IEEE 802.11e is standardized to enhance real time multimedia applications’ quality of service (QoS). This standard introduces two access mechanisms called Enhanced distributed channel access (EDCA) and HCF Controlled Channel Access (HCCA) as well as four Access Categor More
IEEE 802.11e is standardized to enhance real time multimedia applications’ quality of service (QoS). This standard introduces two access mechanisms called Enhanced distributed channel access (EDCA) and HCF Controlled Channel Access (HCCA) as well as four Access Categories (ACs) for different types of applications. Each AC has four adjustable parameters : Arbitrary Inter-Frame Space Number(AIFSN), minimum Size of Contention Window(CWmin), maximum size of Contention Window (CWmax), and TXOP_limit. A TXOP_limit (TXOP) is time interval, in which a wireless station can transmit a number of frames consecutively, without releasing the channel and any further contention with other wireless stations. TXOP improves network throughput as well as service differentiation. Proper TXOP adjustment can lead to better bandwidth utilization and QoS provisioning. This paper studies the determination of TXOP in EDCA mode of IEEE 802.11e using a game theory based approach called GDTXOP. Based on GDTXOP, each wireless node chooses its appropriate TXOP according to its queue length and media access delay. OPNET simulator simulated the proposed method and its accuracy is evaluated and verified. The results of the simulation indicate that tuning TXOP appropriately improves both channel utilization for all levels of traffic priority and fairness. This improvement does not impair the quality of high-priority traffics. The proposed approach improves channel utilization, while preserving fairness and efficiency in WLANs and minimizing selfishness behaviours of stations in a distributed environment. Simulation results show the proposed method improves fairness while not disrupting the quality of service.
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In this paper, the problem of joint energy efficient spectrum sensing and determining the mobile primary user location is proposed based on compressive sensing in cognitive sensor networks. By utilizing compressive sensing, the ratio of measurements for the sensing node More
In this paper, the problem of joint energy efficient spectrum sensing and determining the mobile primary user location is proposed based on compressive sensing in cognitive sensor networks. By utilizing compressive sensing, the ratio of measurements for the sensing nodes are considerably reduced. Therefore, energy consumption is improved significantly in spectrum sensing. The multi-antenna sensors is also considered to save more energy. On the other hand, multi-antenna sensor utilization is a proper solution instead of applying more sensors. The problem is formulated to maximize the network lifetime and find the mobile primary user position by sensors selection under the detection performance and accuracy of localization constraints. For this purpose, a cooperative game is proposed to study this problem. It is shown that with the proposed game, the network lifetime is maximized while the proper sensors which participate in spectrum sensing and primary user localization are determined. Simulation results show that the network lifetime is improved while the detection performance constraint is satisfied and the location of the primary user is determined with high accuracy.
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One of the challenging problems in directional sensor networks is maximizing target coverage while minimizing the amount of energy consumption. Considering the high redundancy in dense directional sensor networks, it is possible to preserve energy and enhance coverage q More
One of the challenging problems in directional sensor networks is maximizing target coverage while minimizing the amount of energy consumption. Considering the high redundancy in dense directional sensor networks, it is possible to preserve energy and enhance coverage quality by turning off redundant sensors and adjusting the direction of the active sensor nodes. In this paper, we address the problem of maximizing network lifetime with adjustable ranges (MNLAR) and propose a new game theory-based algorithm in which sensor nodes try to adjust their working direction and sensing range in a distributed manner to achieve the desired coverage. For this purpose, we formulate this problem as a multiplayer repeated game in which each sensor as a player tries to maximize its utility function which is designed to capture the tradeoff between target coverage and energy consumption. To achieve an efficient action profile, we present a distributed payoff-based learning algorithm. The performance of the proposed algorithm is evaluated via simulations and compared to some existing methods. The simulation results demonstrate the performance of the proposed algorithm and its superiority over previous approaches in terms of network lifetime.
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