is used. Similarly, through taking into users’ generosity, the dynamic scenarios were investigated in [10], including arrivals and departures of users, and whitewashers. The obtained result is that imposing penalty on all users that join the system is effective for combating whitewashing under many scenarios. Unlike work above, our paper does not assume any altruistic feature (or user generosity) at all, and illustrates that, through exerting arbitrary round-based entry fee and fully respecting users’ rationality, community-based applications based on VPEF scheme can evolve the networking environment into the state of almost whole punishers who not only are cooperative, but also voluntarily punish defectors. Interestingly, the roundbased entry fee can play the role of penalty in [10]. In a sense, our paper provided an evolutionary interpretation about the penalty in [10]. However, the most distinguished feature in our scheme is that entry fee can be arbitrarily set. Based on public goods game, recent work investigated how to effectively design service differentiation based resourceprovision incentive mechanisms in dynamic and autonomous P2P networks [11]. This paper significantly extended the work in [11]: Theoretically analyze the efficiency loss of punishmentbased VPEF scheme, and provide a realistic scenario to illustrate the applicability of VPEF scheme.
III. T HEORETICAL A NALYSIS F RAMEWORK OF VPEF A. Basic Concepts About EGT Models Considering the rationality of each participant, game theory is an appropriate tool to analyze rule-based incentive schemes. However, most classical game theory based incentive mechanisms always adopt the best response model to analyze the equilibrium of a rational system, in which a player updates its strategy by choosing best reply to the current mean population strategy: Each wants to maximize her/his utility, which depends on her/his benefit (the resources of the system (s)he can use) and her/his cost (his contribution).We argue that their drawbacks are twofold. First, they cannot completely characterize the evolutionary dynamics of strategies as a whole. That is, in a sense, examining a single equilibrium does not give much insight about how strategies spread, when facing strategy mutation. Specifically, strategy mutation means that some users may occasionally not follow perfect rationality, and changes their strategies due to curiosity or mistake. Second, those schemes always assume forward-looking model of perfect rationality of participants: Assume each individual has the knowledge about the global information and selects strategies as a best response to current system’s global state (for example, in the fixedcontribution scheme, the distribution of participants’ types is assumed to be known). The above perfect rationality brings great burden to individual’s cognitive ability, and in most cases, it is unfeasible.
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On the contrary, our paper adopts EGT inspired approach, in which individuals attempt to optimize their utilities by imitating the behaviors of individuals with better payoff. The above stochastic learning is a backward-looking approach, and thus assumes much lighter cognitive capabilities on the part of individuals than does traditional rationality. EGT-based incentive approach generally include three phases: interaction phase, evolution phase and mutation phase [11]. • Interaction phase specifies some rules by which entities interact. Interactions among individuals are always modeled as some specific games, e.g., public goods game in our paper. • In evolution (reproduction) phase, each agent differentially reproduces children based on its utility. The reproduction can be genetic (entities actually reproduce next generation) or cultural (entities are seen as behaviors or ideas that can be replicated horizontally among peers within a generation). The above interpretation of cultural reproduction gives us a clue as to how evolutionary models can be used in modeling of community-based incentive mechanisms. • Mutation phase means that, in the evolution, with very small probability, agents change their strategies to incorporate innovation. In our framework, small mutation can be intuitively interpreted as small percentage of users trying to exploit the “new world”. Note that the above mutation phase cannot be appropriately modeled in classical game theory, which is also one motivation that we design the EGT-based incentive schemes. Traditionally, the generic mathematical description of evolutionary game dynamics is the replicator equation. It is the system of nonlinear ordinary differential equations that describes how the relative abundances (frequencies) of strategies change over time as a consequence of frequency-dependent selection. The implicit assumption underlying the replicator equation is that individuals meet each other at random in an infinitely large and well-mixed population (for more details about replicator equation, please refer to [12]). We argue that the analysis of evolutionary dynamics based on replicator equation has the following drawbacks: First, in fact, the number of users in autonomous network is finite, and most interactions are constrained by the dynamically formed community; second, in reproduction phase, not all users imitate the strategy of user with larger utility, that is, a small percentage of users may bravely exploit other strategies, so-called mutation. B. A General Analytical Framework of EGT-Based Evolutionary Dynamics To characterize the evolutionary dynamics of strategies in finite population, a stochastic description is necessary, in which the fixation probability of a strategy plays a key role. The fixation probability of a strategy represents the probability that a single mutant strategy overtakes a homogeneous population which uses another strategy. Moreover, the analysis of the stochastic dynamics of incentive mechanisms with n strategies (n > 2), could be greatly simplified in the limiting case: The mutation probability is near to zero, for in the above case, the
whole system almost always consists of one or two types at most. This holds because, when the mutation probability is zero, all monomorphic states are absorbing, and for sufficient small mutation probability, the fate of a mutant (i.e. its elimination or fixation) is settled before the next mutant appears. Summarizing over above results, we proposed the generic model to characterize the evolutionary dynamics. That is, the pairwise comparison model is adopted to infer the fixation probability between any pair of strategies; the fixation probabilities define the transition probabilities of a Markov process among the n homogeneous states of the population, and the stationary distribution can approximately indicate the probability to find the system in one of the n homogeneous states. It is shown that pairwise comparison rule can provide a convenient framework of game dynamics at all intensities of selection [13]. According to this rule, two individuals with respective i-strategy and j-strategy are randomly selected for update (only the selection of mixed pair can change the composition of the population). The strategy of i will replace that of j with a probability given by the Fermi function: p=
1 1+
e−β·(Pi −Pj )
.
