Cross-layer collaboration handoff mechanism based on multi-attribute decision in mobile computation offloading

Decision making is the hot topic in business management. Recently, there have been investigations in Call Admission Control (CAC) to handle the handover events in wireless network environments. Wireless networks have been seen to support different handoff mechanisms, in which the user can switch from one wireless network to another such as from IEEE 802.11 to IEEE 802.16. Many wireless standards, such as IEEE 802.16e and IEEE 802.11, have been proposed to satisfy diverse requirements for mobile users.. Several strategies have been proposed to realize seamless handoff in the heterogeneous networks; in this paper, we analyze the performance of some novel intelligent handoff mechanisms with multiple attributes. The mobile set dynamically selects the optimal network and provides the user with high throughput of Quality of Service (QoS) as to minimize the blocking probability of the system in heterogeneous networks. We evaluate some intelligent handoff mechanisms such as Technique for Order Preference by Similarity to Ideal Solutions (TOPOSIS), Simple Additive Weight (SAW), Analytic Hierarchy Process (AHP) and Fuzzy Logic and Grey Relational Analysis (GRA). From the results, blocking probability and dropping rate can be referenced to support better QoS.

TOPSIS-based dynamic approach for mobile network interface selection

As the technology is developing with a huge rate, the functionality of smartphone is also getting higher. But the smartphones have some resource constraints like processing power, battery capacity, limited bandwidth for connecting to the Internet, etc. Therefore, to improve the performance of smartphone in terms of processing power, battery and memory, the technology namely, augmented execution is the best solution in the mobile cloud computing (MCC) scenario. Mobile cloud computing works as the combination of mobile computing and cloud computing. Augmented execution alleviates the problem of resource scarcity of smartphone. To get the benefits from the resource-abundant clouds, massive computation intensive tasks are partitioned and migrated to the cloud side for the execution. After executing the task at the cloud side, the results are sent back to the smartphone. This method is called as the computation offloading. The given survey paper focuses on the partitioning techniques in mobile cloud computing.

Penelitian ini bertujuan untuk merancang dan mengimplementasikan Executive Support System (ESS) yang efektif menggunakan metode Simple Additive Weighting (SAW), Analytic Hierarchy Process (AHP), dan Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) dalam konteks perencanaan strategis. Kemampuan ESS dalam mendukung pengambilan keputusan eksekutif dihadapkan pada kompleksitas faktor dan kriteria yang melibatkan banyak aspek bisnis. Metode SAW digunakan untuk memberikan nilai relatif pada setiap kriteria, memberikan pendekatan yang sederhana dan mudah dimengerti. AHP digunakan untuk menangani penilaian kriteria secara hierarkis, memungkinkan penetapan bobot yang lebih akurat berdasarkan tingkat hierarki kriteria. TOPSIS digunakan untuk memilih alternatif terbaik dengan mempertimbangkan kedekatan dengan solusi ideal. Penelitian ini adalah penelitian kuantitatif dengan menggunakan metode kepustaakaan. Hasil penelitian menunjukkan bahwa integrasi metode SAW, AHP, dan TOPSIS dalam ESS memberikan kontribusi positif dalam meningkatkan kemampuan sistem dalam memberikan dukungan yang lebih optimal bagi pengambilan keputusan eksekutif. Kesederhanaan SAW, penilaian hierarkis AHP, dan ketepatan pemilihan TOPSIS bekerja bersama untuk memberikan solusi yang akurat dan efisien.

Optimization problems are relevant to various areas of human activity. In different cases, the problems are solved by applying appropriate optimization methods. A range of optimization problems has resulted in a number of different methods and algorithms for reaching solutions. One of the problems deals with the decision-making area, which is an optimal option selected from several options of comparison. Multi-Attribute Decision-Making (MADM) methods are widely applied for making the optimal solution, selecting a single option or ranking choices from the most to the least appropriate. This paper is aimed at providing MADM methods as a component of mathematics-based optimization. The theoretical part of the paper presents evaluation criteria of methods as the objective functions. To illustrate the idea, some of the most frequently used methods in practice—Simple Additive Weighting (SAW), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Complex Proportional Assessment Method (COPRAS), Multi-Objective Optimization by Ratio Analysis (MOORA) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE)—were chosen. These methods use a finite number of explicitly given alternatives. The research literature does not propose the best or most appropriate MADM method for dealing with a specific task. Thus, several techniques are frequently applied in parallel to make the right decision. Each method differs in the data processing, and therefore the results of MADM methods are obtained on different scales. The practical part of this paper demonstrates how to combine the results of several applied methods into a single value. This paper proposes a new approach for evaluating that involves merging the results of all applied MADM methods into a single value, taking into account the suitability of the methods for the task to be solved. Taken as a basis is the fact that if a method is more stable to a minor data change, the greater importance (weight) it has for the merged result. This paper proposes an algorithm for determining the stability of MADM methods by applying the statistical simulation method using a sequence of random numbers from the given distribution. This paper shows the different approaches to normalizing the results of MADM methods. For arranging negative values and making the scales of the results of the methods equal, Weitendorf’s linear normalization and classical and author-proposed transformation techniques have been illustrated in this paper.

