Mobile Cost Research Articles

Consolidating building greening: Integrating mobile modular vertical greening systems into prefabricated building

Vertical greening systems increase green plant coverage within built environments, enhancing urban air quality while reducing building energy consumption. But they encounter challenges related to intensive maintenance and significant costs. As a mainstream future building form, the characteristics of modularization and factory customization inherent in prefabricated buildings can effectively address the demands of vertical greening system. This study employs structural equation model and cloud model to examine the feasibility of integrating mobile modular vertical greening system into prefabricated structures, based on a survey of 403 respondents in Shandong Province, China. The study highlights the supportive roles and mediating effects of two maintenance management measures. It confirms the strong correlations between cost control strategies and the successful integration of mobile modular vertical greening system into prefabricated building. The results indicate that integrating intelligent technology and plant-microalgae combined systems are essential exogenous and endogenous maintenance management strategies. They exert a positive influence on prefabricated modular vertical greening systems. A positive interconnection exists between integrating intelligent technology and the plant-microalgae combined system. Carbon credit trading incentive proves more advantageous in reducing mobile modular vertical greening system costs. Plant-microalgae combined system playing a partial mediating role. Although crowdfunding-community participation does not significantly reduce the costs of mobile modular vertical greening system, integrated intelligent technology mediates this relationship. This highlights the indirect role of crowdfunding-community participation in facilitating cost control for these systems.

Skilling in the Informal Economy in India: Identifying and Addressing Inequities in Designing and Delivering Lifelong Learning Opportunities

Lifelong learning opportunities (LLOs) for adults enhance their capabilities and resilience in adapting to changing circumstances, especially in the context of livelihoods. However, informal economy workers face barriers to accessing LLOs, including the digital divide among other challenges. An action research case study of an Indian LLO provider shifting to hybrid vocational education reveals the potential of digital LLOs, where low mobile data costs, high phone penetration, and flexible options mitigate learning expenses. The study details the constraints that the informal economy learners and the LLO providers face and thus contributes to the literature analyzing the broader implications of market changes and skill development for sustainable development.

Adoption of Digital Technologies as a Driver for Cost Leadership Strategy in Tour Firms within Nairobi City County, Kenya

Digital technologies play a major role in reducing the cost of operations in tour firms. The main focus of the study was to find out the adoption of digital technologies as a driver of cost leadership strategy in tour firms within Nairobi City County. The specific objective was to investigate the influence of mobile applications on cost leadership strategy in tour firms in Nairobi City County. Embedded research design was applied in the study. The study adopted the null hypothesis that mobile applications had no statistically significant influence on cost leadership strategy in tour firms within Nairobi City County. The study population was 278 tour firms obtained from the Kenya Association of Tour Operators (KATO) list where a sample of 164 tour firms were selected through the application of Yemane’s (1967) sample size formula. 10 tour firm mangers were selected for interviews. Systematic random sampling was utilized for the determination of the sample size of 164 tour firms. Semi-structured questionnaires and interview schedules were utilized as data instruments. Questionnaires were filled by tour consultants while interviews were conducted with tour firm managers. 133 respondents filled out the questionnaires representing 81% response rate while 7 interviewees were conducted representing 70% response rate. Both descriptive and inferential statistics were used during data analysis. The study utilized both ANOVA and multiple regression tests to test the hypotheses, and influence between the adoption of digital technology and cost leadership strategy. The results of the R Square equals 0.919 therefore revealing that 91.9% of cost leadership is attributed to mobile applications. The study confirmed there was a statistical significance (p =.000) between mobile applications and cost leadership. Mobile applications as a digital technology tool accounted for a positive significant increase (B = 637) in cost leadership strategy. The study concluded that there is a positive strong influence between the adoption of mobile applications and cost leadership strategy. The study recommends that tour firms should fully adopt digital technologies to maximize cost control, minimize marketing costs, and promote operational efficiency

An improved deep Q-network approach for charging sequence scheduling with optimal mobile charging cost and charging efficiency in wireless rechargeable sensor networks

