A multi-attribute decision making procedure using fuzzy numbers and hybrid aggregators

The classical Analytical Hierarchy Process (AHP) has two limitations. Firstly, it disregards the aspect of uncertainty that usually embedded in the data or information expressed by human. Secondly, it ignores the aspect of interdependencies among attributes during aggregation. The application of fuzzy numbers aids in confronting the former issue whereas, the usage of Choquet Integral operator helps in dealing with the later issue. However, the application of fuzzy numbers into multi-attribute decision making (MADM) demands some additional steps and inputs from decision maker(s). Similarly, identification of monotone measure weights prior to employing Choquet Integral requires huge number of computational steps and amount of inputs from decision makers, especially with the increasing number of attributes. Therefore, this research proposed a MADM procedure which able to reduce the number of computational steps and amount of information required from the decision makers when dealing with these two aspects simultaneously. To attain primary goal of this research, five phases were executed. First, the concept of fuzzy set theory and its application in AHP were investigated. Second, an analysis on the aggregation operators was conducted. Third, the investigation was narrowed on Choquet Integral and its associate monotone measure. Subsequently, the proposed procedure was developed with the convergence of five major components namely Factor Analysis, Fuzzy-Linguistic Estimator, Choquet Integral, Mikhailov‘s Fuzzy AHP, and Simple Weighted Average. Finally, the feasibility of the proposed procedure was verified by solving a real MADM problem where the image of three stores located in Sabak Bernam, Selangor, Malaysia was analysed from the homemakers‘ perspective. This research has a potential in motivating more decision makers to simultaneously include uncertainties in human‘s data and interdependencies among attributes when solving any MADM problems

A multi-criteria approach to dry port location in developing economies with application to Vietnam

This paper presents a conceptual framework for the inclusion of multiple criteria in the evaluation of dry port location in developing countries from a multiple stakeholder perspective. We present the framework in four steps. The first step encompasses preliminary research to filter the alternative locations for dry port development. In the second step, the stakeholders are clustered in three groups: dry port users, dry port service providers and the wider community. Then, we present the sub-criteria related to dry port location including the associated measuring methods. The third step includes an explanation on the methods used for weighing these criteria and sub-criteria. A multicriteria analysis is carried out in the final step. We apply the methodological framework to Vietnam. The location of a new dry port project in Vinh Phuc province will be evaluated against two existing inland clearance depots (ICD) in Lao Cai and Phu Tho province

Context-aware multi-attribute decision multi - attribute decision making for radio access technology selection in ultra dense network

Ultra Dense Network (UDN) is the extreme densification of heterogeneous Radio Access Technology (RAT) that is deployed closely in coordinated or uncoordinated manner. The densification of RAT forms an overlapping zone of signal coverage leading to the frequent service handovers among the RAT, thus degrading overall system performance. The current RAT selection approach is biased towards network-centric criteria pertaining to signal strength. However, the paradigm shift from network-centric to user-centric approach necessitates a multi-criteria selection process, with methodology relating to both network and user preferences in the context of future generation networks. Hence, an effective selection approach is required to avoid unnecessary handovers in RAT. The main aim of this study is to propose the Context-aware Multiattribute decision making for RAT (CMRAT) selection for investigating the need to choose a new RAT and further determine the best amongst the available methods. The CMRAT consists of two mechanisms, namely the Context-aware Analytical Hierarchy Process (CAHP) and Context-aware Technique for Order Preference by Similarity to an Ideal Solution (CTOPSIS). The CAHP mechanism measures the need to switch from the current RAT, while CTOPSIS aids in decision making to choose the best target RAT. A series of experimental studies were conducted to validate the effectiveness of CMRAT for achieving improved system performance. The investigation utilises shopping mall and urban dense network scenarios to evaluate the performance of RAT selection through simulation. The findings demonstrated that the CMRAT approach reduces delay and the number of handovers leading to an improvement of throughput and packet delivery ratio when compared to that of the commonly used A2A4-RSRQ approach. The CMRAT approach is effective in the RAT selection within UDN environment, thus supporting heterogeneous RAT deployment in future 5G networks. With context-aware selection, the user-centric feature is also emphasized

