Abstract-Cognitive radio has been proposed as a novel approach for improving the utilization of the precious limited radio resources by dynamically accessing the spectrum. One of the major design challenges is to coordinate and cooperate in accessing the spectrum opportunistically among multiple distributive users with only local information. In this paper, we propose a game theoretical approach with a new solution concept, the correlated equilibrium, which is better compared to the noncooperative Nash equilibrium in terms of spectrum utilization efficiency and fairness among the distributive users. To achieve this correlated equilibrium, we construct an adaptive algorithm based on no-regret learning that guarantees convergence. From the simulation results, the optimal correlated equilibria achieve better fairness and 5%∼15% performance gain, compared to the Nash equilibria.
In this paper, we study the impact of cooperative routing for maximizing the network lifetime in sensor network applications. We assume nodes in the network are equipped with a single omnidirectional antenna and they perform cooperative transmission to achieve transmit diversity. We propose a joint cooperative transmission and energy aware routing algorithm to prolong the network lifetime. In contrast to the previous works, our approach uses the maximum lifetime power allocation, instead of minimum power allocation. Using the maximum lifetime power allocation, the cooperative nodes allocate their transmit power according to the channel condition and the residual energy in the nodes. Our proposed scheme combines the maximum lifetime power allocation and the energy aware routing to maximize the network lifetime. We study the performance of the cooperative routing in terms of network lifetime (defined as the time until the first node dies) and the total delivered packets before the first node dies. We demonstrate that the proposed solution achieves 1 ∼ 3.5 and 1 ∼ 2 times longer network lifetime and more total delivered packets compared to noncooperative routing, when it is used with MTE and FA algorithms, respectively. Furthermore, the maximum lifetime power allocation achieves 1 ∼ 2 times longer lifetime, compared to minimum power allocation in MTE and FA routing schemes. We also provide distributed implementation of the proposed algorithm. I. INTRODUCTIONAdvances in low power integrated circuit devices and communications technologies have enabled the deployment of lowcost, low power sensors that can be integrated to form sensor networks. This sensor network has vast important applications and has been identified as one of the most important technologies nowadays. The deployment of the low cost and energy limited sensors implies that the energy efficient communication protocol is imperative to extend the lifetime of the network. The problem of energy efficient protocol can be approached from different communication layers; from physical layer, data-link layer, MAC layer, network layer to the application layer. Moreover, the cross layer approach has been shown to be an effective energy saving method in the energy constrained communication [1], [2]. In ad hoc networking environment, most of the energy consumption is due to the packet transmission [3]. Motivated by this fact, we focus on the cross layer approach by jointly design the energy efficient routing algorithm in network layer and the energy efficient signal combining in physical layer.The energy efficient routing and transmit diversity have been studied separately in the literatures. The transmit diversity, pioneered by Alamouti's paper [4] shows the significant performance gain can be achieved in the multiple-input-multiple-output (MIMO) systems. However, multiple antennas in a sensor node may be impractical due to the cost. To overcome this problem, the cooperative communication concept has been recently proposed [5]. This cooperative communication explore...
Abstract-We consider the problem of average throughput maximization per total consumed energy in packetized sensor communications. Our study results in a near-optimal transmission strategy that chooses the optimal modulation level and transmit power while adapting to the incoming traffic rate, buffer condition, and the channel condition. We investigate the point-to-point and multinode communication scenarios. Many solutions of the previous works require the state transition probability, which may be hard to obtain in a practical situation. Therefore, we are motivated to propose and utilize a class of learning algorithms [called reinforcement learning (RL)] to obtain the near-optimal policy in point-to-point communication and a good transmission strategy in multinode scenario. For comparison purpose, we develop the stochastic models to obtain the optimal strategy in the point-to-point communication. We show that the learned policy is close to the optimal policy. We further extend the algorithm to solve the optimization problem in a multinode scenario by independent learning. We compare the learned policy to a simple policy, where the agent chooses the highest possible modulation and selects the transmit power that achieves a predefined signal-to-interference ratio (SIR) given one particular modulation. The proposed learning algorithm achieves more than twice the throughput per energy compared with the simple policy, particularly, in high packet arrival regime. Beside the good performance, the RL algorithm results in a simple, systematic, self-organized, and distributed way to decide the transmission strategy.Index Terms-Energy-aware sensor communications, Markov decision process (MDP), reinforcement learning (RL).
Abstract-In this paper, we consider a class of energy-aware routing algorithm that explicitly takes into account the connectivity of the remaining sensor network. In typical sensor network deployments, some nodes may be more important than other nodes because the failure of these nodes causes the network disintegration, which results in early termination of information delivery. To mitigate this problem, we propose a class of routing algorithms called keep-connect algorithms, that use computable measures of network connectivity in determining how to route packets. The proposed algorithms embed the importance of the nodes in the routing cost/metric. The importance of a node is characterized by the algebraic connectivity of the remaining graph when that node fails. We prove several properties of the proposed routing algorithm including the energy consumption upper bound. Using extensive simulations, we demonstrate that the proposed algorithm achieves significant performance improvement compared to the existing routing algorithms. More importantly, we show that our proposed algorithm is more robust in terms of algebraic network connectivity compared to the existing algorithm. Finally, we present the distributed implementation of our proposed algorithm.
Abstract-In wireless ad hoc networks, autonomous nodes are reluctant to forward others' packets because of the nodes' limited energy. However, such selfishness and noncooperation deteriorate both the system efficiency and nodes' performances. Moreover, the distributed nodes with only local information may not know the cooperation point, even if they are willing to cooperate. Hence, it is crucial to design a distributed mechanism for enforcing and learning the cooperation among the greedy nodes in packet forwarding. In this paper, we propose a selflearning repeated-game framework to overcome the problem and achieve the design goal. We employ self-transmission efficiency as the utility function of individual autonomous node. The self transmission efficiency is defined as the ratio of the power for self packet transmission over the total power for self packet transmission and packet forwarding. Then, we propose a framework to search for good cooperation points and maintain the cooperation among selfish nodes. The framework has two steps: First, an adaptive repeated game scheme is designed to ensure the cooperation among nodes for the current cooperative packet forwarding probabilities. Second, self-learning algorithms are employed to find the better cooperation probabilities that are feasible and benefit all nodes. We propose three learning schemes for different information structures, namely, learning with perfect observability, learning through flooding, and learning through utility prediction. Starting from noncooperation, the above two steps are employed iteratively, so that better cooperating points can be achieved and maintained in each iteration. From the simulations, the proposed framework is able to enforce cooperation among distributed selfish nodes and the proposed learning schemes achieve 70% to 98% performance efficiency compared to that of the optimal solution.
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