(1)
Where Pi and Pj respectively represent the users’ utilities with strategy i and strategy j, which can be obtained from the interactions modeled as corresponding games. The quantity β, which in physics corresponds to an inverse temperature, controls the intensity of selection. When β is large, the individual with the lower payoff will always adopt the strategy of the other individual. For β 1, we recover the weak selection limit of the frequency dependent Moran process [13]. For community based networks in the technological domain, strong selection is more probable than weak selection, i.e. users strongly prefer to mimic behaviors of users that have better utility. Then, as given in [13], the probability to increase the number of i-strategy users from l to l + 1, and the probability to decrease the number of i-strategy users from l to l − 1, can be represented as: Tl± =
1 l M −l · · . ∓β·(P i (l)−Pj (l) M M 1+e
(2)
As described above, the quantity of interest in finite population dynamics is the fixation probability ρij , denoting the probability that a population of j-strategy users invaded by a single i-strategy user evolves with mutations to a population of all i-strategy users. The fixation probability ρij depends only on the ratio γl = Tj− /Tj+ . For the pairwise comparison process, the ratio reduces to γl = exp[β · (Pi (l) − Pj (l))]. Finally, the fixation probability of ρij is given as follows [26]: 1 ρij = M −1 . k k=0 Πl=1 γl
(3)
The transition matrix of a Markov process among the n different homogeneous states of the population can be represented as the following matrix (4), (See equation at the bottom of next page) and the stationary distribution can be easily
WANG et al.: VPEF: A SIMPLE AND EFFECTIVE INCENTIVE MECHANISM IN AUTONOMOUS NETWORKS
Fig. 1.
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Schematic framework of community-based collaborative applications with built-in incentive mechanism (VPEF).
calculated, which approximately indicates the probability to find the system in one of the n homogeneous states. Where μ is the strategy mutation probability. In VPEF, the small mutation could be intuitively interpreted as mistake when users imitate strategy of neighbors with better utility. The mistake may be caused by the fact that nodes could not be (correctly) aware of the utilities gained by neighbors. In other words, VPEF can accommodate the cases of unaware utility or incorrect utility obtained, and interpret those cases as strategy mutation. C. Framework of Community-Based Collaborative Applications and Theoretical Analysis of VPEF Basically, community-based collaborative application framework with built-in incentive mechanism is organized in four basic layers (as shown in Fig. 1). The networking layer is composed of various networking technologies that were used for building physical and/or virtual communities. The goal of networking layer is to enable the researcher and developer use Bluetooth, Wi-Fi and overlay networking technologies through unified interfaces without having to deal with the complexities of networking in a multi-peer, multi-transport environment. The core layer includes mandatory functionalities that are strictly necessary for the community-based collaborative network to function properly. At the core layer, our focus is on the
⎛ ⎜1 − ⎜ ⎜ ⎜ ⎝
abstraction of community formation. The dynamically formed community provides a minimum set of given services and/or share specific interests. The service layer includes fundamental higher-level services, designed for flexibility and extensibility. The application layer includes a variety of applications that can be developed on top of the other two layers. Here, incentive mechanism/fairness was designed as an independent component to support the application development and deployment of community-based applications. The right part of Fig. 1 shows the detailed process of the proposed incentive scheme VPEF for community based collaborative networking environment with the feature of contributing while consuming. Theoretically, the participation and resource contribution mechanism in community-based collaborative environment, VPEF, can be modeled as common goods game with constant user size M . For general case, N individuals are randomly selected to form virtual community and offered with the option to participate in a risky but potentially profitable resource provision scheme. As shown in Fig. 1, specifically, there exist four types of users in our scheme: The non-participants, denoted as ‘L’, represent individuals who, by default, do not join the public enterprise, and thus do not pay the roundbased entry fee; defectors, ‘D’, who participate, but do not contribute; cooperators ‘C’ who contribute but do not punish the defectors; and punishers, ‘P’, who not only contribute to the commonwealth but also punish the defectors. And moreover,
strategy1
strategy2
μ(ρ21 +···+ρn1 ) n−1 μρ21 n−1
μρ12 n−1 μ(ρ12 +···+ρn2 ) n−1
···
μρn1 n−1
1−
···
μρn2 n−1
··· ··· ··· ··· ···
strategy n μρ1n n−1 μρ2n n−1
1−
···
μ(ρ1n +···+ρ(n−1)n ) n−1
⎞ ⎟ ⎟ ⎟ ⎟ ⎠
(4)
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TABLE I S YMBOLS AND T HEIR D EFINITIONS U SED IN T HIS PAPER
Furthermore, in the following description, let the number 1, 2, 3 and 4 denote the strategies of C, D, P and L respectively. Theoretically, in finite population, the groups engaging in resource provision can be modeled by multivariate hypergeometric sampling. For transitions between two pure states, this reduces to sampling (without replacement) from a hypergeometric distribution. In a population of size M with mi individuals of type i and (M − mi ) of type j, the probability of selecting k individuals of type i and (N − k) individuals of type j in N trials, is given as follows: mi M −mi
H(k, N, mi , M ) =
k
MN −k .
