Resource Management Model Research Articles

A critical review of biomonitoring in East African rivers: fostering community-based collaboration for environmental change observation

The contribution of citizen scientists to environmental monitoring is notably increasing significantly. Governments worldwide establish Water Users Associations (WUAs) as a good practice model to implement Integrated Water Resource Management (IWRM) at local levels. These associations target different stakeholders depending on the expected outcome. However, their effectiveness, especially in East Africa, can only be determined case by case. In Kenya, Water Resources Users Associations are registered, voluntary community groups whose members are water users and land owners. Similarly, in Uganda, community-based initiatives exist, especially along degraded rivers, involving cooperation between local communities and regional water authorities. On the contrary, it has been reported that in Tanzania, many community initiatives created become non-functional within 2–3 years of induction. In general, the main responsibility of Water Users Associations or its equivalent is to manage and conserve water resources for sustainable uses. In most cases, water quality monitoring by communities through citizen science is limited to the visual appearance of water in the river channel. While this can indicate the water’s esthetic value, it is insufficient for assessing the ecological status of rivers, which is influenced by a variety of physical, chemical, biological, and socioeconomic factors. The use of bioindicators has been advocated as a feasible method for community-based water quality river monitoring. Therefore, this review explores the commonly used bio-indicators and bio-assessment tools for river health assessment and their complexities when using a biomonitoring community-based approach through citizen science. Tolerance and intolerance macroinvertebrate and fish metric protocols that utilize identification keys have been recommended. These protocols are designed to be user-friendly and require minimal taxonomic expertise, making them easy for community volunteers to use with minimal basic training. The sustainability of these citizen science initiatives relies on the motivation of volunteers, the frequency of monitoring activities, and collaboration with researchers and government agencies. These initiatives not only facilitate environmental monitoring but also foster community engagement and awareness regarding the ecological status of rivers, thereby addressing knowledge and data gaps necessary for effective policy-making. This approach provides a practical model for environmental stewardship and participatory resource management in East Africa.

Bee-inspired insights: Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction.

Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in water resource management have become prevalent due to their proven success in hydrological studies. This study proposed a hybrid model that combines the artificial neural network (ANN) and the artificial bee colony optimization (ABC) algorithm, along with the ensemble empirical mode decomposition (EEMD) and the local mean decomposition (LMD) techniques, to model groundwater levels in Erzurum province, Türkiye. GWL estimation results were evaluated with mean square error (MSE), coefficient of determination (R2), and residual sum of squares (RSS) and visually with violin, scatter, and time series plot. The study results indicated that the EEMD-ABC-ANN hybrid model was superior to other models in estimating GWL, with R2 values ranging from 0.91 to 0.99 and MSE values ranging from 0.004 to 0.07. It has also been revealed that promising GWL predictions can be made with previous GWL data.

High-resolution estimates of water availability for the Iberian Peninsula under climate scenarios

Water availability is of paramount importance for sustainable development and environmental planning, specifically in regions such as the Iberian Peninsula, renowned for diverse landscapes and varying climatic conditions. Due to climate change, understanding the potential impacts on water resources becomes essential for effective water management strategies. This research effort aims to assess future potential water availability for the Iberian Peninsula in different climate scenarios, employing cutting-edge water resource modelling techniques integrated within a geographic information system (GIS) framework. In this study, potential water availability is defined as the annual demand for water that can be satisfied at a specific point in the fluvial network with certain reliability. An ensemble of state-of-the-art climate models is utilised to project runoff for the Iberian Peninsula during the mid- and late-twenty-first century periods. These climate projections were subsequently processed using the GIS-based water resource management model, WAAPA, to derive potential water availability under a range of realistic hypotheses. The results indicate that anticipated shifts in precipitation patterns will lead to alterations in hydrological regimes across the region, significantly impacting future water availability. By using GIS-based methodologies, we can facilitate the identification of vulnerable areas susceptible to changes in water availability, offering spatially explicit information along the main rivers of the Iberian Peninsula for decision-makers and stakeholders. High-resolution spatial outputs from this research and detailed water availability estimates serve as valuable input for integrated water resource management and climate change adaptation planning. By combining advanced GIS-based hydrological modelling with climate scenarios, this research presents a robust framework for assessing water resources amidst a changing climate, applicable to other regions struggling with analogous challenges. Ultimately, our study provides vital insights for policymakers and stakeholders, empowering them to make informed decisions and devise adaptive measures to ensure sustainable use of water resources despite uncertain future climatic conditions.

