Aware Computing Research Articles

An Extended Clustering Approach for Extended Energy Aware Computing

An Extended Clustering Approach for Extended Energy Aware Computing

Context-Aware Vertical Handoff Algorithms for IoT-enabled Environments

-The proliferation of Internet of Things (IoT) devices has ushered in a paradigm shift in the way we interact with our surroundings. As these devices seamlessly exchange data across heterogeneous networks, maintaining uninterrupted connectivity and optimal quality of service (QoS) becomes a paramount concern. The process of vertical handoff, which facilitates the seamless transfer of communication sessions between different networks, is of particular significance in this dynamic IoT landscape. This research paper delves into the realm of "Context-Aware Vertical Handoff Algorithms for IoT-enabled Environments," aiming to harness the power of context-awareness to enhance the efficacy of vertical handoff decisions in IoT scenarios. The introduction highlights the intricate network challenges posed by IoT's diverse devices and applications. These challenges necessitate the development of innovative solutions that can adapt to varying communication technologies, device constraints, and QoS demands. The focal point of this exploration lies in the integration of context-awareness into vertical handoff algorithms. Context-awareness leverages real-time contextual information, encompassing factors such as device location, network conditions, application requirements, and user preferences, to make informed handoff decisions that align with the unique demands of each IoT situation. Drawing from the wellspring of context- aware computing, the paper delves into the mechanics of context-aware vertical handoff algorithms. It explores their attributes and capabilities, shedding light on how these algorithms leverage context to optimize handoff decisions. The paper discusses the diversity of International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 07 Issue: 09 | September - 2023 SJIF Rating: 8.176 ISSN: 2582-3930 © 2023, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM25730 | Page 2 context-aware algorithms, including fuzzy logic- based, machine learning-driven, and rule-based approaches, and their adaptability to the distinct challenges posed by IoT environments. As the IoT landscape brings forth its own set of challenges, the paper addresses considerations like limited resources, heterogeneous data, and intermittent connectivity. It delves into the spectrum of algorithm types and approaches that align with the IoT's intricacies, ensuring that vertical handoff decisions resonate with the unique characteristics of IoT devices. In the quest to gauge the effectiveness of context-aware vertical handoff algorithms in IoT scenarios, the paper outlines a comprehensive methodology for performance evaluation. This methodology involves defining IoT-centric performance metrics, such as data delivery reliability, energy efficiency, latency, and adaptive decision-making, which accurately capture the impact of context-awareness on QoS enhancement and device efficiency. Through practical case studies, the paper demonstrates how context-aware vertical handoff algorithms find application across various IoT domains, from smart cities to healthcare. It unveils how these algorithms address the dynamic challenges presented by real- world scenarios, where devices navigate between heterogeneous networks and diverse application requirements. Keywords: IoT ,QoS, LPWAN,

Emergency treatment mechanism of laboratory safety accidents in university based on IoT and context aware computing

In recent years, safety accidents in university laboratories have occurred frequently. Not only do the accidents result in property damage, but also in injuries. Real-time environmental monitoring of the laboratory through IoT enables early detection of potential safety risks such as high temperatures, high humidity and gas leaks, and timely action to reduce the likelihood of accidents. To ensure laboratory safety, in the paper, an emergency treatment mechanism for laboratory safety accidents was proposed based on IoT and context perception. The mechanism uses sensors to collect environmental information and fill a feature characterization architecture for unified safety management. Subsequently, the meta-rule algorithm is used to discover services in the prior knowledge model to form a workflow engine, so as to drive the security business management. Additionally, based on the standard measurement model, we normalize the fuzzy uncertainty measurement model with different granularities and define the fuzzy uncertainty of different emergency decision-making knowledge. Based on this, a knowledge fusion method for emergency decision-making under different fuzzy uncertainties is proposed, which improves laboratory safety emergency response performance based on situational awareness. The implementation of the proposed mechanism in a chemical laboratory demonstrates its efficacy in optimizing operational processes and discovering operational flow through multi-dimensional information analysis. This capability significantly aids safety administrators in their daily laboratory safety management.