(5)
N
Thus, in a population of XG cooperators and (M − XG ) defectors, the average payoff to cooperators P12 , and defector P21 can be respectively denoted as follows: N −1
P12 (XG ) =
H(k, N − 1, XG − 1, M − 1)
k=0
k+1 ·α N
· cs − cs − ce = α · csN 1 + N − 1M users can voluntarily join the resource provision scheme, and all participants can voluntarily quit the scheme. Note that the costly punishment has been shown to invade when a rare percentage of individuals are allowed to opt out of cooperative ventures [14]. Our work is greatly inspired from the above result. The key point in VPEF is to set arbitrary round-based entry fee, and naturally result in one special type of users, L, which roughly represent those that hesitate about whether to join the resource provision scheme or not, and can watch the obtained utilities of those participants who already joined the scheme. In brief, L strategy provides one possible hatch from the formed deadlock, when only C, D and P strategies exist in the system. Theoretically, VPEF is divided into multiple rounds. Each round include two stages: In the first stage, each participant in same community voluntarily invests same amount of resource cs to common pool, and after times with multiplexing/diversity factor α, the multiplied resource in pool is equally shared by all participants in the group: Irrespective of whether they contributed or not; in the second stage, each P participant will impose penalty cp , on a defector, and similarly incur punishment cost cu for the punishment behavior. Each participant’s average payoff in a round is summed over both stages (each loner’s utility is naturally zero). Furthermore, to formalize user’s rationality, we assume each user simply imitates the strategy of other users with better utilities. Note that, to make the theoretical analysis feasible, our paper assumes extremely simple and ideal model for resource provision. Specifically, all parameters in VPEF scheme are converted into cost-comparative metrics, so that users’ utilities and average utility of users can be simply calculated (shown as follows). But, even though our economic model is rather crude, and abstracts many practical aspects of the implementations, we can still see some interesting implications. To help clearly describe the theoretical model, Table I provides a list of symbols and their definitions used in this paper.
− 1(XG − 1) − cs − ce P21 (XG ) =
N −1
H(k, N − 1, XG , M −1)
k=0
(6)
k · α · cs −ce N
α · cs N − 1 = · · XG − c e . N M −1
(7)
Note that, in P12 , the focal user is C participant, so the total number of other C participants is denoted as (XG − 1). Similarly, we can get: (8) P13 = P31 = α · cs − cs − ce
XL M −1 P14 (XL ) = P34 (XL ) = 1 − / · N −1 N −1
XL M −1 / · ce (α · cs − cs − ce ) − N −1 N −1 (9)
N − 1 α · cs · − c p · XP − c e P23 (XP ) = M −1 N
(10)
P24 (XL ) = − ce
(11)
α · cs N −1 − cs − ce − (N − 1) · cu + N M −1 α · c s + cu · (XP − 1) · (12) N = P42 = P43 = 0. (13)
P32 (XP ) =
P41
In summary, from the (6)to (13), we can obtain the average payoffs of users with corresponding strategies, then the fixation probability ρij (i, j = 1, 2, 3, 4) can be calculated through the (2) and (3). As described in the above subsection, the fixation probability ρij define the transition matrix of a Markov process among the four different homogeneous states of the population. Then, the stationary distribution approximately indicates the
WANG et al.: VPEF: A SIMPLE AND EFFECTIVE INCENTIVE MECHANISM IN AUTONOMOUS NETWORKS
probability to find the system in one of the four homogeneous states. In a sense, the stationary probabilities summed on states D and L can be regarded as the efficiency loss of VPEF scheme. Then, based on the proposed analytical framework, we can straightforwardly obtain the following theorem. Theorem 1: Due to the evolutionary nature of individuals, although VPEF based approaches cannot converge to and stabilize at the static state of full cooperators or full punishers, VPEF does drive the community-based networking environment into the acceptable status: system time will be stochastically dominated by punishers who not only provide resource, but meanwhile voluntarily punish defectors. Specifically, when selection intensity is limiting to infinite (the probability that individual imitates better another’s strategy approaches 1), the stationary distribution (time averages of C, D, P, and L strategies) can be given as: (2; 2; 2 + M ; 2)/(8 + M ), where M is the number of users in community-based collaborative system. That is, the efficiency loss of VPEF scheme is 4/(8 + M ), which is regarded as the stationary probabilities summed on states of D and L (that is, the ratio of system time, in which no users will contribute resource). The brief proof of Theorem 1 is given as Appendix I.
