This paper discusses the importance of an emergency response system for the ocean disasters that affect the life of the fisher community. Ocean emergencies can vary from the extreme climatic conditions, collision between boats and ships as well as other unexpected health emergencies that may be faced by the older fishermen while at the ocean. We discuss the challenges in implementing disaster response activities in ocean scenarios and propose an IoT solution for the fisher community to help them during emergencies as well as to maintain frequent communication with the shore. This paper also presents how this IoT solution can bring a change in the fishermen’s life and how our solution can be used when they are in danger. We also proposed a partial-context aware algorithm that helps to monitor the fishing vessel movements and how this algorithm can help during an emergency.
{"title":"An IoT Based Disaster Response Solution for Ocean Environment","authors":"S. Anand, M. Ramesh","doi":"10.1145/3427477.3429273","DOIUrl":"https://doi.org/10.1145/3427477.3429273","url":null,"abstract":"This paper discusses the importance of an emergency response system for the ocean disasters that affect the life of the fisher community. Ocean emergencies can vary from the extreme climatic conditions, collision between boats and ships as well as other unexpected health emergencies that may be faced by the older fishermen while at the ocean. We discuss the challenges in implementing disaster response activities in ocean scenarios and propose an IoT solution for the fisher community to help them during emergencies as well as to maintain frequent communication with the shore. This paper also presents how this IoT solution can bring a change in the fishermen’s life and how our solution can be used when they are in danger. We also proposed a partial-context aware algorithm that helps to monitor the fishing vessel movements and how this algorithm can help during an emergency.","PeriodicalId":435827,"journal":{"name":"Adjunct Proceedings of the 2021 International Conference on Distributed Computing and Networking","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127793759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sharing the information on damaged roads is a critical matter in disaster evacuation. We aim to develop a heterogeneous DTN based-disaster information sharing system, which uses both short-range wideband media (e.g., Wi-Fi) and long-range narrowband media (e.g., LoRa) to share disaster information even if communication infrastructures are damaged. In the system, evacuees carry a Wi-Fi mobile device, and fixed relay nodes and devices at disaster headquarters are equipped with Wi-Fi and LoRa. We investigated the influence of the DTN on evacuees’ behavior by using the cellular automaton-based mobility and communication simulation model. The results show that “leaving behind phenomena”, in which evacuees who hold the damaged road information (early comers) leave the damaged road before evacuees who have not obtained the damaged road information (latecomers) come to the vicinity of the damaged road occur. We also derived a strategy for the effective placement of fixed relay nodes to avoid leaving behind phenomena based on the simulation results and confirmed the strategy’s effectiveness.
{"title":"An Effect of Sharing Damaged Road Information via Heterogeneous DTN on Evacuation","authors":"Yudai Yahara, Arata Kato, M. Takai, S. Ishihara","doi":"10.1145/3427477.3428192","DOIUrl":"https://doi.org/10.1145/3427477.3428192","url":null,"abstract":"Sharing the information on damaged roads is a critical matter in disaster evacuation. We aim to develop a heterogeneous DTN based-disaster information sharing system, which uses both short-range wideband media (e.g., Wi-Fi) and long-range narrowband media (e.g., LoRa) to share disaster information even if communication infrastructures are damaged. In the system, evacuees carry a Wi-Fi mobile device, and fixed relay nodes and devices at disaster headquarters are equipped with Wi-Fi and LoRa. We investigated the influence of the DTN on evacuees’ behavior by using the cellular automaton-based mobility and communication simulation model. The results show that “leaving behind phenomena”, in which evacuees who hold the damaged road information (early comers) leave the damaged road before evacuees who have not obtained the damaged road information (latecomers) come to the vicinity of the damaged road occur. We also derived a strategy for the effective placement of fixed relay nodes to avoid leaving behind phenomena based on the simulation results and confirmed the strategy’s effectiveness.","PeriodicalId":435827,"journal":{"name":"Adjunct Proceedings of the 2021 International Conference on Distributed Computing and Networking","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116318001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, various Internet of things (IoT) devices have become widely used in our daily lives and made houses and cities easier to live in. This paper proposes a machine learning scheme to take advantage of IoT devices. The proposed scheme realizes cooperation between devices to improve their performance, rather than learning independently. However, it is difficult to share local data directly because those data may contain private information, such as a picture with a user's face or lifelog data. Therefore, this paper provides a way of preserving privacy in interconnected IoT devices by sharing only learners from each device without sharing the original data directly. The proposed algorithm shares decision trees locally learned at each device and utilizes a random forest as a way of combining them together.
