Matti Siekkinen

How low energy is bluetooth low energy? Comparative measurements with ZigBee/802.15.4

Ultra low power communication mechanisms are essential for future Internet of Things deployments. Bluetooth Low Energy (BLE) is one promising candidate for such deployments. We study the energy consumption of BLE by measuring real devices with a power monitor and derive models of the basic energy consumption behavior observed from the measurement results. We investigate also the overhead of Ipv6-based communication over BLE, which is relevant for future IoT scenarios. We contrast our results by performing similar measurements with ZigBee/802.15.4 devices. Our results show that when compared to ZigBee, BLE is indeed very energy efficient in terms of number of bytes transferred per Joule spent. In addition, IPv6 communication energy overhead remains reasonable. We also point out a few specific limitations with current stack implementations and explain that removing those limitations could improve energy utility significantly.

Energy Efficient Multimedia Streaming to Mobile Devices — A Survey

Energy conservation in battery powered mobile devices that perform wireless multimedia streaming has been a significant research problem since last decade. This is because these mobile devices consume a lot of power while receiving, decoding and ultimately, presenting the multimedia content. What makes things worse is the fact that battery technologies have not evolved enough to keep up with the rapid advancement of mobile devices. This survey examines solutions that have been proposed during the last few years, to improve the energy efficiency of wireless multimedia streaming in mobile hand-held devices. We categorize the research work according to different layers of the Internet protocol stack they utilize. Then, we again regroup these studies based on different traffic scheduling and multimedia content adaptation mechanisms. The traffic scheduling category contains those solutions that optimize the wireless receiving energy without changing the actual multimedia content. The second category on the other hand, specifically modifies the content, in order to reduce the energy consumed by the wireless receiver and to decode and view the content. We compare them and provide evidence of the fact that some of these tactics already exist in modern smaprtphones and provide energy savings with real measurements. In addition, we discuss some relevant literature on the complementary problem of energy-aware multimedia delivery from mobile devices and contrast with our target approaches for multimedia transmission to mobile devices.

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

Previous studies have shown that a significant part of the overall energy consumption of battery-powered mobile devices is caused by network data transmission. Power models that describe the power consumption behavior of the network data transmission are therefore an essential tool in estimating the battery lifetime and in minimizing the energy usage of mobile devices. In this paper, we present a simple and practical power model for data transmission over an 802.11g WLAN and show its accuracy against physical data measured from three popular mobile platforms, Maemo, Android and Symbian. Our model estimates the energy usage based on the data transmission flow characteristics which are easily available on all the platforms without modifications to low-level software components or hardware. Based on our measurements and experimentation on real networks we conclude that our model is easy to apply and of adequate accuracy.

A System-Level Model for Runtime Power Estimation on Mobile Devices

The growing popularity of mobile internet services, characterized by heavy network transmission, intensive computation and an always-on display, poses a great challenge to the battery lifetime of mobile devices. To manage the power consumption in an efficient way, it is essential to understand how the power is consumed at the system level and to be able to estimate the power consumption during runtime. Although the power modeling of each hardware component has been studied separately, there is no general solution at present of combining them into a system-level power model. In this paper we present a methodology for building a system-level power model without power measurement at the component level. We develop a linear regression model with nonnegative coefficients, which describes the aggregate power consumption of the processors, the wireless network interface and the display. Based on statistics and expert knowledge, we select three hardware performance counters, three network transmission parameters and one display parameter as regression variables. The power estimation, based on our model, exhibits 2.62 percent median error on real mobile internet services.

Performance limitations of ADSL users: a case study

We report results from the analysis of a 24-hour packet trace containing TCP traffic of approximately 1300 residential ADSL clients. Some of our observations confirm earlier studies: the major fraction of the total traffic originates from P2P applications and small fractions of connections and clients are responsible for the vast majority of the traffic. However, our main contribution is a throughput performance analysis of the clients. We observe suprisingly low utilizations of upload and download capacity for most of the clients. Furthermore, by using our TCP root cause analysis tool, we obtain a striking result: in over 90% of the cases, the low utilization is mostly due to the (P2P) applications clients use, which limit the transmission rate and not due to network congestion, for instance. P2P applications typically impose upload rate limits to avoid uplink saturation that hurt download performance. Our analysis shows that these rate limits are very low and, as a consequence, the aggregate download rates for these applications are low.

