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ML for IEEE 802.15. 4e/TSCH: Energy Efficient Approach to Detect DDoS Attack Using Machine Learning 17

ML for IEEE 802.15. 4e/TSCH: Energy Efficient Approach to De...

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17th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Bhale, Pradeepkumar Biswas, Santosh Nandi, Sukumar IIT Guwahati Dept CSE Gauhati India IIT Bhilai Dept EECS Bhilai India

ISBN: (纸本)9781728186160

internet of things (IoT) is a way to communicate with the real world without much human involvement. It is booming in today's computing world, with billions of devices having sensors and actuators connected to the internet using various low power technologies. Despite several profits, it experiences multiple security threats that impel catastrophic crashes in the IEEE 802.15.4e (6TiSCH) network. Various threats like jamming attacks, DDoS, abnormal behavior, etc., are detected using multiple Machine Learning (ML) and Deep Learning (DL) approaches. In this paper, an edge -based ML enables Intrusion Detection Systems (IDS) is proposed to detect distributed denial of-service (DDoS) attack patterns from a particular source. Experimental outcomes confirm that the proposed approach is scalable and efficient in terms of computation and storage. Hence, the intended approach gives a faster response as (24.2-68.9)Sec. the average memory utilization (ROM/RAM), energy usage, and accuracy achieved by our intended solution are 35834B/5378B, 85916mJ, 98.7%, respectively, which outperform closely related work.

关键词： IEEE 802.15.4e (6TiSCH) network distributed Denial of Service (DDoS) attack Machine Learning Edge-Based Intrusion Detection System (IDS) Naive Bayes Classifier

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A Game theoretical-based Competitive Incentive Mobile Caching in internet of Vehicles 17

A Game Theoretical-based Competitive Incentive Mobile Cachin...

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17th Annual IEEE international conference on distributed computing in Sensor Systems (IEEE DCOSS)

作者： Alioua, Ahmed Bounib, Sara Bounaira, Soumia Khelifi, Manel Jijel Univ Mohamed Seddik Benyahia LaRIA Lab Jijel 18000 Algeria Jijel Univ Mohamed Seddik Benyahia Dept Comp Sci Jijel 18000 Algeria

ISBN: (纸本)9781665439299

Nowadays, there is significant growth in mobile data traffic, mostly due to the explosion in demand for high-bandwidth applications. this increased traffic volume threatens to saturate the backhaul links between the access network and the core network. Edge caching has been proposed as an innovative solution to this problem, by moving popular content near the end-users to reduce access/ downloading delays and relieve backhaul pressure. Most existing caching solutions propose to store the popular contents in edge caches deployed on cellular base stations. In this paper, we study an incentive caching policy in the context of the internet of vehicles. To this end, we consider a market consisting of several content providers (CPs) that own a set of popular files and wish to bring them closer to their end-users. Besides, at several Public Transportation Companies (PTCs) that own embedded caches on their mobile vehicles. the PTCs offer to lease their on-board cache storage space to the CPs to monetize the caches and earn monetary profit. In our caching policy, CPs and PTCs interact and cooperate to improve the performance of the caching process. We formulated this strategic interaction using a Stackelberg game with multiple leaders (PTCs) and multiple followers (CPs) and derive optimal strategies. To validate and prove the effectiveness of the proposed model, several simulations have been performed. the obtained results showed that our Stackelberg game-based model allows CPs and PTCs to reach optimal utilities in a more efficient caching process.

关键词： internet of Vehicles Incentive Policy Edge Caching Game theory Stackelberg Game

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Smart contracts for decentralized vehicle services 17

Smart contracts for decentralized vehicle services

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17th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Mhamdi, Halima Zouinkhi, Ahmed Sakli, Hedi Univ Gabes ENIG MACS Lab Modeling Anal & Control Syst LR16ES22 Gabes Tunisia EITA Consulting 5 Rue Chant Oiseaux F-78360 Montesson France

ISBN: (纸本)9781728186160

internet of Vehicles (IoV), whose basic concepts are like those of the internet of things (IoT), refers to the ability of vehicles to communicate intelligentiy with each other through the exchange of information. To guarantee the services offered by IoV, it is necessary to consider the exchange of a large amount of information without human intervention and in a decentralized manner. To overcome these challenges, the use of blockchain technology is required. this technology is supported by the concept of smart contracts and implements the nodal of decentralized applications (DApp). Our objective is, then, to design a decentralized application that brings together all the services accessible by vehicles. the user, depending on the type of service, can filter the search result and benefit from the appropriate service. Via this paper, we present the basic concepts to be used in the development of our platform such as blockchain, smart contracts, DApp and their relationship with IoV. We model the communication between the different actors of the system as well as the design of the proposed framework.