{"title":"Cooperative Random Forest for Privacy-Preserving IoT Devices","authors":"Yui Yamashita, Akihito Taya, Y. Tobe","doi":"10.1145/3427477.3428188","DOIUrl":"https://doi.org/10.1145/3427477.3428188","url":null,"abstract":"Recently, various Internet of things (IoT) devices have become widely used in our daily lives and made houses and cities easier to live in. This paper proposes a machine learning scheme to take advantage of IoT devices. The proposed scheme realizes cooperation between devices to improve their performance, rather than learning independently. However, it is difficult to share local data directly because those data may contain private information, such as a picture with a user's face or lifelog data. Therefore, this paper provides a way of preserving privacy in interconnected IoT devices by sharing only learners from each device without sharing the original data directly. The proposed algorithm shares decision trees locally learned at each device and utilizes a random forest as a way of combining them together.","PeriodicalId":435827,"journal":{"name":"Adjunct Proceedings of the 2021 International Conference on Distributed Computing and Networking","volume":"428 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115649098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, various approaches for smart home technology have been developed, such as home appliances control, services for energy saving and support of daily life. In order to realize such services, we need a system which is able to accurately recognize various human activities using low-cost devices. To realize such a system, we need to address several problems: the required sensors are too expensive (P1); it is difficult to precisely recognize place-independent activities like reading (P2), and putting on a device causes a burden to people (P3) the information such as images infringe on the privacy of the occupants (P4). In this paper, we propose a method for activity recognition by utilizing a doppler sensor as a motion detection sensor and a machine learning technique to solve the problems above (P1-P4). Specifically, frequency characteristic is obtained from the signals of the doppler sensor and we construct a machine learning model using effective features, which is presented by Anguita, and speed of target calculated from the doppler frequency. In order to examine the usefulness of the proposed method and find out critical issues of realizing activity recognition, we have collected sensor data of 6 kinds of activities(stationary, smartphone operation, PC operation, reading, writing, and eating) performed by 10 participants. For leave-one-session-out cross-validation, the maximum average recognition accuracy was 95.7%, and the average for 10 participants was 81.0%. For leave-one-person-out cross validation, the average recognition accuracy of logistic regression shows maximum accuracy of 42.1%.
{"title":"Non-Contact In-Home Activity Recognition System Utilizing Doppler Sensors","authors":"Shinya Misaki, Keisuke Umakoshi, Tomokazu Matsui, Hyuckjin Choi, Manato Fujimoto, K. Yasumoto","doi":"10.1145/3427477.3429463","DOIUrl":"https://doi.org/10.1145/3427477.3429463","url":null,"abstract":"In recent years, various approaches for smart home technology have been developed, such as home appliances control, services for energy saving and support of daily life. In order to realize such services, we need a system which is able to accurately recognize various human activities using low-cost devices. To realize such a system, we need to address several problems: the required sensors are too expensive (P1); it is difficult to precisely recognize place-independent activities like reading (P2), and putting on a device causes a burden to people (P3) the information such as images infringe on the privacy of the occupants (P4). In this paper, we propose a method for activity recognition by utilizing a doppler sensor as a motion detection sensor and a machine learning technique to solve the problems above (P1-P4). Specifically, frequency characteristic is obtained from the signals of the doppler sensor and we construct a machine learning model using effective features, which is presented by Anguita, and speed of target calculated from the doppler frequency. In order to examine the usefulness of the proposed method and find out critical issues of realizing activity recognition, we have collected sensor data of 6 kinds of activities(stationary, smartphone operation, PC operation, reading, writing, and eating) performed by 10 participants. For leave-one-session-out cross-validation, the maximum average recognition accuracy was 95.7%, and the average for 10 participants was 81.0%. For leave-one-person-out cross validation, the average recognition accuracy of logistic regression shows maximum accuracy of 42.1%.","PeriodicalId":435827,"journal":{"name":"Adjunct Proceedings of the 2021 International Conference on Distributed Computing and Networking","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114557507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pratik Goswami, A. Mukherjee, Pushpita Chatterjee, Lixia Yang
The recent technical evolution is revolving around Internet of Things (IoT). The Internet of Softwarized Things (IoST) as a subset of IoT, is making its mark mostly towards industrial applications to connect all the devices and improve the computation capability and networking flexibility. The Industrial IoT (IIoT) consists of a large network, where the multiple works are processed continuously at a time. Therefore, multi-objective interference issue in the path remains as obstacle, for which the networking resources are lost. The existing works were performed with fixed resources and dedicated channel states which make the network less flexible with more time response. In this paper, the problem is addressed with optimal resource allocation using convolutional neural network (CNN) to extract the optimal channel state for different applications, which ease the computations along with efficiency. Furthermore, the proposed method is validated with the mathematical analysis and simulation.