A multi-criteria decision making approach for scaling and placement of virtual network functions

This paper investigates the joint scaling and placement problem of network services made up of virtual network functions (VNFs) that can be provided inside a cluster managing multiple points of presence (PoPs). Aiming at increasing the VNF service satisfaction rates and minimizing the deployment cost, we use both transport and cloud-aware VNF scaling as well as multi-attribute decision making (MADM) algorithms for VNF placement inside the cluster. The original joint scaling and placement problem is known to be NP-hard and hence the problem is solved by separating scaling and placement problems and solving them individually. The experiments are done using a dataset containing the information of a deployed digital-twin network service. These experiments show that considering transport and cloud parameters during scaling and placement algorithms perform more efficiently than the only cloud based or transport based scaling followed by placement algorithms. One of the MADM algorithms, Total Order Preference by Similarity to the Ideal Solution (TOPSIS), has shown to yield the lowest deployment cost and highest VNF request satisfaction rates compared to only transport or cloud scaling and other investigated MADM algorithms. Our simulation results indicate that considering both transport and cloud parameters in various availability scenarios of cloud and transport resources has significant potential to provide increased request satisfaction rates when VNF scaling and placement using the TOPSIS scheme is performed.This work was partially funded by EC H2020 5GPPP 5Growth Project (Grant 856709), Spanish MINECO Grant TEC2017-88373-R (5G-REFINE), Generalitat de Catalunya Grant 2017 SGR 1195 and the National Program on Equipment and Scientifc and Technical Infrastructure, EQC2018-005257-P under the European Regional Development Fund (FEDER). We would also like to thank Milan Groshev, Carlos Guimarães for providing dataset for scaling of robot manipulator based digital twin service

AHP-based Adaptive Resource Selection for Cognitive Platform in Cloud Gaming Service

Cloud gaming service enables offloading heavy video-processing tasks up to the cloud server so that simple computers or mobile devices can be eligible to run sophisticated games, but on the expense of high network communications. In this regard, the adequate network utilization must be realized for delivering good gaming experiences to the game players. This necessitates a cognitive platform, which is capable of modifying its multimedia quality requirement in response to the network constraint, and notifying the cloud gaming server for updating the corresponded workload. In this regard, the Analytic Hierarchy Process (AHP) method has been proposed to deploy at the cognitive platform for cloud gaming service to select an optimal resource allocation strategy that satisfies various multimedia requirements and energy-awareness. Experiment results can confirm that the proposed method is flexible to enhance the capability of cloud gaming service in term of more efficient cloud gaming resource utilization, particularly during heavy-congested periods, while players’ quality of gaming experience can be still maintained under the mandate of intelligent agent on the player devices

Comparison of vertical handover decision-based techniques in heterogeneous networks

Industry leaders are currently setting out standards for 5G Networks projected for 2020 or even sooner. Future generation networks will be heterogeneous in nature because no single network type is capable of optimally meeting all the rapid changes in customer demands. Heterogeneous networks are typically characterized by some network architecture, base stations of varying transmission power, transmission solutions and the deployment of a mix of technologies (multiple radio access technologies). In heterogeneous networks, the processes involved when a mobile node successfully switches from one radio access technology to the other for the purpose of quality of service continuity is termed vertical handover or vertical handoff. Active calls that get dropped, or cases where there is discontinuity of service experienced by mobile users can be attributed to the phenomenon of delayed handover or an outright case of an unsuccessful handover procedure. This dissertation analyses the performance of a fuzzy-based VHO algorithm scheme in a Wi-Fi, WiMAX, UMTS and LTE integrated network using the OMNeT++ discrete event simulator. The loose coupling type network architecture is adopted and results of the simulation are analysed and compared for the two major categories of handover basis; multiple and single criteria based handover methods. The key performance indices from the simulations showed better overall throughput, better call dropped rate and shorter handover time duration for the multiple criteria based decision method compared to the single criteria based technique. This work also touches on current trends, challenges in area of seamless handover and initiatives for future Networks (Next Generation Heterogeneous Networks)