关键词： internet of vehicles Decentralized application Smart contracts Blockchain Vehicle to object communication

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Secure and Privacy-aware Blockchain-based Remote Patient Monitoring System for internet of Healthcare things 17

Secure and Privacy-aware Blockchain-based Remote Patient Mon...

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17th international conference on Wireless and Mobile computing, Networking and Communications (WiMob)

作者： Zaabar, Bessem Cheikhrouhou, Omar Ammi, Meryem Awad, Ali Ismail Abid, Mohamed Univ Sfax Comp Embedded Syst Lab CES ENIS Sfax Tunisia Univ Monastir Higher Inst Comp Sci Mandia Monastir Tunisia Natl Ctr Nucl Sci & Technol Sidi Thabet Tunisia Naif Arab Univ Secur Sci Forens Sci Dept Criminal Justice Coll Riyadh Saudi Arabia Lulea Univ Technol Dept Comp Sci Elect & Space Engn Lulea Sweden United Arab Emirates Univ Coll Informat Technol POB 17551 Al Ain U Arab Emirates Al Alzhar Univ Fac Engn POB 83513 Qena Egypt

ISBN: (纸本)9781665428545

Remote Patient Monitoring (RPM) is a form of telehealth or virtual health that strengthens online medical services and allows delivering healthcare remotely. Nowadays, remote patient monitoring systems (RPMS) are widely used by healthcare providers to remotely monitor the vital signs of patients. As the RPM field expands, concerns about efficient and secure medical data transmission are raised. this kind of patient medical data is collected by the mean of internet of Healthcare things (IoHT) or sometimes denoted as internet of Medical things (IoMT) devices. the collected data needs to be stored and retrieved with highly assured levels of security and privacy as the data comprises private and critical patients' information. To secure medical data, this paper proposes a blockchain-based architecture to manage access control to medical data and to preserve patient's data privacy. the blockchain-based system is built on Hyperledger Fabric, a permissioned distributed ledger solution, and the ledgers and transactions are stored in the cloud. the proposed architecture is designed to contribute to the robustness of the RPM systems and to avoid recorded security limitations in commonly used permissioned blockchains methods. Performance evaluation has proved the robustness and superiority of the proposed system in terms of data confidentiality, integrity, availability, traceability, scalability, and data privacy while integrated with the RPM services.

关键词： internet of things (IoT) internet of Healthcare things (IoHT) Security and Privacy Blockchain Electronic Medical Record (EMR) Electronic Healthcare Record (EHR)

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Network-Aware AutoML Framework for Software-Defined Sensor Networks 17

Network-Aware AutoML Framework for Software-Defined Sensor N...

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17th Annual IEEE international conference on distributed computing in Sensor Systems (IEEE DCOSS)

作者： Horsanali, Emre Yigit, Yagmur Secinti, Gokhan Karameseoglu, Aytac Canberk, Berk Istanbul Tech Univ Dept Comp Engn Istanbul Turkey BTS Grp Istanbul Turkey Istanbul Tech Univ Dept AI & Data Engn Istanbul Turkey

ISBN: (纸本)9781665439299

As the current detection solutions of distributed denial of service attacks (DDoS) need additional infrastructures to handle high aggregate data rates, they are not suitable for sensor networks or the internet of things. Besides, the security architecture of software-defined sensor networks needs to pay attention to the vulnerabilities of both software-defined networks and sensor networks. In this paper, we propose a networka-ware automated machine learning (AutoML) framework, which detects DDoS attacks in software-defined sensor networks. Our framework selects an ideal machine learning algorithm to detect DDoS attacks in network-constrained environments, using the metrics such as variable traffic load, heterogeneous traffic rate, and detection time while preventing over-fitting. Our contributions are two-fold: (i) we first investigate the trade-off between the efficiency of ML algorithms and network/traffic state in the scope of DDoS detection. (ii) we design and implement a software architecture containing open-source network tools, with the deployment of multiple ML algorithms. Lastly, we show that under the denial of service attacks, our framework ensures the traffic packets are still delivered within the network with additional delays.

