Streaming systems that can maintain Quality of Experience (QoE) for users have attracted much attention because they can be applied in various fields, such as emergency response training and medical surgery. Dynamic Adaptive Streaming over HTTP (DASH) is a typical protocol for streaming system. In order to improve QoE in DASH, a multi-server system has been presented by pseudo-increasing bandwidth through multiple servers. This multi-server system is designed to share streaming content efficiently in addition to having redundant server resources for each streaming content, which is excellent for fault tolerance. Assigning DASH server to users in these multi-servers environment is important to maintain QoE, thus a method of server assignment of users (user allocation method) for multi-servers is presented by using cooperative game theory. However, this conventional user allocation method does not take into account the size of the server bandwidth, thus users are concentrated on a particular server at the start of playback. Although the average required bit rate of video usually fluctuates, bit rate fluctuations are not taken into account. These phenomena may decrease QoE. In this paper, we propose a novel user allocation method using coalition structure generation in cooperative game theory to improve the QoE of all users in an immediate and stable manner in DASH environment. Our proposed method can avoid user concentration, since the bandwidth used by the overall system is taken into account. Moreover, our proposed method can be performed every time the average required bit rate changes. We demonstrate the effectiveness of our method through simulations using Network Simulator 3 (NS3).

Sharding is a solution to the blockchain scalability problem. A sharded blockchain divides consensus nodes (validators) into groups called shards and processes transactions separately to improve throughput and latency. In this paper, we analyze the rational behavior of users in account/balance model-based sharded blockchains and identify a phenomenon in which accounts (users' wallets and smart contracts) eventually get concentrated in a few shards, making shard loads unfair. This phenomenon leads to bad user experiences, such as delays in transaction inclusions and increased transaction fees. To solve this problem, we propose two load balancing methods in account/balance model-based sharded blockchains. Both methods perform load balancing by periodically reassigning accounts: in the first method, the blockchain protocol itself performs load balancing and in the second method, wallets perform load balancing. We discuss the pros and cons of the two protocols, and apply the protocols to the execution sharding in Ethereum 2.0, an existing sharding design. Further, we analyze by simulation how the protocols behave to confirm that we can observe smaller transaction delays and fees. As a result, we released the simulation program as “Shargri-La,” a simulator designed for general-purpose user behavior analysis on the execution sharding in Ethereum 2.0.

In the context of Web 2.0, the interaction between users and resources is more and more frequent in the process of resource sharing and consumption. However, the current research on resource pricing mainly focuses on the attributes of the resource itself, and does not weigh the interests of the resource sharing participants. In order to deal with these problems, the pricing mechanism of resource-user interaction evaluation based on multi-agent game theory is established in this paper. Moreover, the user similarity, the evaluation bias based on link analysis and punishment of academic group cheating are also included in the model. Based on the data of 181 scholars and 509 articles from the Wanfang database, this paper conducts 5483 pricing experiments for 13 months, and the results show that this model is more effective than other pricing models - the pricing accuracy of resource resources is 94.2%, and the accuracy of user value evaluation is 96.4%. Besides, this model can intuitively show the relationship within users and within resources. The case study also exhibits that the user's knowledge level is not positively correlated with his or her authority. Discovering and punishing academic group cheating is conducive to objectively evaluating researchers and resources. The pricing mechanism of scientific and technological resources and the users proposed in this paper is the premise of fair trade of scientific and technological resources.

Cognitive radio network (CRN) provides an effective way of improving efficiency and flexibility in spectrum usage. Due to the coexistence of secondary user (SU) and primary user (PU), managing interference is a critical issue to be addressed if we are to reap the full benefits. In this paper, we consider the problem of joint interference management and resource allocation in a multi-channel ad hoc CRN. We formulate the problem as an overlapping coalition formation game to maximize the sum rate of SU links while guaranteeing the quality of service (QoS) of PU links. In the game, each SU link can make an autonomous decision and is allowed to participate in one or more cooperative coalitions simultaneously to maximize its payoff. To obtain the solution of the formulated game, a distributed, self-organizing algorithm is proposed for performing coalition formation. We analyze the properties of the algorithm and show that SU links can cooperate to reach a final stable coalition structure. Compared with existing approaches, the proposed scheme achieves appreciable performance improvement in terms of the sum rate of SU links, which is demonstrated by simulation results.

Many countries have deregulated their electricity retail markets to offer lower electricity charges to consumers. However, many consumers have not switched their suppliers after the deregulation, and electricity suppliers do not tend to reduce their charges intensely. This paper proposes an electricity market model and evolutionary game to analyze the behavior of consumers in electricity retail markets. Our model focuses on switching costs such as an effort at switching, costs in searching for other alternatives, and so on. The evolutionary game examines whether consumers choose a strategy involving exploration of new alternatives with the searching costs as “cooperators” or not. Simulation results demonstrate that the share of cooperators was not improved by simply giving rewards for cooperators as compensation for searching costs. Furthermore, the results also suggest that the degree of cooperators in a network among consumers has a vital role in increasing the share of cooperators and switching rate.

