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... the evaluation, we tested the video streaming services (i.e. only QL1 is used) via WiFi and LTE in four different locations every hour during the day time (i.e. from 9am to 6pm). In Fig. 6, the energy efficiency (i.e. average throughput/average energy consumption) is used to characterize the difference in terms of energy consumption between WiFi and LTE. We observe that the energy efficiency for WiFi is 58.13%, 54.12%, 54.90% and 57.62% higher than that for LTE, measured in Henry Grattan Building, The Hub, Outside ...
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Mobile communication systems experienced high technical revolution. Nowadays, smart mobile devices have advanced computing aspects. In addition, they are provided with multiple network interfaces, wifi and 3G. Traffic redirection between these networks is addressed by the vertical handover process. Recently, vertical handover enhanced by the IEEE80...
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Citations
... On the client side, based on the given energy consumption models, several studies have focused on reducing energy consumption for end-user devices [6]- [7]. Along with the issue of energy consumption, the quality of user experience (QoE) at client side in video streaming system is also an issue that needs to be considered. ...
... In [6], an Arduino based low-cost platform is designed to measure the energy consumption of the smart mobile device. The experiment results of this method show that the energy consumption at enduser device depends on radio interface and bitrate of network. ...
... To achieve this reduction, an energy consumption estimation model for user devices must be developed. Previous studies have employed two primary approaches to estimate energy consumption: hardware-based method [6] and software-based method [26]. In [6], a low-cost device based on an Arduino board was designed to measure the energy consumption of a smartphone. ...
p style="-qt-block-indent: 0; text-indent: 0px; margin: 0px;">The reduction of greenhouse gas emissions in the Internet and ICT sectors has become a critical challenge. According to recent research, the key contributors to greenhouse gas emissions in Internet include high energy consumption factors such as data centers, transmission network devices, and end-user devices. Among Internet services, video streaming is one of the services having the highest traffic volume and number of users. Consequently, developing energy-efficient solutions for video streaming networks, particularly for end-user devices, is an urgent research priority. Reducing energy consumption in end-user devices in a video streaming system often requires compromises in parameters that impact the quality of user experience (QoE). Therefore, achieving an optimal trade-off between minimizing energy consumption and maintaining an acceptable QoE is a key objective. In this study, a cost function that integrates QoE and energy consumption is developed using the Lagrange multiplier method. Based on this function, an adaptive bitrate algorithm is proposed to select optimal video segments for video players, ensuring maximum QoE while minimizing energy consumption. The performance of the proposed method is evaluated using various types of video samples under varying network bandwidth conditions. Experimental results show that the proposed method reduces energy consumption of end-user devices by up to 6.7% and enhances QoE by 20% compared to previous methods.</p
... Cellular technology solutions such as 3G/4G provide the necessary range but consume too much power [2]. Low Power Wide Area (LPWA) technologies including NB-IoT, Sigfox, and LoRa have emerged as the most suitable solution for smart agriculture due to their long-range transmission, low-cost implementation, and low power consumption [3]. ...
Agriculture is a sector that plays a crucial role in ensuring food security and sustainable development. However, traditional agriculture practices face challenges such as inefficient irrigation methods and lack of real-time monitoring, leading to water waste and reduced crop yield. Several systems that attempt to address these challenges exist, such as those based on Wi-Fi, Bluetooth, and 3G/4G cellular technology; but also encounter difficulties such as low transmission range, high power consumption, etc. To address all these issues, this paper proposes a smart agriculture monitoring and automatic irrigation system based on LoRa. The system utilizes LoRa technology for long-range wireless communication, Blynk platform for real-time data visualization and control , and ThingSpeak platform for data storage, visualization, and further analysis. The system incorporates multiple components, including a sensor node for data collection, a gateway for data transmission, and an actuator node for irrigation control. Experimental results show that the proposed system effectively monitors collected data such as soil moisture levels, visualizes data in real time, and automatically controls irrigation based on sensor data and user commands. The system proposed in this study provides a cost-effective and efficient solution for sustainable agriculture practices.