Sustainable forest resources management model through Pythagorean fuzzy MEREC–MARCOS approach

Sustainable forest resources management model through Pythagorean fuzzy MEREC–MARCOS approach

Unlocking the tapestry of conservation: Navigating ecological resettlement policies in Nepal

Conservation initiatives involve a complex interplay of various ecological, socio-political, and economic factors. Ecological resettlement (ER), implemented within the context of nature conservation policies, stands as one of the most contested issues worldwide. This study aims to navigate the domain of ER policy in conservation through discursive institutionalism and a policy arrangement approach. Focusing on Nepal's conservation policy pathways over the last seven decades, we critically analyze policy ideas and narratives, trends, patterns of policy development, institutional arrangements, driving factors, and responses to contemporary ER policies. Methods involved a systematic literature review (n = 271), a comprehensive review of policy documents and project reports (n > 150), and expert interviews (n = 20). Over the past 50 years, >7600 households in Nepal have been displaced in the name of ER and are still persisting despite the rhetoric of participatory conservation. With changes in political regimes, conservation policy has shifted from a hunting-focused approach to landscape-level and transboundary conservation. Initially influenced by internal factors such as economic and political governance, conservation policies were later shaped by international conservation discourse. Also, the operational sphere of such policy ideas and narratives ˗ including actors, resources, discourses, and rules ˗ along with trends, priorities, institutional arrangements, and driving factors of ER policies, has changed over time. Further, the exclusion of deprived communities and the capture of conservation benefits by elites have undermined conservation values. This research stresses the importance of a judicious balance between people's welfare and nature's integrity, emphasizing community-based natural resource management models accredited to a conservation standard. We further urge the revision of displacement-oriented conservation policies to secure the rights of Indigenous people and traditional landholders, thereby ensuring conservation and sustainable development at both national and global levels.

RESPONSES TO DIGITAL HUMAN RESOURCE MANAGEMENT MODEL: SUGGESTIONS FROM DISRUPTIVE INNOVATION THEORY

Purpose – Considering the popularity of digital technologies, the study aims to provide additional perspective of why managers have low commitment to digital human resource management (HRM) model and how they should interpret and respond to this model.Body of knowledge – The study indicates that rushing to adopt digital HRM model is not encouraged, even though the model has potential benefits in terms of organizational efficiency and employee experience. However, the ignorance of its usage possibilities can make HR managers and professionals lose their control over HR activities to other business units.Implications – Managers need to understand the driving factors of digital HRM model, do critical analysis of its relation to the current business model, and evaluate potential impacts of its adoption on the organization. Based on these analyses, managers can establish a suitable response plan to the disruption, which can range from ignorance at the first time to full integration in the long run.Originality/Value – The emergence of digital technologies have disrupted the way organization operate their internal business processes. Compared to other functions, human resource management is likely to be overlooked in organization’s digital transformation plan, even though human resource is considered to be an important resource for its competitive advantages in today’s uncertain and fast changing world. The study contributes to drawing additional attention to the importance of digital HRM in organization’s digital transformation plan and provides additional perspective to interpret and implement digital HRM.

Using MODFLOW to Model Riparian Wetland Shallow Groundwater and Nutrient Dynamics in an Appalachian Watershed