The requirement of safety, roadway capacity and efficiency in the vehicular network, which makes vehicular platoons concept continue to be of interest. For the authentication in vehicular platoons, efficiency and cooperation are the two most important things. Cooperative authentication is a way to recognize false identities and messages as well as saving resources. However, taking part in cooperative authentication makes the vehicle more vulnerable to privacy leakage which is commonly done by location tracking. Moreover, vehicles consume their resources when cooperating with others during the process of cooperation authentication. These two significant factors cause selfish behaviors of the vehicles not to participate in cooperate cooperation actively. In this paper, an infinitely repeated game for cooperative authentication in vehicular platoons is proposed to help analyze the utility of all nodes and point out the weakness of the current collaborative authentication protocol. To deal with this weakness, we also devised an enhanced cooperative authentication protocol based on mechanisms which makes it easier for vehicles to stay in the cooperate strategy rather than tend to selfish behavior. Meanwhile, our protocol can defense insider attacks.

This paper studies the problem of real-time routing in a multi-autonomous robot enhanced network at uncertain and vulnerable tactical edge. Recent network protocols, such as opportunistic mobile network routing protocols, engaged social network in communication network that can increase the interoperability by using social mobility and opportunistic carry and forward routing algorithms. However, in practical harsh environment such as a battlefield, the uncertainty of social mobility and complexity of vulnerable environment due to unpredictable physical and cyber-attacks from enemy, would seriously affect the effectiveness and practicality of these emerging network protocols. This paper presents a GT-SaRE-MANET (Game Theoretic Situation-aware Robot Enhanced Mobile Ad-hoc Network) routing protocol that adopt the online reinforcement learning technique to supervise the mobility of multi-robots as well as handle the uncertainty and potential physical and cyber attack at tactical edge. Firstly, a set of game theoretic mission oriented metrics has been introduced to describe the interrelation among network quality, multi-robot mobility as well as potential attacking activities. Then, a distributed multi-agent game theoretic reinforcement learning algorithm has been developed. It will not only optimize GT-SaRE-MANET routing protocol and the mobility of multi-robots online, but also effectively avoid the physical and/or cyber-attacks from enemy by using the game theoretic mission oriented metrics. The effectiveness of proposed design has been demonstrated through computer aided simulations and hardware experiments.

Signed networks are ubiquitous in the real world. It is of great significance to study the problem of community detection in signed networks. In general, the behaviors of nodes in a signed network are rational, which coincide with the players in the theory of game that can be used to model the process of the community formation. Unlike unsigned networks, signed networks include both positive and negative edges, representing the relationship of friends and foes respectively. In the process of community formation, nodes usually choose to be in the same community with friends and between different communities with enemies. Based on this idea, we proposed a game theory model to address the problem of community detection in signed networks. Taking nodes as players, we build a gain function based on the numbers of positive edges and negative edges inside and outside a community, and prove the existence of Nash equilibrium point. In this way, when the game reaches the Nash equilibrium state, the optimal strategy space for all nodes is the result of the final community division. To systematically investigate the performance of our method, elaborated experiments on both synthetic networks and real-world networks are conducted. Experimental results demonstrate that our method is not only more accurate than other existing algorithms, but also more robust to noise.

In encryption schemes, the sender may not generate randomness properly if generating randomness is costly, and the sender is not concerned about the security of a message. The problem was studied by the first author (2016), and was formalized in a game-theoretic framework. In this work, we construct an encryption scheme with an optimal round complexity on the basis of the mechanism of repeated games.

Clustering is known to be an effective means of reducing energy dissipation and prolonging network lifetime in wireless sensor networks (WSNs). Recently, game theory has been used to search for optimal solutions to clustering problems. The residual energy of each node is vital to balance a WSN, but was not used in the previous game-theory-based studies when calculating the final probability of being a cluster head. Furthermore, the node payoffs have also not been expressed in terms of energy consumption. To address these issues, the final probability of being a cluster head is determined by both the equilibrium probability in a game and a node residual energy-dependent exponential function. In the process of computing the equilibrium probability, new payoff definitions related to energy consumption are adopted. In order to further reduce the energy consumption, an assistant method is proposed, in which the candidate nodes with the most residual energy in the close point pairs completely covered by other neighboring sensors are firstly selected and then transmit same sensing data to the corresponding cluster heads. In this paper, we propose an efficient energy-aware clustering protocol based on game theory for WSNs. Although only game-based method can perform well in this paper, the protocol of the cooperation with both two methods exceeds previous by a big margin in terms of network lifetime in a series of experiments.

