A survey on energy estimation and power modeling schemes for smartphone applications

In the last decade, the rising trend in the popularity of smartphones motivated software developers to increase application functionality. However, increasing application functionality demands extra power budget that as a result, decreases smartphone battery lifetime. Optimizing energy critical sections of an application creates an opportunity to increase battery lifetime. Smartphone application energy estimation helps investigate energy consumption behavior of an application at diversified granularity (eg, coarse and fine granular) for optimal battery resource use. This study explores energy estimation and modeling schemes to highlight their advantages and shortcomings. It classifies existing smartphone application energy estimation and modeling schemes into 2 categories, ie, code analysis and mobile components power model–based estimation owing to their architectural designs. Moreover, it further classifies code analysis–based modeling and estimation schemes in simulation‐based and profiling‐based categories. It compares existing energy estimation and modeling schemes based on a set of parameters common in most literature to highlight the commonalities and differences among reported literature. Existing application energy estimation schemes are low‐accurate, resource expensive, or non‐scalable, as they consider marginally accurate smart battery's voltage/current sensors, low‐rate power capturing tools, and labor‐driven lab‐setting environment to propose power models for smartphone application energy estimation. Besides, the energy estimation overhead of the components power model–based estimation schemes is very high as they physically run the application on a smartphone for energy profiling. To optimize smartphone application energy estimation, we have highlighted several research issues to help researchers of this domain to understand the problem clearly.     

e‐LiteSense: Self‐adaptive energy‐aware data sensing in WSN environments

Currently deployed in a wide variety of applicational scenarios, wireless sensor networks (WSNs) are typically a resource‐constrained infrastructure. Consequently, characteristics such as WSN adaptability, low‐overhead, and low‐energy consumption are particularly relevant in dynamic and autonomous sensing environments where the measuring requirements change and human intervention is not viable. To tackle this issue, this article proposes e‐LiteSense as an adaptive, energy‐aware sensing solution for WSNs, capable of auto‐regulate how data are sensed, adjusting it to each applicational scenario. The proposed adaptive scheme is able to maintain the sensing accuracy of the physical phenomena, while reducing the overall process overhead. In this way, the adaptive algorithm relies on low‐complexity rules to establish the sensing frequency weighting the recent drifts of the physical parameter and the levels of remaining energy in the sensor. Using datasets from WSN operational scenarios, we prove e‐LiteSense effectiveness in self‐regulating data sensing accurately through a low‐overhead process where the WSN energy levels are preserved. This constitutes a step‐forward for implementing self‐adaptive energy‐aware data sensing in dynamic WSN environments.     

Energy‐efficient data transmission technique for wireless sensor networks based on DSC and virtual MIMO

In a wireless sensor network (WSN), the data transmission technique based on the cooperative multiple‐input multiple‐output (CMIMO) scheme reduces the energy consumption of sensor nodes quite effectively by utilizing the space‐time block coding scheme. However, in networks with high node density, the scheme is ineffective due to the high degree of correlated data. Therefore, to enhance the energy efficiency in high node density WSNs, we implemented the distributed source coding (DSC) with the virtual multiple‐input multiple‐output (MIMO) data transmission technique in the WSNs. The DSC‐MIMO first compresses redundant source data using the DSC and then sends it to a virtual MIMO link. The results reveal that, in the DSC‐MIMO scheme, energy consumption is lower than that in the CMIMO technique; it is also lower in the DSC single‐input single‐output (SISO) scheme, compared to that in the SISO technique at various code rates, compression rates, and training overhead factors. The results also indicate that the energy consumption per bit is directly proportional to the velocity and training overhead factor in all the energy saving schemes.     

Effective-SNR estimation for wireless sensor network using Kalman filter

In many Wireless Sensor Network (WSN) applications, the availability of a simple yet accurate estimation of the RF channel quality is vital. However, due to measurement noise and fading effects, it is usually estimated through probe or learning based methods, which result in high energy consumption or high overheads. We propose to make use of information redundancy among indicators provided by the IEEE 802.15.4 system to improve the estimation of the link quality. A Kalman filter based solution is used due to its ability to give an accurate estimate of the un-measurable states of a dynamic system subject to observation noise. In this paper we present an empirical study showing that an improved indicator, termed Effective-SNR, can be produced by combining Signal to Noise Ratio (SNR) and Link Quality Indicator (LQI) with minimal additional overhead. The estimation accuracy is further improved through the use of Kalman filtering techniques. Finally, experimental results demonstrate that the proposed algorithm can be implemented on resource constraints devices typical in WSNs.     

