Measurement-based methodology for modelling the energy consumption of mobile devices

As energy efficiency has become a key consideration in the engineering of mobile applications, an increasing number of perfective maintenance tasks are concerned with optimizing energy consumption. However, optimizing a mobile application to reduce its energy consumption is non-trivial due to the highly volatile nature of mobile execution environments. Mobile applications commonly run on a variety of mobile devices over mobile networks with divergent characteristics. Therefore, no single, static energy consumption optimization is likely to yield across-the-board benefits, and may even turn to be detrimental in some scenarios. In this paper, we present a novel approach to perfective maintenance of mobile applications to reduce their energy consumption. The maintenance programmer declaratively specifies the suspected energy consumption hotspots in a mobile application. Based on this input, our approach then automatically transforms the application to enable it to offload parts of its functionality to the cloud. The offloading is highly adaptive, being driven by a runtime system that dynamically determines both the state-to-offload and its transfer mechanism based on the execution environment in place. In addition, the runtime system continuously improves its effectiveness due to a feedback-loop mechanism. Thus, our approach flexibly reduces the energy consumption of mobile applications behind the scenes. Applying our approach to third- party Android applications has shown that it can effectively reduce the overall amount of energy consumed by these applications, with the actual numbers ranging between 25% and 50%. These results indicate that our approach represents a promising direction in developing pragmatic and systematic tools for the perfective maintenance of mobile applications.

Task offloading and scheduling is an important issue in mobile edge computing (MEC). A good offloading decision can fully utilize the computing capabilities of edge servers to deliver high-quality computing services. This paper takes into account the concerns about task processing latency and mobile device's energy consumption, and formulates the task offloading problem as a deadline-constrained energy-minimization integer program. We propose a partial offloading and scheduling method based on the whale optimization algorithm to solve the formulated optimization problem. This method employs a probability model-based mapping operator to convert an individual whale into a valid offloading solution represented by a task sequence. This mapping scheme is advantageous over sorting-based rules in producing high-quality task sequence. We develop an efficient heuristic strategy to decide each task should be processed locally on the mobile device or offloaded to the edge server for execution. With all tasks in the sequence having been scheduled, the mobile device's energy consumption for task execution and data transmission can be calculated. Accordingly, we can identify the best individual whale in the population leading to the solution with lowest energy consumption. We iteratively apply the population updating rule to explore better task sequence and offloading solution. We perform extensive experiments to verify that the proposed algorithm achieves more energy-efficient offloading solutions as compared to baseline algorithms while satisfying the deadline constraint.

Mobile Edge Computing is a new technology which aims to reduce latency, to ensure highly efficient network operation and to offer an improved user experience. Considering offloading will introduce additional wireless transmission overhead, the key technical challenge of mobile edge computing is tradeoff between computation cost and wireless transmission cost, reducing energy consumption of mobile edge devices and response time of computation task at the same time. A Mobile Edge Computing System composed of Mobile Edge Device and Edge Cloud, connecting with Wireless Stations, comes out. To protect user privacy, Data Preprocessing is proposed which includes irrelevant property clean and data segmentation. Aimed at reducing total energy consumption and response time, an energy consumption priority offloading (ECPO) algorithm and a response time priority offloading (RTPO) algorithm are put forward, based on Energy Consumption Model and Response Time Model. Combining both ECPO and RTPO, a dynamic computing offloading algorithm is raised which is more universal. Finally, simulations in four scenarios, including network normal scenario, network congested scenario, device low battery scenario and task time limited scenario, demonstrate that our algorithms can effectively reduce energy consumption of mobile edge device and response time of computation task.

ENOrMOUS: ENergy Optimization for MObile plateform using User needS

Reduction of energy consumption is important for reaching a sustainable future. This paper presents a novel method for optimizing the energy consumption of robotic manufacturing systems. The method embeds detailed evaluations of robots' energy consumptions into a scheduling model of the overall system. The energy consumption for each operation is modeled and parameterized as function of the operation execution time, and the energy-optimal schedule is derived by solving a mixed-integer nonlinear programming problem. The objective function for the optimization problem is then the total energy consumption for the overall system. A case study of a sample robotic manufacturing system and an experiment on an industrial robot are presented. They show that there exists a real possibility for a significant reduction of the energy consumption in comparison to state-of-the-art scheduling approaches. Note to Practitioners-The motivation for this work is the great interest of companies on energy consumption optimization, looking for cost reduction and sustainability in manufacturing. Existing optimization methods focus on different levels of details. A high-level model would be able to optimize the overall system. Unfortunately, due to the high computational cost, it can hardly consider the deep level of mechanical and electrical parameters, which determine the real energy consumption. This paper presents a novel method to embed detailed energy consumption models into a scheduling optimization problem. An effective parameterization of the time variables reduces the model complexity, allowing the optimizer to reschedule the complete sequence of operations for minimizing the total energy consumption, while keeping a fixed cycle time. The method has been integrated into a commercial tool for robot programming. The optimization is applicable both on new and existing robotic systems, since the required modifications are limited to the operations rescheduling, and no investments on new hardware are expected.