IV. P RACTICAL I SSUES AND S IMULATION S CENARIO A. Practical Issues Related to the Enforcement of VPEF VPEF aims to provide the suitable incentive for ensuring that users would contribute to the system as a whole while consuming resources. For the application of VPEF in community-based collaborative networking environment, the following practical problems should be considered. Here, we focus on the application specific components, and omit the underlying functions, like mechanisms for low-level communications and connection management, etc. • How to form dynamic physical and/or virtual community/group? • How to enforce the punishment behavior? • How to deal with the whitewashing behavior? Basically, each community may be governed by a set of policy rules that all participants within the community must adhere to. A policy may be presented to the agent during the community formation phase, and must be accepted prior to the agent being allowed to operate within the community. To manage the policy, some peer acting the role of community manager that can be dynamically formed or statically designated, may mediate interaction between agents within a community, and may support a number of common services. Naturally, physical or virtual group formation is clearly a critical first step for collaborative community-based applications. Physical community formation could be spontaneously organized by users within the wireless radio range. A user would switch on its 802.11 radio if she wants to join the system. For example, it is relatively easy to form P2P community and negotiate the role of community manager with the emerging Wi-Fi Direct technology. For virtual community, community manager are usually designated by system owner statically, and community manager can help or-
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ganize users into virtual community according to some criteria or simply in random way. The next issue in VPEF that needs to be addressed is related to enforcement, for example how to enforce the round-based entry fee and punishment etc. For theoretical analysis, all parameters in VPEF scheme are converted into cost-comparative metrics, so that users’ realized utilities (and average utility of users with corresponding strategies) can be simply calculated. In fact, entry fee and punishment in VPEF depend on the nature of community-based applications and value of contents. Intuitively, it is feasible to make payment in kind. For example, an arbitrary small fixed-contribution level could be analogous to the round-based entry fee. And a standard punishment strategy is to deny access to defectors, and, in some cases, available service level could be cut down for potentially suspicious users, so-called the differential services based punishment. For example, a simple rule-based incentive mechanism was preliminarily introduced in [15], which advocates defining small meaningful classes of services and assigning sites to these classes according to their overall resource contribution. Our paper models the community-based collaborative system with users leaving and joining by multivariate hypergeometric sampling. Sometimes, the modeling is too ideal for realistic environments, but if the arrival and departure are type-neutral and therefore do not alter the type distribution, then our scheme still work. Note that a free-rider might choose to whitewash, i.e., leaves and rejoins the network with a new identity, to avoid the penalty imposed on the free-rider. VPEF incentive scheme is intentionally divided into multiple rounds, and from one round to another round, community memberships and their identities were actively reshuffled, which can more or less accommodate the whitewashing. Furthermore, it is shown that it is possible to counter the whitewashing by imposing the penalty on all newcomers, which is so-called social cost [16]. In a sense, round-based entry fee in VPEF scheme acts as the role of the social cost. Note that, for real applications, the partition of multiple rounds depends on the nature of specific applications. For example, for physical community, rounds could be naturally introduced by users’ leaving and joining; for virtual community, the community manager can intentionally start multiple rounds according to some criteria or simply in random way. B. Simulation Scenario: Content Availability in Community-Based Collaborative Networks Here, we use content availability to illustrate the application of VPEF scheme in community-based collaborative networks. In online communities, the majority of the distributed contents belongs to the long tail: large part of those contents are not popular, but together constitute the majority of the total requests [17]. Same as [17], a special file sharing system is investigated, in which the probability of a file being requested is low but the overall value of satisfying such requests is much greater than for popular items (since it is more difficult to find unpopular or rare items in many cases, even if one wishes to pay for them). We will attempt to give incentives for providing any content item regardless of request rate. Specifically, we assume that all files have a similar (low) rate of requests, either because they
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Fig. 2. Effect of selection intensity on the time average.
