29,515 research outputs found

    Modeling the Internet of Things: a simulation perspective

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    This paper deals with the problem of properly simulating the Internet of Things (IoT). Simulating an IoT allows evaluating strategies that can be employed to deploy smart services over different kinds of territories. However, the heterogeneity of scenarios seriously complicates this task. This imposes the use of sophisticated modeling and simulation techniques. We discuss novel approaches for the provision of scalable simulation scenarios, that enable the real-time execution of massively populated IoT environments. Attention is given to novel hybrid and multi-level simulation techniques that, when combined with agent-based, adaptive Parallel and Distributed Simulation (PADS) approaches, can provide means to perform highly detailed simulations on demand. To support this claim, we detail a use case concerned with the simulation of vehicular transportation systems.Comment: Proceedings of the IEEE 2017 International Conference on High Performance Computing and Simulation (HPCS 2017

    Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks

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    Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems. However, the application of ML to networking systems raises concerns among network operators and other stakeholders, especially regarding trustworthiness and reliability. In this paper, we devise the role of network simulators for bridging the gap between ML and communications systems. In particular, we present an architectural integration of simulators in ML-aware networks for training, testing, and validating ML models before being applied to the operative network. Moreover, we provide insights on the main challenges resulting from this integration, and then give hints discussing how they can be overcome. Finally, we illustrate the integration of network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential Wi-Fi network

    The Road Ahead for Networking: A Survey on ICN-IP Coexistence Solutions

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    In recent years, the current Internet has experienced an unexpected paradigm shift in the usage model, which has pushed researchers towards the design of the Information-Centric Networking (ICN) paradigm as a possible replacement of the existing architecture. Even though both Academia and Industry have investigated the feasibility and effectiveness of ICN, achieving the complete replacement of the Internet Protocol (IP) is a challenging task. Some research groups have already addressed the coexistence by designing their own architectures, but none of those is the final solution to move towards the future Internet considering the unaltered state of the networking. To design such architecture, the research community needs now a comprehensive overview of the existing solutions that have so far addressed the coexistence. The purpose of this paper is to reach this goal by providing the first comprehensive survey and classification of the coexistence architectures according to their features (i.e., deployment approach, deployment scenarios, addressed coexistence requirements and architecture or technology used) and evaluation parameters (i.e., challenges emerging during the deployment and the runtime behaviour of an architecture). We believe that this paper will finally fill the gap required for moving towards the design of the final coexistence architecture.Comment: 23 pages, 16 figures, 3 table

    Leading Undergraduate Students to Big Data Generation

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    People are facing a flood of data today. Data are being collected at unprecedented scale in many areas, such as networking, image processing, virtualization, scientific computation, and algorithms. The huge data nowadays are called Big Data. Big data is an all encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications. In this article, the authors present a unique way which uses network simulator and tools of image processing to train students abilities to learn, analyze, manipulate, and apply Big Data. Thus they develop students handson abilities on Big Data and their critical thinking abilities. The authors used novel image based rendering algorithm with user intervention to generate realistic 3D virtual world. The learning outcomes are significant

    Improvement of strength and water absorption of Interlocking Compressed Earth Bricks (ICEB) with addition of Ureolytic Bacteria (UB)

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    Interlocking Compressed Earth Brick (ICEB) are cement stabilized soil bricks that allow for dry stacked construction. This characteristic resulted to faster the process of building walls and requires less skilled labour as the bricks are laid dry and lock into place. However there is plenty room for improving the interlocking bricks by increase its durability. Many studies have been conducted in order to improve the durability of bricks by using environmentally method. One of the methods is by introducing bacteria into bricks. Bacteria in brick induced calcite precipitation (calcite crystals) to cover the voids continuously. Ureolytic Bacteria (UB) was used in this study as a partial replacement of limestone water with percentage of 1%, 3% and 5%. Enrichment process was done in soil condition to ensure the survivability of UB in ICEB environment. This paper evaluates the effect of UB in improving the strength and water absorption properties of ICEB and microstructure analysis. The results show that addition of 5% UB in ICEB indicated positive results in improving the ICEB properties by 15.25% in strength, 14.72% in initial water absorption and 14.68% reduction in water absorption. Precipitation of calcium carbonate (CaCo3) in form of calcite can be distinguish clearly in microstructure analysis

    Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks

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    Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to a base station. The underlying clustering protocol allows the current state and uncertainty of the target position to be easily handed off among clusters as the object is being tracked. This allows Kalman filter-based object tracking to be carried out in a distributed manner. An extended Kalman filter is necessary since measurements acquired by the cameras are related to the actual position of the target by nonlinear transformations. In addition, in order to take into consideration the time uncertainty in the measurements acquired by the different cameras, it is necessary to introduce nonlinearity in the system dynamics. Our object tracking protocol requires the transmission of significantly fewer messages than a centralized tracker that naively transmits all of the local measurements to the base station. It is also more accurate than a decentralized tracker that employs linear interpolation for local data aggregation. Besides, the protocol is able to perform real-time estimation because our implementation takes into consideration the sparsit- - y of the matrices involved in the problem. The experimental results show that our distributed object tracking protocol is able to achieve tracking accuracy comparable to the centralized tracking method, while requiring a significantly smaller number of message transmissions in the network
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