• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1325-1344
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• 2019

Recently, to satisfy mobile users' increasing data transmission requirement, energy efficiency (EE) resource allocation in distributed antenna systems (DASs) has become a hot topic. In this paper, we aim to maximize EE in DASs subject to constraints of the minimum data rate requirement and the maximum transmission power of distributed antenna units (DAUs) with different density distributions. Virtual cell is defined as DAUs selected by the same user equipment (UE) and the size of virtual cells is dependent on the number of subcarriers and the transmission power. Specifically, the selection rule of DAUs is depended on different scenarios. We develop two scenarios based on the density of DAUs, namely, the sparse scenario and the dense scenario. In the sparse scenario, each DAU can only be selected by one UE to avoid co-channel interference. In order to make the original non-convex optimization problem tractable, we transform it into an equivalent fractional programming and solve by the following two sub-problems: optimal subcarrier allocation to find suitable DAUs; optimal power allocation for each subcarrier. Moreover, in the dense scenario, we consider UEs could access the same channel and generate co-channel interference. The optimization problem could be transformed into a convex form based on interference upper bound and fractional programming. In addition, an energy-efficient DAU selection scheme based on the large scale fading is developed to maximize EE. Finally, simulation results demonstrate the effectiveness of the proposed algorithm for both sparse and dense scenarios.

• Journal of the Korea Society of Computer and Information
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• v.22 no.5
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• pp.81-88
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• 2017

Solar-powered wireless sensor nodes can use extra energy to obtain additional data to increase the precision. However, if the amount of data sensed is increased indiscriminately, the overhead of relay nodes may increase, and their energy may be exhausted. In this paper, we introduce a sensing and compression rate selection scheme to increase the amount of data obtained while preventing energy exhaustion. In this scheme, the neighbor nodes of the sink node determine the limit of data to be transmitted according to the allocated energy and their descendant nodes, and the other nodes select a compression algorithm appropriate to the allocated energy and the limitation of data to be transmitted. A simulation result verifies that the proposed scheme gathers more data with a lower number of blackout nodes than other schemes. We also found that it adapts better to changes in node density and the amount of energy harvested.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3542-3566
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• 2018

In this paper, we investigate a buffer-aided wireless powered cooperative communication network (WPCCN), in which the source and relay harvest the energy from a dedicated power beacon via wireless energy transfer, then the source transmits the data to the destination through the relay. Both the source and relay are equipped with an energy buffer to store the harvested energy in the energy transfer stage. In addition, the relay is equipped with a data buffer and can temporarily store the received information. Considering the buffer-aided WPCCN, we propose two buffer-aided relaying protocols, which named as the buffer-aided harvest-then-transmit (HtT) protocol and the buffer-aided joint mode selection and power allocation (JMSPA) protocol, respectively. For the buffer-aided HtT protocol, the time-averaged achievable rate is obtained in closed form. For the buffer-aided JMSPA protocol, the optimal adaptive mode selection scheme and power allocation scheme, which jointly maximize the time-averaged throughput of system, are obtained by employing the Lyapunov optimization theory. Furthermore, we drive the theoretical bounds on the time-averaged achievable rate and time-averaged delay, then present the throughput-delay tradeoff achieved by the joint JMSPA protocol. Simulation results validate the throughput performance gain of the proposed buffer-aided relaying protocols and verify the theoretical analysis.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.140-146
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• 2023

Resource allocation is one of the top challenges in Internet of Things (IoT) networks. This is due to the scarcity of computing, energy and communication resources in IoT devices. As a result, IoT devices that are not using efficient algorithms for resource allocation may cause applications to fail and devices to get shut down. Owing to this challenge, this paper proposes a novel algorithm for managing computing resources in IoT network. The fog computing devices are placed near the network edge and IoT devices send their large tasks to them for computing. The goal of the algorithm is to conserve energy of both IoT nodes and the fog nodes such that all tasks are computed within a deadline. A bi-partite graph-based algorithm is proposed for stable matching of tasks and fog node computing units. The output of the algorithm is a stable mapping between the IoT tasks and fog computing units. Simulation results are conducted to evaluate the performance of the proposed algorithm which proves the improvement in terms of energy efficiency and task delay.