Mobile charging is a practical solution to overcome energy constraints in wireless rechargeable sensor networks (WRSNs). It utilizes a mobile charger (MC) to charge sensors based on the wireless power transfer technique via a charging scheduling scheme. However, how to schedule MC to achieve higher charging efficiency with less mobile charging cost, while ensuring network functionality remains a challenge. Specifically, the mobile charging cost is the total energy consumed by the MC during the charging task. This paper studies the charging sequence scheduling problem with optimal mobile charging cost and charging efficiency (CSCE), considering dynamic changes in sensor energy consumption. Moreover, we model the CSCE as a multi-objective mixed integer nonlinear programming. To address this issue, we propose an improved deep Q network approach for CSCE (IDQN-CSCE), where the MC acts as an agent to explore the WRSN and determine a charging policy based on the charging demand of sensors. IDQN-CSCE employs Q-learning and deep Q-networks (DQN) to train the Q-table and DQN network simultaneously. Charging actions are selected based on probability, and the probability of using Q-learning decreases as iteration steps increase. Additionally, we design a novel reward function consisting of a weighted sum of rewards and penalties fed back by the environment after performing a charging action. Simulation results show that IDQN-CSCE achieves higher charging efficiency than other baseline approaches with less mobile charging cost.

Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications

Mobile cloud computing (MCC) provides resources to users to handle smart mobile applications. In MCC, task scheduling is the solution for mobile users’ context-aware computation resource-rich applications. Most existing approaches have achieved a moderate service reliability rate due to a lack of instance-centric resource estimations and task offloading, a statistical NP-hard problem. The current intelligent scheduling process cannot address NP-hard problems due to traditional task offloading approaches. To address this problem, the authors design an efficient context-aware service offloading approach based on instance-centric measurements. The revised machine learning model/algorithm employs task adaptation to make decisions regarding task offloading. The proposed MCVS scheduling algorithm predicts the usage rates of individual microservices for a practical task scheduling scheme, considering mobile device time, cost, network, location, and central processing unit (CPU) power to train data. One notable feature of the microservice software architecture is its capacity to facilitate the scalability, flexibility, and independent deployment of individual components. A series of simulation results show the efficiency of the proposed technique based on offloading, CPU usage, and execution time metrics. The experimental results efficiently show the learning rate in training and testing in comparison with existing approaches, showing efficient training and task offloading phases. The proposed system has lower costs and uses less energy to offload microservices in MCC. Graphical results are presented to define the effectiveness of the proposed model. For a service arrival rate of 80%, the proposed model achieves an average 4.5% service offloading rate and 0.18% CPU usage rate compared with state-of-the-art approaches. The proposed method demonstrates efficiency in terms of cost and energy savings for microservice offloading in mobile cloud computing (MCC).

An integrated MCDM approach for mobile app cost predictor based on DEMATEL extended with choquet integral

An integrated MCDM approach for mobile app cost predictor based on DEMATEL extended with choquet integral

Grouping Reduces Energy Cost in Directionally Rechargeable Wireless Vehicular and Sensor Networks

The use of Wireless Charging Vehicles (WCVs) to wirelessly recharge sensors is a promising but not yet fully developed technology. The existing WCV trajectory planning schemes either fail to consider the directional characteristics of wireless recharging or fail to jointly optimise the mobile feature and the one-to-many feature. The objective of this paper is to find the optimal recharging scheme to minimise the energy costs of WCVs (recharging cost and mobile cost) in rechargeable networks. While recharging, the recharging efficiency has a trade-off between the distance and recharging angle. For the recharging distance, if the WCV comes close to the sensors, the recharging efficiency is high, but the entire recharging trajectory of the WCV will be long and vice versa. For the recharging angle, if the recharging deflection angle is small, the recharging efficiency is high, but the calculation complexity will be high and vice versa. To solve this problem, we prove that the recharging group can reduce mobile costs. Based on this idea, we optimise the recharging trajectory of the WCV to reduce the mobile energy consumption. The optimal recharging group generation problem is discussed, and the problem is proven to be NP-hard. We propose a greedy recharging group generation algorithm with an approximate ratio of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{ln} \, N$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> is the number of sensors. On this basis, we propose a linear programming equation to minimise the recharging delay of the recharging group to reduce the energy cost during recharging. Experiments show that the number of recharging groups generated by this scheme is 24.5% less than the circle coverage baseline method on average. The energy consumption is reduced by 49.8% on average compared with one-to-one recharging.

