Optimizing Green Computing Awareness for Environmental Sustainability and Economic Security as a Stochastic Optimization Problem
Abstract
:1. Introduction
2. Background and Related Work
2.1. Electronic Waste
2.2. Green Computing and Sustainable Development
2.3. Metaheuristic Algorithm for Optimal Green Computing Awareness
- establishing functional regulatory bodies;
- use of traditional media awareness;
- use of web-based social media;
- organizing workshops and seminars;
- establishing waste collection and quantification systems; and
- organizing a road walk campaign.
- Q = set of states that is finite
- A = set of actions that is not empty
- q0 ϵ Q = start state
- F Q = set of final states;
- Q A Q = finite set of transition between states; where
- p is a function ∂ [0,1] and all q ϵ Q; for all a ϵ A
- ∑ p(q, a, q') = 1.
- a0 = Knowledge gap analysis of computer end-users in Sub-Saharan Africa
- a1 = Identification of suitable campaign option for creating maximum awareness in the socio-cultural context of each African country
- a2 = Application of identified approach
- a3 = Evaluation of the impact of applied approach.
Algorithm 1: Tabu Search |
---|
s ← s0 sBestInitiative ← s awarenessTabuList ← null while (not awarenessSearchStoppingCondition()) awarenessCandidateList ← null for(awarenessCandidate in searchNeighborhood) if(not containsTabuElements(awarenessCandidate, awarenessTabuList)) awarenessCandidateList ← awarenessCandidateList + awarenessCandidate end end awarenessCandidate ← LocateBestAwarenessCandidate(awarenessCandidateList) s ← awarenessCandidate if(fitness(awarenessCandidate) > fitness(sBestAwareness)) awarenessTabuList ← featureDifferences(awarenessCandidate, sBestAdmission) sBestAwareness ← awarenessCandidate while(size(awarenessTabuList) > maxAwarenessTabuListSize) ExpireFeatures(awarenessTabuList) s ← awarenessTabuLIstFirstElements end end end return(sBestAwareness) |
Algorithm 2: Green Computing Awareness Maximization Function |
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Max Green Computing awareness Subject to Availability of funds Access to network infrastructure Literacy level |
2.4. Related Work
3. Methodology—The e-Green Computing System
3.1. Requirements Analysis and Specification
3.2. System and Software Design
3.3. Implementation and Unit Testing
3.4. System Integration
3.5. System Verification and Validation
3.6. Operation Support and Maintenance
4. Results and Discussion
4.1. Results of Software Experiment
4.2. Results of End-Users Survey
4.3. Evaluation Threats
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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SN | Objective | Methodology |
---|---|---|
1. | To drive online real-time conversation on environmentally friendly computing in the African cyberspace | Design and implement an e-Green Computing system |
2. | To ascertain proposed system can bring about desired end-user behaviour | Verify and validate the e-Green Computing system |
Requirement ID | Requirement | Brief Description |
---|---|---|
R01 | Add Information | The e-Green Computing system will allow computer users to add information on green computing techniques based on assigned rights and privileges |
R02 | Access Information | The e-Green Computing system will allow computer users to retrieve and view information on green computing practices within assigned rights and privileges |
R03 | Edit Information | The e-Green Computing system will allow computer users to edit information related to environmentally friendly computing in line with assigned rights and privileges |
R04 | Delete Information | The e-Green Computing system will allow users to delete information from the database based on allocated rights and privileges |
SN | Component | Function Points |
---|---|---|
1. | e-Green Computing | Add Green Computing Info, |
Access Green Computing Info, | ||
Edit Green Computing Info, | ||
Delete Green Computing Info |
SN | Statement | Response | |||
---|---|---|---|---|---|
Yes | No | Abstained | Total | ||
1. | Familiar with Green Computing? | 3 (15%) | 15 (75%) | 2 (10%) | 20 (100%) |
2. | Green Computing is also referred to as environmentally sustainable computing? | 4 (20%) | 12 (60%) | 4 (20%) | 20 (100%) |
3. | The aim of Green Computing is to mitigate hazardous material and protect our environment. | 7 (35%) | 11 (55%) | 2 (10%) | 20 (100%) |
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Okewu, E.; Misra, S.; Maskeliūnas, R.; Damaševičius, R.; Fernandez-Sanz, L. Optimizing Green Computing Awareness for Environmental Sustainability and Economic Security as a Stochastic Optimization Problem. Sustainability 2017, 9, 1857. https://doi.org/10.3390/su9101857
Okewu E, Misra S, Maskeliūnas R, Damaševičius R, Fernandez-Sanz L. Optimizing Green Computing Awareness for Environmental Sustainability and Economic Security as a Stochastic Optimization Problem. Sustainability. 2017; 9(10):1857. https://doi.org/10.3390/su9101857
Chicago/Turabian StyleOkewu, Emmanuel, Sanjay Misra, Rytis Maskeliūnas, Robertas Damaševičius, and Luis Fernandez-Sanz. 2017. "Optimizing Green Computing Awareness for Environmental Sustainability and Economic Security as a Stochastic Optimization Problem" Sustainability 9, no. 10: 1857. https://doi.org/10.3390/su9101857