Simulating shallow groundwater (SGW) flow dynamics and stream–SGW interactions using numerical modeling tools is necessary to develop a mechanistic understanding of water flow systems and improve confidence in water resource management practices. A three-dimensional (3D) SGW flow model was developed for a riparian wetland in a mixed forest and agricultural catchment in West Virginia (WV), Appalachia, USA, using a Modular 3D Groundwater Model (MODFLOW). The MODFLOW simulation was calibrated in steady (R2 = 0.98, ME = −0.21, and RMSE = 0.77), transient state (R2 = 0.97, ME = −0.41, and RMSE = 1.28) and validated (R2 = 0.97, ME = −0.28, and RMSE = 1.05) using observed SGW levels from thirteen nested piezometers under steady and transient states. An experimental MT3D transport scenario was developed to show the lateral transport of NO₃-N from the aquifer to stream cells. Relatively stable SGW head distribution was observed. In the downstream reach, SGW discharge varied from 948 m3/day to 907 m3/day in 2020, with creek seepage ranging from 802 m3/day to 790 m3/day. Similarly, SGW input to the stream ranged from 891 m3/day to 978 m3/day, while creek seepage ranged from 796 m3/day to 800 m3/day in 2021. In upstream reaches, losing stream conditions were observed in January, June, and September 2020 and January to April 2021, while gaining stream conditions prevailed during other months. Thus, an approximately monthly alternating gaining–losing stream condition was observed in the upstream area. An experimental MT3D transport scenario resulted in an advection–dispersion scenario, showing a cumulative loss of 947 g of NO3-N from SGW to the stream. Denitrification accounted for the cumulative loss of 1406 g of NO3-N from SGW, surpassing 639 g of nitrate from the SGW to the stream during the study period. Additionally, particle tracking using MODPATH indicated a long residence time for SGW nutrients, affirming the efficiency of nitrogen transformation through denitrification. This study is among the first to simulate hydrologic and nutrient interactions in riparian wetlands of a mixed land use catchment in the Appalachian region of the northeastern United States. The results better inform water resource management decisions and modeling efforts in the Appalachian region and similar physiographic regions globally.

РИЗИКИ В УПРАВЛІННІ ПЕРСОНАЛОМ ПІДПРИЄМСТВА

The purpose of the work is to identify the essence of personnel management risk, with a focus on its components and their impact on enterprise development goals. Risk arises from conducting business in conditions of uncertainty. It has been established that risks in personnel management are difficult to identify and assess, as they are associated with human resources, which are based on personal essence. Hence, two problems arise. Firstly, risk increases the complexity of evaluating the consequences of decisions made. Secondly, risk in personnel management at the conceptual level affects potential effectiveness. Therefore, it is more appropriate to use the term "uncertainty" in personnel management instead of the concept of risk. A person making decisions regarding human resource management in conditions of wartime economy deals with uncertainty, not risk. It is proven that uncertainty and risk are functions of such factors as the quantity and quality of information available to management personnel, and the variability of conditions for implementing personnel decisions. Two main approaches to defining the determinants of decision-making success by managers are distinguished. The first relates to factors influencing task execution, and the second to personal factors. It is argued that in the field of human resource management, the level of risk depends on the subjective perception of risk by personnel. Considering the attitude toward risk of the decision-maker, three main types of behavior are identified: risk-prone, risk-averse, and risk-neutral. Identifying the purpose of personnel activity is related to the formation of enterprise development strategy and the development of prospective human resource management plans. The purpose directs the will of the employee and determines the future outcome. Personal (individual) goals are defined in the form of a postulate or model. The purpose of HR activity based on both traditional and modern human resource management models is identified. Managers in conditions of wartime economy make decisions based on external data over which they have limited control. Therefore, their attention should be focused on an environment unfavorable for making personnel decisions.

Sustainable Irrigation Systems in Vineyards: A Literature Review on the Contribution of Renewable Energy Generation and Intelligent Resource Management Models

Sustainable Irrigation Systems in Vineyards: A Literature Review on the Contribution of Renewable Energy Generation and Intelligent Resource Management Models

Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation

The Yeşilırmak River Basin in northern Türkiye is crucial for the region’s water supply, agriculture, hydroelectric power generation, and clean drinking water. The primary goal of this study is to determine which modeling approach is most appropriate for various locations within the basin and how well meteorological data can predict river flow rates. Hydrological and meteorological forecasting both depend on the prediction of river flow rates. An artificial neural network (ANN), Univariate and Multivariate Long Short-Term Memory (LSTM) models have been utilized for streamflow forecasting. This research aims to determine the best model for several provinces in the basin area and give decision-makers a tool for reliable river flow rate estimates by combining LSTM and ANN models. According to research findings, the supervised multivariate LSTM model performed better than the unsupervised model in accuracy and precision. The sliding window methodology is suitable for estimating river flow based on meteorological datasets because it offers a primary method for reinterpreting time-series data in a supervised learning style. Compared to LSTM models, the ANN model that has been statistically optimized through experiments (DoE) design performs better in forecasting the river flow rate in the Yeşilırmak River basin (R2 = 0.98, RMSE = 0.18). The study’s findings provided prospective cognitive models for the strategic management of water resources by forecasting future data from flow monitoring stations.