Energy consumption and energy modeling are important issues in designing and implementing of Wireless Sensor Networks (WSNs), which help the designers to optimize the energy consumption in WSN nodes. Good knowledge of the sources of energy consumption in WSNs is the first step to reduce energy consumption. Therefore, an accurate energy model is required for the evaluation of communication protocols. In this paper, an energy model for WSNs is provided considering the physical layer and MAC layer parameters by determining the energy consumed per payload bit transferred without error over AWGN channel. This model has been tested with real data and and NS-2 simulator. Results show good agreement between proposed model, experimental measurements and NS-2 simulator with mean absolute percentage error less than 5.18%. Furthermore, the proposed model is exploited to optimize transmitted power to achieve minimum energy consumption. Finally, a closed-form expression for optimum transmitted power is derived for M-QAM modulation scheme.

Energy consumption caused by wireless data transmission takes a significant part of the overall energy consumption of mobile devices, due to the popularity of Internet applications. There are many factors to influence energy consumption such as signal strength, data rate and so on. In this paper we proposed a system to measure the transmission energy consumption in Wi-Fi and 3G. We did some experiments to show the result, and the data in our research is correct to use in future work. As a result, we concluded the relationship between power and some important factors in Wi-Fi and 3G.

With the popularity of mobile devices such as smartphones and tablets, the improvement of service of quality is an important issue facing great challenges. The improvement of user service of quality is mainly reflected in reducing the energy consumption of mobile devices and the delay of task execution. Multi-access edge computing sinks computing and storage capabilities from the remote cloud to the edge network, which can effectively reduce the high latency caused by the transmission of tasks between the mobile device and the remote cloud and the high energy consumption of tasks performed locally. Most of the previous work was limited to service of quality optimization through dynamic service layout, while ignoring the critical impact of access network selection on network congestion. This article studies the task offloading model of multiple tasks and services with several MEC servers, and jointly optimizes the MEC's access network selection and service placement issues. Considering the delay and energy consumption caused by task offloading and execution, this article designs an effect function on delay and energy consumption, and aims to minimize this function to solve the MEC problem. Since this problem is NP-hard, this article designs a new optimization algorithm based on particle swarm optimization to solve this problem. Extensive simulation experiments show that the proposed optimization algorithm realize better performance than other algorithms. The algorithm has achieved good results in terms of time delay and energy consumption, which effectively reduces the system cost.

The intensification of biological wastewater treatment requires the high usage of electric energy, mainly for aeration processes. Publications on energy consumption have been mostly related to municipal wastewater treatment plants (WWTPs). The aim of the research was to elaborate on models for the estimation of energy consumption during dairy WWTP operation. These models can be used for the optimization of electric energy consumption. The research was conducted in a dairy WWTP, operating with dissolved air flotation (DAF) and an activated sludge system. Energy consumption was measured with the help of three-phase network parameter transducers and a supervisory control and data acquisition (SCADA) system. The obtained models provided accurate predictions of DAF, biological treatment, and the overall WWTP energy consumption using chemical oxygen demand (COD), sewage flow, and air temperature. Using the energy consumption of the biological treatment as an independent variable, as well as air temperature, it is possible to estimate the variability of the total electric energy consumption. During the summer period, an increase in the organic load (expressed as COD) discharged into the biological treatment causes higher electric energy consumption in the whole dairy WWTP. Hence, it is recommended to increase the efficiency of the removal of organic pollutants in the DAF process. An application for the estimation of energy consumption was created.

With the increasing energy consumption in the mining industry, the effective prediction and optimization of energy consumption in mining equipment have become pressing challenges. Traditional energy consumption prediction methods suffer from data processing delays and the fixed nature of monitoring devices, making them inadequate for meeting the real-time and flexible demands of modern mining operations. The advent of mobile computing platforms has introduced new possibilities for the dynamic prediction and optimization of energy consumption in mining equipment. In recent years, energy consumption prediction techniques based on mobile computing platforms have gained significant attention, enabling realtime data acquisition and analysis for a more precise understanding of energy consumption patterns and the implementation of efficient optimization strategies. However, existing studies predominantly focus on conventional models and methodologies, lacking effective mechanisms to capture spatiotemporal dynamics and optimize energy consumption accordingly. In this study, a spatiotemporal gated graph convolutional prediction model was proposed for the dynamic prediction of energy consumption in mining equipment based on a mobile computing platform. Additionally, an energy consumption optimization strategy was explored using the prediction results. This study provides a novel approach to energy consumption optimization in mining equipment, offering both theoretical significance and practical value.