Proof of Evaluation-based energy and delay aware computation offloading for Digital Twin Edge Network

The increasing availability of Internet of Things (IoT) applications has led to the development of new technologies. Specifically, the deployment of edge servers close to IoT devices has strengthened the edge computing paradigm. With the collaboration of Mobile Edge Computing (MEC) and cloud computing, delay-sensitive and computation-intensive tasks can be offloaded to the edge/cloud servers to improve system performance in terms of the delay and energy consumption of IoT devices. However, there is a need to schedule the computation tasks for an efficient management. More importantly, the task scheduling strategy can face data tampering attacks to deliberately modify, destroy or manipulate the decisions. To solve the above problems, in this paper, we newly propose to integrate digital twin and blockchain into the edge networks. However, it is unclear (i) how energy and delay-aware computation should be combined, and (ii) which mining computations should be executed for a secure task scheduling. The state-of-the-art focuses on task scheduling and blockchain mining, separately. Therefore, we propose a novel blockchain-based digital twin-edge network architecture where our proposed algorithm solves these two challenges at the same execution. We design a three-layer system architecture, composed of physical entity layer, digital twin edge layer and blockchain layer. In the physical entity layer, we formulate an energy and delay-aware task scheduling problem. In the digital twin edge layer, we propose a novel Proof of Evaluation (PoE)-based secure energy and delay-aware task scheduling algorithm where optimization is executed by the genetic algorithm implementation of Warehouse Location Problem (WLP). In the blockchain layer, the best-found solutions are shared with the topology in a blockchain. Here, each block includes the hash of the previous block, a genetic algorithm-based solution, nonce value, and a hash of whole blocks in the blockchain. Thus, we aim to execute the computation tasks with an acceptable delay in an energy-efficient manner and prevent data tampering attacks against the optimal computation decisions. We validate the outcomes of our PoE-based secure digital twin-edge network model with extensive evaluations. Since the proposed model distributes the task not only to the local device but also to the MEC and cloud server for delay awareness, it reduces the delay but consumes more energy. Nevertheless, the additional energy consumption can be neglected against the delay reduction. The proposed scheme is also more scalable to compare with the conventional solution. The numerical results clearly show that the proposed model provides energy and delay awareness, maintaining both data integrity and trustworthiness at the same execution of algorithm.

Impacts on Context Aware Systems in Evidence-Based Health Informatics: A Review.

Background: The application of Context Aware Computing (CAC) can be an effective, useful, feasible, and acceptable way to advance medical research and provide health services. Methods: This review was conducted in accordance with the principles of the development of a mixed methods review and existing knowledge in the field via the Synthesis Framework for the Assessment of Health Information Technology to evaluate CAC implemented by Evidence-Based Health Informatics (EBHI). A systematic search of the literature was performed during 18 November 2021–22 January 2022 in Cochrane Library, IEEE Xplore, PUBMED, Scopus and in the clinical registry platform Clinicaltrials.gov. The author included the articles in the review if they were implemented by EBHI and concerned with CAC technologies. Results: 29 articles met the inclusion criteria and refer to 26 trials published between 2011 and 2022. The author noticed improvements in healthcare provision using EBHI in the findings of CAC application. She also confirmed that CAC systems are a valuable and reliable method in health care provision. Conclusions: The use of CAC systems in healthcare is a promising new area of research and development. The author presented that the evaluation of CAC systems in EBHI presents positive effects on the state of health and the management of long-term diseases. These implications are presented in this article in a detailed, clear, and reliable manner.

Survey on Context Aware Computing and IoT Environment

Context awareness enables the system to collect the information about the environment at any time and act according to it. It makes use of hardware as well as software to collect and analyze the data automatically. The mobile users particularly the smart phone users are mostly focused by context due to it’s pliability and is often used as a part of ubiquitous and wearable computing. The Internet and upcoming technologies combine to transform every object into smart objects. Where this can be applied to user’s activities on the device or to the physical environment in which the device is being use. Here we survey the context awareness from an IoT perspective to help users by automating the task of selecting the sensors according to the problems/tasks at hand.

