Internet of Things-Based Energy Efficiency Optimization Model in Fog Smart Cities

In recent years, the IoT) Internet of Things (IoT) allows devices to connect to the Internet that has become a promising research area mainly due to the constant emerging of the dynamic improvement of technologies and their associated challenges. In an approach to solve these challenges, fog computing came to play since it closely manages IoT connectivity. Fog-Enabled Smart Cities (IoT-ESC) portrays equitable energy consumption of a 7% reduction from 18.2% renewable energy contribution, which extends resource computation as a great advantage. The initialization of IoT-Enabled Smart Grids including (FESC) like fog nodes in fog computing, reduced workload in Terminal Nodes services (TNs) that are the sensors and actuators of the Internet of Things (IoT) set up. This paper proposes an integrated energy-efficiency model computation about the response time and delays service minimization delay in FESC. The FESC gives an impression of an auspicious computing model for location, time, and delay-sensitive applications supporting vertically -isolated, service delay, sensitive solicitations by providing abundant, ascendable, and scattered figuring stowage and system associativity. We first reviewed the persisting challenges in the proposed state-of-the models and based on them. We introduce a new model to address mainly energy efficiency about response time and the service delays in IoT-ESC. The iFogsim simulated results demonstrated that the proposed model minimized service delay and reduced energy consumption during computation. We employed IoT-ESC to decide autonomously or semi-autonomously whether the computation is to be made on Fog nodes or its transfer to the cloud. Keywords— Computation time; energy efficiency optimization; fog-enabled smart grid; Internet of Things (IoT); response time; service


I. INTRODUCTION
Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals, or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. One of the revolutions that have provided infinite benefits to society is the Internet of Things (IoT) [1]. Their applicability is seen in transportation, healthcare system, organizations, or industrial automation and has gained much attraction.
IoT was planned to decrease data entry efforts and use sensors to collect data from the surroundings in the first step. It allows us to store and processing of data automatically, such as objects that can fall into the scope of the Internet of Things include connected security systems, thermostats, cars, electronic appliances, lights in household and commercial environments, alarm clocks, speaker systems, vending machines and more [2]. This step is illustrated in Fig. 1. However, nowadays, we encounter a new stage of IoT that allows many objects to connect to the Internet and communicate with each other without a human helping. Above all, IoT is specified by limited storage and slow processing, and various other issues. IoT used cloud computing to overcome these issues, popularly known as the Cloud of Things (CoT). This system helps us to simplify the flow of IoT data storing and processing. The IoT data is transported to the Cloud center in CoT, where they are processed, and the result is sent to defined applications. If the cloud center required IoT data, it would store for future decision-making. It is economical for the cloud of things popularly [3].
But today, with huge data, it has become very difficult to send a large amount of data to the cloud with IoT data. This method requires a high transfer speed. So, to solve these issues, fog computing becomes a fundamental factor. Cisco first presented the term fog computing, and it can positively affect the field of IoT. Data processing and storage of data are provided by fog computing at a local level. It is like a smart layer sitting in between the cloud and IoT. Because of its countless benefits, research opportunities have been increasing in this area [4].
When it comes to security in IoT, there have been some studies down in scaling security and privacy in virtualized resources, software, and hardware in-network computing demand to consumers like in the cloud, fog among others [5]. The inversion of this new high-tech depicted that the possible way to deliver computing resources as service is paramount. The authors mainly showed that the main challenge in computing, like cloud adoption, is security. A Multidimensional Mean Failure Cost model was developed to tackle security risk in computing. The model was able to show different points that cause network security loopholes in computing [6].
Recently, the cloud-based Internet of Things (IoT) heartbeat medical applications has grown progressively due to global services to heart patients. Generally, different healthcare sensors generate data for heart patients and offload these data to the hospital fog server for further processing. Therefore, the scheduling of these data with different operations is a critical question. Numerous task scheduling problems for healthcare applications in the cloud system have been investigated in the literature. To minimize total delay, the author has focused on the studies related to offloading healthcare tasks. During the last era of technology, highly intensive research activities took place in IoMT [7].
Many studies have presented their works based on portable health care devices; for instance, Shafik et al. [8] proposed computational frameworks for healthcare monitoring systems in mobile environments [9] and presented fog-computing based heart-beat detection for arrhythmia classifications.
Patient-centric heart monitoring systems [10] using fog computing were proposed, the system established a connection between patient and medical specialists to perform the efficient operation of detecting abnormality in the heartbeat. State-of-the-art approaches have mainly focused on heart arrhythmia and heart disease prediction from the noninvasive attributes of the morphological structure of the beat.
However, the study deals with minimizing the delaysensitive task and scheduling issues in critical heart-beat detection. Whereas these studies have focused on delay optimal task scheduling or task assignment problems in the fog cloud environment for heart-beat healthcare applications, the aforementioned goal is to minimize the total cost of and delay of each application during processing to the cloud system. Furthermore, the task assignment and task offloading problems related to the healthcare applications are formulated in these studies [11]. The prior studies have focused on offload computation tasks to the cloud system to improve application performance on the user's devices and measure the optimal delay results of healthcare data without any risk.
The delay and cost-optimal task scheduling of heart-beat healthcare applications into cloud networks were investigated in Akrivopoulos et al. [12]. The studies accepted the input of data from real-time sensors and provided the application tasks for the actions. These actions are performed by different clouds concerning application requirements and their constraints. To the best of this author's information, costefficient task scheduling for healthcare applications in fog cloud networks has not been investigated yet.
Regardless of the benefits that can be obtained in the combination of a drone in the 5G, there are still big challenges that are affecting the current technology that is likely to be passed on to the predictive assumptions. The technological problem is major technical issues that need to be solved during immigration, including inter-cell interference, efficient medium access control, and traffic management. Some common challenges are still persistent currently, including the multiple serving (services), standardized infrastructure.