Task recommendation method for fusion of multi-view social relationship learning and reasoning in the mobile crowd sensing system

With the powerful sensing ability of mobile smart devices, sensing users can obtain crowd sensing services for intelligent devices in the Internet of Things. However, due to the low accuracy of task recommendations, the uneven quality of data provided by sensing users has always been a problem. In this paper, a novel mobile crowd sensing task recommendation framework is proposed by integrating multi-view social relationship reasoning, which aims to construct a social relation network through multi-view to ensure task recommendation accuracy and improve data quality. Firstly, the social relationship network is jointly built by location matching view, time series view, and preference view. Secondly, the sensing user preferences and candidate task representations are learned by extracting task name features, task subject features, and task category features. Finally, the key nodes are used to divide the social relationship sub-network. The preference representation of the key nodes and the candidate task representation is used to do the inner product to obtain the probability of the social relationship sub-network selection task, and then complete the task recommendation. Evaluations based on two real datasets, Gowalla and Brightkite, show that the average recommendation accuracy is about 93%, and the participation rate of sensing users is about 97%. At the same time, the running time is reduced by 15% on average compared with the baseline algorithm, and the mobile cost of sensing users is reduced.

Patient- and Health-System-Related Barriers to Treatment Adherence for Patients with Drug-Resistant Tuberculosis in the Philippines: A Mixed-Methods Study.

In the Philippines, drug-resistant tuberculosis (DRTB) is a growing concern. Healthcare workers face challenges in retaining patients with DRTB in care. This study intends to understand their perspectives on the factors that influence patient treatment outcomes and to propose potential programmatic solutions for strengthening care services for the patients. A mixed-methods study was conducted in the Philippines between December 2017 and March 2018 to understand the major barriers for healthcare workers to provide quality care to DRTB patients across the care continuum. In the quantitative phase, healthcare workers participated in an online survey; in the qualitative phase, in-depth interviews were conducted with a select number of the survey respondents to better understand their survey responses. 272 healthcare workers participated in the survey, and of those, 11 were interviewed. Survey results identified economic constraints, patient perceptions of care, family-related concerns, and limited accessibility to healthcare services as the major patient-related barriers across the care continuum. Major health-system-related barriers were insufficient human resources, lack of financial and political support, and limited knowledge about DRTB by healthcare providers. Interviews revealed more elaborate, contextualized, and nuanced aspects of each of the major challenges. The elaborated patient-related barriers included expenses needed during treatment (e.g., transportation); fear of being stigmatized by family, community, or healthcare staff; worries about adverse drug reactions from medication; a lack of family support; and the location of patients' homes. The health-system-related barriers revealed through interviews included the limited capacities of facility staff to provide DRTB care due to insufficient human resources; the shortage of funds to support treatment completion (e.g., transportation allowance and food package for patients, service vehicles and mobile phone costs for outreach actions at the facility level); and discrimination by healthcare staff against patients with DRTB attributed to the staff's limited knowledge and experiences of treating the patients. This study identified the main barriers for DRTB facility staff in the Philippines from the perspectives of providers. Further exploration of the barriers and best practices in facilities may be useful for improving DRTB care in the Philippines.

Invalidation of tunnelling attacks in ubiquitous IoT & wireless sensor enviroment using ML methods

Wireless sensors are interconnected are is ubiquitous in nature and provides great advantage are normally constituting perfect communication on wireless networks like infrastructure and Ad Hoc. The wireless connected devices environment can also be created using Internet of Things and also it can be adhoc. These wireless sensors are installed in the military campus to collect information movement of people, sound, capturing pictures of their activities. These kinds of sensors can be used in most secured places, where there is a need of remote monitoring of the security. It can be either used in atomic power stations or military camps. Based on the same, Ad Hoc networks attract great attention because data obstruction is made in communication interval. The communication interval helps to improve the mobile node convenience, node mobility, scalability and network cost. Perhaps, due the overload in data packet transmission, the routing makes the packet blockage between the nodes. Mobile nodes are always roaming around the particular region for supervise the external conditions or surroundings characteristics in order to accumulates the selective information about alive nodes and its communication route. On certain occasions these communication routes are vulnerable to various attacks due to functionality and deployment scenario, especially wormhole attack. This is an Ad Hoc assembling of mobile nodes that can randomly alter their route with respect to the constrained resources such as bandwidth, energy, security and resource discovery. Based on the above resources wormhole nodes create the fraudulent route within the network; the fraudulent route can confuse routing mechanisms which creates the tunnel between nodes. In order to invalidate the wormhole attacks in the route within the network, this research article proposed the “Train and Avoiding” process. The train and avoid process is the machine learning algorithm to find the optimal route where there are no attacks in the route. The process trains the routing to perform network traffic analysis. In addition to this, avoiding process helps to restrict the packets modification as well as fake route creation. Proposed Train and Avoiding based route Selection is used to achieve more prominent reliable routing. It improves PDR, average lifetime of network and minimizes communication issues, and average delay.