Using crowd-sourced viewing statistics to save energy in wireless video streaming

Video streaming on smartphones is one of the most popular but also most energy hungry services today. Using mobile video services results in two contradictory sources of energy waste for smartphones: i) energy waste because of excessively aggressive prefetching of content that the user will not watch because of abandoning the session, and ii) excessive amount of tail energy, which is energy wasted by keeping the wireless interface powered on after receiving a chunk of content; this is caused by prefetching chunks that are too small. To remedy this, we propose a novel download scheduling algorithm based on crowd-sourced video viewing statistics. Our algorithm judiciously evaluates the probability of a user interrupting a video viewing in order to perform the right amount of prefetching. In this way, the algorithm balances the amount of the two above-mentioned kinds of energy waste. By simulations, we show that our scheduler cuts the energy waste to half compared to existing download strategies. We have also developed an Android prototype that implements the download scheduler together with a novel downloader that speeds up the download by exploiting the Fast Start technique. The prototype exhibits the desired properties of the scheduler, and its faster downloading mechanism yields further energy savings of up to 80% compared to the default Android YouTube app.

Streaming over 3G and LTE: how to save smartphone energy in radio access network-friendly way

Energy consumption of mobile devices is a great concern and streaming applications are among the most power hungry ones. We evaluate the energy saving potential of shaping streaming traffic into bursts before transmitting it over 3G and LTE networks to smartphones. The idea is that in between the bursts, the phone has sufficient time to switch from the high-power active state to low-power states. We investigate the impact of the network parameters, namely inactivity timers and discontinuous reception, on the achievable energy savings and on the radio access network signaling load. The results confirm that traffic shaping is an effective way to save energy, even up to 60% of energy saved when streaming music over LTE. However, we note large differences in the signaling load. LTE with discontinuous reception and long inactivity timer value achieves the energy savings with no extra signaling load, whereas non-standard Fast Dormancy in 3G can multiply the signaling traffic by a factor of ten.

On the energy efficiency of proxy-based traffic shaping for mobile audio streaming

We study how much energy can be saved by reshaping audio streaming traffic before receiving at the mobile devices. The rationale is the following: Mobile network interfaces (WLAN and 3G) are in active mode when they transmit or receive data, otherwise they are in idle/sleep mode. To save energy, minimum possible time should be spent in active mode and maximum in idle/sleep mode. It is well known that by reshaping the usually constant bit rate multimedia traffic into bursts, it is possible to spend more time in idle/sleep mode leading to impressive energy savings. We propose a proxy-based solution that shapes an audio stream into bursts before relaying the traffic to the mobile device. The novelty of our work is an evaluation of the energy savings using such a proxy with different configurations for both WLAN access with standard 802.11 Power Saving Mode and 3G access. We conclude that for WLAN access, proxy causes power savings of 30%-65% depending on the audio stream rate, location of the proxy and amount of cross traffic. In the case of 3G, the effectiveness of our proxy seems to vary depending on the phone model and operator. In some cases, the energy savings are encouraging, while in other cases the proxy turns out to be ineffective due to abnormal delay variation and TCP flow control behavior.

Modeling Energy Consumption of Data Transmission over Wi-Fi

Wireless data transmission consumes a significant part of the overall energy consumption of smartphones, due to the popularity of Internet applications. In this paper, we investigate the energy consumption characteristics of data transmission over Wi-Fi, focusing on the effect of Internet flow characteristics and network environment. We present deterministic models that describe the energy consumption of Wi-Fi data transmission with traffic burstiness, network performance metrics like throughput and retransmission rate, and parameters of the power saving mechanisms in use. Our models are practical because their inputs are easily available on mobile platforms without modifying low-level software or hardware components. We demonstrate the practice of model-based energy profiling on Maemo, Symbian, and Android phones, and evaluate the accuracy with physical power measurement of applications including file transfer, web browsing, video streaming, and instant messaging. Our experimental results show that our models are of adequate accuracy for energy profiling and are easy to apply.

A root cause analysis toolkit for TCP

TCP is the most widely used protocol for data transfer over the Internet and for most applications the performance metric of interest is throughput. Identifying the reasons for the throughput limitation of an observed connection is a complex task. We present a set of techniques, called the TCP root cause analysis toolkit, that allow users to determine from a passively captured packet trace the primary cause for the throughput limitation. We present the details of the toolkit and its validation and apply the toolkit to carry out a case study of ADSL traffic.