A keyword-set search system for peer-to-peer networks

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 63-65).The Keyword-Set Search System (KSS) is a Peer-to-Peer (P2P) keyword search system that uses a distributed inverted index. The main challenge in a distributed index and search system is finding the right scheme to partition the index across the nodes in the network. The most obvious scheme would be to partition the index by keyword. A keyword partitioned index requires that the list of index entries for each keyword in a search be retrieved, so all the lists can be joined; only a few nodes need to be contacted, but each sends a potentially large amount of data. In KSS, the index is partitioned by sets of keywords. KSS builds an inverted index that maps each set of keywords to a list of all the documents that contain the words in the keyword-set. When a user issues a query, the keywords in the query are divided into sets of keywords. The document list for each set of keywords is then fetched from the network. The lists are intersected to compute the list of matching documents. The list of index entries for each set of words is smaller than the list of entries for each word. Thus search using KSS results in a smaller query time overhead. Preliminary experiments using traces of real user queries show that the keywordset approach is more efficient than a standard inverted index in terms of communication costs for query. Insert overhead for KSS grows exponentially as the size of the keyword-set used to generate the keys for index entries. The query overhead for the target application (metadata search in a music file sharing system) is reduced to the result of the query as no intermediate lists are transferred across the network for the join operation. Given our assumption that free disk space is plenty, and queries are more frequent than insertions in P2P systems, we believe this is a good tradeoff.by Omprakash D. Gnawali.M.Eng

Deception Detection with Feature-Augmentation by soft Domain Transfer

In this era of information explosion, deceivers use different domains or mediums of information to exploit the users, such as News, Emails, and Tweets. Although numerous research has been done to detect deception in all these domains, information shortage in a new event necessitates these domains to associate with each other to battle deception. To form this association, we propose a feature augmentation method by harnessing the intermediate layer representation of neural models. Our approaches provide an improvement over the self-domain baseline models by up to 6.60%. We find Tweets to be the most helpful information provider for Fake News and Phishing Email detection, whereas News helps most in Tweet Rumor detection. Our analysis provides a useful insight for domain knowledge transfer which can help build a stronger deception detection system than the existing literature

Surviving sensor network software faults

We describe Neutron, a version of the TinyOS operating system that efficiently recovers from memory safety bugs. Where existing schemes reboot an entire node on an error, Neutron’s compiler and runtime extensions divide programs into recovery units and reboot only the faulting unit. The TinyOS kernel itself is a recovery unit: a kernel safety violation appears to applications as the processor being unavailable for 10–20 milliseconds. Neutron further minimizes safety violation cost by supporting “precious ” state that persists across reboots. Application data, time synchronization state, and routing tables can all be declared as pre-cious. Neutron’s reboot sequence conservatively checks that pre-cious state is not the source of a fault before preserving it. Together, recovery units and precious state allow Neutron to reduce a safety violation’s cost to time synchronization by 94 % and to a routing protocol by 99.5%. Neutron also protects applications from losing data. Neutron provides this recovery on the very limited resources of a tiny, low-power microcontroller

Respirable Dust Monitoring in Construction Sites and Visualization in Building Information Modeling Using Real-time Sensor Data

Construction activities, involving cutting, drilling, and grinding of materials, often produce toxic respirable dust that can cause fatal diseases and illnesses. To protect workers from breathing excessive amounts of respirable dust at job sites, superintendents should continuously monitor the level of respirable dust in workspaces and make timely interventions for overexposed workers. However, current practices of respirable dust monitoring have critical drawbacks, and superintendents cannot accurately estimate workers’ exposures to respirable dust or make prompt decisions to protect the workers. Therefore, there is a need for real-time air dust monitoring that can be deployed ubiquitously at a construction site and be integrated as part of daily construction management. In this research, we developed a real-time dust monitoring system that comprises a network of low-cost mobile dust sensors and visualization in building information modeling (BIM). Single-board computers and dust sensors were integrated as field deployment units. Inaccurate sensors were calibrated automatically on the basis of an accurate ground truth sensor. A BIM-based visualization system was developed to present the data collected from dust sensors in real time. A prototype system was developed and tested in a controlled environment

Feasibility of LoRa for Smart Home Indoor Localization

With the advancement of low-power and low-cost wireless technologies in the past few years, the Internet of Things (IoT) has been growing rapidly in numerous areas of Industry 4.0 and smart homes. With the development of many applications for the IoT, indoor localization, i.e., the capability to determine the physical location of people or devices, has become an important component of smart homes. Various wireless technologies have been used for indoor localization includingWiFi, ultra-wideband (UWB), Bluetooth low energy (BLE), radio-frequency identification (RFID), and LoRa. The ability of low-cost long range (LoRa) radios for low-power and long-range communication has made this radio technology a suitable candidate for many indoor and outdoor IoT applications. Additionally, research studies have shown the feasibility of localization with LoRa radios. However, indoor localization with LoRa is not adequately explored at the home level, where the localization area is relatively smaller than offices and corporate buildings. In this study, we first explore the feasibility of ranging with LoRa. Then, we conduct experiments to demonstrate the capability of LoRa for accurate and precise indoor localization in a typical apartment setting. Our experimental results show that LoRa-based indoor localization has an accuracy better than 1.6 m in line-of-sight scenario and 3.2 m in extreme non-line-of-sight scenario with a precision better than 25 cm in all cases, without using any data filtering on the location estimates

Poster Abstract: A Benchmark for Low-power Wireless Networking

International audienceExperimental research in low-power wireless networking lacks a reference benchmark. While other communities such as databases or machine learning have standardized benchmarks, our community still uses ad-hoc setups for its experiments and struggles to provide a fair comparison between communication protocols. Reasons for this include the diversity of network scenarios and the stochastic nature of wireless experiments. Leveraging on the excellent testbeds and tools that have been built to support experimental validation, we make the case for a reference benchmark to promote a fair comparison and reproducibility of results. This abstract describes early design elements and a benchmarking methodology with the goal to gather feedback from the community rather than propose a definite solution