{"title":"An Optimal Resource Allocation Method for IIoT Network","authors":"Pratik Goswami, A. Mukherjee, Pushpita Chatterjee, Lixia Yang","doi":"10.1145/3427477.3429988","DOIUrl":"https://doi.org/10.1145/3427477.3429988","url":null,"abstract":"The recent technical evolution is revolving around Internet of Things (IoT). The Internet of Softwarized Things (IoST) as a subset of IoT, is making its mark mostly towards industrial applications to connect all the devices and improve the computation capability and networking flexibility. The Industrial IoT (IIoT) consists of a large network, where the multiple works are processed continuously at a time. Therefore, multi-objective interference issue in the path remains as obstacle, for which the networking resources are lost. The existing works were performed with fixed resources and dedicated channel states which make the network less flexible with more time response. In this paper, the problem is addressed with optimal resource allocation using convolutional neural network (CNN) to extract the optimal channel state for different applications, which ease the computations along with efficiency. Furthermore, the proposed method is validated with the mathematical analysis and simulation.","PeriodicalId":435827,"journal":{"name":"Adjunct Proceedings of the 2021 International Conference on Distributed Computing and Networking","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129384364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yugo Nakamura, J. P. Talusan, Teruhiro Mizumoto, H. Suwa, Yutaka Arakawa, H. Yamaguchi, K. Yasumoto
In this paper, we propose ProceThings, a new middleware platform to provide smart community services by utilizing computational resources of IoT devices in a target community area. To realize ProceThings, we address three key challenges: (1) dynamic load balance management among numerous IoT devices; (2) distributed task assignment/execution over the IoT devices taking into account fault-tolerance; and (3) fulfillment of a service level agreement (SLA) for each service. For (1), ProceThings employs a heuristic monitoring mechanism which hierarchically aggregates load and resource conditions from all IoT devices in the target area (or belonging to a service). For (2), ProceThings employs a cluster-based architecture where proximity IoT devices are grouped into clusters with a fail over function, where they are allocated processing tasks from user queries. For (3), ProceThings employs demand-aware in-situ resource provisioning which dynamically predicts and assigns a sufficient amount of computational resources within the area where the service is provided to meet the SLA while preventing over-provisioning of resources. We have implemented a prototype of ProceThings running on commodity small computers consisting of Raspberry Pis and Intel NUCs and confirmed that the above mechanisms can properly work satisfying the corresponding SLAs when running smart community services.