A. Cloud Computing
Cloud computing is the innovative development of running computer applications and data savings over the Internet platform. Cloud computing combines distributed computing, parallel computing, and grid computing. In terms of the architecture of cloud computing, the 'cloud' concept means groups of computers. Each group of computers includes millions of computers connected by the network. Each 'cloud' is a computing center designed to provide cloud users with cloud applications and cloud data storage [13].
Cloud users can run cloud application interfaces, such as web searches, via web browsers. Data can be accessible from and storable in databases in the 'cloud'. Importantly, cloud computing has laid down a solid technological base for academic libraries to design and develop web-based coursereserved materials, digital libraries, essays, and these databases, tutorials, and other archived information repositories in the cloud computing environments [14].

B. Fog Computing
Fog computing is a distributed network environment computing worldview in which logic and computing power are passed on in the most effective way between the cloud and the users. Fog computing stretches out cloud computing. It presented a fog computing architecture that included homogeneous physical resources, a fog abstraction layer, and a fog service orchestration layer. Heterogeneous physical resources include segments, instance, servers, set-up boxes, and end-gadgets with various capacity and memory abilities to help extra functionalities [15].

C. Edge Computing
Edge computing is focused on bringing computing as close to users as possible to reduce latency and bandwidth use. In simpler terms, edge computing means running fewer processes in the cloud and moving those processes to local places, such as on an IoT device or an edge server. Bringing computation to the network's edge minimizes the amount of long-distance communication that has to happen between a client and server [16].
Cloud computing has focused on centralizing services into a handful of large data centers. Edge computing addresses those use cases that cannot be sufficiently addressed by the centralization approach of cloud computing, often because of networking requirements or other constraints. It concentrates on several small computing sites that reduce network cost, avoid bandwidth constraints, reduce transmission delays, limit service failures, and better control the movement of subtle data. Load times are cut by hundreds of milliseconds, and online services deployed closer to users enable both dynamic and static caching capabilities [17].
Fog-Enabled Smart Cities (FESC) is a technique that permits computing resources to be pre-processed through constrained latency with minimum energy consumption. It was revealed that energy consumption efficiency and estimations in a wide range of computing areas, for instance, in energy-based models using smart high-tech [18], [19], ensuring the energy-efficient quality of service [20]. The node density impact on energy consumption was demonstrated explicitly [21], enactment assessment of metaheuristics in resource-awareness like the energy in specifics simultaneous programming issues (i.e., energy-aware real-time scheduling problems) in different networks [22]; the projected model is depicted in Fig. 2. Energy equity solves eventually, reduced the social privacy web of things, and improved the superiority of communication performance [23]. The invention of FESC initiated the extension of onsite computing through its increased energy consumption of Terminal Nodes (TNs), the sensors and actuators of the devices connected on the Internet, IoT of their architecture; depicted in Fig. 2. The FESC opened up a promising computing model insight analysis on the location, response time during computation. The FESC was also employed in the industrial Internet to meet the rigorous prerequisite of truncated expectancy by divesting fractional reckoning responsibilities from FN to mist servers like cloud servers [24].
In particular, this paper mainly introduces a new energy optimization model about response time, service delay minimization. Some other contributions of this paper are provides a survey for proposed models challenges the cut across the networking paradigms including FESC. The proposed framework is mathematically expressed and demonstrated in Fig. 2 about response time, and service delay approaches to minimization.
The traditional architecture of Fog computing is for one data center and multiple FNs. It cannot keep pace with the existing progress of private Clouds. Furthermore, virtual machines leveraged for Cloud computing are also used for FESC as the resource unit, which cannot meet the requirement of FESC. Moreover, the incomplete volume of battery power has been one of the main limitations depicted in Abreha et al. [25].
The perception of fog and cloud computing remains right related to each other; yet we discuss point-by-point differences that using some virtual parameters as exemplified in table1 here is a comparison of fog and cloud computing. Cloud paradigm uses inaccessible servers transversely the Internet to accomplish data techniques, storage, and management of statistical data as an alternative to expanding an indigenous server. Fog comprises a reorganized atmosphere for computing in which the arrangement affords stowage, submissions, records, and multiplications [26].
The early approach in admitting requests of social networks, quality, and grade of services on web technologies like Fog network set-ups facilitated further attention to the Internet. It was observed that the combination of different networks would have a different impact on social, human behavior with different solutions were suggested. It is due to the significant focus on the privacy, energy as the resource is desirable to be determined as well [27]- [31].