A PMC-driven methodology for energy estimation in RVC-CAL video codec specifications

In this paper, an energy estimation methodology based on performance monitor counters (PMC) is proposed to estimate the energy consumption of RVC-CAL video codec specifications. The proposed PMC-driven methodology is able to automatically identify the most appropriate events and training data to cover the main application characteristics. In addition, knowledge of the hardware platform employed is not required. Therefore, this methodology can be easily implemented on other PMC-available systems while keeping the estimation accuracy. It is worth noting that this is an attractive asset to analyze the energy consumption of RVC-CAL codec specifications. Besides, the methodology reduces the PMC redundancy and, thus, the overhead introduced when applied to on-line power management. Experimenting on two RVC-CAL decoders, H.264 and MPEG4 Part2 SP, a coarse estimation model based on instructions per cycle (IPC) and the proposed PMC-driven model are compared. The results show that the PMC-driven model can achieve for the H.264 and MPEG4 Part2 SP decoders average estimation errors of 5.95% and 5.01%, respectively, in comparison to the 17.11% and 13.65% average errors obtained with the IPC-based model. As a consequence, this methodology is suggested to be combined into the RVC framework to help the designer to have an overview of the energy consumption of the specification actors at earlier design stages.     

无线物联网中基于网络编码的能量受限数据传输机制

本文针对无线物联网能量有限的应用场景,提出一种基于网络编码的能量受限数据传输机制NCDT.NCDT引入传染病路由的思想,对网络编码数据包的传输方式进行建模.通过限制编码数据包在网络中的复制次数,以及参与数据包交换的移动终端数量,控制网络的整体能量消耗.NCDT机制在限制网络编码资源消耗的同时,保证了在传输过程中取得较高的传输可靠性.     

An Energy Consumption Model for Performance Analysis of Routing Protocols for Mobile Ad Hoc Networks

A mobile ad hoc network (or manet) is a group of mobile, wireless nodes which cooperatively form a network independent of any fixed infrastructure or centralized administration. In particular, a manet has no base stations: a node communicates directly with nodes within wireless range and indirectly with all other nodes using a dynamically-computed, multi-hop route via the other nodes of the manet.Simulation and experimental results are combined to show that energy and bandwidth are substantively different metrics and that resource utilization in manet routing protocols is not fully addressed by bandwidth-centric analysis. This report presents a model for evaluating the energy consumption behavior of a mobile ad hoc network. The model was used to examine the energy consumption of two well-known manet routing protocols. Energy-aware performance analysis is shown to provide new insights into costly protocol behaviors and suggests opportunities for improvement at the protocol and link layers.     

Performance optimization of duty‐cycled MAC in delay‐energy constrained sensor network under uniform and nonuniform traffic generation

Duty‐cycle at the media access control (MAC) layer plays a key role in energy savings and network lifetime extension. It consists in putting a node's radio in the sleep state as soon as it has no communication activity. Traditional wireless sensor network MAC protocols are designed with short duty‐cycles at the cost of long delays. Careful design is required for joint energy‐delay constrained applications, where the optimal parameters should be thoroughly derived. The present paper deals with this issue and mathematically derives optimal values of key MAC parameters under low data rate applications for 3 well‐known duty‐cycled MAC protocols, WiseMAC, SCP‐MAC, and LMAC as representatives of 3 MAC protocol categories, respectively, preamble‐sampling, slotted contention‐based, and frame‐based. The analysis provides also the optimum traffic sampling rate that guarantees the minimum energy consumption. It shows the role of these parameters in achieving the targeted end‐to‐end delay constraints under network models with uniform traffic generation, for ring and grid topologies. As a second contribution, the model is extended to nonuniform traffic scenarios, where a certain percentage of deployed nodes are relays whose role is to balance traffic forwarding and save the overall network energy. The results reveal that different optimal internal MAC parameters and traffic generation rates can be found for different configurations of relay nodes deployment, which achieve minimal network energy consumption while satisfying the application required end‐to‐end delay threshold.     

