Resource Allocation in Cooperative Cognitive Maritime Wireless Mesh ...

Resource Allocation in Cooperative Cognitive Maritime Wireless Mesh/Ad Hoc Networks: An Game Theory View Tingting Yang1,2,3(B) , Chengming Yang1,2,3 , Zhonghua Sun1,2,3 , Hailong Feng1,2,3 , Jiadong Yang1,2,3 , Fan Sun1,2,3 , and Ruilong Deng1,2,3 1

2

Navigation College, Dalian Maritime University, 1 Linghai Road, Dalian, Liaoning, China [email protected] School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China 3 School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

Abstract. In this paper, an innovative paradigm cooperative cognitive maritime wireless mesh/ad hoc networks (CCMWMAN) is proposed to provide high-speed and low-cost communications for maritime environment. The framework of CCMWMAN is exploited firstly, as well as the analysis of available white space at sea as well as the regulation requirement and standards relatively, to efficiently use the limited frequency resource. Specially, game theory method is applied within the cooperative SUs. An symmetrical system model is constructed, and a price game based on a payoff function is proposed. Then we describe the game theory process to converge to Nash equilibrium, which is verified the results effectiveness of proposed scheme. The simulation results show that the strategy can effectively increases the payoffs of the system. Keywords: Cooperative cognitive maritime wireless mesh/ad hoc networks · Game theory · Symmetrical model

1

Introduction

There has been proliferation interest in the emerging maritime wideband communication networks, which could be a low-cost alternative for current maritime satellite system. It is envisioned that building up a “maritime highway” system will greatly contribute to the maritime communication services with different priority of distress, urgency, safety and general and so that promote a myriad of glamorous applications with respect to monitoring, safety etc. It is envisaged that maritime wideband communication networks could refashion the navigation traffic more energetic, as well as expand wideband communications to the ocean with lower cost. Recent years, a plenty of wireless technologies have been exploited to achieve maritime data transmission. Adopting Worldwide Interoperability for Microwave Access (WiMAX) technology, wireless-broadband-access c Springer International Publishing Switzerland 2015 K. Xu and J. Zhu (Eds.): WASA 2015, LNCS 9204, pp. 674–684, 2015. DOI: 10.1007/978-3-319-21837-3 66

Resource Allocation in Cooperative Cognitive Maritime Networks

675

for Seaport (WISEPORT) project in Singapore could achieve broadband data access, with data rate up to 5 Mbps [1]. In addition, cognitive technology has been applied to maritime mesh/ad hoc networks [2], to mitigate the scarcity of dedicated operation spectrums due to the existing overcrowded frequency bandwidth resources. It is envisioned that a cognitive mesh/ad hoc network could explore the connection between neighboring ships, sea farm, oil/gas platform, as well as marine beacons and buoys, to form an attractive network and connect to the land-based network. However, the notable inherent characteristics of maritime communication are long distance requirement and occasionally deteriorated wireless channel due to the obstacles such as sea clutters. Hence the communication coverage range is still short for the advanced land-based technology to implement on the sea. Cooperative technology is a communication technology developing rapidly in recent years. It can get diversity gain through cooperation between users, which bases on relay transmission principle [3]. It serves to confront the decadence of wireless channel, enhance the reliability of communication, enlarge the coverage and lower transmitting power effectively. The state-of-the-art cooperative and cognitive technology are combined to construct an innovative maritime communication network paradigm in this paper, i.e., cooperative cognitive maritime wireless mesh/ad hoc network, to efficient exploiting White Space (WS) on the sea, and address the limited transmission range and distorted signal transmission simultaneously. Although the research on maritime scenario is still in the early stage, the counterparts on land-based network could provide a solid foundation for our work [4–7]. In cognitive system, not only the secondary users could cooperate with primary users, but also the secondary users themselves could cooperate with each other. Generally speaking, in the research of cooperative system, it always assume relays would like to help source to transmit information. However a challenge in practice maritime communication system is how to effectively build up a cooperative system in limited transmitter power level on the sea. And nodes are independent selfish individuals and they are not voluntary to help other nodes. So the user must pay fee to buy resource to attain corresponding help. Then the other user calculate payoff of itself to decide to join or not the cooperative communication. Therefore, its inevitable to consume resources such as power and rate helping other nodes to transmit, rational nodes are not obligatory to involve in this cooperate activity. Game theory is a good mathematic method to research effective cooperative condition and requirement. In [8], the authors investigated multi-channel assignment in wireless sensor networks utilizing the game theoretic approach. In [9] the author proposed adjustable price and rate algorithm based on cooperative stimulating method in Ad hoc networks. In [10], cooperative spectrum access of primary and secondary users, as well as MAC protocol for multi-channel cognitive radio networks is developed. In [11] authors discussed a non-symmetrical cooperative three-node model. But in practice, the two users are all likely the potential relay which is better accordant with fairness of network. In this paper, game theory method and price mechanism are applied into the cooperative cognitive mesh/ad hoc maritime network to formulate when to cooperate. An symmetrical system model is constructed, and a price game based

