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HSSIW: Hybrid Squirrel Search and Invasive Weed Based Cost-Makespan Task Scheduling for Fog-Cloud Environment 3

HSSIW: Hybrid Squirrel Search and Invasive Weed Based Cost-M...

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3rd international conference on Information and Communication technology for Development for Africa (ICT4DA)

作者： Tsegaye, Abate Assefa, Beakal Gizachew AASTU Dept Software Engn CoE HPC & BDA Addis Ababa Ethiopia Addis Ababa Univ Sch Informat Technol & Engn AAiT Addis Ababa Ethiopia

ISBN: (纸本)9781665436663

The large-scale development of internet of Things devices emerged a new computing environment called fog computing to reduce the makespan and cost spent on the cloud devices as a result of distant communication. However, unless the appropriate assignment of tasks is strictly allocated on an available resource of fog nodes, it results in wastage of resources and unachievable quality of service. In this paper, the balance of the most common conflicting objectives in task scheduling that is makespan and cost for the distributed fog-cloud environment is investigated. A novel hybrid squirrel search and invasive weed (HSSIW) algorithm is adapted to assign generated tasks from the internet of Things(IoT) devices at appropriate fog and cloud nodes so that reduction in cost and makespan is assured. The proposed algorithm has been compared with three related state-of-the algorithms such as genetic algorithm (GA), particle swarm optimization algorithm (PSO), and squirrel search algorithm(SS). The experiment conducted on Cloudsim shows that the proposed algorithm reduces makespan 18% better than classic algorithms such as First Come First Serve(FCFS) and Short Job First(SJF) algorithms. Similarly, it has made a reduction in latency 4% better than GA and PSO with optimal cost.

关键词： Fog computing Fog-Cloud computing Cost-Makespan internet of Things Task Scheduling squirrel search invasive weed algorithm

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Performance Issues and Research Challenges in Designing a Secure IoT with Blockchain

Performance Issues and Research Challenges in Designing a Se...

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international conference on Issues and Challenges in Intelligent computing Techniques (ICICT)

作者： Hemani Dayashankar Singh Rajendra Kumar Dwivedi Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Gorakhpur India

Now a day, internet of Things (IoT) are being used in several areas such as vehicular systems, smart city, healthcare, and supply chain management. Sensor data is stored in the cloud in an IoT system. Therefore, there is always a security concern with this data. There are several techniques for maintaining security, but most of them work in a centralized manner. However, blockchain is an emerging technology that works in a distributed manner and is based on peer-to-peer computing. Security can be enhanced using blockchain in any system. Blockchain technology offers decentralised security and privacy. Sensors used in loT systems are resource constrained. Therefore, implementing blockchain in any loT system creates several research challenges. This paper provides a discussion on such performance issues and research challenges while integrating blockchain with loT. The paper also gives some insights to overcome such issues.

关键词： Privacy Cloud computing Supply chain management Smart cities Medical services Blockchains Peer-to-peer computing

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Deep Forest-Based Intrusion Detection System for Edge Intelligence Assisted Smart Homes 3rd

Deep Forest-Based Intrusion Detection System for Edge Intell...

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3rd international conference on computing, Communication and Learning, CoCoLe 2024

作者： Jakka, Aishwarya Rani, J. Vakala Budhathoki, Binay Swathi, Y. Jayanthi, M. University of Pittsburgh Pittsburgh United States CMR Institute of Technology Bengaluru India Lead Engineer Memoria Inc. Nepal Kathmandu India Gopalan College of Engineering Bengaluru India CMR University Bengaluru India

