Sangdon Park

Maximizing energy efficiency in off-peak hours: A novel sleep scheme for WLAN access points

The number of wireless access points (APs) have increased rapidly in recent years to support increasing demands of wireless local area network. However, some of the research findings show that many of these APs are actually idle mainly during off-peak hours, i.e., there is no active user to support within their coverage areas during that time. Nevertheless, to identify availability of users, all APs are remained powered-on always regardless the presence or absence of any active user inside their coverage areas. To save energy during off-peak hours in idle APs, sleep mechanism could be applied. In this paper, we propose a novel sleep mechanism for IEEE 802.11 wireless local area network APs. In our solution, an AP moves into sleep mode to maximize its energy efficiency (EE) while not harming traffic delay requirements. To decide length of sleep interval of an AP, we propose a dynamic sleep boundary decision algorithm which dynamically sets upper and lower bound of sleep duration considering traffic arrival rate and traffic delay requirements at a given time. Numerical results show the proposed scheme can improve EE of an AP significantly compared to the existing schemes without violating traffic delay requirements.

Joint optimal access and sensing policy on distributed cognitive radio networks with channel aggregation

In this paper we consider distributed cognitive radio networks (CRNs) with channel aggregation (CA) scheme where there are multiple wireless channels and multiple secondary users (SUs). SUs in CRNs with CA can transmit bits of data through more than one idle channels which are not used by primary users (PUs). SUs decide whether to access idle channels or not based on the access probability (AP), and we call users who decide to access active SUs. Each active SU uses the sequential sensing policy to sense randomly chosen L channels at the beginning of each access frame (AF) and accesses all the idle channels among the sensed channels during the rest of the AF. We analyze the average number of bits transmitted successfully by an SU per AF, call it the throughput performance of an SU. We derive a closed form of the throughput performance of an SU as a function of AP and L. By maximizing throughput, we propose a joint optimal access and sensing policy, that is, we provide the optimal value of AP a* and the optimal number of sensing channels (NSC) L*. We validate our analysis and investigate the throughput performance of the proposed optimal policy using numerical and simulation studies. Based on our investigation we further propose a near-optimal simplified policy.

Competitive Partial Computation Offloading for Maximizing Energy Efficiency in Mobile Cloud Computing

In this paper, we newly model computation offloading competition when multiple clients compete with each other so as to reduce energy cost and improve computational performance. We consider two types of destination of offloading request, such as a cloudlet and a remote cloud. Here, the cloudlet consists of locally connected mobile terminals with low-latency and high bandwidth but suffering from task overload due to its limited computational capacity. On the other hand, the remote cloud has a high and stable capacity but the high latency. To facilitate the competition model, on the destination sides, we have designed an energy-oriented task scheduling scheme, which aims to maximize the welfare of clients in terms of energy efficiency. Under this proposed job scheduling, as a joint consideration of the destination and client sides, competition behavior among multiple clients for optimal computation offloading is modeled and analyzed as a non-cooperative game by considering a trade-off between different types of destinations. Based on this game-theoretical analysis, we propose a novel energy-oriented weight assignment scheme in the mobile terminal side to maximize mobile terminal energy efficiency. Finally, we show that the proposed scheme converges well to a unique equilibrium and it maximizes the payoff of all participating clients.

Optimal Throughput Analysis of Random Access Policies for Cognitive Radio Networks with Multiple Channel Access

In this paper we consider a time slotted cognitive radio (CR) network with N wireless channels and M secondary users (SUs). We focus on a random access policy where each SU stochastically decides whether to access idle channels or not based on the given access probability (AP) that is adapted to the channel state information (CSI), if possible. The AP plays an important role in the random access policy because it can control the number of SUs who can access idle channels in a simple manner and hence alleviate packet collisions among SUs. We assume that each SU can access at most L idle channels simultaneously at a time slot whenever possible. We consider two extreme cases - a) where all SUs have full CSI and b) where all SUs have no CSI. We analyze the throughput of an arbitrary SU for the two extreme cases, and rigorously derive a closed-form expression of the optimal AP values that maximize the throughput of an arbitrary SU for the two extreme cases. In the analysis, we also show the impact of multiple channel access and the acquisition of CSI on throughput performance.

Energy efficient relay selection scheme with DRX mechanism in 3GPP LTE network

As bandwidth scarcity problem is widely agreed, communication through high frequency band has been considered for future commercial cellular networks. However, according to its radio propagation property, out of service area will be increased. So, relay-based cooperative communication has been emerging as a promising solution. In relay-based cooperative communication, energy efficiency is crucial factor due to battery issue of relay stations. But, most of conventional relay selection schemes does not consider both energy efficiency and sleep operation of relay stations which can reduce power consumption. Thus, we construct Discrete Time Markov Chain (DTMC) model for the relay station which has DRX mechanism, and analyze that model. Also, we propose a novel energy efficiency metric called Relay Energy Efficiency (REE), and REE-based energy efficient relay selection scheme. Numerical results show that proposed relay selection scheme achieves higher energy efficiency than other conventional schemes.

Event-Driven Energy Trading System in Microgrids: Aperiodic Market Model Analysis With a Game Theoretic Approach

This paper presents the design of an event-driven energy trading system among microgrids. Each microgrid can be either a provider or a consumer depending on the status of its energy generation and local demands. Under this approach, an aperiodic market model is newly proposed such that trading occurs when one of the consumers requests energy from the trading market. To promote the trading system, a consumer-side reward concept is introduced. The consumer makes a decision on the size of the posted reward to procure energy depending on its required energy level. Providers then react to this posted reward by submitting their energy bid. Accordingly, the posted reward is allocated to the providers in proportion to their energy bids. Moreover, for practical concerns, a transmission and distribution loss factor is considered as a heterogeneous energy trading system. The problem is then formulated as a non-cooperative Stackelberg game model. The existence and uniqueness of Stackelberg equilibrium (SE) are shown and the closed form of the SE is derived. Using the SE, an optimal trading algorithm for microgrids is provided. The stability of the energy trading system is verified due to the unique SE. In this approach, no expected waiting time for trading is required for sustaining an energy trading market.