Trust and reputation systems represent a significant trend in decision support including selection of best match cloud providers to process Big Data. Reputation is often considered as a collective measure of trustworthiness based on the referrals or ratings from members in a community. Reputation systems have been applied in various applications such as online service provision. However, reputation models do not reflect user's quality of service (QoS) preferences and thus they might not be satisfied with the recommendations from others. In this paper, we propose a de-centralized reputation-based trust model that incorporates the user QoS preferences to select the best match Cloud Service Provider to process Big Data. Our trust model relies on three multi-attribute decision-making (MADM) methods including Simple Additive Weighting (SAW), Weighted Product Method (WPM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). We conducted several experiments using simulated cloud environment to validate our trust model and assess the three MADM methods. The results show that the proposed model is pliable to users' requirements and efficiently evaluate trust of cloud providers.

SMK TI LABBAIKA provides scholarships every semester to students who meet the requirements. The selection process still uses the manual method based on the Headmaster's knowledge. The suitable decision support systems and methods are needed to minimize errors in decision making and get fair decision results. In this study, two decision support system methods were chosen, namely Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The SAW and TOPSIS methods were chosen because they have simple calculations. A comparison of the two methods is carried out to determine the best method for selecting scholarships. The criteria used in this study are student status, number of family members, parent's occupation, parent's income, and semester grades. The 20 alternative data is used for testing. Based on the results of testing the accuracy for the SAW method is 45% while for TOPSIS is 60%.

Decision making problem is the process of finding the best option out of all feasible alternatives. There are some methods for solving Multiple Criteria Decision-Making problems and Simple Additive Weighting (SAW) is one of the most popular ones. In this paper, among multi-criteria models in making complex decisions and multiple attribute models for the most preferable choice, SAW technique is extended using interval numbers. For this purpose, we first propose a method for extending Entropy method for dealing with interval data, and then the extended SAW method with interval data is proposed by using the interval weights derived by the proposed interval Entropy method. The extended SAW method is an algorithm to determine the most preferable choice out of all possible choices, when the input data are stated in interval.

Recently, the popularity of big data and business analytics has increased with advanced technological developments. Supply chain analytics (SCA) notion was born with the implementation of these technologies in supply chains that become more global, more complex, more extended, and more connected each day. SCA aims to find meaningful patterns in supply chain processes with the application of statistics, mathematics, machine-learning techniques, and predictive modeling. In this context, companies try to find ways to create business value for their supply chains by leveraging SCA. However, the selection of the most appropriate SCA tool is a complicated process that contains many influencing factors. For instance, the graphical and intuitive features, the data extraction method and real-time operability can be the influencing factors for such a selection. Therefore, in this study, it is aimed to provide an integrated technique for prioritizing SCA success factors and for evaluating SCA tools. For addressing these problems, fuzzy logic and multi-criteria decision making (MCDM) techniques are used. An integrated fuzzy simple additive weighting (SAW) - a technique for order preference by similarity to ideal solution (TOPSIS) approach is applied. The weights of the success factors are calculated by using fuzzy SAW technique, and the SCA tools are evaluated by using fuzzy TOPSIS technique. The success factors and the SCA tool alternatives are determined by reviewing the literature and industry reports, and by collecting experts' opinions. An application is given to illustrate the potential of the proposed approach. At the end of the study, the suggestions for future studies are presented.

With the ever increasing data and computational demands from mobile users, heterogenous wireless networks (HetNets) and mobile cloud computing (MCC) have been advocated as a promising solution to meet these demands. Insufficient bandwidth is one of the most important challenges being faced by a successful implementation of the MCC technology due to heavy data traffic. The MCC implementation on HetNet increases the bandwidth available to each base station (BS) by frequency reuse. In this paper, a novel data storage method for big data files is proposed, along with a data correction technique to deal with the issue of failure of a data chunk retrieval. The proposed algorithm exploits the multiple paths that are available between a user and the cloud storage system in a MCC-HetNet environment. Since the bottleneck in the MCC is the wireless link between the user equipment (UE) and the BS, we have implemented the algorithm on the wireless links between the UE and the BS. The simulated results show that the proposed method outperforms the conventional data storage method between the mobile device and the cloud system.