Analysis of Smart Parking System Using IOT Environment

The typical parking experience has been transformed by smart parking systems that use the Internet of Things (IoT) environment to integrate technology to improve efficiency, convenience, and sustainability. In order to monitor and manage parking spaces in real-time, this unique technique makes use of IoT devices, such as sensors, cameras, and networking technologies. As a result of the system's reliable information on parking availability, drivers may find and book parking spaces in advance, which eases traffic and reduces aggravation. Additionally, parking systems with IoT capabilities optimize resource use, lowering carbon emissions and fostering sustainability. The adoption of IoT in parking systems is a crucial step towards building smarter, more connected cities that will enhance both drivers' and parking operators' experiences with parking. There are numerous crucial elements in the process for developing a smart parking system in an IoT context. First, sensors are placed in parking places to gather up-to-the-minute occupancy information. Then, using wireless communication protocols, this data is sent to a central server or cloud computing platform. After that, a data processing and analysis module interprets the gathered data using algorithms and machine learning techniques and presents parking availability information to users via a mobile application or other user interfaces. For effective management and monitoring of parking spaces, the system also includes automated payment methods and interacts with existing infrastructure. Taken as Alternative parameters is Park Smart, Street line, Park Whiz, ParkMobile, Spot Hero. Taken as evaluation parameters is Light Sensor, CCTV coins, SMS, Cost-effectiveness, Timestamp. This demonstrates the rank of the data set Park Smart is on 1st Rank, ParkMobile is on 2nd Rank, Park Whiz is on 3rd Rank, Street line is on 4th Rank and Spot Hero is on 5th Rank. To sum up, implementing a smart parking system employing IoT technology has shown to be a potential way to deal with the problems associated with urban parking. The system increases parking efficiency, lessens traffic congestion, and enhances user experience by utilising IoT sensors, data analytics, and real-time communication. The parking scene in smart cities has the potential to change dramatically, enhancing ease and sustainability

Mobility management in 5G heterogeneous networks

In recent years, mobile data traffic has increased exponentially as a result of widespread popularity and uptake of portable devices, such as smartphones, tablets and laptops. This growth has placed enormous stress on network service providers who are committed to offering the best quality of service to consumer groups. Consequently, telecommunication engineers are investigating innovative solutions to accommodate the additional load offered by growing numbers of mobile users. The fifth generation (5G) of wireless communication standard is expected to provide numerous innovative solutions to meet the growing demand of consumer groups. Accordingly the ultimate goal is to achieve several key technological milestones including up to 1000 times higher wireless area capacity and a significant cut in power consumption. Massive deployment of small cells is likely to be a key innovation in 5G, which enables frequent frequency reuse and higher data rates. Small cells, however, present a major challenge for nodes moving at vehicular speeds. This is because the smaller coverage areas of small cells result in frequent handover, which leads to lower throughput and longer delay. In this thesis, a new mobility management technique is introduced that reduces the number of handovers in a 5G heterogeneous network. This research also investigates techniques to accommodate low latency applications in nodes moving at vehicular speeds

