A measurement study for transmission energy on mobile device

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.

Energy consumption is the result of interactions between hardware, software, users, and the application environment. Optimisation of energy consumption has become crucial, and energy is considered a critical resource, so it is important to know and understand both how energy is measured and consumed on mobile devices. An accurate knowledge will allow us to develop efficient solutions to reduce energy consumption in order to improve the user experience. In this paper, we propose an experimental methodology to build a model of the energy consumption of mobile applications. Based on precise measurements, we elaborate predictive models of energy consumption for both unconnected and connected applications.

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.

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.

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.

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.

ENOrMOUS: ENergy Optimization for MObile plateform using User needS

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.

Battery-operated mobile devices may lose their freedom of mobility because of their dependence on being in close proximity to a power outlet as applications with higher computation and processing capabilities consume more energy and drain battery life faster. One of the techniques used to minimize energy consumption in battery-operated mobile devices is user cooperation at the time of wireless transmission. In this paper, we formulate minimization of energy consumption through user cooperation routing in mobile devices that have multiinterfaces under a multi-hop network. User cooperation optimization will be exploited by selecting a short-range wireless communication that has the lowest energy consumption as a proxy for establishing long-range wireless communication. Two algorithms are proposed to solve the problem. Simulation results show that both algorithms are able to solve this cooperation problem and optimize energy efficiency.

Reducing the communication energy is essential to facilitate the growth of emerging mobile applications. In this paper, we introduce signal strength into location-based applications to reduce the energy consumption of mobile devices for data reception. First, we model the problem of data fetch scheduling, with the objective of minimizing the energy required to fetch location-based information without impacting the application's semantics adversely. To solve the fundamental problem, we propose a dynamic-programming algorithm and prove its optimality in terms of energy savings. Then, we perform postoptimal analysis to explore the tolerance of the algorithm to signal strength fluctuations. Finally, based on the algorithm, we consider implementation issues. We have also developed a virtual tour system integrated with existing Web applications to validate the practicability of the proposed concept. The results of experiments conducted based on real-world case studies are very encouraging and demonstrate the applicability of the proposed algorithm toward signal strength fluctuations .

This chapter proposes a Cognitive Radio network architecture that enables for the efficient operation of mobile devices over TV White Spaces. The proposed network architecture comprises of a Geo-location database and a spectrum broker that coordinates TV White Spaces access, by a number of 4G secondary communication systems, competing/requesting for the available radio spectrum. Furthermore, it introduces an innovative methodology for evaluation of energy and resource consumption in mobile cognitive devices that does not require any external metering device but exploits the advanced software and hardware features of modern smart phones to this end. In particular, the various APIs provided, by such operating systems for access to their functionality can be used for adequately auditing and reporting resource consumption on such mobile platforms. More specifically, we evaluate energy consumption and CPU utilisation in various communication scenarios via a number of experimental tests, carried out under controlled conditions. Network connectivity, calling and multimedia playback are some of the scenarios that are evaluated and presented here.KeywordsCognitive Radio NetworksEnergy Consumption EvaluationResource ConsumptionMobile Devices

Mobile devices may use mobile edge computing to improve energy efficiency and responsiveness by offloading computation tasks to edge servers. However, the transmissions of mobile devices may result in interference that decreases the upload rate and prolongs transmission delay. Clustering has been shown as an effective approach to improve the transmission efficiency for dense devices, but there is no distributed algorithm for the optimization of clustering and computation offloading. In this work, we study the optimization problem of computation offloading to minimize the energy consumption of mobile devices in mobile edge computing by adaptively clustering devices to improve the transmission efficiency. To address the optimization problem in a distributed manner, the decision problem of clustering and computation offloading for mobile devices is formulated as a potential game. We introduce the construction of the potential game and show the existence of Nash equilibrium in the game with a finite enhancement ability. Then, we propose a distributed algorithm of clustering and computation offloading based on game theory. We conducted a simulation to evaluate the proposed algorithm. The numerical results from our simulation show that our algorithm can improve offloading efficiency for mobile devices in mobile edge computing by improving transmission efficiency. By offloading more tasks to edge servers, both the energy efficiency of mobile devices and the responsiveness of computation-intensive applications can be improved simultaneously.

Scalable video coding with adaptive modulation and coding is a promising technique to provide real-time multicast services for heterogeneous mobile devices. Nevertheless, as the rapid growth of data communication for emerging applications, energy consumption is an critical challenge of mobile devices. This paper targets the problem of resource allocation for scalable video multicast with adaptive modulation-coding schemes in next generation cellular wireless networks, with an objective to minimize the total energy consumption of all mobile devices for reception. We show the NP-hardness of the target problem and propose a 2-approximation algorithm. Extensive simulations are conducted to compare the proposed algorithm with a brute-force optimal algorithm and a conventional approach, which provides some useful insights into power-aware scalable video multicast in 4G wireless systems.

As the network becomes dense, multi-server mobile edge computing (MEC) systems with multiple candidate MEC servers, bring new opportunities to enrich user experience through computation offloading. Specifically, MEC server selection, as a new dimension, arises to strike a well balance between energy consumption and task execution delay of mobile device (MD). Since channel conditions between the MD and MEC server (or its connected access point) as well as available computing capability at MEC servers are time-varying, this paper aims to devise dynamic computation offloading mechanism to account for delay-energy tradeoffs in multi-server MEC systems. To this end, we jointly optimize transmit power and MEC server selection for the MD to minimize time average expected task execution delay, under the constraint of average energy consumption. With partial current network status available, we then combine Lyapunov optimization framework and multi-armed bandit framework for an online learning based computation offloading algorithm, whose feasibility and regret bound are given through theoretical analyses. Finally, simulation results are presented to demonstrate its efficiency and superiority.

Energy consumption of wireless data transmission, a significant part of the overall energy consumption on a mobile device, is context-dependent—it depends on both internal and external contexts, such as application workload and wireless signal strength. In this paper, we propose an event-driven framework that can be used for efficient power management on mobile devices. The framework adapts the behavior of a device component or an application to the changes in contexts, defined as events, according to developer-specified event-condition-action (ECA) rules that describe the power management mechanism. In contrast to previous work, our framework supports complex event processing. By correlating events, complex event processing helps to discover complex events that are relevant to power consumption. Using our framework developers can implement and configure power management applications by editing event specifications and ECA rules through XML-based interfaces. We evaluate this framework with two applications in which the data transmission is adapted to traffic patterns and wireless link quality. These applications can save roughly 12 percent more energy compared to normal operation.