676

T. Yang et al.

on a payoff function is proposed. The idea of this paper is that source node can get relevant help just when it pay for to purchase corresponding resource, and relay node determine whether to involve into the cooperative communication according to profit in this action. In this paper, an innovative paradigm cooperative cognitive maritime wireless mesh/ad hoc networks (CCMWMAN) is proposed to provide high-speed and low-cost communications for maritime environment. The framework of CCMWMAN is exploited firstly, as well as the analysis of available white space, regulation requirement and standards, to efficiently use the limited frequency resource. Specially, game theory method is applied within the cooperative SUs. An symmetrical system model is constructed, and a price game based on a payoff function is proposed. Then we will describe the game theory process to converge to Nash equilibrium. The remainder of this paper is organized as follows. System model and WS analysis are illustrated in Sect. 2 and problem formulation is shown in Sect. 3. Section 4 describes the game theory based resource allocation of symmetrical cooperative three-node model, as well as the proof of value boundary of some parameters. In Sect. 5, simulation results validate the performance of our scheme. Finally, conclusions and references end this paper.

2

System Model and WS Analysis

This section presents the details of the system model as well as the analysis of WS on the sea. 2.1

System Model

We consider the scenario that an innovative paradigm cooperative cognitive maritime wireless mesh/ad hoc networks (CCMWMAN) could be formed by exploring the connection between neighboring ships, sea farm, oil/gas platform, maritime security/safe monitors as well as marine beacons and buoys. Depending to the node mobility, the network close to shore could be the combination of the classed two network: (1) immobile marine infrastructure mesh network; (2) mobile ship-to-ship/shore mesh/ad hoc network. The nodes are equipped with a mesh/ad hoc module that is capable of implementing cognitive radio functions. The cognitive nodes which sense or collect data regularly, could be the relay to deliver traffic. And then, the immobile or mobile nodes could connect to the land-based network via shore-based base station easily. In this cognitive system, primary users could be the radio devices installed near the coastal region such as authorities on land, or licensed vessels. Thereby, the secondary users could be the devices on the sea, such as the unlicensed vessels neighboring, sea farm, oil/gas platform, as well as marine beacons and buoys. And they could cooperate with each other, and form a cluster to cooperate with primary users further to strive for transmission opportunities. Therefore, the unlicensed users could utilize the unused frequency spectrum resources opportunistically. And the sensed data or monitoring data by the mesh/ad hoc nodes could be transmitted to the land-based administrative agencies through wireline/wireless network on


677

Fig. 1. System model.

land dynamically adopting the unused frequency channels by the primary users. Therefore, this network could provide high-speed and low-cost communications for maritime environment. Network topologies are shown in Fig. 1. 2.2