Semantics-aware planning methodology for automatic web service composition

Service-Oriented Computing (SOC) has been a major research topic in the past years. It is based on the idea of composing distributed applications even in heterogeneous environments by discovering and invoking network-available Web Services to accomplish some complex tasks when no existing service can satisfy the user request. Service-Oriented Architecture (SOA) is a key design principle to facilitate building of these autonomous, platform-independent Web Services. However, in distributed environments, the use of services without considering their underlying semantics, either functional semantics or quality guarantees can negatively affect a composition process by raising intermittent failures or leading to slow performance. More recently, Artificial Intelligence (AI) Planning technologies have been exploited to facilitate the automated composition. But most of the AI planning based algorithms do not scale well when the number of Web Services increases, and there is no guarantee that a solution for a composition problem will be found even if it exists. AI Planning Graph tries to address various limitations in traditional AI planning by providing a unique search space in a directed layered graph. However, the existing AI Planning Graph algorithm only focuses on finding complete solutions without taking account of other services which are not achieving the goals. It will result in the failure of creating such a graph in the case that many services are available, despite most of them being irrelevant to the goals. This dissertation puts forward a concept of building a more intelligent planning mechanism which should be a combination of semantics-aware service selection and a goal-directed planning algorithm. Based on this concept, a new planning system so-called Semantics Enhanced web service Mining (SEwsMining) has been developed. Semantic-aware service selection is achieved by calculating on-demand multi-attributes semantics similarity based on semantic annotations (QWSMO-Lite). The planning algorithm is a substantial revision of the AI GraphPlan algorithm. To reduce the size of planning graph, a bi-directional planning strategy has been developed

End-to-End Trust Fulfillment of Big Data Workflow Provisioning over Competing Clouds

Cloud Computing has emerged as a promising and powerful paradigm for delivering data- intensive, high performance computation, applications and services over the Internet. Cloud Computing has enabled the implementation and success of Big Data, a relatively recent phenomenon consisting of the generation and analysis of abundant data from various sources. Accordingly, to satisfy the growing demands of Big Data storage, processing, and analytics, a large market has emerged for Cloud Service Providers, offering a myriad of resources, platforms, and infrastructures. The proliferation of these services often makes it difficult for consumers to select the most suitable and trustworthy provider to fulfill the requirements of building complex workflows and applications in a relatively short time. In this thesis, we first propose a quality specification model to support dual pre- and post-cloud workflow provisioning, consisting of service provider selection and workflow quality enforcement and adaptation. This model captures key properties of the quality of work at different stages of the Big Data value chain, enabling standardized quality specification, monitoring, and adaptation. Subsequently, we propose a two-dimensional trust-enabled framework to facilitate end-to-end Quality of Service (QoS) enforcement that: 1) automates cloud service provider selection for Big Data workflow processing, and 2) maintains the required QoS levels of Big Data workflows during runtime through dynamic orchestration using multi-model architecture-driven workflow monitoring, prediction, and adaptation. The trust-based automatic service provider selection scheme we propose in this thesis is comprehensive and adaptive, as it relies on a dynamic trust model to evaluate the QoS of a cloud provider prior to taking any selection decisions. It is a multi-dimensional trust model for Big Data workflows over competing clouds that assesses the trustworthiness of cloud providers based on three trust levels: (1) presence of the most up-to-date cloud resource verified capabilities, (2) reputational evidence measured by neighboring users and (3) a recorded personal history of experiences with the cloud provider. The trust-based workflow orchestration scheme we propose aims to avoid performance degradation or cloud service interruption. Our workflow orchestration approach is not only based on automatic adaptation and reconfiguration supported by monitoring, but also on predicting cloud resource shortages, thus preventing performance degradation. We formalize the cloud resource orchestration process using a state machine that efficiently captures different dynamic properties of the cloud execution environment. In addition, we use a model checker to validate our monitoring model in terms of reachability, liveness, and safety properties. We evaluate both our automated service provider selection scheme and cloud workflow orchestration, monitoring and adaptation schemes on a workflow-enabled Big Data application. A set of scenarios were carefully chosen to evaluate the performance of the service provider selection, workflow monitoring and the adaptation schemes we have implemented. The results demonstrate that our service selection outperforms other selection strategies and ensures trustworthy service provider selection. The results of evaluating automated workflow orchestration further show that our model is self-adapting, self-configuring, reacts efficiently to changes and adapts accordingly while enforcing QoS of workflows