List of Titles

Invariance principles and observability in switched systems with an application in consensus

- Lee, Ti-Chung, Tan, Ying, Su, Youfeng, Mareels, Iven

Authors: Lee, Ti-Chung , Tan, Ying , Su, Youfeng , Mareels, Iven
Date: 2021
Type: Journal article
Relation: IEEE transactions on automatic control Vol. 66, no. 11 (2021), p. 5128-5143
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Description: Using any nonnegative function with a nonpositive derivative along trajectories to define a virtual output, the classic LaSalle invariance principle can be extended to switched nonlinear time-varying (NLTV) systems, by considering the weak observability associated with this output. The concept of weak observability reflects the information of the limiting behavior of state trajectories hidden in the zero locus of the output. In the context of switched NLTV systems, weak observability can be explored using the recently established framework of limiting zeroing-output solutions. Adding to this, a new approach to show uniform global attractivity of a closed set (without assuming uniform Lyapunov stability or dwell-time conditions) is proposed. A further extension of the generalized LaSalle invariance principle allows the consideration of cascaded switched NLTV systems, which is used to characterize how consensus in a leaderless swarm of nonholonomic robots allowing for switching communication topologies may be established.

Energy efficiency maximization for downlink cloud radio access networks with data sharing and data compression

- Tung Thanh, Vu, Duy Trong, Ngo, Dao, Minh, Durrani, Salman, Nguyen, Duy, Middleton, Richard

Authors: Tung Thanh, Vu , Duy Trong, Ngo , Dao, Minh , Durrani, Salman , Nguyen, Duy , Middleton, Richard
Date: 2018
Type: Text , Journal article
Relation: IEEE transactions on wireless communications Vol. 17, no. 8 (2018), p. 4955-4970
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Description: This paper aims to maximize the energy efficiency of a downlink cloud radio access network (C-RAN). Here, data is transferred from a baseband unit in the core network to several remote radio heads via a set of edge routers over capacity-limited fronthaul links. The remote radio heads then send the received signals to their users via radio access links. Both data sharing and compression-based strategies are considered for fronthaul data transfer. New mixed-integer nonlinear problems are formulated, in which the ratio of network throughput and total power consumption is maximized. These challenging problem formulations include practical constraints on routing, predefined minimum data rates, fronthaul capacity, and maximum remote radio head transmit power. By employing the successive convex quadratic programming, iterative algorithms are proposed with guaranteed convergence to the Fritz John solutions of the formulated problems. Significantly, each iteration of the proposed algorithms solves only one simple convex program. Numerical examples with practical parameters confirm that the proposed joint optimization designs markedly improve the C-RAN's energy efficiency compared to benchmark schemes. They also show that the fronthaul data-sharing strategy outperforms its compression-based counterpart in terms of energy efficiency, in both single-hop and multi-hop network scenarios.

Spectral and energy efficiency maximization for content-centric c-rans with edge caching

- Vu, Tung Thanh, Ngo, Duy Trong, Dao, Minh, Durrani, Salman, Middleton, Richard

Authors: Vu, Tung Thanh , Ngo, Duy Trong , Dao, Minh , Durrani, Salman , Middleton, Richard
Date: 2018
Type: Text , Journal article
Relation: IEEE transactions on communications Vol. 66, no. 12 (2018), p. 6628-6642
Full Text: false
Reviewed:
Description: This paper aims to maximize the spectral and energy efficiencies of a content-centric cloud radio access network (C-RAN), where users requesting the same contents are grouped together. Data are transferred from a central baseband unit to multiple remote radio heads (RRHs) equipped with local caches. The RRHs then send the received data to each group's user. Both multicast and unicast schemes are considered for data transmission. We formulate mixed-integer nonlinear problems in which user association, RRH activation, data rate allocation, and signal precoding are jointly designed. These challenging problems are subject to minimum data rate requirements, limited fronthaul capacity, and maximum RRH transmit power. Employing successive convex quadratic programming, we propose iterative algorithms with guaranteed convergence to Fritz John solutions. Numerical results confirm that the proposed joint designs markedly improve the spectral and energy efficiencies of the considered content-centric C-RAN compared to benchmark schemes. Importantly, they show that unicasting outperforms multicasting in terms of spectral efficiency in both cache and cache-less scenarios. In terms of energy efficiency, multicasting is the best choice for the system without cache whereas unicasting is best for the system with cache. Finally, edge caching is shown to improve both spectral and energy efficiencies.

P2DCA: A Privacy-preserving-based data collection and analysis framework for IoMT applications

- Usman, Muhammad, Jan, Mian, He, Xiangjian, Chen, Jinjun

Authors: Usman, Muhammad , Jan, Mian , He, Xiangjian , Chen, Jinjun
Date: 2019
Type: Text , Journal article
Relation: IEEE journal on selected areas in communications Vol. 37, no. 6 (2019), p. 1222-1230
Full Text: false
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Description: The concept of Internet of Multimedia Things (IoMT) is becoming popular nowadays and can be used in various smart city applications, e.g., traffic management, healthcare, and surveillance. In the IoMT, the devices, e.g., Multimedia Sensor Nodes (MSNs), are capable of generating both multimedia and non-multimedia data. The generated data are forwarded to a cloud server via a Base Station (BS). However, it is possible that the Internet connection between the BS and the cloud server may be temporarily down. The limited computational resources restrict the MSNs from holding the captured data for a longer time. In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server. However, this data collection may create privacy issues, such as revealing identities and location information of MSNs. Therefore, there is a need to preserve the privacy of MSNs during mobile data collection. In this paper, we propose an efficient privacy-preserving-based data collection and analysis (P2DCA) framework for IoMT applications. The proposed framework partitions an underlying wireless multimedia sensor network into multiple clusters. Each cluster is represented by a Cluster Head (CH). The CHs are responsible to protect the privacy of member MSNs through data and location coordinates aggregation. Later, the aggregated multimedia data are analyzed on the cloud server using a counter-propagation artificial neural network to extract meaningful information through segmentation. Experimental results show that the proposed framework outperforms the existing privacy-preserving schemes, and can be used to collect multimedia data in various IoMT applications.

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