Unraveling heterogeneity in farmer's adoption of mobile phone technologies: A systematic review

Adoption and usage of mobile phones enable information and knowledge flows among value chain players, including farmers, and therefore contribute to improved efficiency. To ensure the successful implementation of MPTs in agriculture, farmers should embrace them. We present a systematic review of determinants, constraints, methods, indicators, and measures of MPT adoption among farmers and a methodological quality assessment of the included studies. Findings from 53 studies showed significant heterogeneity in research. Most studies targeted developing regions in Africa and Asia and only a few in developed countries. Although studies mainly focused on MPT adoption, they varied significantly in the outcome indicators assessed and their measurement. This heterogeneity in the conceptualization and measurement of adoption indicators underscores the need for standardized approaches in future studies. There is also sub-optimal use of established psychological-behavioral theories to underpin MPT adoption.Cross-sectional designs and quantitative approaches dominate the research landscape. Concerning farmers' MPT adoption, key drivers were education, age, gender, perceived ease of use, perceived usefulness, perceived cost, performance and effort expectancy, attitude, skills, and knowledge. Factors such as mobile phone cost, inadequate infrastructure, and language barrier constitute major hurdles constraining farmers' adoption of MPT in developing countries. The generally low quality of the reviewed studies suggests that future studies should invest in transparently providing their study objectives, methods, and interpretation of the findings. This systematic review contributes to a better understanding of farmers' MPT adoption drivers and suggests areas for future research. It provides relevant information to policy-makers, public and private sector agencies, mobile phone companies and app developers, researchers, agricultural extension workers, academicians, and other stakeholders when designing and implementing policies for MPT adoption in agriculture.

Optimization of Multi-Objective Mobile Emergency Material Allocation for Sudden Disasters.

The mobile emergency system is a new emergency mode that provides a solution to deal with increasingly frequent sudden disasters by reasonably allocating mobile emergency facilities and optimizing the allocation of mobile emergency materials. We consider mobile emergency cost and mobile emergency time as two objective functions. This paper establishes a multi-objective mobile emergency material allocation model, and transforms the multi-objective. We choose the emergency material transportation path for coding, and apply the hybrid leapfrog algorithm for material allocation to obtain the optimal solution. Finally, the feasibility of the model is verified by taking Zhengzhou urban area under the “21.7” severe rainstorm and flood disaster in Henan Province. The result analyses show that the model can correspond to each stage of mobile emergency material allocation based on the value of cost preference, and put forward suggestions on the location of mobile emergency facilities and the amount of material allocation.

MONICA2: Mobile Neural Voice Command Assistants towards Smaller and Smarter

In this paper, we propose on-device voice command assistants for mobile games to increase user experiences even in hands-busy situations such as driving and cooking. Since most of the current mobile games cost large memory (e.g. more than 1GB memory), so it is necessary to reduce memory usage further to integrate voice commands systems on mobile clients. Therefore a need to design an on-device automatic speech recognition system that costs minimal memory and CPU resources rises. To this end, we apply cross layer parameter sharing to Conformer, named MONICA2 which results in lower memory usage for on-device speech recognition. MONICA2 reduces the number of parameters of deep neural network by 58%, with minimal recognition accuracy degradation measured in word error rate on Librispeech benchmark. As an on-device voice command user interface, MONICA2 costs only 12.8MB mobile memory and the average inference time for 3-seconds voice command is about 30ms, which is profiled in Samsung Galaxy S9. As far as we know, MONICA2 is the most memory efficient yet accurate on-device speech recognition which could be applied to various applications such as mobile games, IoT devices, etc.

