Wirelessly streaming high quality 360 degree videos is still a challenging problem. When there are many users watching different 360 degree videos and competing for the computing and communication resources, the strea...
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ISBN:
(数字)9798350363999
ISBN:
(纸本)9798350364002
Wirelessly streaming high quality 360 degree videos is still a challenging problem. When there are many users watching different 360 degree videos and competing for the computing and communication resources, the streaming algorithm at hand should maximize the average quality of experience (QoE) while guaranteeing a minimum rate for each user. In this paper, we propose a cross layer optimization approach that maximizes the available rate to each user and efficiently uses it to maximize users' QoE. Particularly, we consider a tile based 360 degree video streaming, and we optimize a QoE metric that balances the tradeoff between maximizing each user's QoE and ensuring fairness among users. We show that the problem can be decoupled into two interrelated subproblems: (i) a physical layer subproblem whose objective is to find the download rate for each user, and (ii) an application layer subproblem whose objective is to use that rate to find a quality decision per tile such that the user's QoE is maximized. We prove that the physical layer subproblem can be solved optimally with low complexity and an actor-critic deep reinforcement learning (DRL) is proposed to leverage the parallel training of multiple independent agents and solve the application layer subproblem. Extensive experiments reveal the robustness of our scheme and demonstrate its significant performance improvement compared to several baseline algorithms.
IoT has proven valuable in many industries such as Supply Chain, Shipping and Transportation providing updates on the status of shipments in real time. This has resulted in a large amount of data created by IoT device...
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ISBN:
(数字)9781728173030
ISBN:
(纸本)9781728173047
IoT has proven valuable in many industries such as Supply Chain, Shipping and Transportation providing updates on the status of shipments in real time. This has resulted in a large amount of data created by IoT devices that require real time processing. Blockchain has also emerged as a trusted common data storage among multiple participants in IoT systems. However, it is essential to verify the authenticity of data before they go in to the blockchain. Some IoT data must be also encrypted in a secure manner. In this paper, we propose a method to collect sensor data from IoT devices and use blockchain to store and retrieve the collected data in a secure and decentralized fashion. A testbed private blockchain system with Ethereum and Raspberry Pi has been developed to test the feasibility and performance of blockchain-based secure IoT system. We employ private PKI for key management and TPM for storing keys, signing, verifying and encrypting data in a trusted way. We describe the structure of the testbed, decentralized storage system based on Ethereum Swarm and IPFS, TPM configuration processes, smart contract codes, and detailed operation sequences. Performance has been measured with 5000 test transactions in 8 different configurations. The result shows that Ethereum blockchain with Swarm can offer about 9 transactions per second. Encryption and decryption is shown to reduce the performance slightly to about 8 transactions per second. The proposed system can provide a secure IoT Blockchain platform for small industrial applications at low cost.
We take an algorithmic approach to a well-known communication channel problem and develop several algorithms for solving it. Specifically, we develop power control algorithms for sensor networks with collaborative rel...
ISBN:
(纸本)9783540730897
We take an algorithmic approach to a well-known communication channel problem and develop several algorithms for solving it. Specifically, we develop power control algorithms for sensor networks with collaborative relaying under bandwidth constraints, via quantization of finite rate (bandwidth limited) feedback channels. We first consider the power allocation problem under collaborative relaying where the tradeoff between minimizing ones own energy expenditure and the energy for relaying is considered under the constraints of packet outage probability and bandwidth constrained (finite rate) feedback. Then we develop bandwidth constrained quantization algorithms (due to the finite rate feedback) that seek the optimal way of quantizing channel quality and power values in order to minimize the total average transmission power and satisfy the given probability of outage. We develop two kinds of quantization protocols and associated quantization algorithms. For separate source-relay quantization, we reduce the problem to the well-known k-median problem [1] on line graphs and show a a simple O((KJ)2N) polynomial time algorithm, where log2 KJ is the quantization bandwidth and N is the size of the discretized parameter space. For joint quantization, we first develop a simple 2-factor approximation of complexity O(KJN + N logN). Then, for Ɛ > 0, we develop a fully polynomial approximation scheme (FPAS) that approximates the optimal quantization cost to within an 1+Ɛ-factor. The running time of the FPAS is polynomial in 1/Ɛ, size of the input N and also ln F, where F is the maximum available transmit power.