The Analysis of WS on the Sea

There is nor TV broadcasting frequency neither cellular signal frequently adopted at sea currently. Therefore, there is prosperity TVWS and cellular WS at sea nowadays, especially the open ocean. Furthermore, the state-of-the-art maritime communication system, christened as Global Maritime Distress and Safety System, comprises of terrestrial and satellite systems [12]. The frequency bands of GMDSS system utilized scattered across very high frequency (VHF, 30–300 MHz), medium frequency band (MF, 300 kHz-3 MHz), high frequency band (HF, 3–30 MHz) as well as Microwave bands such as ultra high frequency (UHF, 300 MHz) adopted by satellite communications. Hence, the frequency bands utilized at sea are highly fragmented, and they are not employed efficiently. In the literature [2], it is noted that the channels of 156–174 MHz in VHF band provide attractive opportunistic access for high-speed communications. The unused bands aforementioned dedicated for maritime communication are called “Maritime WS”. Moreover, the utilization rule of maritime WS should be: (1) The primary users like authorities or licensed vessels should be solidly protected, and avoid harmful interference [13]; (2) The priority of communication at sea should be subordinated restricted; (3) The cognitive nodes at sea should have the capability to sense the “Maritime WS” spectrum, wherever they go across the world.

3

Problem Formulation

Some research focused on the resource allocation between primary users and secondary users in cognitive mesh/ad hoc network. Although the cooperation

678

T. Yang et al.

between secondary users are equally significant. And the secondary users could cooperate with each other, and form a cluster to cooperate with primary users further to strive for transmission opportunities. However, the secondary users are independent selfish individuals and they are not voluntary to help other nodes. So the user must pay fee to buy resource to attain corresponding help. Then the other user calculate payoff of itself to decide to join or not the cooperative communication. Therefore, its inevitable to consume resources such as power and rate helping other nodes to transmit. In this paper, we will discuss the cooperative scheme between secondary users, especially the symmetric model. Game Theory Model. Firstly, its need to confirm the participant element (playersstrategy space, payoffs) to establish game theory model. In this cooperative symmetrical network of triple nodes, we define the source node s and the destination node d, which the status of two is equal. The source nodedestination node d and the relay node r are players. Strategies space are inspirit price μ and ν, which are the price paid to the partner of source node and relay node, and the speed Rrs and Rsr , which are helping partner to tranmit data. Payoff function is defined as the margin of utility function and price function when adopt the strategies as P ayof f = U − P , here U denotes utility function and P is price function. – Utility Function: Here we employ a common utility function expressed by received data when consuming one unit energy the same as literature [14] . So the definition of Utility function is unit throughout utility multiply throughout. The unit throughout of local operation is defined as 1/bit. The following expression Rs and bersd are respectively the transmitting speed and bit error rate(BER) of source nodewhich is in the local operation. Rr and berrd are respectively the transmitting speed and BER of source node which is in the local operation. T is transmitting time. The utility of non-cooperative source node is: Us non = 1 · T hroughput = Rs · T · (1 − bersd )

(1)

The utility of cooperative source node is: Us coop = 1 · T hroughputlocal + 1 · T hroughputrelay

(2)

The utility of non-cooperative relay node is: Ur

non

= 1 · T hroughput = Rr · T · (1 − berrd )

(3)

The utility of cooperative relay node is: Ur

coop

= 1·T hroughput = (Rr −Rrs )·T ·(1−berrd )+Rsr T ·(1−bersr ) (4)

i – Price Function: The service price is defined as λi = λ0 RRmax , here λ0 is criterion price. Ri and Rmax respectively indicate transmitting rate and maximum


679

rate supported by this system. λs denotes source unit price of local service; λr denotes relays unit price of local service; λrs denotes unit price of relay helping source to transmit; λsr denotes unit price of source helping relay to transmit; μ and ν are respectively stimulating price of source and relay; λr−rs is unit price of relay node when cooperation; λs−sr is unit price of source node when cooperation. The price function of non-cooperative source node is: Ps−non = λs · Rs · T · (1 − bersd )

(5)