A. Energy Utilization Challenges
In this section, we examine some of the energy consumption or utilization challenge and service delays. In the FESC environment, the functioning challenges for the research community are reducing energy consumption and load balancing. The main challenge is that if we need some level of edge computation to diminish the task delays, it comes at the higher dynamism depletion of the FNs.
Supplementary to that, focusing on QoS while minimizing energy consumption is proposed, especially in some papers for energy-constrained networks. Unquestionably, the records composed through the entire system are the foundation of the higher-layer resolution and the establishment for all the solicitations, which necessitates efficient energy etiquettes. Furthermore, if the composed records are inaccurate and undependable, the records fortification and solicitation become an impracticable aim that advances, leading to superfluous energy costs.
For delay-sensitive applications, the routine of a prolonged-distance distant Cloud server increases interruption that destroys the QoS. Certainly, this gain cannot realize unless the FNs consume more energy which causes a trade-off between the TNs and FNs in the Fog-IoT system. Minimization of energy consumption should be done before reckoning responsibilities are consummate in the interior and anticipated energy directly above and adjournment.
As the generation of technology advances, the fifthgeneration willpower over 100 intervals quicker than contemporary cellular networks and additional dimensions and approachable than with the compeers above of wireless. Many technologies that researchers have experienced on 5G.
This expectancy enhancement can assist in bringing an increased lifespan of approximately of the newest tendencies in machinery, like virtual reality, drones, and surgery from afar. However, planned 5G real-time and mission-critical applications cannot be realized except the issues of user scheduling, and beamforming for energy-efficient Fog Radio Access Networks (RAN) resolved.
Even though simulators were endeavors to prototypical and incarceration the realistic behavior and productivity of announcement systems, which originates with the sustenance of the maximum prevalent types of machinery and systems in the application of the today, for example, the fifth generation, IoT, the proposed movable-cloud-fog environments tranquil non-existence of the customizable software apparatuses for the enactment recreation of their computing-schmoosing edifice chunks. In processer system explorations, system imitation is a practice whereby software plug-in facsimiles the performance of cities by scheming the communication amongst the diverse network entities like (routers, adjustments, protuberances, entree points, acquaintances, among others).
The execution time of such simulations would be tremendously high, significantly plummeting the enterprise interplanetary that can be premeditated. A realistic estimate to study the effect of design parameters on the performance of the FESC consists of modeling the interruption and energy ingesting of a separate module of the FESC.
Formerly, for example, an anticipated set of design parameters, the deferment, and energy depletion of every piece is subtracted, determining the critical path and the number of resources required for each operation, and calculating which an uncluttered delinquent relic. Besides, the given protagonist of Big data in FESC is to progress a huge quantity of records on a simultaneous center and stowage them employing diverse storing machinery. However, the development of Cyber-physical IoT systems suffers severely from Big data.
In this situation, the remaining challenge is how FESC can tackle the big data bottleneck. Nonetheless, the Internet of Things and Big data regressed unconventionally; they have become interrelated over the period. We now have unprecedented amounts of IoT data, and it is up to organizations to harness the data to extract useful, actionable insights.
Nonetheless, since traditional clouds cannot store, process, and analyze massive amounts of unstructured data in real-time, organizations turn to FESC solutions. Moreover, the relativeness amongst FESC with Big data has publicized the dualistic machinery that is disarranging the tools in the superlative imaginable technique. Data analytics is emerging as a key to FESC that aids in yielding the inventiveness to advance pronouncement assembly.
Moreover, offloading among the FNs has been introduced by the resource and capability sharing of cooperative Fogs. In this way, services to clients can be offered again with a lesser reliance on the cloud by strengthening the intermediary Fog layer in FESC before the Cloud layer. However, the coordination challenge provokes energy against primary intention in reducing the energy consumption of the FNs.