Energy consumption is a main research issue in
\nwireless sensor networks; and particularly in those where nodes
\ncollaborate to reach a goal. This article explores the energy
\nconsumption in mobile devices participating in a human-based
\nwireless sensor network. Specifically, the paper proposes the use
\nof a message predictor to help detect and reduce the number of
\nunnecessary control packets delivered by the nodes as a way to
\nkeep updated the network topology. In order to evaluate this
\nproposal, the Optimized Link State Routing protocol was
\nmodified to add a message predictor between the routing and the
\nnetwork layers. Eleven simulations were performed using a
\nparticular setting. The preliminary results indicate the use of the
\nmessage predictor can help reduce considerably the nodes energy
\nconsumption without affecting the routing capability of the
\nprotocol. Although these results are still preliminary, they are
\nhighly encouraging.

With the progress of the eras and the development of science and technology, the requirements of device-to-device (D2D) connectivity increased rapidly. As one important service in future systems, ultra-reliable low-latency communication (URLLC) has attracted attention in many applications, especially in the Internet of Things (IoT), smart cities, and other scenarios due to its characteristics of ultra-low latency and ultra-high reliability. However, in order to achieve the requirement of ultra-low latency, energy consumption often increases significantly. The optimization of energy consumption and the latency of the system in the communication field are often in conflict with each other. In this paper, in order to optimize the energy consumption and the latency jointly under different scenarios, and since the detailed requirements for latency and reliability are diverse in different services, we propose an adaptive UE aggregation (AUA)-based transmission scheme that explores the diversity gain of multiple simultaneous paths to reduce the overall latency of data transmission, wherein multiple paths correspond to multiple coordination nodes. Furthermore, it could provide the feasibility of link adaptation by adjusting the path number according to the real transmission environment. Then, unnecessary energy waste could be avoided. To evaluate the performance, the energy-delay product (EDP) is proposed for the latency and energy comparison. The provided simulation results align with the numerical data. Through the analysis, it can be proven that the proposed scheme can achieve a joint optimization of latency and energy consumption to meet different types of URLLC services.

Utilizing peoples' mobile devices for gathering sensor data has attracted a lot of attention within the last few years. As a result, a great variety of systems for sensing environmental phenomena like temperature or noise have been proposed. However, most of these systems do not take into account that mobile devices have only limited energy resources. For instance, an often assumed prerequisite is that mobile devices are always aware of their position. Given the fact that a position fix is a very energy consuming operation, continuous positioning would quickly drain a device's battery. Since the owners of the mobile devices will not tolerate a significant reduction of the devices' battery lifetime, such an approach is not suitable. To address this issue we present PSense, a flexible system for efficiently gathering sensor data with mobile devices. By avoiding unnecessary position fixes, PSense reduces the energy consumption of mobile devices by up to 70% compared to existing mobile sensing approaches. This is achieved by introducing an adaptive positioning mechanism and by utilizing energy efficient short-range communication to exchange position related information.

Mobile devices play an integral role in our day lives and have brought the revolutionary change in business, education, and entertainment. Moreover, the emergence of cloud computing technology greatly extended the significance of smart device. On the other hand, the smart devices experience the problem when obtaining the multiple cloud services during the handoff process. In this paper, we propose an energy detection (ED) analytical model for handoff process that calculates the energy consumption for each handoff process in the cloud computing environment. Our ED analytic model is developed to examine the consumed energy for different handoff processes in cloud computing. The model helps the mobile users to get prior information for the status of the mobile when executing the handoff process. To reconfirm the validity of ED analytical model, we have test programmed in NS2. The results demonstrate that the ED analytical model efficiently detects the energy consumption of mobile devices during the handoff process in cloud computing environment.

Given the problems of high energy consumption and unreasonable resource utilization in current heterogeneous cloud computing platforms, this paper proposes an energy-efficient task scheduling strategy. First, by analyzing the relationship between the energy consumption of physical machines and the utilization rate of CPU-GPU resources, we can establish the task scheduling model and energy consumption model. Second, a task scheduling method HCGTS (Heterogeneous CPU-GPU Task Scheduling) for energy consumption optimization of CPU-GPU heterogeneous cloud platform is proposed. Different from traditional subtasks, we divide the multi-task data flows into CPU tasks, GPU tasks and pending tasks, which are scheduled and executed by different processors to implement multi-task parallel scheduling. In addition, the execution order of subtasks is dynamically adjusted to realize the load balance of CPU and GPU, thereby reducing the task completion time and overall energy consumption. Finally, the simulation experiment is completed on the cloud simulation software. Through comparing with the other three algorithms, the HCGTS algorithm has better performance in terms of execution time, resource utilization and energy consumption optimization.