A Security and Privacy Aware Computing Approach on Data Sharing in Cloud Environment

Today, the role of cloud computing in our day-to-day lives is very prominent. The cloud computing paradigm makes it possible to provide demand-based resources. Cloud computing has changed the way that organizations manage resources due to their robustness, low cost, and pervasive nature. Data security is usually realized using different methods such as encryption. However, the privacy of data is another important challenge that should be considered when transporting, storing, and analyzing data in the public cloud. In this paper, a new method is proposed to track malicious users who use their private key to decrypt data in a system, share it with others and cause system information leakage. Security policies are also considered to be integrated with the texts encrypted to ensure system safety and to prevent the violation of data owners ' privacy. For this purpose, before sending the data to the cloud, it must be encrypted in such a way that operations such as max, min, etc. can be performed on it. The proposed method uses order-preserving symmetric encryption (OPES), which does not require decryption or re-encryption for mathematical operations. This process leads to a great improvement in delay. The OPES scheme allows comparison operations to be performed directly on encrypted data without decryption operands. According to the results, it is obvious that the proposed strategy is in a better position compared to the base paper in terms of the system's ability to find the malicious elements that cause the problem of leakage and in terms of system security to prevent the violation of privacy.

Awareness Modeling and Computing for Quality-Aware Coordination

In this paper, we address the issues of the trade-off between QoS and QoE with an analytical analysis based on mathematical modeling under a unified normalization measurement. We model through computation the awareness of QoS and QoE with a strategy of quality-aware QoE-QoS coordination. A balanced coordination is proposed using modeling correlations between user experience and service performance. The main contributions of this paper include three main parts. First, a comprehensive mapping is modeled in a close form to illustrate the analytic correlations between QoS, QoE, and data communication. Second, an analytical method to analyze and coordinate the nonlinear trade-off between QoE and QoS is proposed based on the theoretical proof with discussions on necessary-sufficient conditions. Third, an algorithmic framework is provided to perform QoE-QoS coordination based on quality-awareness computing with a test proof. An assessment model for user experience quantification is built with the mean opinion score (MOS) test. Quality-aware QoE and QoS models are built based on the subspace learning strategy. Simulations are given to prove the feasibility and effectiveness of the proposed method. The results show that the operations with the proposed solution can be obtained analytically with balanced efficiency in both user experience performance and network performance.

A comprehensive model and computational methods to improve Situation Awareness in Intelligence scenarios

This paper presents a comprehensive model for representing and reasoning on situations to support decision makers in Intelligence analysis activities. The main result presented in the paper stems from a work of refinement and abstraction of previous results of the authors related to the use of Situation Awareness and Granular Computing for the development of analysis methods and techniques to support Intelligence. This work made it possible to derive the characteristics of the model from previous case studies and applications with real data, and to link the reasoning techniques to concrete approaches used by intelligence analysts such as, for example, the Structured Analytic Techniques. The model allows to represent an operational situation according to three complementary perspectives: descriptive, relational and behavioral. These three perspectives are instantiated on the basis of the principles and methods of Granular Computing, mainly based on the theories of fuzzy and rough sets, and with the help of further structures such as graphs. As regards the reasoning on the situations thus represented, the paper presents four methods with related case studies and applications validated on real data.

Preface

Thanks for your passionate involvements and professional supports to 2020 2nd International Conference on Computer, Communications and Mechatronics Engineering (CCME2020), which was held to carry out new attempts in Computer, Communications and Mechatronics Engineering fields through related academic researches and practical actions based on future directions. And special thanks also to all and Technical Program Committees and reviewers for their professional works and selfless giving of their precious time.CCME2020 was held via Tencent Online meeting on December 20, 2020 to avoid offline contact and inconvenience caused by traffic restriction against the backdrop of the current normalized the epidemic. While improving the research motivation and academic literacy of all participants, CCME2020 will more strengthen safety protection, actively respond to the current epidemic prevention and cooperate with the normalization of epidemic situation.In order to ensure the normal running of CCME2020 online meeting, our staff preset the test room in advance for participants for their testing on the connection of relevant equipment and the stability of normal operation. We also arrange dedicated staff to provide the authors with questions and guidance. CCME2020 online meeting was held normally with supports of Prof. Gaurang S Patkar, Prof. Manik Sharma and all attendees. Prof. Gaurang S Patkar from Don Bosco College of Engineering, India made keynote speech about Automated Grading of Oil Palm Fruits using Soft Computing, and Prof. Manik Sharma from DAV University, India shared his viewpoints on Design of Artificial Intelligent Stress Diagnostic System using Machine Learning, Soft Computing and Context Aware Computing Techniques.With professional assistances from Technical Program Committee on paper reviewing and checking, CCME2020 selected 44 paper contributions among 105 paper submissions covering Computer, Communications and Mechatronics Engineering eventually. All these selected papers have been reviewed by 2-3 reviewers and editors who major in relevant research areas and be detected instances of overlapping and similar text in submitted manuscripts via iThenticate software before submitting for publication in Journal of Physics: Conference Series.In terms of the main topics and subject areas involved in all selected submissions, and professional, rigorous and meticulous guidance from the reviewer, CCME2020 Conference Proceeding mainly cover Computer Technology, Image Recognition, Communication, Intelligent Information and Electronic Engineering.Sincerely hope for future cooperation with these rigorous and responsible reviewers, Technical program Committee, these authors with a high enthusiasm for academic research, and accurate and efficient publication of IOP Publisher. Thanks again for your active participation and contributions!CCME2020 Organization Committees