Assessment of Human Resource Management Framework from Islamic Paradigm: Proposition of an Islamic Model of Human Resource Management

The study of Human Resource Management (HRM) in the context of Islamic philosophy has gained traction especially in the last two decades. This paper aims to assess the HRM purpose, processes and practices in the light of Islamic doctrine to eventually present an Islamic model of HRM that is hinged upon Islamic principles and values. The postulated model offers a unique perspective by incorporating both contemporary and spiritual dimensions to HRM framework in order to provide a holistic outlook to the study of Islamic Human Resource Management (IHRM). The proposed model named The “AF Islamic HRM Model” is driven by two key Islamic concepts of Adl (justice) and Falah (wellbeing or success), where Adl represents an essential underlying element for all HRM processes as per the proposed IHRM model and the conception of Falah implies an ultimate reward or success in this world as well as in the world hereafter, as perceived from the Islamic belief system. The paper entails a qualitative research approach by evaluating the existing HRM models as well as analyzing the already available literature regarding Islamic perspective to the HRM theory to ultimately posit a model which contributes value to the existing knowledge base of Islamic HRM. There is not much research regarding the application of Islamic ideology to various facets of HRM and this study endeavors to address this research gap by proposing an IHRM model which consolidates both the contemporary HRM philosophy as well as the Islamic tenets and guidelines, encompassing diverse aspects of both religion and worldly life by providing insights about the conventional HRM body of knowledge with a touch of ethics and spirituality.

Investigation into New Models of Corporate Human Resource Management Through the Lens of the Sharing Economy

The emergence of the sharing economy model has significant and complex effects on company human resource management. This paper first evaluates the positive impact of the sharing economy on human resource management, and then delves into the challenges it faces, including job instability, recruitment dilemmas, data protection issues, and compliance with legal regulations. Subsequently, the article proposes innovative strategies for human resource management in the context of the sharing economy, involving the adoption of high-tech recruitment methods, flexible work plans, data-driven performance evaluation, and enhanced training and development. These innovative proposals aim to help companies more effectively manage human resources in the broader context of the sharing economy, providing practical references and guidance.

Refining the Farm Aquaculture Resource Management Model for Shellfish Nitrogen Removal at the Local Scale

Nutrient-related environmental degradation in coastal waters is a continuing global problem. Bivalve shellfish farms show nutrient removal capabilities similar to some traditional management strategies and in some places have been incorporated into nutrient management programs to help achieve water quality goals. Bioextractive nutrient removal varies by farmed species and is influenced by environment parameters; thus, data and information for both are needed to estimate nutrient mitigation potential of shellfish farms. The Farm Aquaculture Resource Management (FARM) model, calibrated for farmed species, uses local environmental and farming practice data to simulate interactions between the farmed population and the local environment and to optimize cultivation practices for economic gain. We calibrated the model to predict nitrogen removal by Eastern oyster (Crassostrea virginica) farms with specific field and experimental data on oysters, their local environment, and farm practices in Long Island Sound, CT, USA. Previous FARM applications were not validated for nitrogen removal with local data. In the harvest when ready (HWR) model scenario (oysters are harvested when they reach harvest size), the farm removed 159 kg N ha−1 year−1 while the non-HWR scenario (all oysters are harvested at one time) removed 274 kg N ha−1 year−1. These estimates are within the range of previously reported in-water bioextraction studies in the Northeastern USA. The robust outputs from this validated model can be reliably used in marine spatial planning efforts and by nutrient managers to predict the nitrogen removal benefits that could be achieved through new or expanded eastern oyster farms in eutrophic environments.

The impact of GHRM and EGB on organization’s financial and operational performance – an empirical analysis on Indian ICT sector

