5,650 research outputs found
Efficient energy management for the internet of things in smart cities
The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy-efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities
Energy-Efficient Power Allocation in OFDM Systems with Wireless Information and Power Transfer
This paper considers an orthogonal frequency division multiplexing (OFDM)
downlink point-to-point system with simultaneous wireless information and power
transfer. It is assumed that the receiver is able to harvest energy from noise,
interference, and the desired signals.
We study the design of power allocation algorithms maximizing the energy
efficiency of data transmission (bit/Joule delivered to the receiver). In
particular, the algorithm design is formulated as a high-dimensional non-convex
optimization problem which takes into account the circuit power consumption,
the minimum required data rate, and a constraint on the minimum power delivered
to the receiver. Subsequently, by exploiting the properties of nonlinear
fractional programming, the considered non-convex optimization problem, whose
objective function is in fractional form, is transformed into an equivalent
optimization problem having an objective function in subtractive form, which
enables the derivation of an efficient iterative power allocation algorithm. In
each iteration, the optimal power allocation solution is derived based on dual
decomposition and a one-dimensional search. Simulation results illustrate that
the proposed iterative power allocation algorithm converges to the optimal
solution, and unveil the trade-off between energy efficiency, system capacity,
and wireless power transfer: (1) In the low transmit power regime, maximizing
the system capacity may maximize the energy efficiency. (2) Wireless power
transfer can enhance the energy efficiency, especially in the interference
limited regime.Comment: 6 pages, Accepted for presentation at the IEEE International
Conference on Communications (ICC) 201
RF-Powered Cognitive Radio Networks: Technical Challenges and Limitations
The increasing demand for spectral and energy efficient communication
networks has spurred a great interest in energy harvesting (EH) cognitive radio
networks (CRNs). Such a revolutionary technology represents a paradigm shift in
the development of wireless networks, as it can simultaneously enable the
efficient use of the available spectrum and the exploitation of radio frequency
(RF) energy in order to reduce the reliance on traditional energy sources. This
is mainly triggered by the recent advancements in microelectronics that puts
forward RF energy harvesting as a plausible technique in the near future. On
the other hand, it is suggested that the operation of a network relying on
harvested energy needs to be redesigned to allow the network to reliably
function in the long term. To this end, the aim of this survey paper is to
provide a comprehensive overview of the recent development and the challenges
regarding the operation of CRNs powered by RF energy. In addition, the
potential open issues that might be considered for the future research are also
discussed in this paper.Comment: 8 pages, 2 figures, 1 table, Accepted in IEEE Communications Magazin
Energy-Efficient Resource Allocation in Multiuser OFDM Systems with Wireless Information and Power Transfer
In this paper, we study the resource allocation algorithm design for
multiuser orthogonal frequency division multiplexing (OFDM) downlink systems
with simultaneous wireless information and power transfer. The algorithm design
is formulated as a non-convex optimization problem for maximizing the energy
efficiency of data transmission (bit/Joule delivered to the users). In
particular, the problem formulation takes into account the minimum required
system data rate, heterogeneous minimum required power transfers to the users,
and the circuit power consumption. Subsequently, by exploiting the method of
time-sharing and the properties of nonlinear fractional programming, the
considered non-convex optimization problem is solved using an efficient
iterative resource allocation algorithm. For each iteration, the optimal power
allocation and user selection solution are derived based on Lagrange dual
decomposition. Simulation results illustrate that the proposed iterative
resource allocation algorithm achieves the maximum energy efficiency of the
system and reveal how energy efficiency, system capacity, and wireless power
transfer benefit from the presence of multiple users in the system.Comment: 6 pages. The paper has been accepted for publication at the IEEE
Wireless Communications and Networking Conference (WCNC) 2013, Shanghai,
China, Apr. 201
Improving the Efficiency of Energy Harvesting Embedded System
In the past decade, mobile embedded systems, such as cell phones and tablets have infiltrated and dramatically transformed our life. The computation power, storage capacity and data communication speed of mobile devices have increases tremendously, and they have been used for more critical applications with intensive computation/communication. As a result, the battery lifetime becomes increasingly important and tends to be one of the key considerations for the consumers. Researches have been carried out to improve the efficiency of the lithium ion battery, which is a specific member in the more general Electrical Energy Storage (EES) family and is widely used in mobile systems, as well as the efficiency of other electrical energy storage systems such as supercapacitor, lead acid battery, and nickel–hydrogen battery etc. Previous studies show that hybrid electrical energy storage (HEES), which is a mixture of different EES technologies, gives the best performance. On the other hand, the Energy Harvesting (EH) technique has the potential to solve the problem once and for all by providing green and semi-permanent supply of energy to the embedded systems. However, the harvesting power must submit to the uncertainty of the environment and the variation of the weather. A stable and consistent power supply cannot always be guaranteed. The limited lifetime of the EES system and the unstableness of the EH system can be overcome by combining these two together to an energy harvesting embedded system and making them work cooperatively.
In an energy harvesting embedded systems, if the harvested power is sufficient for the workload, extra power can be stored in the EES element; if the harvested power is short, the energy stored in the EES bank can be used to support the load demand. How much energy can be stored in the charging phase and how long the EES bank lifetime will be are affected by many factors including the efficiency of the energy harvesting module, the input/output voltage of the DC-DC converters, the status of the EES elements, and the characteristics of the workload.
In this thesis, when the harvesting energy is abundant, our goal is to store as much surplus energy as possible in the EES bank under the variation of the harvesting power and the workload power. We investigate the impact of workload scheduling and Dynamic Voltage and Frequency Scaling (DVFS) of the embedded system on the energy efficiency of the EES bank in the charging phase. We propose a fast heuristic algorithm to minimize the energy overhead on the DC-DC converter while satisfying the timing constraints of the embedded workload and maximizing the energy stored in the HEES system. The proposed algorithm improves the efficiency of charging and discharging in an energy harvesting embedded system.
On the other hand, when the harvesting rate is low, workload power consumption is supplied by the EES bank. In this case, we try to minimize the energy consumption on the embedded system to extend its EES bank life. In this thesis, we consider the scenario when workload has uncertainties and is running on a heterogeneous multi-core system. The workload variation is represented by the selection of conditional branches which activate or deactivate a set of instructions belonging to a task. We employ both task scheduling and DVFS techniques for energy optimization. Our scheduling algorithm considers the statistical information of the workload to minimize the mean power consumption of the application while satisfying a hard deadline constraint. The proposed DVFS algorithm has pseudo linear complexity and achieves comparable energy reduction as the solutions found by mathematical programming. Due to its capability of slack reclaiming, our DVFS technique is less sensitive to small change in hardware or workload and works more robustly than other techniques without slack reclaiming
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