are unpopular or because they are distributed in proportion to their popularity. Based on the above consideration, it is feasible to make the following assumptions in our simulation: the provision cost cs equals the number of files that a cooperator (or punisher) provides; the entry cost can be set as 1 file; assume there exist iG cooperators, iB defectors, iP punishers, iL loners in a formed community, and D behavior can be successfully found with probability pb, thus the penalty cp that punisher imposes on defector can be represented as: cp = pb · α · cs /(iG + iB + iP ); The punishment cost cu is simply assumed to be proportional with pb, that is, the more accuracy that D peers could be detected, the more cost that each punisher should bear. We use a cycle-based simulation model, in which time consists of rounds. Note that, due to the lasting dynamics of imitating and mutating process in the scheme, in our simulations, we define the homogeneous state of each strategy as follows: Whenever more than 90% of the users opt for one strategy, then it is counted as being in the respective homogeneous state. Then, based on the number of homogeneous states, the ratio of time average for each strategy was calculated. Note that we also use other value, like 95% as threshold, and get very similar results. Fig. 2 illustrates the theoretical values of time ratios spent in various strategies, with the change of selection intensity β, when existing entry fee. Obviously, when selection intensity is zero, that is, the whole system is completely determined by random natural drift, and then in the long runs, probability to find the system in one of the four homogenous states should be identical. As the selection intensity gradually increases, P strategy quickly dominates all other strategies. As described above, for modeling evolution of community-based collaborative networks, strong selection is more feasible than weak selection, that is, users always mimic the behavior of stronger people. Thus, in the following simulations, we adopt the strong selection (that is, let β = 1). Fig. 3 illustrates the experimental values of time ratios spent in various strategies, with the change of the probability, pb, that
Fig. 3. Effect of the probability that defect peers are found on the time average.
Fig. 4.
Effect of round-based entry fee on the time average.
defect peers are successfully found (when existing entry fee). Intuitively, only when pb is high (approaching to 100%), VPEF can work well. In other words, when pb is relatively low, the performance of VPEF is bad. We think the reason is following: when only small percentage of D peers can be found, even though they are punished, after learning and imitating in evolution phase, they will again change into D behavior, for most of D peers will not be punished (due to the low probability), which leads to their relatively high utility. Fortunately, VPEF scheme does not rely on tracking the long term behaviors of peers to categorize peers, instead, only relies on the time a peers is consuming resources (namely downloading a file), and uses the uploading peer as the enforcing entity, thus it is reasonable to assume high probability that D peers can be found. Thus, in the following experiments, pb is set as 100%. Fig. 4 shows the theoretical and experimental time averages of various strategies with the change of entry fee (representing the number of contributed files). We can see that, the small entry
WANG et al.: VPEF: A SIMPLE AND EFFECTIVE INCENTIVE MECHANISM IN AUTONOMOUS NETWORKS
Fig. 5. Effect of multiplexing/diversity factor on the time averages of various strategies.
fee can effectively facilitate the emergence of almost whole P state. Here, small entry fee means that the entry fee is just slightly larger than zero. But, it does not mean that the larger entry fee is, the more frequency the system will be spent in the P state. On the contrary, if the entry fee was too large, intuitively, it will prevent more users from joining the resource provision venture, and more users will select the ‘L’ state. The increase of the theoretical time average of ‘L’ strategy in Fig. 4 slightly shows the above trend. This result is very attractive, for the entry fee should not be set very high, otherwise, instead of joining the community-based collaborative networks, most participants will choose to be loners. Fig. 5 illustrates the theoretical and experimental time averages of various strategies with the change of multiplexing/diversity factor, from which we can draw the following two conclusions. First, just like intuitive thinking, when the multiplexing/diversity factor equals 1, that is, there exists no multiplexing effect in community-based collaborative system, users’ best choice is not to participate in resource provision system, namely act as loners (‘L’ strategy). Second, when multiplexing/diversity factor is larger than 1 (even though it is a little larger than 1), our scheme VPEF can drive the system into the state that almost full P participants. Fig. 5 implies that small multiplexing/diversity factor can drive the communitybased collaborative system into desirable state. In other word, as long as the multiplexing/diversity effect in community-based applications, VPEF scheme can be used. Fig. 6 illustrates the impact of the number of users in community on the time averages of various strategies. Interestingly, in all experiments and theoretical analysis, the whole collaborative system is mostly occupied by the P, not matter what parameters (the number users in each group and the total number of users in system) are set. The reason is straightforward: For strong selection, those parameters can hardly affect the transition probability, and thus the ratios of time average of those four states in various parameters almost keep same.
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Fig. 6. Effect of the total number of users in community on the time average.