Additionally, for the aim of service provisioning, presentday elucidations adopt full collaboration amongst the FNs. However, if every Fog influence fit a diverse system operative, or overhaul providers reduced the partnership. How can we solve the problem of integration and compatibility among the FNs? FESC comes across with many safeties defies, exclusively from internal outbreaks. The difference is that the quid pro quo amongst confidence, broadcast enactment, and energy depletion meet simultaneous conduction.
Pseudo Code for the proposed Model 1 Initialize all parameters 2 Count node, generate, consider population size 3 Initialize initial chromosome via every casual for node for each ask 4 do while i=1 to the number of generation 5 Select random chromosome and apply mutation 6 Sort chromosome with evaluate response time and energy intake 7 Sort chromosome with calculate service delay 8 Select population size from all chromosomes 9 End process 10 Show be the matrix of node and tasks It vestiges a perplexing interrogation on the dynamismoperative collaboration strategy amongst FNs to advance the QoS lower than equality. The even-handedness safeguards that FNs are enthusiastic about taking part in the assistance.
On the other hand, the geo-longitudinal evidence fluctuates terminated regions. Furthermore, the storage and dispensation of the records of all sections exclusive to the cloud records centers are not competent. The challenges concerning latency (expectancy) and managed resource consumption in real-time applications using geospatial information remained unsolved yet.

II. MATERIAL AND METHOD
In this section, the proposed new model to minimize energy consumptions concerning response time and service delays is presented. Although the application, strategy, modeling, computing, announcement, and numerous architectural encounters, enactment, in addition to energy-awareness FESC computing has not accomplished substantial devotion by academics recently.
How to optimize the energy consumption of application requests from TNs sustaining the deadline constraint is a significant challenge. The FESC next to the TNs has lower service delays but fewer resources than the remote cloud. FNs do not know the guise to substitute cloud, they are accompanying each other, and the collaboration between them is worth studying.
We assumed a FESC node having a customary N fog node. Every IoT node is connected to another with either one or more of these fog nodes. The associativity within the physical representation is often visible or logical through the channel where the resources are pooled between fog nodes, considering the frequency circumstances or previous arrangements amongst customers and system overhaul benefactors. The following is the pseudo-code demonstrating the operation presented in Fig. 2.
Considered a fixed FESC node with standby energy, this means that there are no energy issues whatsoever. In the situation where the fog nodes in the fog layer are busy, then requested are forwarded to the cloud for computation. The appearance rate of request is in the fog node's form to fog layer per second of Poisson set-up and the mean or an average number of arrivals per period as the mean arrival rate.
This depicts new request rates to nodes from the IoT, where the show the fitness factor between the zeros to one in equation (1). The dispensation control of the fog protuberance is shown by indicating the number of instructions per second. The average processing load is given by the exponential IoT. This opens the need to examine the best proportionality of the service given by the through estimated waiting time for Fog node which the delay is optimized from the requests to the fog layer.
The depicting new requests to nodes from the IoT shown by the _ , therefore the shows the average services rate that the fog node can have the capacity to the process resulting in the equations below.
= becomes the complete delay computation of the request done by the fog node within the fog layer. According to the request queue within the network using equation (2), and are to be used to cater for through = . The minimum time for every request is depicted by the * that is given by the = ∈ . The becomes the minimum waiting time for the request can be presented based on the traffic. In the case of increased traffic, it is forwarded automatically to the cloud for computation. Noting that the delay of each request of IoT to the fog layer is depicted by the ! " of the node.
Where T represents the request forwarded to the cloud if holds the request of IoT and FESC and depicts the request of the IoT. the node that the response time of where . shows the mapping of the nodes to Fog node to . In case the fog node offloads its loads to neither the next node nor the cloud given by the * . On observation on equation (3), This leads to the best completion of the request producing the minimum delay, thus increased services minimizing the service delay of the nodes given by the optimization equation for the node in equation (4).