... Unfortunately, we still lack comprehensive data to draw key conclusions on the energy intensity, emissions, and broader environmental impacts of different broadband technologies (Pihkola et al., 2018), such as cellular versus Wi-Fi services. In general, the available evidence suggests that Wi-Fi is >50% more energy efficient than cellular 4G LTE (Zou et al., 2017). When we consider newer technologies, such as 5G, empirical measurement exercises suggest this escalates power consumption by 2−3x when compared to legacy 4G (Xu et al., 2020). ...
With the arrival of the peak smartphone era, users are upgrading their smartphones less frequently, and data growth is decelerating. To ensure effective spectrum management decisions, policy makers require a thorough understanding of prospective wireless broadband technologies, current trends and emerging issues. Here, we review the sixth cellular generation (‘6G’), in comparison to two new Wi-Fi standards, including IEEE 802.11be (‘Wi-Fi 7’) and IEEE 802.11bn (‘Wi-Fi 8’). We identify three emerging issues necessary for successful telecommunication policy. Firstly, evidenced-based policy making needs to be able to measure effectively how much demand takes place where and how. Thus, new datasets are needed reflecting real usage by different wireless broadband technologies, for indoor and outdoor users. Secondly, with data consumption growth slowing, there needs to be an urgent reassessment of spectrum demand versus allocation. Past forecasts do not reflect recent data and regulators urgently need to re-evaluate the implications for spectrum management. Finally, regulators need new and improved Lifecycle Impact Assessment metrics of cellular versus Wi-Fi architectures, to support successful policy decisions which mitigate energy and emissions impacts.
... Monsoon was used to measure the power consumption of the Android-based smartphone and showed a power saving of 25%. In [19], an open source measurement system has been proposed to detect the power consumption while transferring the video stream on smart devices over LTE or Wi-Fi connection. A small resistor has been connected between the smartphone and its battery in series, it has been aimed to pass current through the resistor and to determine the power consumption of the smartphone. ...
... Power Monitor, a commercial product offered by Monsoon Solutions Inc, has been used in numerous other research projects. In this study, a power measurement platform is set up in a similar way to the research in [19]. The block diagram of the experimental setup is shown in Fig. 5. ...
Internet usage and user interaction on mobile smart devices have grown exponentially with technological advancements, leading to a major challenge: rapid battery depletion. This study addresses this issue by proposing a novel communication network designed to reduce power consumption on smart mobile devices during internet communication. The proposed architecture leverages Bluetooth Low Energy (BLE) technology, known for its energy efficiency. The system comprises two main components: a hardware unit that manages internet tasks via Wi-Fi and transmits notifications to the mobile device using BLE, and a new dedicated smartphone application that receives these notifications and alerts the user. To evaluate the system's effectiveness, a custom measurement system was developed. This system assessed the power consumption of the smartphone in standby mode, measured the power consumption of an email application selected for testing purposes while different connectivity options (3G, LTE and Wi-Fi) were active, and finally assessed the power consumption of the new mobile application using the developed communication methods. The findings demonstrate that the proposed architecture offers significant advantages in scenarios requiring frequent communication.
... Cellular technology solutions such as 3G/4G provide the necessary range but consume too much power [2]. Low Power Wide Area (LPWA) technologies including NB-IoT, Sigfox, and LoRa have emerged as the most suitable solution for smart agriculture due to their long-range transmission, low-cost implementation, and low power consumption [3]. ...
Agriculture is a sector that plays a crucial role in ensuring food security and sustainable development. However, traditional agriculture practices face challenges such as inefficient irrigation methods and lack of real-time monitoring, leading to water waste and reduced crop yield. Several systems that attempt to address these challenges exist, such as those based on Wi-Fi, Bluetooth, and 3G/4G cellular technology; but also encounter difficulties such as low transmission range, high power consumption, etc. To address all these issues, this thesis proposes a smart agriculture monitoring and automatic irrigation system based on LoRa. The system utilizes LoRa technology for long-range wireless communication, Blynk platform for real-time data visualization and control, and ThingSpeak platform for data storage, visualization, and further analysis. The system incorporates multiple components, including a sensor node for data collection, a gateway for data transmission, and an actuator node for irrigation control. Experimental results show that the proposed system effectively monitors collected data such as soil moisture levels, visualizes data in real-time, and automatically controls irrigation based on sensor data and user commands. The system proposed in this study provides a cost-effective and efficient solution for sustainable agriculture practices.