{"title":"ProceThings: Data Processing Platform with In-situ IoT Devices for Smart Community Services","authors":"Yugo Nakamura, J. P. Talusan, Teruhiro Mizumoto, H. Suwa, Yutaka Arakawa, H. Yamaguchi, K. Yasumoto","doi":"10.1145/3427477.3429275","DOIUrl":"https://doi.org/10.1145/3427477.3429275","url":null,"abstract":"In this paper, we propose ProceThings, a new middleware platform to provide smart community services by utilizing computational resources of IoT devices in a target community area. To realize ProceThings, we address three key challenges: (1) dynamic load balance management among numerous IoT devices; (2) distributed task assignment/execution over the IoT devices taking into account fault-tolerance; and (3) fulfillment of a service level agreement (SLA) for each service. For (1), ProceThings employs a heuristic monitoring mechanism which hierarchically aggregates load and resource conditions from all IoT devices in the target area (or belonging to a service). For (2), ProceThings employs a cluster-based architecture where proximity IoT devices are grouped into clusters with a fail over function, where they are allocated processing tasks from user queries. For (3), ProceThings employs demand-aware in-situ resource provisioning which dynamically predicts and assigns a sufficient amount of computational resources within the area where the service is provided to meet the SLA while preventing over-provisioning of resources. We have implemented a prototype of ProceThings running on commodity small computers consisting of Raspberry Pis and Intel NUCs and confirmed that the above mechanisms can properly work satisfying the corresponding SLAs when running smart community services.","PeriodicalId":435827,"journal":{"name":"Adjunct Proceedings of the 2021 International Conference on Distributed Computing and Networking","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131495937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Delivering the right information to the right people in a timely manner can greatly improve outcomes and save lives in emergency response. A communication framework that flexibly and efficiently brings victims, volunteers, and first responders together for timely assistance can be very helpful. With the burden of more frequent and intense disaster situations and first responder resources stretched thin, people increasingly depend on social media for communicating vital information. This paper proposes ONSIDE, a framework for coordination of disaster response leveraging social media, integrating it with Information-Centric dissemination for timely and relevant dissemination. We use a graph-based pub/sub namespace that captures the complex hierarchy of the incident management roles. Regular citizens and volunteers using social media may not know of or have access to the full namespace. Thus, we utilize a social media engine (SME) to identify disaster-related social media posts and then automatically map them to the right name(s) in near-real-time. Using NLP and classification techniques, we direct the posts to appropriate first responder(s) that can help with the posted issue. A major challenge for classifying social media in real-time is the labeling effort for model training. Furthermore, as disasters hits, there may be not enough data points available for labeling, and there may be concept drift in the content of the posts over time. To address these issues, our SME employs stream-based active learning methods, adapting as social media posts come in. Preliminary evaluation results show the proposed solution can be effective.
{"title":"Online Delivery of Social Media Posts to Appropriate First Responders for Disaster Response","authors":"Viyom Mittal, Mohammad Jahanian, K. Ramakrishnan","doi":"10.1145/3427477.3429272","DOIUrl":"https://doi.org/10.1145/3427477.3429272","url":null,"abstract":"Delivering the right information to the right people in a timely manner can greatly improve outcomes and save lives in emergency response. A communication framework that flexibly and efficiently brings victims, volunteers, and first responders together for timely assistance can be very helpful. With the burden of more frequent and intense disaster situations and first responder resources stretched thin, people increasingly depend on social media for communicating vital information. This paper proposes ONSIDE, a framework for coordination of disaster response leveraging social media, integrating it with Information-Centric dissemination for timely and relevant dissemination. We use a graph-based pub/sub namespace that captures the complex hierarchy of the incident management roles. Regular citizens and volunteers using social media may not know of or have access to the full namespace. Thus, we utilize a social media engine (SME) to identify disaster-related social media posts and then automatically map them to the right name(s) in near-real-time. Using NLP and classification techniques, we direct the posts to appropriate first responder(s) that can help with the posted issue. A major challenge for classifying social media in real-time is the labeling effort for model training. Furthermore, as disasters hits, there may be not enough data points available for labeling, and there may be concept drift in the content of the posts over time. To address these issues, our SME employs stream-based active learning methods, adapting as social media posts come in. Preliminary evaluation results show the proposed solution can be effective.","PeriodicalId":435827,"journal":{"name":"Adjunct Proceedings of the 2021 International Conference on Distributed Computing and Networking","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131970513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Usman Ahmed, Chun-Wei Lin, Gautam Srivastava, Y. Djenouri
With the establishment of the 5G network, a number of data-intensive applications will be developed. Privacy of information over the network is increasingly relevant, and require protection. The privacy of information while utilizing data is a trade-off that needs to be addressed. In this paper, we propose data privacy of 5G connected devices over heterogeneous networks (5G-Hetnets). A deep Q learning (DQL) based technique is applied to sensitize sensitive information from a given database while keeping the balance between privacy protection and knowledge discovery during the sanitization process. It takes transaction states as input and results in state and action pair. The DQL discovers the transactions dynamically, then the sanitization operation hide the sensitive information by minimizing side effects. The proposed approach shows significant improvement of performance compared to greedy and meta-heuristics and heuristics approaches.