Area and Power Reduction of Embedded DSP Systems using Instruction Compression and Re-configurable Encoding

In embedded control applications, system cost and power/energy consumption are key considerations. In such applications, program memory forms a significant part of the chip area. Hence reducing code size reduces the system cost significantly. A significant part of the total power is consumed in fetching instructions from the program memory. Hence reducing instruction fetch power has been a key target for reducing power consumption. To reduce the cost and power consumption, embedded systems in these applications use application specific processors that are fine tuned to provide better solutions in terms of code density, and power consumption. Further fine tuning to suit each particular application in the targeted class can be achieved through reconfigurable architectures. In this paper, we propose a reconfiguration mechanism, called Instruction Re-map Table, to re-map the instructions to shorter length code words. Using this mechanism, frequently used set of instructions can be compressed. This reduces code size and hence the cost. Secondly, we use the same mechanism to target power reduction by encoding frequently used instruction sequences to Gray codes. Such encodings, along with instruction compression, reduce the instruction fetch power. We enhance Texas Instruments DSP core TMS320C27x to incorporate this mechanism and evaluate the improvements on code size and instruction fetch energy using real life embedded control application programs as benchmarks. Our scheme reduces the code size by over 10% and the energy consumed by over 40%. *A preliminary version of this paper has appeared in the International Conference on Computer Aided Design (ICCAD-2001), San Jose, CA, November 2001.     

Cuckoo: flexible compute‐intensive task offloading in mobile cloud computing

Mobile cloud computing (MCC) is an emerging technology to facilitate complex application execution on mobile devices. Mobile users are motivated to implement various tasks using their mobile devices for great flexibility and portability. However, such advantages are challenged by the limited battery life of mobile devices. This paper presents Cuckoo, a scheme of flexible compute‐intensive task offloading in MCC for energy saving. Cuckoo seeks to balance the key design goals: maximize energy saving (technical feasibility) and minimize the impact on user experience with limited cost for offloading (realistic feasibility). Specifically, using a combination of static analysis and dynamic profiling, compute‐intensive tasks are fine‐grained marked from mobile application codes offline. According to the network transmission technologies supported in mobile devices and the runtime network conditions, adopting “task‐bundled” strategy online offloads these tasks to MCC. In the task‐hosted stage, we propose a skyline‐based online resource scheduling strategy to satisfy the realistic feasibility of MCC. In addition, we adopt resource reservation to reduce the extra energy consumption caused by the task multi‐offloading phenomenon. Further, we evaluate the performance of Cuckoo using real‐life data sets on our MCC testbed. Our extensive experiments demonstrate that Cuckoo is able to balance energy consumption and execution performance. Copyright © 2016 John Wiley & Sons, Ltd.     

Genetic spider monkey‐based routing protocol to increase the lifetime of the network and energy management in WSN

Wireless sensor networks (WSNs) include large distributed nodes in the sensing field. However, the sensor nodes may die due to energy deficiency as they are situated in a hostile environment. Therefore, an energy‐efficient WSN routing protocol is necessary in order to better accommodate the various environmental conditions. In this paper, we have proposed a new Energy‐Efficient Genetic Spider Monkey‐based Routing Protocol (EGSMRP) to improve the stability and lifetime of sensor nodes. The operation of EGSMRP is classified into two stages: (i) setup phase and (ii) steady‐state phase. In the setup phase, GSMO‐based cluster head selection procedure is done. In this phase, the base station utilizes the GSMO algorithm as a device to generate energy‐efficient clusters. Followed with this, the steady‐state phase solves the load balancing issue by utilizing the intracluster data broadcast and dual‐hop intercluster broadcasting algorithm. Thereby, the proposed EGSMRP protocol has shown the energy‐based opportunistic broadcasting with reduced control overhead. Simulation is performed in various conditions to evaluate the effectiveness of the proposed EGSMRP protocol using different metrics such as throughput, control overhead, energy consumption, end‐to‐end delay, and network lifetime. From the simulation results, it was evident that EGSMRP has achieved a higher performance compared to other traditional approaches such as EBAR, MCTRP, IEEMARP, HMCEER, and EFTETRP.     