B. Energy Efficiency in FESC Node
The power or the intended energy to the user in the computation is considered to be static (static energy), where dynamic consumption is done the network processing to either single or extra adjacent fog nodes to route the capabilities acquiesced by their consumers. This is denoted by the static energy and the dynamic energy within the fog node intended to process a nonnegative portion. This demonstrates that the input rate from the users' workload using the utilization of its local resources. It contains all computation rates and is constant in this method that caters to remaining workloads, in case any, is forwarded to the cloud for complete execution.

C. Energy Efficiency in FESC Node
Further still, the study assumed that the energy efficiency by the quantity of energy depleted on dispensation accomplishment of the conventional capability. Optimizing the energy efficiency enumerates to enhance the energy depletion for dispensation an identified capacity. The aggregate quantity of energy disbursed by slightly automated maneuvers in the incident, for example, a fog node, is contingent on the power usage efficiency and immobile and energetic energy depletion.
The energy usage efficiency is the input energy after the energy network is separated by the energy depletion of the specified expedient. The stagnant control or verve depletion at times entitled trickle control due to trickle fluxes incongruent to the convention of the computing possessions at a fog node. Energetic dynamism depletion is frequently the consequence of the course commotion and is strong-minded by the commotion of computing properties. " = " 6 " 7 + " 8 " " 9 The diversity of FESC nodes may partake in diverse workloads during the arrival rates equation (5). Consequently, this allows fog nodes to collaborate or cooperatively process their customary workload to progress the inclusive capability dispensation propensities supplementarily.
Explicitly, the nodes that achieve additional capability than their treating competencies can chase sustenance from nearby fog nodes with excess workout possessions. The chief aim, in this situation, is to heighten the run-of-the-mill reaction interval of consumers supplementary with all buttressed nodes.
In this case, the aggregate quantity of assignment implemented by every node, resolve not lonely, be subject to on its particular conventional assignment, nevertheless then on the assignment accelerated from other the nodes. The study writes the reaction stretch of the node further down is the power efficiency on the nodes, to be the total consumption of the power in the fog node for a given specified time of execution. Notably, where the node, the received computation is administered by the node. The workload of computation over a cloud in (6).

D. Service of End-Users
In this analysis, the reaction interval comprises the roundtrip stretch for communicating between the workloads of an operator and the related node with the queue-up deferment as well as the fog defenses. Provided the proximity to the operators, FESC nodes are expected to parade slighter computation times about the remote cloud data centers. Nevertheless, because of their incomplete incomes, nodes that progress a bulky quantity of capability can probably have a long queuing delay.
Hence, it is significant to accomplish balances the capability discharged by FESC nodes. The response time associated with a gi ven FESC node ? : 5 is the reaction stretch . The capability treated Round-Trip Time (RTT) where the devices in the network locally @ A in fog or cloud serve are fixed. ? :" = @ A + @ B portrayed the computation was done in the cloud.
In case D , the need to analyze performance raises represented in equation (7). is a portion of the workload of the node is at times partially in the fog and the rest within the computations of the cloud. This leads to the constrain that 0 D D 1by the node , then processed one in the cloud is − 1. Therefore, the need to have D is to obtain and satisfy (7). * = /,0F " " " ∈ G0,1I The equation (8) above is followed in the condition that? " " D ? " . Equation (8), " " show that the amount of energy used and ? " " depicts the execution of the " processing of the workload.
However, some limitations were noticed, mainly professional, technological, and dynamical factors. For instance, insurances, international energy policy, optimal energy consumption voltages, and device capacities have not been considered in this study. This study is not focused on telecommunication and transmission path standards against other prevailing technical issues like dynamics of technology, latency, bandwidth assumptions, and restriction on devices.

