Energy detection analytical model for handoff process to support mobile cloud computing environment

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.

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.

Secure and quality-of-service-supported service-oriented architecture for mobile cloud handoff process

The paper concerns issues related to evaluation of processing in computational clouds. The effectiveness of the computational clouds depends on many factors and different metrics can be used to asses effects of processing. In this work potentiality for evaluation of processing in cloud virtual machines that takes into account also financial cost constraints is investigated. There are considered KVM and Hyper–V based cloud platform configurations. In the paper the APPI index (Application Performance and Price Index) that can be used for selection of recommended configuration considering acceptable price of processing is defined. Presented experiments show the impact of use of the proposed metric on the choice of recommended platform for processing.

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.

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.

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.

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.

Mobile cloud computing has become a research hotspot in the fields of cloud computing and mobile computing. At present, many researches on offloading computation to cloud resource and their related work have been done and some achievements have been obtained in the field of mobile cloud computing. However, some problems of the current researches have also been found such as the separation between computation and data, limited network resource, and lack of location-aware, etc. In this paper, we have presented a novel mobile cloud computing model and proposed a heuristic approach MCC-particle swarm optimization (PSO) of placement selection of mobile applications’ partitions for minimizing the overall processing time and energy saving. The algorithm of the proposed MCC-PSO approach includes two parts. One is that it combines the PSO idea with the simulated annealing (SA) idea to achieve an improved PSO-based approach with the better global search’s ability. The other one is that it uses the probability theory and mathematical statistics and once again utilizes the SA idea to deal with the data obtained from the improved PSO-based process to get the final solution. And thus the whole approach achieves a long-term optimization of mobile cloud computing. The experimental results demonstrate that MCC-PSO evidently reduces the overall processing time of mobile applications and energy consumption of mobile devices while better guaranteeing the performance of executing mobile applications. MCC-PSO better achieves the idea of mobile cloud computing and makes the mobile cloud computing model more high-effective and meaningful.

Mobile cloud computing (MCC) is introduced as a supporting architecture for mitigating the resources and energy limitations of mobile devices. And security is considered as a key factor which significantly affects MCC's widely deployment. Thus data protection methods such as encryption were applied to ensure the security of data. However, the complexity increased by data protection methods brings down the performance of transaction processing in MCC. In order to reduce the hurts brought to users' satisfactory by data protection methods, we firstly carried out some experiments based on the Hadoop cloud computing platform for quantitative analysis. We used the two popular encryption algorithms DES and AES(512), simulated the transaction processing, measured the time consumption, and logged the battery voltage change of mobile terminals. Results show that data protection methods have significance effects on the efficiency of transaction processing and the energy consumption of mobile devices. Furthermore, the degree of these effects is closely related to the complexity of protection methods. Based on the analysis, this paper proposed a framework for dynamic data protection, expecting to balance the security and resource consumption in MCC.

ENOrMOUS: ENergy Optimization for MObile plateform using User needS

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.

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.

In this paper, a dynamic offloading model is proposed to minimize the energy consumption of mobile devices by exploiting cloud computational resources for view synthesis. The computational complexity of view synthesis, the processing capability of the cloud, the processing capability and the power consumption of the mobile are considered jointly into the model to provide an optimized solution. Several simulations based on parameters of real mobile devices demonstrate that the proposed method can save an average of 42.59%(4-partition case) and 46.40%(8-partition case) of total energy on different mobile devices and an average of 67.58%(4-partition case) and 69.76%(8-partition case) of total energy under different transmitting rates than the existing algorithms for view synthesis, respectively.

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.