Foundations and affordances of workplace assistive technology: The case of mobile and enabling IT for workers with visual impairments

ABSTRACT Smartphones, with their built-in cameras, speech synthesizers, processing power, sensors, and network connectivity have established themselves as a fertile and ubiquitous platform for software developers interested in augmenting the capabilities of users with visual impairments. When fitted with readily available nonvisual apps, they comprise a mobile and enabling IT bundle (ME-IT) which can afford users new capabilities to interact with visual aspects of their environment. In this study, we investigated the potential of ME-IT as a workplace enabler for workers with visual impairments (WVI). Adopting a Delphi approach, this study contributes five categories of extended capabilities afforded by ME-IT deemed highly relevant to WVI and their employers: reading text on the fly, understanding the visual properties of people and things in the workplace, orienting to the physical features of one’s work environment, tagging objects in one’s work environment, and locating things in the work environment. Two characteristics of the ME-IT bundle emerged as the basis for these extended capabilities: portable computer vision and context awareness. Findings extend literature on the use of mobile technology in the workplace, contextually aware computing, and assistive technology, and invites additional work to calibrate ME-IT to specific jobs, job contexts, and the needs of users.

Statistical Properties of a New Social Media Context Awareness Scale (SMCA)—A Preliminary Investigation

In the Internet of Things era, or in the digitalization and mediatization of everything paradigm, where context awareness computing is on the rise, people are also facing a new challenge, that of being aware of the digital contexts, in all situations when surfing the internet’s ocean of row information. The emerging social media context awareness competency refers to a new emerging skill regarding the trust load people give to a specific social media context they encounter. Since it is an emergent competence, it cannot be understood as standalone. If the digital context would not be available, we would not develop such a competence. Being a competence, it must be defined by three core elements: Knowledge, skills, and attitudes. Consequently, we have operationalized the competence of social media context awareness in terms of social media literacy, social media communication process understanding, social media content impact awareness, and social media confidence. An online questionnaire was created under the Erasmus+ project Hate’s Journey, addressing a convenience sample of 206 online youth respondents from Turkey, Spain, Latvia, and Romania. Our team has computed a reliability analysis on the social media context awareness scale designed with four items referring to social media literacy (m = 3.79, SD = 1), social media communication process understanding (m = 3.77, SD = 0.9), social media content impact awareness (m = 3.88, SD = 1), and social media confidence (m = 3.45, SD = 1). Cronbach’s alpha coefficient and the Exploratory Factor Analysis demonstrated the acceptable reliability of the SMCA scale, α = 0.87. Conclusions, implications, and limitations are discussed in the context of social sustainability.