PurposeThe study aims to conduct an empirical research to examine how green human resource management (GHRM) can influence sustainable organizational performance (SOP) in terms of financial performance (FP) and operational performance (OpP), both directly and through employee green behavior (EGB), in the backdrop of the Indian information and communication technology (ICT) sector.Design/methodology/approachThe study is based on primary data collected through a questionnaire survey from 387 employees engaged in the ICT sector. Confirmatory factor analysis validates the measurement model, while structural equation modeling (SEM) using the partial least squares (PLS) method examines the hypothesized relations.FindingsResults confirm that GHRM significantly contributes to EGB, FP and OpP, with a theoretical underpinning concerning resource-based view (RBV) and ability-motivation-opportunity (AMO) concepts. The study also confirms that connections of GHRM with both FP and OpP are significantly and positively mediated by EGB.Practical implicationsThe study provides evidence-based implications of different GHRM practices toward EGB, FP and OpP, which are highly pertinent to strategists, policymakers, managers and supervisors in implementing GHRM for adding value to both operational and financial statements while protecting nature and natural resources through the green efforts of employees.Originality/valueThe study bridges a significant theoretical and practitioner gap by providing a deeper understanding of GHRM as a sustainable human resource management (HRM) model in the context of the Indian ICT sector. It empirically explores the connections between GHRM and OpP, which is a crucial dimension of SOP, and highlights noteworthy implications for promoting EGB, an individual micro-sustainability dimension directed toward the achievement of broader organizational goals, which are essentially oriented to macro-sustainability.

Characterizing streamflow regimes using a distributed model for sustainable resource management in the humid tropics

Estimating streamflow regimes on small tropical islands is challenging due to limited observational data, poor accuracy of remote sensing, and the limitations of coarse-scale climate models, restricting our understanding of hydrological processes and the management of water resources. The inadequacy of data has also affected the development of instream flow standards (i.e., environmental flows, e-flows) needed to protect ecological and cultural values central to island communities. Estimates of water availability for agriculture, hydropower, and drinking water supply are also critical for informed investments in water resource planning. We utilized high-resolution (250 m) gridded daily rainfall data to parameterize the Soil and Water Assessment Tool (SWAT) to characterize streamflow regimes at ungauged locations in a watershed on Kaua‘i Island. The resultant flow statistics were then used to describe the daily, seasonal, and annual availability of surface water to evaluate the implications of potential e-flow scenarios on water available for run-of-river hydropower. Compared to rainfall data derived from a single station within the watershed, the gridded daily rainfall product increased the accuracy of model output from not satisfactory to good. Model techniques using high resolution rainfall data may substitute for long-term hydrological data collection across complex tropical environments and provide data for making sustainable management decisions at ungauged locations.

Rainfall-runoff Modelling Using MIKE11 NAM Model for Ravishankar Sagar Catchment, Chhattisgarh, India

Estimating rainfall-runoff within a catchment is inherently intricate and crucial for water resource planning via hydrological evaluations. The study focuses on utilizing the MIKE 11 NAM model to simulate rainfall-runoff dynamics within the Ravishankar Sagar Reservoir catchment in the Chhattisgarh state. In order to ensure accurate estimation, data on stream flows from 2004 to 2015 was used for calibration, and from 2016 to 2020 was used for validation. The MIKE 11 NAM model accurately predicted daily runoff and adequately reproduced the hydrological response of the Ravishankar Sagar watershed to rainfall. The calibrated model outputs were good to employ in the water resources management model, specifically for MIKE BASIN. During calibration, the optimal values of the nine NAM model parameters were determined and subsequently employed in the simulation. The reliability of the MIKE 11 NAM model was assessed for the study area using the Coefficient of Determination (R2) and the Nash–Sutcliffe Efficiency Index (EI). The sensitivity analysis helped to determine the most important model parameters. R2 values of 0.730 and 0.704 were obtained from the model's calibration and validation, respectively. With an Efficiency Index of 81%, the model demonstrated its efficiency and ability to forecast runoff for Ravishankar Sagar Reservoir over an extended period. This study helps to manage the water resource and to improve the reservoir operation policy for the reservoir.

Community participation and effective water management: A study on water user organizations (WUOs) in Thailand

This study examines the factors enhancing the effectiveness of Water User Organizations (WUOs) in Thailand, spotlighting the integration of geo-informatics and community involvement in water management. Employing a community-based participatory research approach, we assessed the impact of advanced technological tools and increased community engagement on WUOs' operations and sustainability. Our methodology included surveys, interviews, and an analysis of water management practices before and after geo-informatics implementation. Findings reveal a significant increase in community participation and an improvement in water management knowledge. The use of geo-informatics led to notable advancements in water allocation efficiency and conflict resolution, directly contributing to the financial sustainability of WUOs. Specifically, the application of geo-informatics facilitated more accurate and timely water resource allocation, evidenced by enhanced community engagement metrics and the resolution of longstanding conflicts. These results underscore the value of combining geo-informatics with community-driven approaches to water management, offering valuable insights for policymakers and practitioners. The study advocates for policies that support the adoption of technological innovations and promote community-centric governance, providing a scalable model for sustainable water resource management. This approach not only improves the functionality and sustainability of WUOs but also sets a precedent for holistic water governance practices globally.