V. D ISCUSSION A. The Underlying Reason for the Divergence Between Theoretical Results and Simulations Interestingly, From Figs. 5 and 6, we can observe that the experimental results are consistently below the theoretical for ‘P’ strategy. The reason for the above phenomenon is following. In finite populations with small mutation rates, the population is homogeneous most of the time. Occasionally a mutation occurs with probability μ and an individual switches to a different random strategy. The mutant either reaches fixation or extinction before the next mutant arises. The average time until a neutral mutant reaches fixation is M · (M − 1) time steps. The average time between two mutations is μ−1 . Thus, for μ M −2 , the time scale of mutation and imitation are completely separated and we only have to consider two strategies at a time. Thus, our theoretical results are derived from stationary probabilities of the transition matrix of a Markov process among the 4 different homogeneous states (C, D, P, L). However, in our simulations, the time scale of mutation and imitation could be not fully separated, that is, the conditions μ M −2 cannot be satisfied (in Figs. 5 and 6, μ = 0.0001, and M = 500–1000), which accounts for the slight divergence between theoretical results and simulations. B. The Philosophy Implication of Entry Fee in VPEF Intrinsically, participating in public enterprises bears considerable risks because whether and how many users participate in, and turn out to be defectors is not known in advance. Thus, in our scheme, the entry fee should not be set very high. Otherwise, instead of joining the resource provision scheme, most users will choose to act as loners. On the other hand, it is shown that, in the case of public goods, the ability of partially excluding agents from their use is a powerful tool for achieving an efficient provision of resource [20]. In a sense, the round-based entry fee in VPEF scheme can play the similar
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role of partially excluding users from the systems. Furthermore, another purpose of round-based entry fee is to alleviate the effect of whitewashing attack, which is made feasible by the availability of low cost identities or cheap pseudonyms. Basically, in the absence of irreplaceable pseudonyms, to counter whitewashing attacks, it may be necessary to impose a penalty on all newcomers, including both legitimate newcomers and whitewashers [21]. Thus, the round-based entry fee could also be regarded as a special kind of social costs. But the key feature of round-based entry fee in our scheme lies in that: The entry fee can be arbitrarily small, and all actions are voluntarily chosen by rational users. C. Incentive Mechanism VPEF as System Rule Our proposal, VPEF, targets at providing a general framework of incentive mechanism (built as system rule) for most community-based collaborative applications with the features of “contributing while consuming”. Most of those applications are built as overlay superposed on underlying networking infrastructure, to provide user-specific services, in which resource type is application specific. As shown in Fig. 2, we argue that, incentive mechanism as system rule should be designed as an independent component located in service layer, which resides on top of networking technologies and itself is the base for the remaining system. Furthermore, specific solutions are even more dependent on the specific environment and the corresponding resources entailed. As a result, freedom should be given to communities to self-organize and configure the corresponding mechanisms to suit with their own needs. In this paper, we follow the similar simulation method used in [7] to evaluate the proposed mechanism in an abstract simulated environment, and illustrate VPEF can drive the system into almost full P state. Furthermore, we simply assume there exist P participants, and in evolutionary phase, P participants can be created by small mutation probability. Actually, experiments in [22] show that if players can choose between joining a public goods game either with or without punishment, they prefer the former. D. Limitations and Future Work Finally, our model and experiments have their limitations (binary choices for users and lack of heterogeneity in user characteristics, etc.). Actually, interactions tend not be as clearcut as black and white or cooperative and defect, and thus in many situations, it might be more appropriate to allow for a continuous range of degrees of cooperation, that is, cooperative investments, i.e., the resource and effort spent in producing the common goods, can vary continuously within a certain range. Note that another framework for evolutionary change is given by adaptive dynamics [33], which describes how continuous traits or strategies change under mutation and frequencydependent selection. One of our future works is to investigate the continuous range of cooperative investments based on adaptive dynamics. Furthermore, it is also imperative to perform simulations to test the VPEF incentive scheme under a wider range of possible resource distributions and user behaviors.