Exploring the influencing factors of public environmental satisfaction based on socially aware computing

As the problem of urban environment and air pollution continues to intensify, the public has developed a very strong perception of the surrounding environment. With this growing perception, their emotions and satisfaction about the environment may also be greatly affected. This article explores the main factors that affect the public’s environmental satisfaction with two main novelties. The first is to develop a data acquisition platform. Based on socially aware computing and a participatory sensing method, the public’s subjective environmental satisfaction is measured by quantifying the collected sensing data. The second is to establish a multiple regression model and a correlation model to analyze the relationship between public’s environmental satisfaction and atmospheric environmental factors, and to compare the influence of each factor. Lanzhou City was taken as a case study, and a total of 33505 data points was acquired over a year of experiment. The experimental results show: (1) Air pollution in Lanzhou affects public environmental satisfaction more strangely than meteorological factors. (2) There is a negative correlation between public environmental satisfaction and different air pollutants in Lanzhou. (3) PM2.5, PM10 and NO2 are the main influencing factors of the public’s environmental satisfaction in Lanzhou. Overall, our study demonstrates a viable method for capturing public environmental satisfaction and identifying probable drivers of it, and our results provide important information for policy makers to prioritize areas that can more effectively improve public satisfaction.

Adaptive Group-based QoS Aware Link State Proactive Multipath Routing for Large-Scale MANETs (GMP-OLSR)

Objectives: To propose a group-based QoS aware proactive multipath routing based on OLSR protocol. Methodology: This work proposes a group-based proactive multipath routing based on the OLSR protocol, which is designed to be adaptive for a big crowded Mobile Ad-hoc Network to satisfy the desire for real-time multimedia applications. This approach will restrict mobile nodes to be participated in the cluster head selection process based on the mobility and energy of a node. It uses a grouping strategy to manage the network topology and performing route maintenance, and preserve multiple prioritized routes with the support of Quality of Service (QoS) aware computation. Findings: The study evaluated the performance of the proposed group-based QoS aware multipath Optimal Link State Routing (GMP-OLSR) routing through the OMNET++ network simulation environment. Within different network density, the proposed approach offered a significant reduction in end to end delay, overhead and an increment in data packet delivery ratio Applications: Using an appropriate groupbased QoS aware multipath routing strategy for mobile ad hoc networks provides better performance and improves the services of mobile applications. Keywords: MANET, GMP-OLSR, Clustering, Multipath Routing, QoS Aware Routing.

A Mechanism for Air Health Monitoring in Smart City using Context Aware Computing

Abstract In recent days, the concept of smart sustainable cities has come up, which guarantee the residents with quality lifestyle. This cities ensure the overall socio economic development by providing smart services contributed by smart technologies. One of the most significant pillars of the Smart City is to ensure healthy and sustainable environment by making the city pollution free as far as possible, to ensure the smart health of the citizens. In this work, a frame work is proposed using Context Aware Computing approach to monitor the air health of urban region. The proposed frame work is designed to help in sensing air pollutant parameters using smart sensors and generate alert using context if there is a threat of air pollution. Here, the Input layer optimization technique is also proposed. It has been shown that, the Input layer optimization technique has discarded the unwanted overheads of the proposed frame work without degrading the efficiency of the system.

Soft Computing for Scalability in Context Aware Location based Services

Ubiquitous computing blended with context awareness gives user the facility of “anywhere anytime” computing. Location based services represents a class of context aware computing. Involvement of location as the primary input in location based services triggered concerns for user’s privacy. Most of the privacy work in domain of location based services relies on obfuscation strategy along with K anonymity. The proposed work acknowledges the idea of calculating value of K for K anonymity using context factors in fuzzy format. However, with increasing number of these fuzzy context factors resulting in more fuzzy rules, the system will tend to get slower. In order to address this issue, requirement is to reduce the size of rule base without hampering the performance much. Goal of the proposed work is to attain scalability and high performance for the above said system. Towards this, reduction of number of rules in the rule base, of fuzzy inference system has been done using Fuzzy C Means and Genetic Algorithm. Results of reduced rule base have been compared with the results of exhaustive rule base. It has been identified that number of rules can be reduced up to considerable extent with comparable performances and acceptable level of error.

Multi-Hop Cooperative Computation Offloading for Industrial IoT–Edge–Cloud Computing Environments

The concept of the industrial Internet of things (IIoT) is being widely applied to service provisioning in many domains, including smart healthcare, intelligent transportation, autopilot, and the smart grid. However, because of the IIoT devices’ limited onboard resources, supporting resource-intensive applications, such as 3D sensing, navigation, AI processing, and big-data analytics, remains a challenging task. In this paper, we study the multi-hop computation-offloading problem for the IIoT–edge–cloud computing model and adopt a game-theoretic approach to achieving Quality of service (QoS)-aware computation offloading in a distributed manner. First, we study the computation-offloading and communication-routing problems with the goal of minimizing each task's computation time and energy consumption, formulating the joint problem as a potential game in which the IIoT devices determine their computation-offloading strategies. Second, we apply a free–bound mechanism that can ensure a finite improvement path to a Nash equilibrium. Third, we propose a multi-hop cooperative-messaging mechanism and develop two QoS-aware distributed algorithms that can achieve the Nash equilibrium. Our simulation results show that our algorithms offer a stable performance gain for IIoT in various scenarios and scale well as the device size increases.