• ETRI Journal
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• v.45 no.6
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• pp.963-973
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• 2023

Non-orthogonal multiple access (NOMA) is considered a key candidate technology for next-generation wireless communication systems due to its high spectral efficiency and massive connectivity. Incorporating the concepts of multiple-input-multiple-output (MIMO) into NOMA can further improve the system efficiency, but the hardware complexity increases. This study develops an energy-efficient (EE) subchannel assignment framework for MIMO-NOMA systems under the quality-of-service and interference constraints. This framework handles an energy-efficient co-training-based semi-supervised learning (EE-CSL) algorithm, which utilizes a small portion of existing labeled data generated by numerical iterative algorithms for training. To improve the learning performance of the proposed EE-CSL, initial assignment is performed by a many-to-one matching (MOM) algorithm. The MOM algorithm helps achieve a low complex solution. Simulation results illustrate that a lower computational complexity of the EE-CSL algorithm helps significantly minimize the energy consumption in a network. Furthermore, the sum rate of NOMA outperforms conventional orthogonal multiple access.

• KIEAE Journal
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• v.2 no.4
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• pp.65-72
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• 2002

In this study, the evaluation of environmental performance of the building energy system of domestic commercial sector was carried out. Based on the theory of linear programming model, we established an evaluation model satisfying object functions and constraint conditions. Employing the model, the evaluation of building energy system was performed under the consideration of cost and environmental constraint conditions. As an evaluation tool, MARKAL (MARKet Allocation) known as a market distribution model was employed. We analyzed scenarios of Case I (Base Scenarios) through Case IX established by the combination of the components of building energy system such as glazing, building skin, core, and heat source system. According to the results of the evaluation, highest contribution on the useful energy demand was obtained from the building energy system combined with solar heat source system, when the total amounts of $CO_2$ exhaust as an environmental constraint condition is assumed to be the level of 1995.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.51-72
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• 2015

Time predictability is crucial in hard real-time and safety-critical systems. Cache memories, while useful for improving the average-case memory performance, are not time predictable, especially when they are shared in multicore processors. To achieve time predictability while minimizing the impact on performance, this paper explores several time-predictable scratch-pad memory (SPM) based architectures for multicore processors. To support these architectures, we propose the dynamic memory objects allocation based partition, the static allocation based partition, and the static allocation based priority L2 SPM strategy to retain the characteristic of time predictability while attempting to maximize the performance and energy efficiency. The SPM based multicore architectural design and the related allocation methods thus form a comprehensive solution to hard real-time multicore based computing. Our experimental results indicate the strengths and weaknesses of each proposed architecture and the allocation method, which offers interesting on-chip memory design options to enable multicore platforms for hard real-time systems.

• ETRI Journal
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• v.36 no.6
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• pp.960-967
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• 2014

In this paper, an analytical framework is proposed for the optimization of network performance through joint congestion control, channel allocation, rate allocation, power control, scheduling, and routing with the consideration of fairness in multi-channel wireless multihop networks. More specifically, the framework models the network by a generalized network utility maximization (NUM) problem under an elastic link data rate and power constraints. Using the dual decomposition technique, the NUM problem is decomposed into four subproblems - flow control; next-hop routing; rate allocation and scheduling; power control; and channel allocation - and finally solved by a low-complexity distributed method. Simulation results show that the proposed distributed algorithm significantly improves the network throughput and energy efficiency compared with previous algorithms.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.7
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• pp.478-488
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• 2009

Using solar power in wireless sensor networks (WSNs) requires adaptation to a highly varying energy supply and to a battery constraint. From an application's perspective, however, it is often preferred to operate at a constant quality level as opposed to changing application behavior frequently. Reconciling the varying supply with the fixed demand requires good tools for allocating energy such that average of energy supply is computed and demand is fixed accordingly. In this paper, we propose a probabilistic observation-based model for harvested solar energy. Based on this model, we develop a time-slot-based energy allocation scheme to use the periodically harvested solar energy optimally, while minimizing the variance in energy allocation. We also implement the testbed and demonstrate the efficiency of the approach by using it.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.7
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• pp.571-579
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• 2013

This paper proposes an energy-efficient mode selection and power allocation scheme in device-to-device (D2D) communication system as an underlay coexistence with cellular networks. We analyze the energy efficiency which is defined as the summation of the energy efficiencies for all devices. The proposed scheme consists of two steps. First, we calculate the transmission power maximizing the energy efficiency for all possible modes of each device. Although the proposed power cannot maximize the system capacity, we prove that the proposed transmission power is the optimal power which maximizes the energy efficiency. In the second step, we select a mode which has the maximal energy efficiency among all possible mode combinations of the devices. Then we can jointly obtain the transmission power and the mode which can maximize the energy efficiency. The proposed scheme has the optimal performance with respect to the energy efficiency and outperforms the conventional schemes.