The price function of cooperative source node is: Ps−coop = λs−sr · (Rs − Rsr ) · T · (1 − bersd ) + (μ + λrs ) · Rrs · T · (1 − berrs ) − ν · Rsr · T · (1 − bersr )

(6)

The price function of non-cooperative relay node is: Pr−non = λr · Rr · T · (1 − berrd )

(7)

The price function of cooperative relay node is: Pr−coop = λr−rs · (Rr − Rrs ) · T · (1 − berrd ) + (ν + λsr ) · Rsr · T · (1 − bersr ) − μ · Rrs · T · (1 − berrs )

(8)

– Payoff Function: Payoff function is defined as difference of utility function and price function adopting strategies, i.e., P ayof f = U − P . (U denotes utility function, P is price function) The payoff function of non-cooperative source node is: payof fs−non = (1 − λs ) · Rs · T · (1 − bersd )

(9)

The payoff function of cooperative source node is: payof fs−coop = (1 − λs−sr ) · (Rs − Rsr ) · T · (1 − bersd ) + ν · Rsr · T · (1 − bersr ) + (1 − μ − λrs ) · Rrs · T · (1 − berrs )

(10)

The payoff function of non-cooperative relay node is: payof fr−non = (1 − λr ) · Rr · T · (1 − berrd )

(11)

The payoff function of cooperative relay node is: payof fr−coop = (1 − λr−rs ) · (Rr − Rrs ) · T · (1 − berrd ) + μ · Rrs · T · (1 − berrs ) + (1 − ν − λsr ) · Rsr · T · (1 − bersr )

4

(12)

Game Theory Based Resource Allocation

In this section, we described the game theory process to converge to Nash equilibrium by the iteration algorithm. Firstly, the value boundary of u and v are given. Exploiting the same rules, the value boundary of relaying rate Rrs and Rsr could be obtained easily.

680

4.1

T. Yang et al.

Value Boundary Analysis

Lemma 1. Using game theory to converge to Nash equilibrium, the stimulating price of source and relay u and v, as well as the are achieved. Proof. – Value boundary of u and v: When cooperation, according to P ayof fs coop − P ayof fs non ≥ 0 and P ayof fr coop − P ayof fr non ≥ 0, we could get, u ≤ 1 − λs + vRrs (1 − bersr ) + λs · Rs (1 − bersd ) − Rsr (1 − bersd ) − λs−sr (Rs − Rsr )(1 − bersd ) Rrs (1 − berrs )

(13)

[Rrs + λr−rs (Rr − Rrs )](1 − berrd ) + (v + λsr − 1)Rsr (1 − bersr ) Rsr (1 − bersr ) (14) [λr − Rrs − λr−rs (Rr − Rrs )](1 − berrd ) + μRrs (1 − berrs ) v≤ + 1 − λsr Rsr (1 − bersr ) (15) v ≥ [Rsr +λs−sr (Rs −Rsr )−λs Rs ](1−bersd )−(1−u−λs )Rrs (1−berrs ) (16) u≥

Jointing the formulas [13]-[16], we could get the value which satisfy the value of μmin and νmin simultaneously. – Value boundary of Rrs and Rsr : payof fr−coop is a quadratic function regarding to Rrs . When u is fixed, we ∗ calculating the first order partial derivative can get the extreme value of Rrs of Rrs . ∂payof fr−coop 2λ0 (Rr − Rrs )T (1 − berrd ) = −T (1−berrd )+ +uT (1−berrs ) ∂Rrs Rmax (17) ∂payof fr−coop When = 0, we can get ∂Rrs ∗ Rrs = Rr − Rrs max [

1 μ(1 − rrs ) ] − 2λ0 2λ0 (1 − berrd )

(18)

Here, we suppose the calculating method of service price are λr−rs = λr −λrs and λs−sr = λs − λsr . Similarly, payof fs−coop is a quadratic function regarding to Rsr . When ν is ∗ calculating the first order partial fixed, we can get the extreme value of Rsr derivative of Rsr . ∂payof fs−coop λ0 = (Rs − Rsr )T (1 − bersd ) − (Rs − Rsr )2 T (1 − bersd ) ∂Rsr Rmax + (1 − μ − λs )Rs T (1 − berrs ) + νRsr T (1 − bersr ) (19)


When

∂payof fs−coop ∂Rsr

681

= 0, we could get

∗ = Rs − Rsr max [ Rsr

1 ν(1 − rsr ) ] − 2λ0 2λ0 (1 − bersd )

(20)

∗ ∗ Hence, we find that Rrs is just relating to μ but not ν; Rsr is just relating to ν but not μ.