In India, utilising low-temperature waste heat is crucial to improving energy efficiency. The regenerative organic Rankine cycle (ORC) process is a promising waste heat recovery technique in this study. Here, the thermodynamic behaviour of eight working fluids: Butane, Heptane, Hexane, Pentane, Cyclopentane, HexMDiSiloxn (MM), Benzene, and Toluene, was carefully examined. An elite Nondominated sorting genetic sorting algorithm (NSGA-II) was used in Multi-objective Optimization (MOO) to address the incongruent behaviour of thermodynamic performance (exergy efficiency) and economic performance (levelized energy cost, LEC). Every eight working fluids are the subject of MOO studies, and the resulting Pareto optimal fronts are created. Several advanced Pareto ranking techniques are taken into account to choose one ideal solution from the Pareto optimal front. These include Simple additive weighting (SAW), The technique for order of preference by similarity to deal Solution (TOPSIS), Grey relation analysis (GRA), and Multi-attributive border approximation area comparison (MABAC). The most economically advantageous working fluid is chosen using an explicit economic performance assessment index called the Static Investment Payback Period (SIPP). The results show that Pentane is the most economically advantageous working fluid with the shortest SIPP and a medium waste heat temperature range. The findings also imply that any working fluid can be used TOPSIS, SAW, GRA, and MABAC with entropy information considered in this study to recover low-temperature waste heat in the ORC.

The increasing demand for high-performance gaming laptops requires a systematic decision-making approach to select the most suitable product. This study compares three Multi-Criteria Decision-Making (MCDM) methods: Simple Additive Weighting (SAW), Weighted Product (WP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to determine the best gaming laptop. A dataset of six premium laptops was evaluated based on seven criteria, including specifications, portability, and price. Numerical scoring and sensitivity analysis were applied to assess method stability. The results indicate differences in rankings produced by each method, providing insight into their reliability for supporting gaming laptop selection decisions

Multimode terminals designed for heterogeneous wireless networks (HWNs) have the capability to support two or more classes of calls simultaneously. When a multimode terminal having an ongoing call initiates a new class of call, the current radio access technology (RAT) may not support the new class of call thereby necessitating a vertical handoff to a RAT that can support both the current and the newly initiated call. However, vertical handoffs incur signaling overhead, handover delay, packet loss, and service disruption. Therefore, it is important to reduce the frequency of vertical handoffs in HWNs. This paper proposes a predictive algorithm that considers the current state and the possible future state of a multimode terminal in making RAT selection decisions, and thereby reduces the frequency of vertical handoffs in HWNs. The history of a user's call logs is used in making predictions about future states of the user's multimode terminal. The paper develops an analytical model to evaluate the performance of the proposed predictive RAT-selection algorithm, using a four-RAT HWN as an example, and considering three classes of calls namely voice, video, and data. Results show that the proposed predictive algorithm significantly reduces the frequency of vertical handoff calls when compared with non-predictive algorithm in HWNs.

The recruitment process for video editing staff is a strategic stage in ensuring the quality of professional and competitive content production. However, candidate assessment often faces challenges of subjectivity and inaccuracy in decision-making when evaluators rely solely on intuitive judgment without a measured approach. This study aims to develop a decision support system based on Multi-Criteria Decision Making (MCDM) by integrating the Entropy method for objective determination of criteria weights and the Simple Additive Weighting (SAW) method in calculating the preference values of alternatives. Five evaluation criteria are used in the selection process, namely Editing, Creativity, Experience, Discipline, and Teamwork, with the final weights obtained through the Entropy method being 0.2867, 0.2248, 0.2573, 0.0685, and 0.1626. The study results show that the SAW method is capable of processing candidate evaluation scores comprehensively based on these weights, producing final scores that indicate the best candidates, namely Eko Firmansyah (0.986), Indra Mahendra (0.9699), and Candra Wijaya (0.9662) as the three candidates with the highest eligibility. This study demonstrates that the integration of the Entropy–SAW method is effective in creating a selection mechanism that is objective, transparent, and scientifically accountable, thus making a significant contribution to decision-making in the field of human resource management

A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design