Rational proofs, introduced by Azar and Micali (STOC 2012), are a variant of interactive proofs in which the prover is rational, and may deviate from the protocol for increasing his reward. Guo et al. (ITCS 2014) demonstrated that rational proofs are relevant to delegation of computation. By restricting the prover to be computationally bounded, they presented a one-round delegation scheme with sublinear verification for functions computable by log-space uniform circuits with logarithmic depth. In this work, we study rational proofs in which the verifier is also rational, and may deviate from the protocol for decreasing the prover's reward. We construct a three-message delegation scheme with sublinear verification for functions computable by log-space uniform circuits with polylogarithmic depth in the random oracle model.

Decentralized channel assignment schemes are proposed to obtain low system-wide spatial overlap regions in wireless local area networks (WLANs). The important point of channel assignment in WLANs is selecting channels with fewer contending stations rather than mitigating interference power due to its medium access control mechanism. This paper designs two potential game-based channel selection schemes, basically each access point (AP) selects a channel with smaller spatial overlaps with other APs. Owing to the property of potential games, each decentralized channel assignment is guaranteed to converge to a Nash equilibrium. In order that each AP selects a channel with smaller overlaps, two metrics are proposed: general overlap-based scheme yields the largest overlap reduction if a sufficient number of stations (STAs) to detect overlaps are available; whereas decomposed overlap-based scheme need not require such STAs, while the performance would be degraded due to the shadowing effect. In addition, the system-wide overlap area is analytically shown to be upper bounded by the negative potential functions, which derives the condition that local overlap reduction by each AP leads to system-wide overlap reduction. The simulation results confirm that the proposed schemes perform better reductions in the system-wide overlap area compared to the conventional interference power-based scheme under the spatially correlated shadowing effect. The experimental results demonstrate that the channel assignment dynamics converge to stable equilibria even in a real environment, particularly when uncontrollable APs exist.

In this letter, we propose two robust and distributed game-based algorithms, which are the modifications of two algorithms proposed in [1], to solve the joint base station selection and resource allocation problem with imperfect information in heterogeneous cellular networks (HCNs). In particular, we repeatedly sample the received payoffs in the exploitation stage of each algorithm to guarantee the convergence when the payoffs of some users (UEs) in [1] cannot accurately be acquired for some reasons. Then, we derive the rational sampling number and prove the convergence of the modified algorithms. Finally, simulation results demonstrate that two modified algorithms achieve good convergence performances and robustness in the incomplete information scheme.

In this paper, we introduce the new concept of temporal diversity utilization based on asymmetric transmission to minimize network interference in wireless ad-hoc networks with a two-hop half-duplex relaying (HDR) protocol. Asymmetric transmission is an interference-aware backoff technique, in which each communication session (source-relay-destination link) adaptively chooses a certain subset of spectrally-orthogonal data streaming which should be delayed by the duration of one time-slot (i.e., half of one subframe). We design the problem in the HDR scenario by applying the concept of asymmetric transmission, and evaluate the game-theoretical algorithm, called asymmetric transmission game (ATG), to derive the suboptimal solution. We show that ATG is an exact potential game, and derive its convergence and optimality properties. Furthermore, we develop an approximated version of ATG (termed A-ATG) in order to reduce signaling and computational complexity. Numerical results verify that two algorithms proposed show significant synergistic effects when collaborating with the conventional methods in terms of interference coordination. Ultimately, the energy consumption to satisfy the rate requirement is reduced by up to 17.4% compared to the conventional schemes alone.

Potential games form a class of non-cooperative games where the convergent of unilateral improvement dynamics is guaranteed in many practical cases. The potential game approach has been applied to a wide range of wireless network problems, particularly to a variety of channel assignment problems. In this paper, the properties of potential games are introduced, and games in wireless networks that have been proven to be potential games are comprehensively discussed.

In P2P applications, networks are formed by devices belonging to independent users. Therefore, routing hotspots or routing congestions are typically created by an unanticipated new event that triggers an unanticipated surge of users to request streaming service from some particular nodes; and a challenging problem is how to provide incentive mechanisms to allocation bandwidth more fairly in order to avoid congestion and other short backs for P2P QoS. In this paper, we study P2P bandwidth game — the bandwidth allocation in P2P networks. Unlike previous works which focus either on routing or on forwarding, this paper investigates the game theoretic mechanism to incentivize node's real bandwidth demands and propose novel method that avoid congestion proactively, that is, prior to a congestion event. More specifically, we define an incentive-compatible pricing vector explicitly and give theoretical proofs to demonstrate that our mechanism can provide incentives for nodes to tell the true bandwidth demand. In order to apply this mechanism to the P2P distribution applications, we evaluate our mechanism by NS-2 simulations. The simulation results show that the incentive pricing mechanism can distribute the bandwidth fairly and effectively and can also avoid the routing hotspot and congestion effectively.