关键词： Automated Machine Learning (AutoML) Sofware-defined Sensor Networks (SDSN) distributed Denial-of-Service (DDoS)

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Large Scale Global Optimization Algorithms for IoT Networks: A Comparative Study 17

Large Scale Global Optimization Algorithms for IoT Networks:...

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17th Annual IEEE international conference on distributed computing in Sensor Systems (IEEE DCOSS)

作者： Goudos, Sotirios K. Boursianis, Achilles D. Mohamed, Ali Wagdy Wan, Shaohua Sarigiannidis, Panagiotis Karagiannidis, George K. Suganthan, Ponnuthurai N. Aristotle Univ Thessaloniki Sch Phys ELEDIA AUTH Thessaloniki Greece Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza Egypt Nile Univ Wireless Intelligent Networks Ctr WINC Sch Engn & Appl Sci Giza Egypt Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan Peoples R China Univ Western Macedonia Dept Informat & Telecommun Engn Kozani Greece Aristotle Univ Thessaloniki Sch Elect & Comp Engn Thessaloniki Greece Nanyang Technol Univ Sch Elect Elect Engn Singapore Singapore

ISBN: (纸本)9781665439299

the advent of internet of things (IoT) has bring a new era in communication technology by expanding the current inter-networking services and enabling the machine-to-machine communication. IoT massive deployments will create the problem of optimal power allocation. the objective of the optimization problem is to obtain a feasible solution that minimizes the total power consumption of the WSN, when the error probability at the fusion center meets certain criteria. this work studies the optimization of a wireless sensor network (WNS) at higher dimensions by focusing to the power allocation of decentralized detection. More specifically, we apply and compare four algorithms designed to tackle Large scale global optimization (LSGO) problems. these are the memetic linear population size reduction and semi-parameter adaptation (MLSHADE-SPA), the contribution-based cooperative coevolution recursive differential grouping (CBCC-RDG3), the differential grouping with spectral clustering-differential evolution cooperative coevolution (DGSC-DECC), and the enhanced adaptive differential evolution (EADE). To the best of the authors knowledge, this is the first time that LSGO algorithms are applied to the optimal power allocation problem in IoT networks. We evaluate the algorithms performance in several different cases by applying them in cases with 300, 600 and 800 dimensions.

关键词： internet of things optimal power allocation wireless sensor network large scale global optimization

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Multi-source Data Analysis Method of Exhibition Site Based on Mobile internet 17

Multi-source Data Analysis Method of Exhibition Site Based o...

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17th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Yin, Xiaoyin He, Jiangnan Gao, Ying Li, Jingxian Yunnan Minzu Univ Lanmei Sch Kunming Yunnan Peoples R China Yunnan Univ Finance & Econ Business Sch Kunming Yunnan Peoples R China Yunnan Minzu Univ Peoples Armed Forces Coll Kunming Yunnan Peoples R China

ISBN: (纸本)9781728186160

With the continuous improvement of information and communication technology, the popularity of mobile terminals and endless new technologies, the application of mobile internet is increasingly extensive. this paper mainly studies the multi-source data analysis method based on mobile internet. through the connection of multi-source database, the data is associated and the foundation of data fusion is constructed. It is stored in the specified storage database or data medium according to certain rules. On this basis, first of all, preprocess the data, sort out and correct those disordered storage formats, disordered data records and redundant fields to form standard data. Secondly, these data are cleaned to remove data redundancy and noise. through the combination of multi-source data, new data records are obtained, and these data are sorted and improved. According to the actual situation and problems of the project, the multi-source data is fused to build an effective data set. Data show that the accuracy of the model can reach more than 85%. the results show that mobile internet technology can provide security and risk information for the participants of the exhibition.

关键词： Mobile internet Exhibition Site Multi-Source Data Analysis Passenger Flow Evacuation

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Research on the Difference Between the internet of things and the Traditional internet Under Artificial Intelligence 17

Research on the Difference Between the Internet of Things an...