This paper presents a fully distributed algorithm for target tracking using a mobile sensor network. It tries to maintain the target being visible to the mobile network all the time while consuming as little motion en...
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ISBN:
(纸本)9781424420575
This paper presents a fully distributed algorithm for target tracking using a mobile sensor network. It tries to maintain the target being visible to the mobile network all the time while consuming as little motion energy as possible. Meanwhile the network connectivity is maintained. At every time, only the nodes around the target are activated while other nodes keep idle. Certain functions are defined to quantify the main aspects in the tracking such as the target escaping probability and the network connectivity status. They transform the tracking into a multi-objective optimization problem. To solve this global problem, a local motion strategy is proposed. Simulation results show that our algorithm yields good performance.
Agent systems are a way to increase flexibility of Manufacturing Execution systems (MES) by scheduling tasks in a distributed autonomous way. Usage of autonomous, collaborative agents and off-the-shelf hardware and so...
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Agent systems are a way to increase flexibility of Manufacturing Execution systems (MES) by scheduling tasks in a distributed autonomous way. Usage of autonomous, collaborative agents and off-the-shelf hardware and software components yet introduce vulnerabilities. This article presents a structural security model for protecting a distributed agent system based on the concept of a hierarchical model. In particular the necessary key system is introduced. The developed security model takes into account typical properties of embedded systems (low computation power, real-time capabilities, autarkic operation) used in automation environments.
sensors link the physical world with the digital by sensing, capturing and revealing real world phenomena and converting them into a form that can be processed, stored and acted upon. The sensors help to avoid infrast...
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ISBN:
(纸本)9781479939152
sensors link the physical world with the digital by sensing, capturing and revealing real world phenomena and converting them into a form that can be processed, stored and acted upon. The sensors help to avoid infrastructure failures, increase productivity, protect natural resources, and enhance security. While sensor networks share information with other distributedsystems, they are subject to a variety of unique challenges and constraints. The most important constraint is Energy in a network. In this paper, we will discuss about the power conservation in normal AODV protocol, with DoS attack and Game theoritic approach.
The Yellow Sea is semi-enclosed shelf sea located between the mainland of China and the Korea Peninsula. It is one of the most important fishing areas in the world. Phytoplankton bloom is defined as a relatively rapid...
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The Yellow Sea is semi-enclosed shelf sea located between the mainland of China and the Korea Peninsula. It is one of the most important fishing areas in the world. Phytoplankton bloom is defined as a relatively rapid increase in the biomass of phytoplankton. The twice bloom in spring and autumn every year is common in Yellow Sea. Chlorophyll concentration is the important parameters to estimate the phytoplankton biomass and its seasonal variation or blooms. Taking the yellow sea as an experimental site, a cruise for phytoplankton bloom was carried out in the Yellow Sea with R/V Beidou founded by 973 projects. Chlorophyll was measured by a fluorescence sensor of sea point installed on a RBR 620 CTD. Using MODIS remote sensing images, unifying the location water quality monitor data, in this paper the model of retrieval of chlorophyll concentration is built using the 250m resolution waveband 1 and 2 reflectivity combination compare with chlorophyll concentration measurements by correlation analysis and multivariate regression. Then, the distribution of chlorophyll concentration in the yellow sea is mapped based on this retrieval model. The intensity, place, process and distributed scope of this phytoplankton spring bloom reflected clearly. The results of this study show that MODIS data is useful in retrieving quantitatively chlorophyll concentration and studying on the onset of phytoplankton spring bloom and its dynamics in the yellow sea.
In this work we present a mobile stress recognition system based on an existing activity recognition system using a hip-worn inertial measurement unit and a chest belt. Integrating activity knowledge, the prediction o...
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ISBN:
(纸本)9781467350754
In this work we present a mobile stress recognition system based on an existing activity recognition system using a hip-worn inertial measurement unit and a chest belt. Integrating activity knowledge, the prediction of different human stress levels in a mobile environment can be enabled while the state of the art is focussed on stress recognition in static environments. Our system has been implemented on an Android mobile phone and evaluated for different Bayesian networks as classifiers. Our implementation is able to operate in real-time with a stress inference rate of 1 Hz. The results of this work indicate that the implemented system is able to differentiate between the states 'No Stress' and 'Stress' in a mobile context. A more detailed distinction of stress in five substates has not been possible in a reliable way to date. With our results, the proposed system can serve as a basis for further improvements with larger data sets and for in-situ testing during disaster assessment.
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