In brief, VPEF scheme is to use simple and relative easyto-implement rules to influence the behaviors of the users in community-based collaborative applications, in which a user is contributing to the system as a whole while consuming resources. The results obtained by VPEF are more qualitative than quantitative, showing the form of the optimal control rather than computing its exact value. Our scheme can find great applications in the dynamic community based collaborative services characterized with contributing while consuming, such as, collaborative downloading through aggregating users’ cellular interfaces, user participation and attention in online game, expert knowledge sharing, etc. VI. C ONCLUSION Under the prerequisite of fully respecting users’ rational behaviors, this paper designs a simple incentive mechanism VPEF, based on voluntary principle and arbitrary round-based entry fee, to encourage users to participate in and provide resource to the community-based collaborative applications with the feature of “contributing while consuming”. Specifically, we model the resource provision in community-based systems as that of creating common goods and apply EGT model to formally analyze its dynamics. The theoretical analysis and experimental results show that: Although VPEF scheme cannot converge to and stabilize at the static state of full cooperators, but, for a wide ranges parameter, VPEF can drive the system into the state of almost full punishers: Most of system time will be spent on the state of punisher. Punishers are ideally cooperative, in that they not only contributed resource to system, but voluntarily punished defectors. Such a simple resource provision scheme does not adopt any artificial stimuli, and is relatively easy to implement. We noticed that, recently, incentive design for heterogeneous User-Generated Content (UGC) Networks has been investigated in [18], where individuals voluntarily establish connections with others to produce and share information. content and resources (e.g. Facebook, Twitter, etc.). Surprisingly, the finding is that allowing a certain level of freeriding behavior may lead to higher social welfare than incentivizing all users to produce. In a sense, our results are compatible with the above finding. That is, the philosophy implication behind our work is that: Like many other mechanisms in real society, it is difficult (always impossible, or even not necessary) for our proposal to be perfect, but it still works with acceptable performance. A PPENDIX I P ROOF OF T HEOREM 1 In finite populations with small mutation rates, the population is homogeneous most of the time. In other words, the quantity of interest in finite population dynamics is the fixation probability ρij , which is the probability that a population of j-strategy users invaded by a single i-strategy user evolves with mutations to a population of all i-strategy users. If only cooperators and defectors are present, the probability that a single cooperator takes over a defector population is zero
WANG et al.: VPEF: A SIMPLE AND EFFECTIVE INCENTIVE MECHANISM IN AUTONOMOUS NETWORKS
i.e., ρ12 = 0 for strong selection; A single defector, however, always takes over a cooperator population, i.e., ρ21 = 1. If only cooperators and punishers are present, since cooperator and punishers have same utility, then the probability that a single cooperator (punisher) takes over a punisher (cooperator) population is neutral transition, i.e., ρ13 = ρ31 = 1/M . Note that in our scheme single C or P participant cannot form the public/common goods game, but a group with at least two C (or P) participants obviously advantages over other L users. Thus, the probability ρ14 (or ρ34 ) that the mutant of C (or P) successfully invades the L population equals 1/2. Therefore, for the strong selection, those fixation probabilities converge to several specific values: 0, 1, 1/2, or 1/M , and are given as follows: ρ12 = 0, ρ13 = 1/M, ρ14 = 1/2; ρ21 = 1, ρ23 = 0, ρ24 = 0; ρ31 = 1/M, ρ32 = 0, ρ34 = 1/2; ρ41 = 0, ρ42 = 1, ρ43 = 0; Then, according to matrix (4), the transition matrix for all four strategies (C, D, P and L) is given as follows: ⎛ C C D ⎜ 1 − μ3 − ⎜ μ ⎜ 3 ⎝ μ P 3·M 0 L
μ 3·M
D 0 1 − μ3 0 μ 3
P
L
μ 3·M
μ 6
0 μ 1 − 3·M 0
0 μ 6
1−
⎞ ⎟ ⎟ ⎟. ⎠ μ 3
Let us explain the first column, i.e. the population is in the C state: If a mutation occurs (with probability μ), the mutant is a defector with probability 1/3, and takes over the population with probability 1. This leads to the entry (the second line and the first column), μ/3; the mutant is a punisher with probability 1/3, and takes over the population with probability 1/M . This results in the entry (the third line and the first column), μ/(3 · M ). Other entries can be explained similarly. The normalized right eigenvector to the largest eigenvalue (which is 1 for the above transition matrix) can be obtained as (2; 2; 2 + M ; 2)/(8 + M ). Naturally, as the number of users in system is relatively large, punishers will prevail. Furthermore, we regard the stationary probabilities summed on states D and L as the efficiency loss of VPEF scheme, for in those states, no users will provide resource for community. Thus, the efficiency loss of VPEF scheme is 4/(8 + M ). Note that the closed result is obtained only for strong selection which means that the user with the lower payoff will always adopt the strategy of another user. We believe, for modeling the strategy evolution in community-based collaborative system, the assumption of strong selection is feasible.
ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers and editors for their valuable comments, which help improve the quality of paper greatly.
