The fairness concept has been widely studied in the area of data networks. The most well-known fairness criterion, max-min fairness, gives priority to the minimum rate session. Kelly questioned its appropriateness in his works on the bandwidth sharing among the end-to-end flows and proposed another fairness criterion preferring short distance flows to enhance the overall throughput, which is called the proportional fairness (PF). A simple scheduler achieving this objective was introduced in wireless access networks and revealed that it can achieve a good compromise between cell throughput and user fairness. Though it has received much attention for some time, research on its performance mainly depended on computer simulations. In this paper, we analyze the PF scheduler to obtain the cell throughput which is a primary performance metric.

Recent advances in the physical layer have demonstrated the feasibility of in-band wireless full-duplex which enables a node to transmit and receive simultaneously on the same frequency band. While the full-duplex operation can ideally double the spectral efficiency, the network-level gain of full-duplex in large-scale networks remains unclear due to the complicated resource allocation in multi-carrier and multiuser environments. In this paper, we consider a single-cell fullduplex OFDMA network which consists of one full-duplex base station (BS) and multiple full-duplex mobile nodes. Our goal is to maximize the sum-rate performance by jointly optimizing subcarrier assignment and power allocation considering the characteristics of full-duplex transmissions. We develop an iterative solution that achieves local Pareto optimality in typical scenarios. Through extensive simulations, we demonstrate that our solution empirically achieves near-optimal performance and outperforms other resource allocation schemes designed for halfduplex networks. Also, we reveal the impact of various factors such as the channel correlation, the residual self-interference, and the distance between the BS and nodes on the full-duplex gain.

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Consumer electricity consumption can be controlled through electricity prices, which is called demand response. Under demand response, retailers determine their electricity prices and customers respond accordingly with their electricity consumption levels. In particular, the demands of customers who own electric vehicles (EVs) are elastic with respect to price. The interaction between retailers and customers can be seen as a game because both attempt to maximize their own payoffs. This study models an at-home EV charging scenario as a Stackelberg game, and proves that this game reaches an equilibrium point at which the EV charging requirements are satisfied and retailer profits are maximized when customers use our proposed utility function. The equilibrium of our game can vary according to the weighting factor for the utility function of each customer, resulting in various strategic choices. Our numerical results confirm that the equilibrium of the proposed game lies somewhere between the minimum generation cost solution and the result of the equalcharging scheme.Index Terms-demand response, electric vehicle, real-time pricing, Stackelberg game. 0018-9545 (c)

Low-power wireless network for the emerging Internet of Things (IoT) should be reliable enough to satisfy the application requirements, and also energy-efficient for embedded devices to remain battery powered. Synchronized communication methods such as Time Slotted Channel Hopping (TSCH) have shown promising results for these purposes, achieving end-to-end reliability over 99% with low dutycycles. However, they lack one thing: flexibility to support a wide variety of applications and services with unpredictable traffic load and routing topology due to ''fixed'' slotframe sizes. To this end, we propose TESLA, a traffic-aware elastic slotframe adjustment scheme for TSCH networks which enables each node to dynamically self-adjust its slotframe size at run time. TESLA aims to minimize its energy consumption without sacrificing reliable packet delivery by utilizing incoming traffic load to estimate channel contention level experienced by each neighbor. We extensively evaluate the effectiveness of TESLA on large-scale 110-node and 79-node testbeds, demonstrating that it achieves up to 70.2% energy saving compared to Orchestra (the de facto TSCH scheduling mechanism) while maintaining 99% reliability.

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