{"title":"A Deep Q-Learning Sanitization Approach for Privacy Preserving Data Mining","authors":"Usman Ahmed, Chun-Wei Lin, Gautam Srivastava, Y. Djenouri","doi":"10.1145/3427477.3429990","DOIUrl":"https://doi.org/10.1145/3427477.3429990","url":null,"abstract":"With the establishment of the 5G network, a number of data-intensive applications will be developed. Privacy of information over the network is increasingly relevant, and require protection. The privacy of information while utilizing data is a trade-off that needs to be addressed. In this paper, we propose data privacy of 5G connected devices over heterogeneous networks (5G-Hetnets). A deep Q learning (DQL) based technique is applied to sensitize sensitive information from a given database while keeping the balance between privacy protection and knowledge discovery during the sanitization process. It takes transaction states as input and results in state and action pair. The DQL discovers the transactions dynamically, then the sanitization operation hide the sensitive information by minimizing side effects. The proposed approach shows significant improvement of performance compared to greedy and meta-heuristics and heuristics approaches.","PeriodicalId":435827,"journal":{"name":"Adjunct Proceedings of the 2021 International Conference on Distributed Computing and Networking","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123134200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abhishek Kumar, Nitin Gupta, Riya Tapwal, Jagdeep Singh
Emerging of Cognitive Radio (CR) technology has provided an optimistic solution for the dearth of the spectrum by improving the spectrum utilization. The opportunistic use of the spectrum is enabled by spectrum sensing which is one of the key functionality of CR systems. To perform the interference-free transmission in cognitive radio networks, an important part for the unlicensed user is to identify a licensed user with the help of spectrum sensing. Recently, the Cooperative Spectrum Sensing has been widely used in the literature where various scattered unlicensed users collaborate to make the final sensing decision. This overcomes the hidden terminal problem and also improve the overall reliability of the decisions made about the presence or absence of a licensed user. Each unlicensed user sends the sensing results to the base station for the final decision. However, there exist some nodes which do not provide the correct sensing results to maximize their own profit which can highly degrade the CR network functionality. In this paper, a trust-aware model is proposed for the detection of misbehaving nodes such that their sensing reports can be filtered out from the final result. The performance evaluation of the proposed scheme is done by checking its robustness and efficiency against various possible attacks.
{"title":"Trust Aware Scheme based Malicious Nodes Detection under Cooperative Spectrum Sensing for Cognitive Radio Networks","authors":"Abhishek Kumar, Nitin Gupta, Riya Tapwal, Jagdeep Singh","doi":"10.1145/3427477.3429992","DOIUrl":"https://doi.org/10.1145/3427477.3429992","url":null,"abstract":"Emerging of Cognitive Radio (CR) technology has provided an optimistic solution for the dearth of the spectrum by improving the spectrum utilization. The opportunistic use of the spectrum is enabled by spectrum sensing which is one of the key functionality of CR systems. To perform the interference-free transmission in cognitive radio networks, an important part for the unlicensed user is to identify a licensed user with the help of spectrum sensing. Recently, the Cooperative Spectrum Sensing has been widely used in the literature where various scattered unlicensed users collaborate to make the final sensing decision. This overcomes the hidden terminal problem and also improve the overall reliability of the decisions made about the presence or absence of a licensed user. Each unlicensed user sends the sensing results to the base station for the final decision. However, there exist some nodes which do not provide the correct sensing results to maximize their own profit which can highly degrade the CR network functionality. In this paper, a trust-aware model is proposed for the detection of misbehaving nodes such that their sensing reports can be filtered out from the final result. The performance evaluation of the proposed scheme is done by checking its robustness and efficiency against various possible attacks.","PeriodicalId":435827,"journal":{"name":"Adjunct Proceedings of the 2021 International Conference on Distributed Computing and Networking","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116895744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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