THE PRICE OF NON-COOPERATION IN RESERVATION-BASED BANDWIDTH SHARING PROTOCOLS

ABSTRACTIn reservation-based bandwidth sharing protocols, the base station relies on the stations’ requests to allocate time slots to them. Like most  other protocols, reservation-based protocols were designed with the assumption that all stationsrespect the rules of the protocols. However, as mobile devices are becoming more intelligent andprogrammable, they can selfishly optimize their operations to obtain a larger share of commonbandwidth. Here, we study reservation-based bandwidth sharing protocols considering the existence of selfish stations through game-theoretic perspectives. We show that this game admits a Nash  equilibrium. Then, we prove the inefficiency of the Nash equilibrium. Game-theoretical analysis shows that local optimization in the bandwidth sharing problem with conflicted interests does not lead to any global optimization.Keywords. Nash equilibrium, Repeated game, Reservation-based

On Selfish Behavior in CSMA/CA Networks

CSMA/CA protocols rely on the random deferment of packet transmissions. Like most other protocols, CSMA/CA was designed with the assumption that the nodes would play by the rules. This can be dangerous, since the nodes themselves control their random deferment. Indeed, with the higher programmability of the network adapters, the temptation to tamper with the software or firmware is likely to grow; by doing so, a user could obtain a much larger share of the available bandwidth at the expense of other users. We use a game-theoretic approach to investigate the problem of the selfish behavior of nodes in CSMA/CA networks, specifically geared towards the most widely accepted protocol in this class of protocols, IEEE~802.11. We characterize two families of Nash equilibria in a single stage game, one of which always results in a network collapse. We argue that this result provides an incentive for cheaters to cooperate with each other. Explicit cooperation among nodes is clearly impractical. By applying the model of dynamic games borrowed from game theory, we derive the conditions for the stable and optimal functioning of a population of cheaters. We use this insight to develop a simple, localized and distributed protocol that successfully guides multiple selfish nodes to a Pareto-optimal Nash equilibrium

Optimal Scanning Bandwidth Strategy Incorporating Uncertainty about Adversary's Characteristics

In this paper we investigate the problem of designing a spectrum scanning strategy to detect an intelligent Invader who wants to utilize spectrum undetected for his/her unapproved purposes. To deal with this problem we model the situation as two games, between a Scanner and an Invader, and solve them sequentially. The first game is formulated to design the optimal (in maxmin sense) scanning algorithm, while the second one allows one to find the optimal values of the parameters for the algorithm depending on parameters of the network. These games provide solutions for two dilemmas that the rivals face. The Invader's dilemma consists of the following: the more bandwidth the Invader attempts to use leads to a larger payoff if he is not detected, but at the same time also increases the probability of being detected and thus fined. Similarly, the Scanner faces a dilemma: the wider the bandwidth scanned, the higher the probability of detecting the Invader, but at the expense of increasing the cost of building the scanning system. The equilibrium strategies are found explicitly and reveal interesting properties. In particular, we have found a discontinuous dependence of the equilibrium strategies on the network parameters, fine and the type of the Invader's award. This discontinuity of the fine means that the network provider has to take into account a human/social factor since some threshold values of fine could be very sensible for the Invader, while in other situations simply increasing the fine has minimal deterrence impact. Also we show how incomplete information about the Invader's technical characteristics and reward (e.g. motivated by using different type of application, say, video-streaming or downloading files) can be incorporated into scanning strategy to increase its efficiency.Comment: This is the last draft version of the paper. Revised version of the paper was published in EAI Endorsed Transactions on Mobile Communications and Applications, Vol. 14, Issue 5, 2014, doi=10.4108/mca.2.5.e6. arXiv admin note: substantial text overlap with arXiv:1310.724

Optimal Channel-Switching Strategies in Multi-channel Wireless Networks.

The dual nature of scarcity and under-utilization of spectrum resources, as well as recent advances in software-defined radio, led to extensive study on the design of transceivers that are capable of opportunistic channel access. By allowing users to dynamically select which channel(s) to use for transmission, the overall throughput performance and the spectrum utilization of the system can in general be improved, compared to one with a single channel or more static channel allocations. The reason for such improvement lies in the exploitation of the underlying temporal, spatial, spectral and congestion diversity. In this dissertation, we focus on the channel-switching/hopping decision of a (group of) legitimate user(s) in a multi-channel wireless communication system, and study three closely related problems: 1) a jamming defense problem against a no-regret learning attacker, 2) a jamming defense problem with minimax (worst-case) optimal channel-switching strategies, and 3) the throughput optimal strategies for a group of competing users in IEEE 802.11-like medium access schemes. For the first problem we study the interaction between a user and an attacker from a learning perspective, where an online learner naturally adapts to the available information on the adversarial environment over time, and evolves its strategy with certain payoff guarantee. We show how the user can counter a strong learning attacker with knowledge on its learning rationale, and how the learning technique can itself be considered as a countermeasure with no such prior information. We further consider in the second problem the worst-case optimal strategy for the user without prior information on the attacking pattern, except that the attacker is subject to a resource constraint, which models its energy consumption and replenishment process. We provide explicit characterization for the optimal strategies and show the most damaging attacker, interestingly, behaves randomly in an i.i.d. fashion. In the last problem, we consider a group of competing users in a non-adversarial setting. We place the interaction among users in the context of IEEE 802.11-like medium access schemes, and derive decentralized channel allocation for overall throughput improvement. We show the typically rule-of-thumb load balancing principle in spectrum resource sharing can be indeed throughput optimal.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108949/1/qingsi_1.pd