ISBN: (纸本)9783031790409

Edge computing has revolutionized distributed architectures, enabling workloads to be strategically positioned at the network’s edge, where data is generated and actions are initiated. This paradigm facilitates various applications, including the internet of Things (IoT), smart grids, and smart homes, by providing cloud services directly to end-users. In particular, delay-sensitive applications benefit from the mobility, low latency, bandwidth, and location awareness offered by edge computing. Edge computing is an ideal platform for efficient data processing in smart homes. However, the security of edge gateways and nodes within smart home environments remains susceptible to security threats. This paper presents a novel approach employing a Deep Forest (DF)-based Intrusion Detection System (IDS) for Edge Intelligence. By embedding an IDS in edge nodes, this approach leverages edge computing to enhance security in smart home environments. The Deep Forest Classifier (DF) is innovative because it effectively addresses data imbalance in training datasets, a crucial factor in anomaly detection for binary-class classification. This integration enables real-time, low-latency threat detection and significantly improves security metrics, demonstrating substantial performance enhancements over traditional machine learning models. Experiments conducted on the UNSW-NB15 dataset demonstrate that the proposed binary classifier improves Accuracy by 26.24% over Naïve Bayes, 11.68% over Logistic Regression and 6.20% over KNN models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep Forest Classifier Edge computing internet of things Machine Learning Smart Home

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An Integrated Approach of 4G LTE and DSRC (IEEE 802.11p) for internet of Vehicles (IoV) by Using a Novel Cluster-Based Efficient Radio Interface Selection Algorithm to Improve Vehicular Network (VN) Performance 1

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3rd international conference on Sustainable Advanced computing, ICSAC 2021

作者： Hussain, Shaik Mazhar Yusof, Kamaludin Mohamad Asuncion, Rolito Hussain, Shaik Ashfaq Ahmad, Afaq School of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia JohorBahru Malaysia Sultan Qaboos University Muscat Oman Middle East College Muscat Oman

ISBN: (数字)9789811690129

ISBN: (纸本)9789811690112

internet of Vehicles (IoV) is a distributed network supporting V2X communications. Two potential technologies of V2X communications are dedicated short-range communication (DSRC) and cellular network technologies. DSRC provides low latency with direct connectivity to connect vehicles in a distributed manner. However, it suffers from reliability and serious link degradation problems. In contrast, cellular network offers high capacity, wider cell coverage range but suffers from high latencies due to long transmission time intervals. Hence, cellular networks limit their ability to support low-latency V2V communications, jeopardizing the effectiveness of safety applications. Hence, considering the above issues, single technology might not suffice the requirements of V2X applications under varying vehicle density scenarios. Therefore, for efficient V2X communications, it is required to interwork with DSRC and cellular technologies. Most of the existing solutions were based on either DSRC alone or cellular networks alone. Very few research works evaluated the performance of vehicular networks using hybrid approach. This paper reviews potential DSRC and wireless integrated solutions for efficient vehicular communications. Secondly, we highlight the limitations of each supporting vehicular communications. Then, we provide brief comparative analysis of each paper. Finally, we propose our algorithm to integrate DSRC and 4G LTE with novel optimal clustering-based data forwarding protocol for efficient data transmission in IoV. The performance of the proposed algorithm is evaluated in terms of end-to-end delay and throughput and compared with the existing approaches using NS-3 simulation tool. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： internet of things

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Multi-agent Reinforcement Learning for Live Video Streaming in Vehicular Networks

Multi-agent Reinforcement Learning for Live Video Streaming ...

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Digital Society and Intelligent Systems (DSInS), international conference on

作者： Meiting Wu Kangli Zhao Xiaowei Lei Penglin Dai School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China College of Computer Science and Technology Aba Teachers University Sichuan China Chongqing Prescholl Education College Chongqing China

Live Video Streaming (LVS) services are critical in supporting real-time applications in internet of Vehicles (IoV) by transmitting real-time generated video content from streaming server to mobile vehicles. However, due to restricted spectrum resources and high mobility of vehicles, LVS suffers from notable performance degradation. Accordingly, our paper investigates the problem of LVS-IoV by synthesizing multicasting and Scalable Video Coding (SVC)-based encoding techniques with the goal of maximizing Quality of Experience (QoE). We resolve the LVS-IoV by decoupling it into three sub-problems: vehicle grouping, quality selection, and resource allocation. Firstly, we propose a K-means-based mechanism for vehicle grouping that considers the geographical distribution and dynamic channels of vehicles. Secondly, we develop the Value Decomposition Network for quality selection which obtains the optimal reward with fast convergence via global value function decomposition and centralized training followed by distributed execution. Lastly, we propose a sub-gradient algorithm to achieve optimal resource allocation within a few iteration steps. We build simulation model and perform extensive evaluation, which demonstrates its superiority compared to other competitive methods.