In this paper, we introduce a new multi-operator pico eNodeB (eNB) concept for cellular networks. It is expected that mobile data offloading will be performed effectively after installing the pico eNBs in cellular networks, owing to the rapid increase in mobile traffic. However, when several different operators independently install the pico eNBs, high costs and large amounts of space will be required for the installation. In addition, when several different operators accommodate their own user equipments (UEs) in the pico eNBs, not enough UEs can be accommodated. This is because the UEs are not evenly distributed in the coverage area of the pico eNBs. In this paper, the accommodation of the UEs of different operators in co-sited pico eNB is discussed as one of the solutions to these problems. For the accommodation of the UEs of different operators, wireless resources should be allocated to them. However, when each operator independently controls his wireless resources, the operator is not provided with an incentive to accommodate the UEs of the other operators in his pico eNBs. For this reason, an appropriate rule for appropriate allocation of the wireless resources to the UEs of different operators should be established. In this paper, by using the concepts of game theory and mechanism design, a resource allocation rule where each operator is provided with an incentive to allocate the wireless resources to the UEs of different operators is proposed. With the proposed rule, each operator is not required to disclose the control information like link quality and the number of UEs to the other operators. Furthermore, the results of a throughput performance evaluation confirm that the proposed scheme improves the total throughput as compared with individual resource allocation.

In cognitive radio networks, the primary user (PU) can lease a fraction of its licensed spectrum to the secondary users (SUs) in exchange for their cooperative transmission if it has a minimum transmission rate requirement and is experiencing a bad channel condition. However, due to the selfish nature of the SUs, they may not cooperate to meet the PU's Quality of Service (QoS) requirement. On the other hand, the SUs may not exploit efficiently the benefit from cooperation if they compete with each other and collaborate with the PU independently. Therefore, when SUs belong to the same organization and can work as a group, how to stimulate them to cooperate with the PU and thus guarantee the PU's QoS requirement, and how to coordinate the usage of rewarded spectrum among these SUs after cooperation are critical challenges. In this paper, we propose a two-level bargaining framework to address the aforementioned problems. In the proposed framework, the interactions between the PU and the SUs are modeled as the upper level bargaining game while the lower level bargaining game is used to formulate the SUs' decision making process on spectrum sharing. We analyze the optimal actions of the users and derive the theoretic results for the one-PU one-SU scenario. To find the solutions for the one-PU multi-SU scenario, we put forward a revised numerical searching algorithm and prove its convergence. Finally, we demonstrate the effectiveness and efficiency of the proposed scheme through simulations.

This paper focuses on learning the economic behaviour of the access point (AP) and users in wireless local area networks (WLANs), and using a game theoretic approach to analyze the interactions among them. Recent studies have shown that the AP would adopt a simple, yet optimal, fixed rate pricing strategy when the AP has an unlimited uplink bandwidth to the Internet and the channel capacity of WLAN is unlimited. However, the fixed rate strategy fails to be optimal if a more realistic model with limited capacity is considered. A substitute pricing scheme for access service provisioning is hence proposed. In particular, the AP first estimates the probable utility degradation of existing users consequent upon the admission of an incoming user. Second, the AP decides: (i) whether the incoming user should be accepted; and (ii) the price to be announced in order to try to maximize the overall revenue. The condition, under which the proposed scheme results in a perfect Bayesian equilibrium (PBE), is investigated.

In this paper, we consider a multi-cell cognitive radio network (CRN), which overlays a multi-cell primary network. To manage the coexistence, a primary-willingness based coexistent architecture and a novel intra-cell spectrum overlay and inter-cell spectrum underlay sharing method are proposed. In the system, primary base stations will broadcast pilot signals and interference margins to assist the CRN for interference channel evaluation and power control. Subject to the interference margins imposed by the primary network, we define a utility (payoff) function that can represent the secondary system performance while taking into account the co-channel interference among secondary cells. A distributed resource allocation scheme is devised to guarantee the primary performance, and at the same time, maximize the secondary utility without any cooperation among cognitive base stations (CBS). Quality of Service among users is also considered by the scheme such that the instantaneous data rate for each secondary user is larger than a given minimum rate. The resource allocation problem can be decomposed into two subproblems: subchannel allocation and distributed power allocation game (DPAG). We prove that there exists a Nash equilibrium in the DPAG and the equilibrium is unique. Moreover, the DPAG is also Pareto optimal in some constrained environments, that is, no CBS can further improve its performance without impairing others. The proposed algorithm turns out to converge to an equilibrium within a small number of iterations.