Energy efficiency and traffic offloading in wireless mesh networks with delay bounds

In the last decade, the rising trend in the popularity of smartphones motivated software developers to increase application functionality. However, increasing application functionality demands extra power budget that as a result, decreases smartphone battery lifetime. Optimizing energy critical sections of an application creates an opportunity to increase battery lifetime. Smartphone application energy estimation helps investigate energy consumption behavior of an application at diversified granularity (eg, coarse and fine granular) for optimal battery resource use. This study explores energy estimation and modeling schemes to highlight their advantages and shortcomings. It classifies existing smartphone application energy estimation and modeling schemes into 2 categories, ie, code analysis and mobile components power model-based estimation owing to their architectural designs. Moreover, it further classifies code analysis-based modeling and estimation schemes in simulation-based and profiling-based categories. It compares existing energy estimation and modeling schemes based on a set of parameters common in most literature to highlight the commonalities and differences among reported literature. Existing application energy estimation schemes are low-accurate, resource expensive, or non-scalable, as they consider marginally accurate smart battery's voltage/ current sensors, low-rate power capturing tools, and labor-driven lab-setting environment to propose power models for smartphone application energy estimation. Besides, the energy estimation overhead of the components power model-based estimation schemes is very high as they physically run the application on a smartphone for energy profiling. To optimize smartphone application energy estimation, we have highlighted several research issues to help researchers of this domain to understand the problem clearly. KEYWORDS application energy, energy estimation, energy profiling, profiling overhead 1 | INTRODUCTION Nowadays, with the proliferation of portable devices, the energy-efficient system design has become a must-to-meet requirement for recent resource-constrained smartphone devices. The ever growing smartphone user's demands encourage application developers to enrich the legacy applications that as a result, increase smartphone's computational and communication cost. Among all smartphone-based applications, on-demand video, 1 multi-agent-based distributed games, 2 spatial locality-based social applications, 3 pedestrian tracking, 4,5 and context-based advertisement services 6 are the most energy-hungry services. The inherent features of these services considerably increase the energy demands of processors when executed on smartphones. 7-9 It is estimated that in last 2 decades, processor power budget has increased significantly because of smartphone application's high resource use. To efficiently exploit smartphone's power budget, software optimization minimizes inter-component interaction among hardware modules that as a result, increases battery's lifetime. ...

Section: Energy Efficiency and Technology Advancementsmentioning

confidence: 99%

A survey on energy estimation and power modeling schemes for smartphone applications

Ahmad

Gani

Hamid

et al. 2016

“…Among all smartphone applications, video on demand, mobile‐gaming, location‐aware social applications, and context‐based advertisement services are the utmost energy‐consuming services . The inherent features of these services significantly increase the energy demands of processors when executed on mobile phones . It is estimated that in the last 2 decades processor power budget has surged from 1 to 50 W because of high resource demands of smartphone applications.…”

Section: Introductionmentioning

confidence: 99%

“…[2][3][4][5][6] The inherent features of these services significantly increase the energy demands of processors when executed on mobile phones. 7,8 It is estimated that in the last 2 decades processor power budget has surged from 1 to 50 W because of high resource demands of smartphone applications. To efficiently exploit smartphone battery's power budget, energy estimation assists designing resource-efficient applications to augment device's lifetime.…”

Section: Introductionmentioning

confidence: 99%

A case and framework for code analysis–based smartphone application energy estimation

Ahmad

Gani

Hamid

et al. 2016

Summary The hype in the popularity of recent wireless technologies has increased applications of smartphones in various fields, particularly, education and health care. The trend of increasing application functionality to enrich smartphone users experience requires detailed insights of application energy consumption behavior. Smartphone application energy estimation helps investigate energy consumption behavior of applications at diversified granularity when it is run on resource‐constrained devices. Fine granular estimation gives more insights to the application energy consumption behavior to assist developers to propose resource‐friendly application designs. This study proposes a lightweight code analysis–based estimation framework to minimize high profiling overhead of use‐based estimation methods. Moreover, it analyzes estimation overhead and accuracy of existing dynamic estimation tools to present a case for code analysis–based energy estimation method. The estimated energy is found 86% accurate to the ground truth value for a set of benchmarks using our proposed framework.

“…This approach has been proved to be an effective way to reduce total energy consumption of heterogeneous networks. However, the sleep mode control is still a challenging research topic because of complexity issue, although it has been investigated widely for wireless sensor networks and wireless local area networks in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in proposed an integer linear programming method to solve the problem of traffic offloading with both guaranteed bandwidth and bounded delay constraint for IEEE 802.11 mesh networks. And the tradeoff between energy consumption and traffic load was analyzed.…”

Section: Introductionmentioning

confidence: 99%

Dynamic programming‐based pico base station sleep mode control in heterogeneous networks

Dong

Qin

et al. 2015

SUMMARYIn this paper, a sleeping mode control for low power wireless node, that is, pico base station (PBS) in heterogeneous networks, is proposed to save energy cost with adaptation to the time-varying traffic load. By formulating the sleep mode control problem into the framework of a standard 0/1 knapsack problem, then a dynamic programming scheme for solving the sleeping on/off mode for PBS in heterogeneous networks was investigated. The complexity of the proposed scheme is only in linear order instead of exponential complexity with conventional scheme. Furthermore, the proposed algorithm is verified in dynamic traffic environment, which consists of two kinds of hot-spot area covered by PBSs. The simulation results show that the proposed scheme can effectively save the system energy consumption during the period of low traffic load.