Assessment Framework Modeling using Location Aware Computing for Fertilizer Management and Crop Recommendation

Indian economy mainly depends on the agriculture, which contributes a major part in the growth and development of the nation. This is directly connected to the standard of life of farmers which covers more than 40 percent of the country population. Looking at the current situation of agriculture in India, agricultural productivity in India is not competitive to the world standards. For increasing the crop productivity farmers are forced to use more fertilizers which will end in health risk for the consumers. Lack of a proper knowledge of the effective usage of fertilizers and the changing soil nutrient values are the major problems for a farmer apart from the loss due to climate change and so many other factors. To address the problem , we propose to build an application model 'Prediction On Fertilizer Management for Crop Productivity' which enables the farmers to understand and effectively utilize their money with effective methods of seasonal crops production by providing recent and intimate information about which has to be produced, soil suitability, its nutrient values and soil moisture level which pasteurization management methods, how to apply the fertilizers in larger areas, what amount of water must be given, how to develop a model maps to the current scenario of farm areas. Crop Yield Production is basically an aggregation of crop and fertilizer recommendation, soil analysis, and crop yield calculations based on recent market scenario. Through our model we are revising and correcting the existing system with predictive analytics of the usage of effective fertilizers with less health risk.

Adoption of Ubiquitous Customer Relationship Management (uCRM) in Enterprise: Leadership Support and Technological Competence as Moderators

Ubiquitous Customer Relationship Management (uCRM) signifies the realization of special characteristics, including proactiveness, context awareness, and mobility. This uCRM helps to enhance promptness and timeliness of the traditional system, thereby creating an environment associated with a state of being everywhere in no time and covering any device and any network. uCRM provides high personalization of service. The uCRM system has opened a new area of satisfaction for customers. Context awareness computing, a uCRM characteristic, is presumed to have enhanced benefits for a firm adopting uCRM in India. In this context, this study has taken a holistic attempt to analyze how technological competence and leadership support can enhance the business performance of a firm adopting uCRM. With input from literature studies, a hypothesis and a conceptual model have been formulated. Statistically, those have been validated with survey work considering 165 usable respondents from 22 different firms situated in three metropolitan cities in India. Results show that the effect of technological competence and leadership support positively help a uCRM-dependent firm towards achieving better business benefits. The study ends with a comprehensive conclusion.

A multivariant secure framework for smart mobile health application

AbstractWireless sensor network enables remote connectivity of technological devices such as smart mobile with the internet. Due to its low cost as well as easy availability of data sharing and accessing devices, the Internet of Things (IoT) has grown exponentially during the past few years. The availability of these devices plays a remarkable role in the new era of mHealth. In mHealth, the sensors generate enormous amounts of data and the context‐aware computing has proven to collect and manage the data. The context aware computing is a new domain to be aware of context of involved devices. The context‐aware computing is playing a very significant part in the development of smart mobile health applications to monitor the health of patients more efficiently. Security is one of the key challenges in IoT‐based mHealth application development. The wireless nature of IoT devices motivates attackers to attack on application; these vulnerable attacks can be denial of service attack, sinkhole attack, and select forwarding attack. These attacks lead intruders to disrupt the application's functionality, data packet drops to malicious end and changes the route of data and forwards the data packet to other location. There is a need to timely detect and prevent these threats in mobile health applications. Existing work includes many security frameworks to secure the mobile health applications but all have some drawbacks. This paper presents existing frameworks, the impact of threats on applications, on information, and different security levels. From this line of research, we propose a security framework with two algorithms, ie, (i) patient priority autonomous call and (ii) location distance based switch, for mobile health applications and make a comparative analysis of the proposed framework with the existing ones.