A multi-method analytical approach to predicting young adults’ intention to invest in mHealth during the COVID-19 pandemic

Mobile-based health (mHealth) systems are proving to be a popular alternative to the traditional visits to healthcare providers. They can also be useful and effective in fighting the spread of infectious diseases, such as the COVID-19 pandemic. Even though young adults are the most prevalent mHealth user group, the relevant literature has overlooked their intention to invest in and use mHealth services. This study aims to investigate the predictors that influence young adults’ intention to invest in mHealth (IINmH), particularly during the COVID-19 crisis, by designing a research methodology that incorporates both the health belief model (HBM) and the expectation-confirmation model (ECM). As an expansion of the integrated HBM-ECM model, this study proposes two additional predictors: mobile Internet speed and mobile Internet cost. A multi-method analytical approach, including partial least squares structural equation modelling (PLS-SEM), fuzzy-set qualitative comparative analysis (fsQCA), and machine learning (ML), was utilised together with a sample dataset of 558 respondents. The dataset—about young adults in Bangladesh with an experience of using mHealth—was obtained through a structured questionnaire to examine the complex causal relationships of the integrated model. The findings from PLS-SEM indicate that value-for-money, mobile Internet cost, health motivation, and confirmation of services all have a substantial impact on young adults’ IINmH during the COVID-19 pandemic. At the same time, the fsQCA results indicate that a combination of predictors, instead of any individual predictor, had a significant impact on predicting IINmH. Among ML methods, the XGBoost classifier outperformed other classifiers in predicting the IINmH, which was then used to perform sensitivity analysis to determine the relevance of features. We expect this multi-method analytical approach to make a significant contribution to the mHealth domain as well as the broad information systems literature.

Sustainability of a mobile phone application-based data reporting system in Myanmar\u2019s malaria elimination program: a qualitative study

BackgroundStrengthening surveillance systems to collect near-real-time case-based data plays a fundamental role in achieving malaria elimination in the Greater Mekong Subregion (GMS). With the advanced and widespread use of digital technology, mHealth is increasingly taking a prominent role in malaria surveillance systems in GMS countries, including Myanmar. In Myanmar’s malaria elimination program, an mHealth system called Malaria Case-based Reporting (MCBR) has been applied for case-based reporting of malaria data by integrated community malaria volunteers (ICMVs). However, the sustainability of such mHealth systems in the context of existing malaria elimination programs in Myanmar is unknown.MethodsFocus group discussions were conducted with ICMVs and semi-structured in-depth interviews were conducted with malaria program stakeholders from Myanmar’s Ministry of Health and Sports and its malaria program implementing partners. Thematic (deductive followed by inductive) analysis was undertaken using a qualitative descriptive approach.ResultsTechnological and financial constraints such as inadequate internet access, software errors, and insufficient financial resources to support mobile phone-related costs have hampered users’ access to MCBR. Poor system integrity, unpredictable reporting outcomes, inadequate human resources for system management, and inefficient user support undermined the perceived quality of the system and user satisfaction, and hence its sustainability. Furthermore, multiple parallel systems with functions overlapping those of MCBR were in use.ConclusionsDespite its effectiveness and efficiency in malaria surveillance, the sustainability of nationwide implementation of MCBR is uncertain. To make it sustainable, stakeholders should deploy a dedicated human workforce with the necessary technical and technological capacities; secure sustainable, long-term funding for implementation of MCBR; find an alternative cost-effective plan for ensuring sustainable system access by ICMVs, such as using volunteer-owned mobile phones for reporting rather than supporting new mobile phones to them; and find a solution to the burden of multiple parallel systems.Trial registrationNot applicable.

Incentive-Driven Deep Reinforcement Learning for Content Caching and D2D Offloading

Offloading cellular traffic via Device-to-Device communication (or D2D offloading) has been proved to be an effective way to ease the traffic burden of cellular networks. However, mobile nodes may not be willing to take part in D2D offloading without proper financial incentives since the data offloading process will incur a lot of resource consumption. Therefore, it is imminent to exploit effective incentive mechanisms to motivate nodes to participate in D2D offloading. Furthermore, the design of the content caching strategy is also crucial to the performance of D2D offloading. In this paper, considering these issues, a novel Incentive-driven and Deep Q Network (DQN) based Method, named IDQNM is proposed, in which the reverse auction is employed as the incentive mechanism. Then, the incentive-driven D2D offloading and content caching process is modeled as Integer Non-Linear Programming (INLP), aiming to maximize the saving cost of the Content Service Provider (CSP). To solve the optimization problem, the content caching method based on a Deep Reinforcement Learning (DRL) algorithm, named DQN is proposed to get the approximate optimal solution, and a standard Vickrey-Clarke-Groves (VCG)-based payment rule is proposed to compensate for mobile nodes' cost. Extensive real trace-driven simulation results demonstrate that the proposed IDQNM greatly outperforms other baseline methods in terms of the CSP's saving cost and the offloading rate in different scenarios.