III. RESULTS AND DISCUSSION
This section contemporaneous the outcomes obtained after the simulation of service delay minimization model response time. Also, this section provides some sample comparisons.

A. Simulation Results
There is a significant decline of 7% of energy due to increased requests, and both algorithms try to optimize resources from the know consumption of electricity of 28% and 18.2% of other energy sources. However, when the cloud computing layer cannot process many incoming requests, it sends them to the cloud computing layer depicted in Fig. 3. When submitting requests to cloud computing, both algorithms have no particular role in optimization, and the energy of cloud computing is unlimited.  Fig. 4 indicates the response time of both algorithms concerning the number of requests received from the Internet layer of objects processed by the system. Notably, the proposed model has been able to perform the minimum queries in the shortest time compared to the ADMM-VS algorithm. The power consumption diagram for both algorithms shows from 100 to 6 requests received from the Internet layer of objects processed by the system that the power consumption in our proposed model is significantly reduced compared to the matching algorithm per Fig. 4. The ADMM-VS algorithm briefly provides a primal-dual interior-point method in which individuals use the alternative direction method of multipliers to reduce consummation. GA mainly imitates the practice of natural miscellany, where the fittest entities are selected for reproduction to produce offspring of the next cohort. Since it is the central system, which is aware of all the free resources according to the distribution system; see  The power consumption for both algorithms is received from 10 to over a thousand requests from the IoT layer that are received and processed by the system verified in Fig. 6. It is shown that the power consumption in the proposed model is at the end equal to the ADMM-VS of the proposed model but at the response time using random data sets.  Fig. 7 shows an observable difference between our proposed model and one of the existing models like. The authors focused on searching the Closest Date Centre for the requests of IoT nodes to be processed in FESC or Cloud server and within our simulation, in GA the proposed model we are searching for the Best Date center in FESC or cloud server layer for the requests of IoT nodes to be processed, so we can say that all the Closest data centers are not the best data centers for processing the demands of IoT nodes, so by implementing such policy, we optimized the response time and Processing time.

Fig. 8. A Service Minimum Evaluation
It is responsive to reducing power consumption and reducing time because of the add-ons of adding a broker layer to the proposed method that rolls into a central server but is a cloud computing layer policy with full knowledge of all the resources on which to work as presented in Fig. 8.
The illustration shows the response time of both algorithms for 1000 to 10,000 thousand requests from the IEEE layer processed by the system, shown in figure 8 to decrease the energy depletion of equal algorithms. Reducing the response time of the proposed model was able to achieve the best result in the least amount of time and with a considerable difference; that is to say, it could use resources that ADMM-VS the proposed model did not optimally use, but our proposed model was able to optimize the resources thus reduced the delay. The demonstration in Fig. 9 depicts the reduced execution times of both algorithms for requests that received 700 requests from the IoT layer and are then processed, and the proposed model has shown better results throughout the execution process, indicating that the superiority of the proposed algorithm.

B. Limitation of Proposed Model
Our proposed model has achieved a satisfactory downward trend compared to the ADMM-VS algorithm, especially for a response. Both algorithms have significant differences in response time results but have similar results in positions of energy depletion, with the results of the proposed prototype achieving the lowest value in both. Their main reason is the central system that all sources can use better and more consciously.

IV. CONCLUSION
This paper presented a novel energy efficiency and service delay minimization model. It is portrayed how our delayminimizing and energy model can be advantageously improved on the computation for IoT resource pooling and data transfers to the cloud; proper energy efficiency increases networking computation. Numerous numerical results are provided to back this claim by showing how variations in constraints could affect the reduced service delay shows a 7% decrease of the total 18.2% renewable contribution. In forthcoming activities, we subjected this model to reinforcement learning to mainly a) reduce greenhouse gas emissions, b) automatically recognize the environment to determine which percentage of energy is needed to accomplish a task at hand, c) regulate energy consumption based on the dynamics of technology, and d) peak reduction.

ACKNOWLEDGMENT
We are grateful to the anonymous reviewers for reviewing this paper.