车联网中整合移动边缘计算与内容分发网络的移动性管理策略

由于车载应用的普及和车辆数量的增加,路边基础设施的物理资源有限,当大量车辆接入车联网时能耗与时延同时增加,通过整合内容分发网络(CDN)和移动边缘计算(MEC)的框架可以降低时延与能耗。在车联网中,车辆移动性对云服务的连续性提出了重大挑战。因此,该文提出了移动性管理(MM)来处理该问题。采用开销选择的动态信道分配(ODCA)算法避免乒乓效应且减少车辆在小区间的切换时间。采用基于路边单元(RSU)调度的合作博弈算法进行虚拟机迁移并开发基于学习的价格控制机制,以有效地处理MEC的计算资源。仿真结果表明,所提算法相比于现有的算法能够提高资源利用率且减少开销。     

Dynamic cache resources allocation for energy efficiency

This article proposes a mechanism of low overhead and less runtime, termed dynamic cache resources allocation (DCRA), which allocates each application with required cache resources. The mechanism collects cache hit-miss information at runtime and then analyzes the information and decides how many cache resources should be allocated to the current executing application. The amount of cache resources varies dynamically to reduce the total number of misses and energy consumption. The study of several applications from SPEC2000 shows that significant energy saving is achieved for the application based on the DCRA with an average of 39% savings.     

Network application profiling with traffic causality graphs

A network application profiling framework is proposed that is based on traffic causality graphs (TCGs), representing temporal and spatial causality of flows to identify application programs. The proposed framework consists of three modules: the feature vector space construction using discriminative patterns extracted from TCGs by a graph‐mining algorithm; a feature vector supervised learning procedure in the constructed vector space; and an application identification program using a similarity measure in the feature vector space. Accuracy of the proposed framework for application identification is evaluated, making use of ground truth packet traces from seven peer‐to‐peer (P2P) application programs. It is demonstrated that this framework achieves an overall 90.0% accuracy in application identification. Contributions are twofold: (1) using a graph‐mining algorithm, the proposed framework enables automatic extraction of discriminative patterns serving as identification features; 2) high accuracy in application identification is achieved, notably for P2P applications that are more difficult to identify because of their using random ports and potential communication encryption. Copyright © 2014 John Wiley & Sons, Ltd.     

Energy‐efficient resource allocation for device‐to‐device communication through noncooperative game theory

Wireless network with high data rate applications has seen a rapid growth in recent years. This improved quality of service (QoS) leads to huge energy consumption in wireless network. Therefore, in order to have an energy‐efficient resource allocation in cellular system, a device‐to‐device (D2D) communication is the key component to improve the QoS. In this paper, we propose a noncooperative game (NCG) theory approach for resource allocation to improve energy efficiency (EE) of D2D pair. A three‐tier network with macrocell base station (MBS), femtocell base station (FBS), and D2D pair is considered, which shares the uplink resource block. A resource allocation strategy with constraints is arrived, which maintains minimum throughput for each user in the network. The proposed resource allocation strategy optimizes the EE of D2D pair in the three‐tier network, which achieves Nash equilibrium (NE) and Pareto optimality (PO). Simulation results validate that EE is uniform and optimum for all D2D pair, which converges to NE when channel is static and it converges to PO when the channel is dynamic.     

基于模拟器的嵌入式操作系统能耗估算与分析

随着嵌入式系统低能耗技术研究的深入,软件对系统能耗的影响越来越受到人们的关注,并向着定量分析方向发展.本文提出一种嵌入式操作系统能耗量化分析方法,通过模拟运行嵌入式操作系统和应用软件,利用微体系结构能耗模型估算单时钟周期指令能耗.提出基于软件功能结构的操作系统内核能耗估算模型并估算分析内核执行路径、服务、例程、原子函数的能耗,发现操作系统内核中显著影响系统能耗的关键软件模块及其特征.实验结果表明,本方法可以从很大程度上提高嵌入式操作系统能耗估算和分析的准确性,估算结果有助于嵌入式操作系统能耗的量化分析和操作系统及应用程序的能耗优化设计.     

A web service‐based internet of things framework for mobile resource augmentation

Mobile devices are the primary communication tool in day to day life of the people. Nowadays, the enhancement of the mobile applications namely IoTApps and their exploitation in various domains like healthcare monitoring, home automation, smart farming, smart grid, and smart city are crucial. Though mobile devices are providing seamless user experience anywhere, anytime, and anyplace, their restricted resources such as limited battery capacity, constrained processor speed, inadequate storage, and memory are hindering the development of resource‐intensive mobile applications and internet of things (IoT)‐based mobile applications. To solve this resource constraint problem, a web service‐based IoT framework is proposed by exploiting fuzzy logic methodologies. This framework augments the resources of mobile devices by offloading the resource‐intensive subtasks from mobile devices to the service providing entities like Arduino, Raspberry PI controller, edge cloud, and distant cloud. Based on the recommended framework, an online Repository of Instructional Talk (RIoTalk) is successfully implemented to store and analyze the classroom lectures given by faculty in our study site. Simulation results show that there is a significant reduction in energy consumption, execution time, bandwidth utilization, and latency. The proposed research work significantly increases the resources of mobile devices by offloading the resource‐intensive subtasks from the mobile device to the service provider computing entities thereby providing Quality of Service (QoS) and Quality of Experience (QoE) to mobile users.     