4.2

Game Theory Process Description

This is two-parties game existing priority decision, so its absolutely information dynamic game. According to the above derivation, payof fs−coop is a monotonous decrease progressively function regarding to μ and monotonous increase progressively function regarding to ν. payof fr−coop is a monotonous decrease progressively function regarding to ν and monotonous increase progressively function regarding to μ. So as to say, the two parties hope the simulating price of opposite side is higher, and the price given to opposite side is lower. So when starting cooperation, they are certain to select μmin and νmin . Then we can get the cooperation transmitting rate given to opposite side calculating by formulation [18]-[20]. If they are lower than maximum transmitting rate, the game theory process finished and cooperation process started. If they not fit the requirement, it need revalue minimum from the value range to game and repeat the above process. payof ftotal−coop = payof fs + payof fr + payof fBS = payof fs + payof fr + Ptotal = payof fs + payof f + Ps + Pr = (Us − Ps ) + (Ur − Pr ) + Ps + Pr = Us + Ur (21) payof ftotal−non = (1 − λs )Rs T (1 − bersd ) + (1 − λr )Rr T (1 − berrd )

(22)

payof ftotal−coop = (1 − λs−sr )(Rs − Rsr )T (1 − bersd ) + (1 − μ − λs )Rrs T (1 − berrs ) + νRsr T (1 − bersr ) + (1 − λr−rs )(Rr − Rrs )T (1 − berrd ) + (1 − ν − λsr )Rsr T (1 − bersr ) + μRrs T (1 − berrs ) = (1 − λs )Rrs T (1 − berrs ) + (1 − λsr )Rsr T (1 − bersr ) + (1 − λs−sr )(Rs − Rsr )T (1 − bersd ) + (1 − λr−rs )(Rr − Rrs )T (1 − berrd )

(23)

We suppose rr = rrs and rs = rsr , the above formulation can similar to payof ftotal−coop = (λr−rs − λs )Rrs T (1 − berrd ) + (λs−sr − λsr )Rsr T (1 − bersd ) + (1 − λs−sr )Rs T (1 − bersd ) + (1 − λr−rs )Rr T (1 − berrd ) (24) when λr−rs − λs > 0 and λs−sr − λsr > 0, then the Nash equilibrium result ∗ ∗ (Rrs max , μmin ), (Rsr max , νmin ) can satisfy Pareto optimum following the increase of Rrs and Rsr thus the whole network could achieve optimum status.

682

5

T. Yang et al.

Simulation Results

In the simulation, we consider the VHF band as the WS. The coordination of destination is (0, 0), source is (4, 0). Here, the unit coordinates are all nautical mile. Easy to observe, we select relay (xx, yy) and the horizontal coordination and vertical coordination are varied in the scope of (1, 10).The transmitting time T is 10msthe λ0 criterion price is 0.1.The local services transmitting rate of source and relay are all 320 Mb/S. Simulation results are the comparison of payoff of system , source and relay when axis X and Y respective indicating horizontal and vertical coordination, as shown in Fig. 2, respectively. We can see that the cooperative system payoff are distinctive increased than non-cooperative. And the distinction between relay and destination is increased, the payoff is larger, the desire is stronger, efficiency is better. But the payoffs of source are nearly when cooperation and non-cooperation. It explains that it does not obtain the best offset from fee. The payoff of source can increase if value of μ is larger.

