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پنج شنبه, 20 آبان 1395 ساعت 14:01

Routing and Sensor Search in the Internet of Things

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اطلاعات تکميلي

  • نام نویسندگان: M.Sc. Cuong Duc Truong
  • چکیده شامل:

    We are witnessing the formation of an Internet of Things (IoT), where real-world entities (e.g., people, plants, cars) augmented with computing devices (e.g., smartphones, tablets, sensor nodes), sensors (e.g., humidity sensors, microphones, cameras), and actuators (e.g.,motors, LED) are connected to the Internet, enabling them to publish their generated data on the Web. By mashing up these \Smart Things" with the services and data available on the Web, novel IoT applications can be created. Two main characteristics of the IoT are its large scale interconnecting billions of Smart Things in the next decade, and the resource limitations of Smart Things (i.e., of their embedded computing devices). For many IoT applications, routing and sensor search are essential services. The sensor search service allows for quickly nding Smart Things in the IoT based on the real-world states perceived by their embedded sensors. To facilitate sensor search and also other IoT applications, a routing service is required to enable e cient communication of information among Smart Things and other Internet nodes. Due to the resource limitations of Smart Things, the design of these two services is challenging. Our thesis is that despite the large scale of the IoT and the resource limitations of Smart Things, efficient solutions for the routing and sensor search services can actually be provided.

    We support this thesis by proposing, implementing, and evaluating two routing algorithms for large-scale wireless sensor networks (which are a building block of the IoT), and two sensor search algorithms for the IoT. The proposed routing algorithms are Recursive Multi-region Geocasting (RMG) and Stochasic Forwarding-based Routing (SFR). The RMG algorithm is targeted to large-scale wireless networks where information needs to be delivered from a source to multiple geographical regions that are remotely located from the source, respectively to all Smart Things that are located therein. RMG's approach is to avoid making routing decisions at every intermediate node. Instead, routing decisions and packet duplication are only performed at a few selected nodes on the routing path of a data packet, thus saving processing and energy resources as well as wireless bandwidth. The SFR algorithm aims to balance the energy consumption caused by the routing task across the network, therefore improving the operational lifetime of the network. Our approach for SFR is to model the route of a data packet as a random

    walk, such that different packets travel on different routing paths between a given source and destination. The random walk is designed such that the ratio between the average length of the routing paths taken by multiple packets and the length of the shortest routing path (path length overhead) is small, thus saving energy. Evaluation results show the efficiency of our routing algorithms. In particular, (i) RMG minimizes the total number of transmissions needed for the successful delivery of a data packet, and incurs little computation overhead on the network; and (ii) SFR fairly balances the routing load across the network while keeping the path length overhead small. Furthermore, both algorithms are scalable, since routing decisions are made using only local information. The proposed search algorithms are sensor similarity search and content-based sensor search .The  first algorithm allows for  finding sensors whose recent measurements (i.e., perceived states of the real world) are similar to that of an example sensor. The second algorithm allows for  finding sensors whose latest measurements fall in a given value range. These two search services are useful because they enable the users to search the real world for objects and places with a given state in real time. Our approach for both algorithms is to encode

    the properties of the real-world states perceived by sensors using a fuzzy set, and store and index those fuzzy sets in a distributed database system in the Internet, such that they can be used for query resolution. Since the size of a fuzzy set is small, its computation is efficient, and the query resolution using those fuzzy sets is fast, our approach is scalable. Moreover, our approach addresses the inherent imperfections of data generated by sensors as it is based on fuzzy sets. Evaluating our search algorithms using sensor data from real-world deployments, we show the high accuracy of the sensor similarity search algorithm, and the low communication overhead of the content-based sensor search algorithm. We also demonstrate the practical feasibility of our sensor search algorithms by developing an online

    search engine that allows for finding sensors on the Web based on their published data, using the above sensor search algorithms.

  • سال انتشار: چهارشنبه, 11 دی 1392
  • دانشگاه: University of Lubeck
  • مقطع: Doctoral Degree
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