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R EFERENCES [1] T. Plagemann, R. Canonico, J. Domingo-Pascual, C. Guerrero, and A. Mauthe, “Infrastructures for community networks,” in Content Delivery Networks, R. Buyya, Ed. Berlin, Germany: Springer-Verlag, 2008, ch. 15. [2] D. Camps Mur, A. Garcia, and P. Serrano, “Device to device communications with Wi-Fi Direct: Overview and experimentation,” IEEE Wireless Commun., vol. 20, no. 3, pp. 96–104, Jun. 2013. [3] R. Motta and J. Pasquale, “Wireless P2P: Problem or opportunity?” in Proc. 2nd Int. Conf. AP2PS, 2010, pp. 32–37. [4] D. D. Clark, J. Wroclawski, K. R. Sollins, and R. Braden, “Tussle in cyberspace: Defining tomorrow’s Internet,” IEEE/ACM Trans. Netw., vol. 13, no. 3, pp. 462–475, Jun. 2005. [5] E. C. Efstathiou, P. A. Frangoudis, and G. C. Polyzos, “Stimulating participation in wireless community networks,” in Proc. IEEE INFOCOM, 2006, pp. 1–13. [6] N. Miller, “Notes on Microeconomic Theory,” Externalities and Public Goods. [Online]. Available: https://business.illinois.edu/nmiller/ documents/notes/notes8.pdf [7] C. Courcoubetis and R. Weber, “Economic issues in shared infrastructures,” IEEE/ACM Trans. Netw., vol. 20, no. 2, pp. 594–608, Apr. 2012. [8] T.-Y. Wu, W.-T. Lee, N. Guizani, and T.-M. Wang, “Incentive mechanism for P2P file sharing based on social network and game theory,” J. Netw. Comput. Appl., vol. 41, pp. 47–55, May 2014. [9] D. K. Vassilakis and V. Vassalos, “An analysis of peer-to-peer networks with altruistic peers,” Peer-to-Peer Netw. Appl., vol. 2, no. 2, pp. 109–127, Jun. 2009. [10] B. Q. Zhao, J. C. S. Lui, and D.-M. Chiu, “A mathematical framework for analyzing adaptive incentive protocols in P2P networks,” IEEE/ACM Trans. Netw., vol. 20, no. 2, pp. 367–380, Apr. 2012. [11] Y. Wang, A. Nakao, A. V. Vasilakos, and J. Ma, “On the effectiveness of service differentiation based resource-provision incentive mechanisms in dynamic and autonomous P2P networks,” Comput. Netw., vol. 55, no. 17, pp. 3811–3831, Dec. 2011. [12] C. Hauert et al., “Exploration dynamics in evolutionary games,” Proc. Nat. Acad. Sci., vol. 106, no. 3, pp. 709–712, 2009. [13] A. Traulsen, M. A. Nowak, and J. M. Pacheco, “Stochastic dynamics of invasion and fixation,” Phys Rev. E, vol. 74, Jul. 2006, Art. ID. 011909. [14] C. Hauert et al., “Via freedom to coercion: The emergence of costly punishment,” Science, vol. 316, no. 5833, pp. 1905–1907, Jun. 2007. [15] P. Antoniadis, T. Friedman, and X. Cuvellier, “Resource provision and allocation in shared network testbed infrastructures,” in Proc. Workshop ROADS, Warsaw, Poland, 2007, pp. 1–8. [16] E. Friedman and P. Resnick, “The social cost of cheap pseudonyms,” J. Econ. Manag. Strategy, vol. 10, no. 2, pp. 173–199, 1998. [17] P. Antoniadis and B. Le Grand, “Self-organised virtual communities: Bridging the gap between web-based communities and P2P systems,” Int. J. Web Based Commun., vol. 5, no. 2, pp. 179–194, 2009. [18] J. Xu and M. van der Schaar, “Incentive design for heterogeneous usergenerated content networks,” ACM SIGMETRICS Perform. Eval. Rev., vol. 41, no. 4, pp. 34–37, 2014. [19] D. G. Rand and M. A. Nowak, “Human cooperation,” Trends Cognitive Sci., vol. 17, no. 8, pp. 413–425, Aug. 2013. [20] J. A. Dearden, “Serial cost sharing of excludable public goods: General cost functions,” Econ. Theory, vol. 12, pp. 189–198, 1998. [21] M. Feldman and J. Chuang, “The evolution of cooperation under cheap pseudonyms,” ACM SIGecom Exchanges, vol. 5, no. 4, pp. 41–50, Jul. 2005. [22] Ö. Gürerk, B. Irlenbusch, and B. Rockenbach, “The competitive advantage of sanctioning institutions,” Science, vol. 312, no. 5770, pp. 108–111, Apr. 2006.
Yufeng Wang received Ph.D degree in State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications (BUPT), China. He acts as Full Professor in Nanjing University of Posts and Telecommunications, China. From March 2008, He acts as Expert Researcher in National Institute of Information and Communications Technology (NICT), Japan. He is guest researcher at Media Lab, Waseda University, Japan; also guest researcher at State Key Laboratory of Networking and Switching Technology, BUPT, China. His research interests focus on mutli-disciplinary inspired networks and systems.
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Athanasios V. Vasilakos is currently Professor at the Computer Science Department, Luleå University of Technology, Sweden. He has authored or co-authored over 200 technical papers in major international journals and conferences. He is author/coauthor of five books, 20 book chapters in the areas of communications. He served as general chair, TPC chair and symposium chair for many international conferences. He served or is serving as an Editor or/and Guest Editor for many technical journals, such as IEEE TNSM, IEEE TSMC-Part B, IEEE TITB, IEEE TWC,IEEE Communications Magazine, ACM TAAS. He is founding Editor-in-chief of the journals: International Journal of Adaptive and Autonomous Communications Systems (IJAACS, http://www.inderscience.com/ijaacs), International Journal of Arts and Technology (IJART, http://www.inderscience.com/ijart). He is chairman of the Intelligent Systems Applications Technical Committee (ISATC) of the IEEE Computational Intelligence Society (CIS).
Jianhua Ma (M’00) received his B.S. and M.S. degrees of Communication Systems from National University of Defense Technology (NUDT), China, in 1982 and 1985, respectively, and the PhD degree of Information Engineering from Xidian University, China, in 1990. He has joined Hosei University since 2000, and is currently a professor at Digital Media Department in the Faculty of Computer and Information Sciences, in Hosei University, Japan. Dr. Ma is a member of ACM. He has edited 10 books/ proceedings, and published more than 150 academic papers in journals, books and conference proceedings. His research interest is ubiquitous computing.