Deep learning-based spectrum prediction collision avoidance for hybrid wireless environments

With a growing number of connected devices relying on the Industrial, Scientific, and Medical radio bands for communication, spectrum scarcity is one of the most important challenges currently and in the future. The existing collision avoidance techniques either apply a random back-off when spectrum collision is detected or assume that the knowledge about other nodes' spectrum occupation is known. While these solutions have shown to perform reasonably well in intra-Radio Access Technology environments, they can fail if they are deployed in dense multi-technology environments as they are unable to address the inter-Radio Access Technology interference. In this paper, we present Spectrum Prediction Collision Avoidance (SPCA): an algorithm that can predict the behavior of other surrounding networks, by using supervised deep learning; and adapt its behavior to increase the overall throughput of both its own Multiple Frequencies Time Division Multiple Access network as well as that of the other surrounding networks. We use Convolutional Neural Network (CNN) that predicts the spectrum usage of the other neighboring networks. Through extensive simulations, we show that the SPCA is able to reduce the number of collisions from 50% to 11%, which is 4.5 times lower than the regular Multiple Frequencies Time Division Multiple Access (MF-TDMA) approach. In comparison with an Exponentially Weighted Moving Average (EWMA) scheduler, SPCA reduces the number of collisions from 29% to 11%, which is a factor 2.5 lower

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Thwarting Selfish Behavior in 802.11 WLANs

The 802.11e standard enables user configuration of several MAC parameters, making WLANs vulnerable to users that selfishly configure these parameters to gain throughput. In this paper we propose a novel distributed algorithm to thwart such selfish behavior. The key idea of the algorithm is for honest stations to react, upon detecting a selfish station, by using a more aggressive configuration that penalizes this station. We show that the proposed algorithm guarantees global stability while providing good response times. By conducting a game theoretic analysis of the algorithm based on repeated games, we also show its effectiveness against selfish stations. Simulation results confirm that the proposed algorithm optimizes throughput performance while discouraging selfish behavior. We also present an experimental prototype of the proposed algorithm demonstrating that it can be implemented on commodity hardware.Comment: 14 pages, 7 figures, journa

Multilevel Pricing Schemes in a Deregulated Wireless Network Market

Typically the cost of a product, a good or a service has many components. Those components come from different complex steps in the supply chain of the product from sourcing to distribution. This economic point of view also takes place in the determination of goods and services in wireless networks. Indeed, before transmitting customer data, a network operator has to lease some frequency range from a spectrum owner and also has to establish agreements with electricity suppliers. The goal of this paper is to compare two pricing schemes, namely a power-based and a flat rate, and give a possible explanation why flat rate pricing schemes are more common than power based pricing ones in a deregulated wireless market. We suggest a hierarchical game-theoretical model of a three level supply chain: the end users, the service provider and the spectrum owner. The end users intend to transmit data on a wireless network. The amount of traffic sent by the end users depends on the available frequency bandwidth as well as the price they have to pay for their transmission. A natural question arises for the service provider: how to design an efficient pricing scheme in order to maximize his profit. Moreover he has to take into account the lease charge he has to pay to the spectrum owner and how many frequency bandwidth to rent. The spectrum owner itself also looks for maximizing its profit and has to determine the lease price to the service provider. The equilibrium at each level of our supply chain model are established and several properties are investigated. In particular, in the case of a power-based pricing scheme, the service provider and the spectrum owner tend to share the gross provider profit. Whereas, considering the flat rate pricing scheme, if the end users are going to exploit the network intensively, then the tariffs of the suppliers (spectrum owner and service provider) explode.Comment: This is the last draft version of the paper. Revised version of the paper accepted by ValueTools 2013 can be found in Proceedings of the 7th International Conference on Performance Evaluation Methodologies and Tools (ValueTools '13), December 10-12, 2013, Turin, Ital

Robust and Listening-Efficient Contention Resolution

This paper shows how to achieve contention resolution on a shared communication channel using only a small number of channel accesses -- both for listening and sending -- and the resulting algorithm is resistant to adversarial noise. The shared channel operates over a sequence of synchronized time slots, and in any slot agents may attempt to broadcast a packet. An agent's broadcast succeeds if no other agent broadcasts during that slot. If two or more agents broadcast in the same slot, then the broadcasts collide and both broadcasts fail. An agent listening on the channel during a slot receives ternary feedback, learning whether that slot had silence, a successful broadcast, or a collision. Agents are (adversarially) injected into the system over time. The goal is to coordinate the agents so that each is able to successfully broadcast its packet. A contention-resolution protocol is measured both in terms of its throughput and the number of slots during which an agent broadcasts or listens. Most prior work assumes that listening is free and only tries to minimize the number of broadcasts. This paper answers two foundational questions. First, is constant throughput achievable when using polylogarithmic channel accesses per agent, both for listening and broadcasting? Second, is constant throughput still achievable when an adversary jams some slots by broadcasting noise in them? Specifically, for $N$ packets arriving over time and $J$ jammed slots, we give an algorithm that with high probability in $N+J$ guarantees $\Theta(1)$ throughput and achieves on average $O(\texttt{polylog}(N+J))$ channel accesses against an adaptive adversary. We also have per-agent high-probability guarantees on the number of channel accesses -- either $O(\texttt{polylog}(N+J))$ or $O((J+1) \texttt{polylog}(N))$, depending on how quickly the adversary can react to what is being broadcast