Real‐time performance evaluation of asynchronous time division traffic‐aware and delay‐tolerant scheme in ad hoc sensor networks

Wireless infrastructureless networks demand high resource availability with respect to the progressively decreasing energy consumption. A variety of new applications with different service requirements demand fairness to the service provision and classification, and reliability in an end‐to‐end manner. High‐priority packets are delivered within a hard time delay bound whereas improper power management in wireless networks can substantially degrade the throughput and increase the overall energy consumed. In this work a new scheme is being proposed and evaluated in real time using a state‐based layered oriented architecture for energy conservation (EC). The proposed scheme uses the node's self‐tuning scheme, where each node is assigned with a dissimilar sleep and wake time, based on traffic that is destined for each node. This approach is based on stream's characteristics with respect to different caching behavioral and storage‐capacity characteristics, and considers a model concerning the layered connectivity characteristics for enabling the EC mechanism. EC characteristics are modeled and through the designed tiered architecture the estimated metrics of the scheme can be bounded and tuned into certain regulated values. The real‐time evaluation results were extracted by using dynamically moving and statically located sensor nodes. A performance comparison is done with respect to different data traffic priority classifications following a real‐time asymmetrical transmission channel. Results have shown the scheme's efficiency in conserving energy while the topology configuration changes with time. Copyright © 2009 John Wiley & Sons, Ltd.     

A load balancing energy efficient clustering algorithm for MANETs

Nodes in mobile ad hoc Networks (MANETs) are characterized by their limited resources. Hence, the concept of clustering was introduced to allow spacial reuse of bandwidth and to minimize routing overhead. However, node mobility perturbs the stability of the network and affects the performance of other protocols such as scheduling, routing, and resource allocation, which makes re‐clustering the network to maintain up‐to‐date information at each node unavoidable. Consequently, clustering models for MANETS should be carefully designed while taking into consideration the fact that mobile nodes are energy constrained. In this paper, we propose a dynamic energy‐efficient clustering algorithm that prolongs the network lifetime by electing cluster‐heads taking into consideration, in addition to other parameters such as mobility, their residual energies and making them dynamically monitor their energy consumption to either diminish the number of their cluster‐members or relinquish their roles. We have evaluated the performance of the proposed clustering model and compared it with other related clustering approaches found in the literature. Obtained results show the efficiency of the proposed algorithm. Copyright © 2010 John Wiley & Sons, Ltd.     

A novel residual energy‐based distributed clustering and routing approach for performance study of wireless sensor network

Non‐uniform energy consumption during operation of a cluster‐based routing protocol for large‐scale wireless sensor networks (WSN) is major area of concern. Unbalanced energy consumption in the wireless network results in early node death and reduces the network lifetime. This is because nodes near the sink are overloaded in terms of data traffic compared with the far away nodes resulting in node deaths. In this work, a novel residual energy–based distributed clustering and routing (REDCR) protocol has been proposed, which allows multi‐hop communication based on cuckoo‐search (CS) algorithm and low‐energy adaptive‐clustering–hierarchy (LEACH) protocol. LEACH protocol allows choice of possible cluster heads by rotation at every round of data transmission by a newly developed objective function based on residual energy of the nodes. The information about the location and energy of the nodes is forwarded to the sink node where CS algorithm is implemented to choose optimal number of cluster heads and their positions in the network. This approach helps in uniform distribution of the cluster heads throughout the network and enhances the network stability. Several case studies have been performed by varying the position of the base stations and by changing the number of nodes in the area of application. The proposed REDCR protocol shows significant improvement by an average of 15% for network throughput, 25% for network scalability, 30% for network stability, 33% for residual energy conservation, and 60% for network lifetime proving this approach to be more acceptable one in near future.