Fig. 2. The payoff comparison between cooperative and non-cooperative system.


6

683

Conclusion

In this paper, an innovative paradigm cooperative cognitive maritime wireless mesh/ad hoc networks (CCMWMAN) is proposed to provide high-speed and low-cost communications for maritime environment. The framework is developed, as well as the analysis of available white space at sea to efficiently use the limited frequency resource. Moreover, game theory method is applied within the cooperative SUs. An symmetrical system model is constructed, and a price game based on a payoff function is proposed. Then the game theory process to converge to Nash equilibrium is described. Simulation results indicate that our propose scheme is desirable with regards to performance of total system, source node and relay node. For future work, we plan to research the design of MAC layer and resource allocation issue between primary user and secondary users. Moreover, the security of secondary users could be considered further. Acknowledgments. This work was supported in part by China Postdoctoral Science Foundation under Grants 2013M530900, Natural Science Foundation of China under Grant 61401057, Science and technology research program of Liaoning under Grants L2014213, NSERC, Canada, Research Funds for the Central Universities 3132015201, China Postdoctoral International Academic Exchange Fund, and also supported by Scientific Research Foundation for the Returned Overseas Chinese Scholars from Ministry of Human Resources and Social Security.

References 1. Cellular-news, Maritime WiMAX Network Launched in Singapore (2008). http:// www.cellular-news.com/story/29749.php 2. Zhou, M.T., Harada, H.: Cognitive maritime wireless mesh/ad hoc networks. J. Netw. Comput. Appl. 35(2), 518–526 (2012) 3. Chen, C., Yan, J., Lu, N., Wang, Y., Yang, X., Guan, X.: Ubiquitous monitoring for industrial cyber-physical systems over relay assisted wireless sensor networks. IEEE Trans. Emerg. Topics Comput. doi:10.1109/TETC.2014.2386615 4. Xu, Q., Su, Z., Han, B., Fang, D., Xu, Z., Gan, X.: Analytical model with a novel selfishness division of mobile nodes to participate cooperation. Peer-to-Peer Netw. Appl. doi:10.1007/s12083-015-0330-6 5. Su, Z., Xu, Q., Zhu, H., Wang, Y.: A novel design for content delivery over software defined mobile social networks. IEEE Netw. 29(4), 1332–1346 (2015) 6. Liu, S., Zhu, H., Du, R., Chen, C., Guan, X.: Location privacy preserving dynamic spectrum auction in cognitive radio network. In: IEEE ICDCS, pp. 256-265 (2013) 7. Gao, Z., Zhu, H., Liu, Y., Li, M., Cao, Z.: Location privacy in database-driven cognitive radio networks: attacks and countermeasures. In: IEEE INFOCOM, pp. 2751-2759 (2013) 8. Yu, Q., Chen, J., Fan, Y., (Sherman) Shen, X., Sun, Y.: Multi-channel assignment in wireless sensor networks: a game theoretic approach. In: IEEE INFOCOM, USA (2010) 9. Shastry, N., Adve, R.S.: Stimulating cooperative diversity in wireless ad hoc networks through Pricing. In: IEEE ICC, pp. 3747-3752 (2006)

684

T. Yang et al.

10. Zhang, N., et al.: Risk-aware cooperative spectrum access for multi-channel cognitive radio networks. IEEE J. Sel. Areas Commun. 32(3), 516C527 (2014) 11. Chen, F., Yang, Y., Wang, Z.: Novel resource allocation for cooperative MB-OFDM UWB Systems. In: IEEE WiCom (2009) 12. Maglogiannis, I., et al.: Next generation maritime communication systems. Int. J. Mob. Commun. 3(3), 231–248 (2005) 13. Gao, Z., Zhu, H., Li, S., Du, S., Li, X.: Security and privacy of collaborative spectrum sensing in cognitive radio networks. IEEE Wire. Commun. 19(6), 106– 112 (2012) 14. Menghan, W.: Cooperative resource management of wireless multi-medial based on game theory. TN929.53 (2007)