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17th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Qi, Yongjun Wu, Haiyan Guangdong Baiyun Univ Fac Megadate & Comp Guangzhou Peoples R China Guangdong Univ Foreign Studies Management Sch South China Business Coll Guangzhou Peoples R China

ISBN: (纸本)9781728186160

the internet of things is the most heated topic in the current electronic information technology industry. It is generally believed that it will become a new driving force for the development of human industry, and its comprehensive application will promote the human society to step forward to the "smart earth". In this paper, based on artificial intelligence and internet of things technology, experimental design of fuzzy control and PID control algorithm anti-interference performance test simulation box. Experimental data show that due to the internet of things control system in the process of application, the controlled object in different degrees of non-linear, large lag, parameter time-varying and model uncertainty characteristics. It can be seen from the principle and simulation results that fuzzy control does not require the precise model of the controlled object and has strong adaptability. the experimental results show that if the pulses with a height of 0.1, a period of 10s, a width of 2S and a delay of 4s are set, interference is added to the output of a sampling time controller. Among them, T1=2, T2=1, =0.5, then the initial PID Kp0=7.6, Ki0=1.12, Kd0=4.88 in the fuzzy PID control system. the internet of things control system server has the advantages of comprehensive functions, good expansibility, easy operation and maintenance, etc., and the adoption of automated testing technology improves the efficiency and standardization of the server design process.

关键词： internet of things Artificial Intelligence Fuzzy Control Algorithm

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An Intelligent Mechanism to Detect Cyberattacks of Mirai Botnet in IoT Networks 17

An Intelligent Mechanism to Detect Cyberattacks of Mirai Bot...

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17th Annual IEEE international conference on distributed computing in Sensor Systems (IEEE DCOSS)

作者： Araujo Cruz, Antonia Raiane S. Gomes, Rafael L. Fernandez, Marcial P. Fed Inst Ceara IFCE Boa Viagem Brazil State Univ Ceara UECE Fortaleza Ceara Brazil

ISBN: (纸本)9781665439299

the evolution of computational resources allowed the innovation and development of new technologies to improve the accessibility and agility of daily tasks. From these new technologies arose the deployment of IoT Networks. However, most of the IoT devices still don't implement security countermeasures, turn them vulnerable to cyberattacks. One of the most dangerous cyberattacks for IoT networks is the Mirai Botnet, a malware that turns networked consumer devices into a botnet to perform distributed Denial of Service (DDoS) attacks. therefore, it is necessary to provide a security solution to detect cyberattacks on IoT networks. A promising approach to improve the IoT network's detection capacity is the use of Machine Learning (ML) based solutions. this paper presents an intelligent mechanism to detect the Scan, Ack Flooding, Syn Flooding, UDP Flooding, and UDPplain Mirai Botnet attacks on IoT networks using ML techniques, comparing distinct ML approaches (KNN, SVM, and LR). the proposed mechanism was evaluated using a real IoT devices traffic dataset, giving 99% of accuracy to detect Mirai Botnet.

关键词： internet of things Machine Learning Mirai Botnet

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Batterfly: Battery-Free Daily Living Activity Recognition System through distributed Execution over Energy Harvesting Analog PIR Sensors 17

Batterfly: Battery-Free Daily Living Activity Recognition Sy...

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17th Annual IEEE international conference on distributed computing in Sensor Systems (IEEE DCOSS)

作者： Stirapongsasuti, Sopicha Misaki, Shinya Matsui, Tomokazu Suwa, Hirohiko Yasumoto, Keiichi Nara Inst Sci & Technol Ikoma Nara 6300192 Japan

ISBN: (纸本)9781665439299

In recent years, the use of energy harvesting (EH) sensors has led to the proposal of activity sensing systems that are easy to install and do not require maintenance such as battery replacement. In this study, we aim to construct a system that can not only sense but also recognize activities of daily living (ADLs) using only power generated by EH sensors. To achieve this goal, in this paper, we propose a fully EH-based ADL recognition system called Batterfly, which consists of EH analog PIR sensor nodes that can operate with indoor light, continuously senses human movement, and recognizes daily activities through machine learning. We applied the distributed execution method of the activity recognition model with five sensor nodes to five types of activities by five participants, and found that the system could recognize them with an average accuracy of 63.59%, comparable to the performance of the centralized model running on a gateway.

关键词： wireless sensor network energy harvesting distributed computing

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