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SIEM architecture for the internet of things and smart city 3

SIEM architecture for the internet of things and smart city

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3rd international conference on Data Science, E-Learning and Information Systems, DATA 2021

作者： Abdalrahman, Hwoij Ashhar, Khamaiseh Mohammed, Ababneh Princess Sumaya University of Technology Jordan

ISBN: (纸本)9781450388382

The internet of things (IoT) is a new technology that shapes the future of a world that is rapidly being invaded by smart devices connected to the internet. Such technology has a great role in developing the idea of a smart city. A smart city is a city that takes advantage of existing infrastructure and integrates it with the internet of things technology to improve the quality of life. internet of Things (IoT) sensors are distributed geographically around the city to collect data from the environment (i.e.: streets, cars, traffic lights...etc.), process, and manage it to provide intelligent actionable information to citizens. All data transferred through networks of a smart city may be threatened and susceptible to illegal actions such as violation, stealing, and inappropriate use. These security threats affect the privacy and security of users;where hackers can get access to user's data and gain control of their smart homes, cars, medical devices and might even gain control over city traffic lights. All the above enforce the need to have a security system that continuously monitors and tracks all data logs to detect any suspicious activity. In this paper, we propose a Security Information and Event Management (SIEM) approach for smart cities by forwarding event logs generated by smart devices to a security operation center that works around the clock to detect security incidents and handle them. Such an approach aims to create a safe smart living environment. © 2021 Association for computing Machinery.

关键词： internet of things

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Research on the Security of Beyond-the-Sight Services Based on Blockchain 3rd

Research on the Security of Beyond-the-Sight Services Based ...

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3rd international conference on Blockchain and Trustworthy Systems, Blocksys 2021

作者： Duan, Xuefei Lu, Hua Bai, Lejin Guo, Zixuan GuangDong Communications and Networks Institute Huangpu District Guangdong Province Guangzhou City China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789811679926

In 2008, blockchain was proposed as the basic technology of Bitcoin, and it is essentially a decentralized database. This technology is widely used in financial industry, data storage, edge computing and other scenarios. With the rapid increase of the number of devices connected to the internet of Things (IoT), the traditional centralized cloud computing system is unable to satisfy the Quality of Service (QoS) of many IoT applications, especially for areas with real-time. IoT services based on Mobile Edge computing (MEC) are gradually becoming popular, and the characteristics of the distributed accounting and consensus mechanism of the blockchain can ensure the security of MEC-based IoT services. In this paper, we first summarize the existing blockchain and MEC related technologies, and then propose a future-oriented internet of Vehicles (IoV) service: beyond-the-sight service. At last, we specifically discuss how the blockchain can ensure the security of beyond-the-sight service in terms of terminal equipment identification and data transmission. © 2021, Springer Nature Singapore Pte Ltd.

关键词： internet of things

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Denial of Service Attack Detection and Mitigation using Ensemble based ML in Software Defined Network

Denial of Service Attack Detection and Mitigation using Ense...

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Applied Artificial Intelligence and computing (ICAAIC), international conference on

作者： N. Sathish K. Valarmathi R. Nagalakshmi K Hema Priya School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu Panimalar Engineering College Computer Science and Engineering. SRM institute of Science and Technology Ramapuram Department of CSE Easwari Engineering College

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Software-defined networking (SDN) introduces a novel networking paradigm by decoupling control logic from forwarding operations, resulting in improved network resource management and administration efficiency. However, centralized network design in SDN gives rise to significant security challenges, notably denial-of-service (DoS) attacks. Attackers exploit SDN’s lack of effective message-verification mechanisms to launch DoS attacks by falsifying source address information. In response, this study proposes an innovative approach to internet of Things (IoT) security leveraging software-based networks and collaborative learning. One key strategy involves partitioning the network domain into smaller subdomains, each with its own controller, facilitating the exchange of security rules among subdomains. However, this topology necessitates routing every packet through the subnet’s control node. To continually monitor network traffic data and detect attacks, each controller node employs an integrated learning model. This model combines statistical insights from data streams with artificial neural networks and the Extreme Gradient Boosting Algorithm to anticipate potential attacks. Additionally, the ICOA model selects relevant characteristics for analysis. Through comprehensive performance evaluation, including empirical values, the ensemble model demonstrates superior performance compared to existing approaches. Furthermore, the study compares various models and methodologies, affirming the efficacy of the proposed strategy in accurately identifying distributed Denial-of-Service (DDoS) attacks in SDNs, thereby presenting a groundbreaking approach to SDN security.