... In contrast to cellular communication, DSRC saves time and energy while transmitting data at a lower cost. Since WiFi and LTE are the foundations for contemporary DSRC and C-V2X, respectively, WiFi has a 60% greater energy efficiency than LTE [23]. Additionally, DSRC and C-V2X have transmission periods of 0.4 ms and 1 ms, correspondingly. ...
Vehicle-to-vehicle (V2V) communication enables a network of automobiles to perform collaborative computing, giving rise to the concept of a "vehicular cloud" (VC). However, without the need for edge nodes or cloud servers, vehicles can carry out applications needing the massive amount of processing cooperatively on their own by creating a Vehicular Ad-Hoc Network (VANET). Managing the recurrent topology alteration caused by vehicle mobility is a significant challenge for VANET cooperative computing. In this research, we present a V2V-based cooperative computing approach. The suggested method takes into account the distance between vehicles while choosing which ones to collaborate with, and it waits task offloading until the last possible moment to ensure a stable and energy-efficient cooperative computing environment. Despite its competitive performance when compared to other MH algorithms, the artificial rabbits optimisation (ARO) algorithm still suffers from poor accuracy and the issue of becoming trapped in solutions. By antagonism methods, this research creates selective opposition version of the artificial rabbit procedure (LARO), which eliminates the negative consequences of these shortcomings. To begin, during the random concealment phase, a Lévy flight strategy is implemented to increase population diversity and dynamics. The algorithm's convergence accuracy is enhanced by the richness of its various population samples. The tracking efficiency is improved, and ARO is kept from getting stuck in its existing local solutions by adopting the selective opposition technique. In comparison to traditional static scheduling techniques, the suggested strategy improves upon both energy efficiency and network reliability.
... Depending on the required transmission speed and distance, the collected stream should be transmitted to a centralized or decentralized administration using, e.g., Zigbee, Bluetooth, Tmote Sky, 4G, etc. It is important to note that the sensor system needs a continuous source of electricity, and the choice of connectivity is influenced by power consumption, with mobile service requiring more power than WiFi, 474.67 to 576.64 mW and 1254.3 to 1540.6 mW, respectively [144]. Safety considerations for both hardware and connectivity are also crucial. ...
Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone areas. In this study, we explore the potential of IoT and cloud infrastructure in realizing a sustainable EEWS that is capable of providing early warning to people and coordinating disaster response efforts. To achieve this goal, we provide an overview of the fundamental concepts of seismic waves and associated signal processing. We then present a detailed discussion of the IoT-enabled EEWS, including the use of IoT networks to track the actions taken by various EEWS organizations and the cloud infrastructure to gather data, analyze it, and send alarms when necessary. Furthermore, we present a taxonomy of emerging EEWS approaches using IoT and cloud facilities, which includes the integration of advanced technologies such as machine learning (ML) algorithms, distributed computing, and edge computing. We also elaborate on a generic EEWS architecture that is sustainable and efficient and highlight the importance of considering sustainability in the design of such systems. Additionally, we discuss the role of drones in disaster management and their potential to enhance the effectiveness of EEWS. Furthermore, we provide a summary of the primary verification and validation methods required for the systems under consideration. In addition to the contributions mentioned above, this study also highlights the implications of using IoT and cloud infrastructure in early earthquake detection and disaster management. Our research design involved a comprehensive survey of the existing literature on early earthquake warning systems and the use of IoT and cloud infrastructure. We also conducted a thorough analysis of the taxonomy of emerging EEWS approaches using IoT and cloud facilities and the verification and validation methods required for such systems. Our findings suggest that the use of IoT and cloud infrastructure in early earthquake detection can significantly improve the speed and effectiveness of disaster response efforts, thereby saving lives and reducing the economic impact of earthquakes. Finally, we identify research gaps in this domain and suggest future directions toward achieving a sustainable EEWS. Overall, this study provides valuable insights into the use of IoT and cloud infrastructure in earthquake disaster early detection and emphasizes the importance of sustainability in designing such systems.