Market Needs and Education of Mass Communication: A Perspective of Media Managers in Leading Private Television News Channels of Pakistan

Mass Communication is a field that gives extensive knowledge about other disciplines of academics and its degree holder can work in various professions due to their grip on different subjects. In Pakistan, it has been observed that many of the professionals who are working in the media industry do not have any educational background of mass communication instead they are specialized in other subjects. A mismatch between the media education and degrees of working professionals is raising serious concerns about the future of mass communication degree. Therefore this research is an attempt to examine the criteria that keeps in mind while hiring graduates in major television news channels of Pakistan. This study also aims to understand the importance of mass communication education in the career building of graduates from the perspective of media management. To investigate their perspective, in-depth interview method of Media Managing directors, Human Resource Managers, Director News, and Bureau Chiefs from the five leading private television channels of Pakistan i.e. GEO, Gourmet News Network (GNN), 92 News HD, Express TV, and Dunya TV are conducted. As this study focuses on the management perspective, therefore, the data is analyzed under the umbrella of the Knowledge, Skills and Abilities (KSA) Model of Human Resource management. The study concludes that there is a gap between academia and the professional field. Media graduates are somehow lacking in the professional training which is making path for non-media graduates to enter in the media field. Apart from that, individual needs such as confidence and communication skills are also making an impact on the hiring of media graduates.

Statistical pathways to low-carbon cities: Analyzing renewable integration, energy-efficient design, and job creation

To address the existing gap in understanding the intricate dynamics of resource management in Cloud Fog Computing (CFC)-driven Smart Grids (SG), this study introduces an innovative adaptive framework integrating Genetic Optimization Algorithm (GOA) and Cuckoo Search Optimization Algorithm (CSOA), aiming to optimize response times and enhance overall efficiency. In the domain of contemporary energy grids, the implementation of intelligent grids (IG) has ushered in a new era of dependable, streamlined, eco-friendly, and economically viable electric services. These grids constitute a fundamental facet of present-day energy resources. Moreover, the advent of cloud and mist computing (CMC) provides on-demand access to computational resources. Overcoming associated challenges, CMC not only delivers advantages like cost-effectiveness, energy conservation, scalability, flexibility, and adaptability but also presents an innovative model for resource management in IG. This study introduces a CMC-driven strategy for managing resources in IG by digitally replicating key grid components. The proposed framework aims to furnish diverse computational services for resource management within IG through a hierarchical arrangement of CMCs. Additionally, this investigation scrutinizes and simulates two optimization approaches: the cuckoo search and grasshopper optimization algorithms. These algorithms operate on an adaptive machine learning mechanism designed to balance the load, and the study meticulously compares their efficacy. These algorithms operate on an adaptive machine learning mechanism designed to balance the load, and the study meticulously compares their efficacy in the context of Renewable Integration, Energy-Efficient Design, and Job Creation.

A cloud-edge collaborative computing framework using potential games for space-air-ground integrated IoT

As a critical component of space-air-ground integrated IoT, the aerial network provides highly reliable, low-latency and ubiquitous information services to ground users by virtue of their high mobility, easy deployment and low cost. However, the current computation and resource management model of air-ground integrated networks are insufficient to meet the latency demanding of emerging intelligent services such as autonomous systems, extended reality and haptic feedback. To tackle these challenges, we propose a computation offloading and optimization method based on potential game. First, we construct an cloud-edge collaborative computing model. Secondly, we construct Offloading Decision Objective Functions (ODOF) with the objective of minimum task processing latency and energy consumption. ODOF is proved to be a Mixed Inferior Nonlinear Programming (MINLP) problem, which is hard to solve. ODOF is converted to be a full potential game, and the Nash equilibrium solution exists. Then, a computational resource allocation algorithm based on Karush–Kuhn–Tucker (KKT) conditions is proposed to solve resource allocation problem. On this basis, a distributed game-based computational offloading algorithm is proposed to minimize the offloading cost. Extensive simulation results demonstrate that the convergence performance of the proposed algorithm is reduced by 50%, the convergence time is reduced by 13.3% and the average task processing delay is reduced by 10%.