关键词： Telecommunication traffic Computer architecture Denial-of-service attack Boosting Data models Topology Security

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Utilizing the Gaode Open API for Evaluating Fire- Fighting Facility Planning

Utilizing the Gaode Open API for Evaluating Fire- Fighting F...

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Cloud computing, Big Data Applications and Software Engineering (CBASE), international conference on

作者： Bing Han Junjun Fan Jing Wang Yunfei Ding College of Horticulture Jinling Institute of Technology Nanjing China School of Government Nanjing University Nanjing China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Big data has been widely used in urban planning and architecture in recent years. However, data collecting techniques limit its broader use. As a result, a lot of work has been done to precisely identify and address urban problems through the mining of legally accessible open-source internet data, which has progressively gained popularity among urban scholars. In light of this, using Nanjing as an example, this study uses the Gaode OPEN API to build urban real-time traffic circles and assess the layout of firefighting facilities. In order to extract the average vehicle speed of all roads within a certain time frame and to automatically collect real-time traffic data in the study region, a batch processing model is first developed based on the Gaode OPEN API. It was discovered that the speed causes Nanjing City to be divided into multiple different zones. After that, a case study is carried out to build an urban real-time traffic circle based on the current firefighting facilities using the Python programming language and Arcgis software. After further analysis, it is found that the current planning for firefighting facilities exhibits a notable regional difference between the urban centre and the suburbs. Traffic speed, station density, and road network density all influence the service coverage area of fire stations. This study offers a comprehensive approach for gathering and analysing Gaode data, mining its spatiotemporal properties, and using it in planning evaluation procedures. This strategy highlights the benefits of streaming data features and distributed big data processing, efficiently uses the real-time nature of big data in emergency facility planning, and is expected to expand ideas for data collection.

关键词： Roads Urban planning distributed databases Real-time systems Software Planning Spatiotemporal phenomena Data mining Software engineering Python

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Advancing IoT Security through Federated Learning: A Comprehensive Approach

Advancing IoT Security through Federated Learning: A Compreh...

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Applied Artificial Intelligence and computing (ICAAIC), international conference on

作者： Shiva Mehta Meenu Khurana Ayush Dogra Shanmugasundaram Hariharan Centre for Research Impact and Outcome Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

IoT devices have changed into one of the mainstays in areas like houses, industry and education, resulting in a level of convenience and efficiency that has never been achieved before. Even though the most important advantage of the distributed network is raising the level of security, the problem of security vulnerabilities with the extended networks remains. This paper is devoted to a novel strategy of Federated Learning (FL) that could become an applied solution for enhancing the safety of the IoT. Therefore, based on its decentralized knowledge process, FL allows IoT devices to cooperate in achieving common purposes without endangering data pri vacy. The paper uses this thinking method to deal with scalability, model personalization, and non-IID data that aren’t IID distributing, suggesting solutions through model aggregation techniques or Artificial Intelligence (AI). Experimental evidence highlights that FL is very useful in enhancing IoT security irrespective of the diversity of its applications. To this end, residents of smart homes experienced a 17% increase in detecting the anomalies through the setting up of FL. This situation was replicated in the industrial internet of Things, where the false positive rates were reduced from 20% to 5%, thus improving the system’s efficiency. Furthermore, in the FL context in healthcare IoT ecosystems, this blazes a trail for privacy-preserving data analytics, preserving patient confidentiality and systematic profiling of noteworthy health trends. These findings thus demonstrate that FL is a feasible way of dealing with the security challenges of IoT scalable, promptly and in a manner that maintains user privacy. The article takes part in a conversation about ways to strengthen the security of IoT networks. Such a framework is based on the computational power of IoT devices. It allows us to detect and prevent threats in time. Nonetheless, it urges extensive R&D attempts to be introduced to develop better security solutio

关键词： Privacy Federated learning Scalability Ecosystems Safety internet of Things Security

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