... Similar studies have already examined various applications like online browsing, video filming, or standard video playback [1], where the focus was put on a high variety of applications instead of a detailed analysis on a single application. Furthermore, different hardware modules being active in the VR streaming pipeline such as the network interface [2], [3] and the display [4] were investigated. Many studies focused on the video decoding process which can be software-bound [5], [6] or hardware-bound [7], [8]. ...
This paper proposes a method to evaluate and model the power consumption of modern virtual reality playback and streaming applications on smartphones. Due to the high computational complexity of the virtual reality processing toolchain, the corresponding power consumption is very high, which reduces operating times of battery-powered devices. To tackle this problem, we analyze the power consumption in detail by performing power measurements. Furthermore, we construct a model to estimate the true power consumption with a mean error of less than 3.5%. The model can be used to save power at critical battery levels by changing the streaming video parameters. Particularly, the results show that the power consumption is significantly reduced by decreasing the input video resolution.
... Many approaches have been proposed for optimizing energy-saving applications, based on the analysis of code and on good energy-saving practices, among these factors that directly influence battery life and autonomy : firstly, the way in which the battery is charged, Horvath et al. [25] found that in cases where the users did not completely drain the battery and never let it reach 100% during charging, the drain coefficient was lower (1.42%) than in the case of a full charge, secondly we can use WI-FI instead of cellular networks as soon as we have the possibility :Zou et al. [26] developed a platform with an Arduino board and an open-source Java application, to monitoring the power consumption of video streaming, they observe that the energy efficiency (average throughput / average power consumption) with WIFI and for different locations is at least 54% higher than that of LTE, they also find that the power consumption increases when the video quality also increases, as well as the density of users and the quality of the channels (signal strength). For developers as well, several researches have been launched and recommendations have been drawn: Nguyen et al [27] propose an interesting way to reduce the energy consumption of smartphones, their main idea is to offload heavy computational tasks to the cloud (more powerful computers) rather than performing them locally and downloading the results, For Machine Learning McIntosh et al. [28] present an empirical study of different machine learning algorithms that developers can use as a guide. ...
The recent mobile devices consist of software and hardware components, such as screens, cameras, sensors (accelerometer, fingerprint reader, GPS …), connectivity adaptors (Bluetooth, WIFI …), different radio cells (H+, 3G, 4G, 5G…), processors with multiple cores, etc. The availability of these elements simultaneously on a given smartphone/tablet provides a rich experience for end users. However, the recent components consume a lot of energy, which severely limits the duration of their use, and reduces the autonomy of the device batteries. It is why the optimization of the energy consumption management becomes of crucial importance. However, most of the mobile applications developed so far have been designed unconsciously from their actual energy consumption and ignoring the energy bugs during the development process that could drain the battery later after deployment. Unfortunately, there are no quantitative approaches to detect specifically these energy bugs introduced in this fast-paced development process. Through this paper, we target to study and compare the different existing energy profilers for mobile devices, available in the literature. We report about the various techniques of energy consumption modelling, detecting energy bugs, and optimizing code structures with energy-saving practices.KeywordsEnergy consumptionpower/energy profilerspower modelsbattery monitoringpower estimationmobile devices
... IBtryMntr is a tool developed by Gokhale et al. [8], it shows that Wi-Fi uses less power than LTE. Zou et al. [9] achieve similar results using an Arduino board, where Wi-Fi was 54% more efficient. They also find that the power consumption increases when the video quality also increases. ...
Despite the rapid and exponential evolution of innovative applications for mobile devices, their batteries still suffer from a limited capacity that cannot keep pace with new and increasingly resource-intensive apps. The gap between development rates of batteries and chips strongly requires optimizing the energy efficiency in order to meet the demands for reduced energy consumption. Therefore, we need to monitor and analyse the energy efficiency of these devices. For this purpose, we have developed an Android application, called AppsDrain. It provides a detailed analysis of battery usage, specifying the energy consumption of each component. This app could assist developers to identify instantly which apps are the sources of battery drain. In some cases, power leaks are due to poor software design, junk code running in the background, or complex code consuming a high number of CPU cycles. To validate our app, we propose to explore the energy consumption of some applications while making a comparison with their respective complexities. Among different applications that aroused our interest, we consider here most known sorting algorithms. The obtained results of our experiment confirm that more complexity implies more energy consumption.
