{"_buckets": {"deposit": "7054c2b2-f9d4-452f-a293-3aecf82a3a97"}, "_deposit": {"created_by": 73, "id": "7928", "owner": "73", "owners": [73], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "depid", "value": "7928"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/00007928", "sets": ["user-miit"]}, "communities": ["miit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Efficient Hybrid Sensor Data Recovery Scheme", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "\"In the emerging Internet of Things (IoT) technolo￾gies, network-connected sensors or sensor-attached devices are\nused for sensing purposes and a large number of devices utilize\nthe sensor data stream to analyze the situations of the real world.\nIn case of the client-server model, to keep the reliability of the\nresult of sensor data analysis, the server has to re-transmit\nthe lost data to the loss-encountered receivers. Reducing the\nnumber of streams for the lost data recovery is important to\ntackle the scalability challenge. To overcome the client-server\nmodel problems, the peer-to-peer model emerges as a paradigm.\nIn the peer-to-peer data recovery, the assisted peer selection\nis an important issue. Although there are many existing peer\nselection methods, these do not consider the fixed communication\nbandwidth and data availability of the assisted peer cooperatively.\nHence, in this paper, we introduce a hybrid sensor data recovery\nscheme considering each peer’s fixed communication bandwidth\nand data availability. According to our simulation results, we\nconfirmed that our method introduces less number of average\nrecovery streams with faster catch-up time.\""}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2021-01-29"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Efficient Hybrid Sensor Data Recovery Scheme.pdf", "filesize": [{"value": "864 KB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_3", "mimetype": "application/pdf", "size": 864000.0, "url": {"url": "https://meral.edu.mm/record/7928/files/Efficient Hybrid Sensor Data Recovery Scheme.pdf"}, "version_id": "c545e6fe-97fe-426e-b00a-f13a9824c946"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "GCCE", "subitem_c_date": "Oct.16, 2019", "subitem_conference_title": "GCCE 2019 (Global Conference on Consumer Electronics)", "subitem_place": "Osaka, Japan", "subitem_website": "http://ieee-gcce.org/#:~:text=2019%20IEEE%208th%20Global%20Conference%20on%20Consumer%20Electronics%20(GCCE%202019,CES%20in%20Las%20Vegas%2C%20USA."}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Ei Khaing Win"}, {"subitem_authors_fullname": "Tomoki Yoshihisa"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Conference paper"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2019-10-06"}, "item_title": "Efficient Hybrid Sensor Data Recovery Scheme", "item_type_id": "21", "owner": "73", "path": ["1582963674932", "1597396989070"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000007928", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-10-06"}, "publish_date": "2019-10-06", "publish_status": "0", "recid": "7928", "relation": {}, "relation_version_is_last": true, "title": ["Efficient Hybrid Sensor Data Recovery Scheme"], "weko_shared_id": -1}