Low-Cost Ultrasonic Range Improvements for an Assistive Device.

To achieve optimal mobility, visually impaired people have to deal with obstacle detection and avoidance challenges. Aside from the broadly adopted white cane, electronic aids have been developed. However, available electronic devices are not extensively used due to their complexity and price. As an effort to improve the existing ones, this work presents the design of a low-cost aid for blind people. A standard low-cost HC-SRF04 ultrasonic range is modified by adding phase modulation in the ultrasonic pulses, allowing it to detect the origin of emission, thus discriminating if the echo pulses come from the same device and avoiding false echoes due to interference from other sources. This improves accuracy and security in areas where different ultrasonic sensors are working simultaneously. The final device, based on users and trainers feedback for the design, works with the user’s own mobile phone, easing utilization and lowering manufacturing costs. The device was tested with a set of twenty blind persons carrying out a travel experiment and satisfaction survey. The main results showed a change in total involuntary contacts with unknown obstacles and high user satisfaction. Hence, we conclude that the device can fill a gap in the mobility aids and reduce feelings of insecurity amongst the blind.

A task recommendation framework for heterogeneous mobile crowdsensing

Aiming at the problems of low data quality and high incentive costs caused by the low enthusiasm of participants in mobile crowd sensing, a new task recommendation framework is proposed in this paper. First, the participants' historical behaviors are analyzed, assuming that user behaviors can be quantified as the user's willingness to participate, and the cosine similarity theorem is used to calculate the similarity between participants, thereby constructing a user-hybrid model. Secondly, probabilistic matrix factorization is developed to predict the willingness of participants, and a ranking model is obtained through learn-to-rank algorithm. Finally, a task recommendation list is generated according to the ranking model, which serves as the target participant's preferred task list for sensing task recommendation. The experiment in this paper is carried out on the MATLAB platform based on two real check-in datasets, Gowalla and Brightkite. The results show that the average allocation precision rate can reach 96%, and the sensing user participation rate is about 97%. Meantime, the user's mobile cost is reduced, and the overall goal of maximizing accuracy and minimizing perceived cost is achieved.

Smart Microgrid Energy Market: Evaluating Distributed Ledger Technologies for Remote and Constrained Microgrid Deployments

The increasing strain on ageing generation infrastructure has seen more frequent instances of scheduled and unscheduled blackouts, rising reliability on fossil fuel based energy alternatives and a slow down in efforts towards achieving universal access to electrical energy in South Africa. To try and relieve the burden on the National Grid and still progress electrification activities, the smart microgrid model and secure energy trade paradigm is considered—enabled by the Industrial IoT (IIoT) and distributed ledger technologies (DLTs). Given the high availability requirements of microgrid operations, the limited resources available on IIoT devices and the high processing and energy requirements of DLT operations, this work aims to determine the effect of native DLT algorithms when implemented on IIoT edge devices to assess the suitability of DLTs as a mechanism to establish a secure, energy trading market for the Internet of Energy. Metrics such as the node transaction time, operating temperature, power consumption, processor and memory usage are considered towards determining possible interference on the edge node operation. In addition, the cost and time required for mining operations associated with the DLT-enabled node are determined in an effort to predict the cost to end users—in terms of fees payable and mobile data costs—as well as predicting the microgrid’s growth and potential blockchain network slowdown.

Reduced power consumption by resource scheduling in mobile cloud using optimized neural network

Mobile cloud computing has developed into a modern platform that combines mobile computing with cloud computing in order to process mobile data. In addition to the benefits of mobile cloud storage, energy usage, resource shortage, service efficiency, protection and computational costs are also a big concern. In this article, the neural network-based optimization approaches using artificial neural network and convolutionary neural network models were applied by various variances and loudness in order to minimize overall energy consumption and improve efficiency. Experimental data indicate that the power consumption was lowered by 53.68% by means of an enhancement of the neural network and enhanced by a convolutionary neural network, which further decreased the consumption of energy by 30.3% with a small root average square error relative to other algorithms.