Practical power modeling of data transmission over 802.11g for wireless applications

Xiao, Yu; Savolainen, Petri; Karppanen, Arto; Siekkinen, Matti; Ylä-Jääski, Antti

doi:10.1145/1791314.1791326

Cited by 67 publications

(51 citation statements)

References 27 publications

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“…Study on energy consumption over IEEE 802.11g network by Xiao et al [26] and nearly similar direction of study was also carried out by Zhang et al [27]. Study towards energy efficiency in 4G networks as well as a WLAN network was done by Harjula et al [28].…”

Section: Related Workmentioning

confidence: 92%

Energy Dissipation Model for 4G and WLAN Networks in Smart Phones

Prasad¹,

Balaji²

2016

ijacsa

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Abstract-With the modernization of the telecommunication standards, there has been considerable evolution of various technologies to assist cost effective communication. In this regard, the fourth generation communication services or commonly known as 4G mobile networks have penetrated almost every part of the world to offer faster and seamless data connectivity. However, such services come at the cost of energy drained from the smart phone supporting 4G services. This paper presents an algorithm that is capable of evaluating the actual amount of energy being dissipated while using next generation mobile networks. The study also performs a comparative analysis of energy dissipation of 4G networks with other wireless local area networks to understand the networks that cause more energy dissipation.

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Section: Related Workmentioning

confidence: 92%

Energy Dissipation Model for 4G and WLAN Networks in Smart Phones

Prasad¹,

Balaji²

2016

ijacsa

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“…The energy model was build taking into account both the size of transfer and the time between consecutive transfers. In [16], the analysis focuses only on the Wi-Fi connection. Even in this case, power consumption was measured using the Nokia Energy Profiler.…”

Section: Application-level Consumptionmentioning

confidence: 99%

On Power and Energy Consumption Modeling for Smart Mobile Devices

Ferroni

Cazzola

Trovò

et al. 2014

2014 12th IEEE International Conference on Embedded and Ubiquitous Computing

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Abstract-In nowadays life, mobile phones are becoming a cheaper and smaller alternative to laptops for simple, everyday tasks. They experienced an astonishing growth in functionalities and, because of their constant presence in our life, mobile phones became fundamental for the interaction with information coming from the environment. Nevertheless, their resources are limited, both in terms of performance and power, and their availability can greatly vary over time. Especially when dealing with power consumption, mobile devices cannot disregard environment conditions and user habits. Both internal and external conditions are rapidly changing and may influence the response of the entire system, e.g., switching between network types may causes an unpredictable power consumption. In order to puzzle out all these issues, we regard the definition of a power/energy model for mobile devices as a first mandatory step. In literature, several attempts to do so are present, basing their approaches on techniques coming from different computer science fields. They differ in the way they consider hardware components, in the operating system they are suitable for and in the scope of their tests and experiments. Within this paper, we categorize techniques presented in the major works in the field, in order to be able to compare different methods, highlight open issues and give suggestions on future works.

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“…Packet interval patterns and throughput, for instance, have a significant impact on communication energy cost [11]. As a consequence, grouping packets to bursts is more energy efficient.…”

Section: How Can Communication Offloading Save Energy?mentioning

confidence: 99%

“…We also collect the packet traces and use models to estimate the energy consumed by the network interfaces. We use the model in [11] to estimate Wi-Fi energy consumption. As for 3G, we use a deterministic power model described in [10].…”

Section: Energy Consumption Of Offloaded Andtweetmentioning

confidence: 99%

“…In estimation one has to make assumptions about the interdependencies of methods within the program, which is why we show two numbers: a lower bound and an upper bound. The reason is that communication energy consumption is heavily dependent on traffic patterns meaning that only the exact number of bits transmitted does not determine the energy consumed but also their timing has a large impact (refer to [11], for instance). The lower bound corresponds to the energy consumed by the packets associated with the method in question, while the upper bound is computed so that it includes also the packets belonging to other methods that arrive between the first and the last packet of this method.…”

Section: Profiling Communication Energymentioning

confidence: 99%

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Can offloading save energy for popular apps?

Saarinen

Siekkinen

Xiao

et al. 2012

Proceedings of the Seventh ACM International Workshop on Mobility in the Evolving Internet Architecture

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Offloading tasks to cloud is one of the proposed solutions for extending battery life of mobile devices. Most prior research focuses on offloading computation, leaving communicationrelated tasks out of scope. However, most popular applications today involve intensive communication that consumes a significant part of the overall energy. Hence, we currently do not know how feasible it is to use offloading for saving energy in such apps. In this paper, we first show that it is possible to save energy by offloading communication-related tasks of the app to the cloud. We use an open source Twitter client, AndTweet, as a case study. However, using a set of popular open source applications, we also show that existing apps contain constraints that have to be released with code modifications before offloading can be profitable, and that the potential energy savings depend on many communication parameters. We therefore develop two tools: the first to identify the constraints and the other for fine-grained communication energy estimation. We exemplify the tools and explain how they could be used to help offloading parts of popular apps successfully.

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Practical power modeling of data transmission over 802.11g for wireless applications

Cited by 67 publications

References 27 publications

Energy Dissipation Model for 4G and WLAN Networks in Smart Phones

Energy Dissipation Model for 4G and WLAN Networks in Smart Phones

On Power and Energy Consumption Modeling for Smart Mobile Devices

Can offloading save energy for popular apps?

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