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Cyber Physical Systems for Disaster Response NetworksConceptual Proposal using Never Ceasing Network
Published in G.R. Karpagam, B. Vinoth Kumar, J. Uma Maheswari, Xiao-Zhi Gao, Smart Cyber Physical Systems, 2020
I. Devi, G.R. Karpagam, J. Uma Maheswari
The NCN is a cognitive radio network which observes the operating environment, formulates decisions, configures the system based on observations and learns from experience obtained. A cognitive network has the ability to adapt to network changes, according to the observations. A cognitive network provides support for self-organization, interoperability, heterogeneity and re-configurability with available networks. This chapter focuses on the development of a prototype of the Never Ceasing Network in disaster-response networks.
Artificial Neural Networks
Published in Paresh Chra Deka, A Primer on Machine Learning Applications in Civil Engineering, 2019
A cognitive network is a data communication network which consists of intelligent devices. By ‘intelligent,’ we mean that these networks are aware of everything happening inside the device and in the network they are connected to. Using this awareness, they can adjust their operation to match current and near-future network conditions. The cognitive network aims to be proactive so that it can predict most of the usual use cases before they occur and adapt to those beforehand. If predictions fail, it falls back to reactive method and looks for the optimal way of handling the new situation. In any case, the cognitive network learns from every situation it encounters and uses that information for future cases. The main goal of the cognitive network is to increase network efficiency and performance. An important aspect of the cognitive network is that it optimizes data communication for the entire network between the sender and the receiver to meet required end-to-end goals of users of the network. A network becomes cognitive when all the statically configured parts of the network are replaced with self-adjusting and self-aware components. Statically configured nodes are not cognitive, because they need an external operator (human) to make decisions and take care of configuration. The promise of cognitive networking is that the network itself can find optimal ways of connecting devices and tuning network parameters to achieve the best performance for data transfers. It can even optimize for events that have not happened but are likely to happen. The conventional network forwards packets using routing algorithms and detects failures after packets are lost. It also knows the status of every node, so it doesn’t send data using a route that cannot deliver the packet, so it prevents congestion. There have been multiple definitions of cognitive networking each one refining and tightening the definition. Thomas et al. define CN as ‘a cognitive network is a network with a cognitive process that can perceive current network conditions, and then plan, decide, and act on those conditions.’ The network can learn from these adaptations and use them to make future decisions, all while taking into account end-to-end goals.
SWAQ: a Semantic Web Application Quality Evaluation Framework
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2018
The conventional weighted-sum based methods cannot be used in SWAQ’s hierarchical structure of attributes, in order to obtain overall ranking. Moreover, some attributes do not have direct measures or any numeric measure at all. Another issue is to compare each SWA based on each attribute, how to quantify them and how to aggregate them in a meaningful metric (Starr & Zeleny, 1977). AHP is based on pairwise comparisons of decision criteria, thus allowing the decision maker to determine trade-offs among the criteria. Pairwise comparison matrices have also been used earlier. We validate the matrix with consistency index, similar to the effort of Yuen (2014b) which uses accordance index. Primitive Cognitive Network Process presented in (Yuen, 2014b) may also be used to make more reliable decisions using AHP data similar to our work. Moreover, AHP has the power to check inconsistencies of evaluation criteria and alternatives (Saaty, 2000) . AHP also helps to capture both subjective and objective assessment measures, and reduces prejudice in decision making. Our premise lies in using subjective pairwise comparisons to render flexibility and control to the end user for assigning ratio scale values to quality attributes of SWAs. For objectivity in healthcare decision making and other application areas, Yuen (2010) proposes prioritisation of the AHP hierarchy. Thus, in SWAQ’s AHP module, a user assigns priorities to the quality attributes of SWAs. Based on the assigned priorities and the value of quality metrics, SWAs are ranked in decreasing order of quality.
EMA-PRBDS: efficient multi-attribute packet rank based data scheduling in wireless sensor networks for real-time monitoring systems
Published in International Journal of Electronics, 2020
N Mahendran, S Shankar, T Mekala
The author proposes the cross-layer mechanism as a cognitive network congestion control technique with network and transport layers in the OSI model. The MAC layer finds the length of the queue, the congestion degree and the degree of cumulative congestion from downstream nodes sends to network and transport layer. TOPSIS gives the best neighbour for the next node with less distance from a positive ideal solution and more distance from a negative ideal solution by considering the above metrics. The methodology along with TOPSIS increases energy efficiency, throughput, and packet delivery ratio also reduces the congestion with intermediate nodes in the network (Gholipour, Haghighat, & Meybodi, 2017).
Performance analysis of a cognitive radio network with adaptive RF energy harvesting
Published in International Journal of Electronics Letters, 2021
S. Mondal, S. D. Roy, S. Kundu
Cognitive radio exploits a dynamic spectrum sharing technology where secondary users or unlicensed users can use the spectrum of primary or licenced users without affecting the quality of service of primary users (PUs) Mitola and Maguire (1999). Due to long distance and deep fading, a signal received at a destination may not be decoded correctly. To overcome this problem, cooperative relay has been incorporated to transfer signal from source to destination successfully via intermediate relays. Several works have investigated the use of a single and multiple intermediate relays in dual-hop scenario and multi-hop scenarios, respectively (Bao & Duong, 2012; Duong, Bao, & Zepernick 2011; Mondal, Dhar Roy, & Kundu, 2017; Tuyen & Bao 2012). In Tuyen and Bao, the outage performance of dual hop amplify and forward (AF) network in an underlay spectrum sharing cognitive radio network has been analysed under different amplifying gains. An analytical expression of outage probability of secondary in AF cognitive scenario has been evaluated in Duong et al. (2011). Further, the work is extended from dual hop to multiple hop and the performance of secondary network is analysed in Bao and Duong (2012). In Mondal et al. (2017), the authors have considered multi-hop network where all SR nodes harvest energy from a periodic transmission signal of primary transmitter. Wireless RF energy harvesting is recently attracting significant research attention, where power is supplied to wireless devices from RF signals. In Kalamkar and Banerjee (2015), the authors have analysed the performance of an energy harvesting based DF relaying secondary network considering the primary outage constraint. In Thanh, Hoan, Vu-Van, and Koo (2019) the authors have focused on energy efficient attack strategy employing partial observed decision process where eavesdropper is powered by RF energy harvesting. An energy-efficient power allocation strategy has been proposed for data transmission against a full-duplex active eavesdropper in a energy harvesting based cognitive network in Do, Koo, et al. (2019). In Tang, Ren, Wang, and Han (2016), the authors have discussed optimal strategies to maximise wiretap rate as well as legitimate transmission in present of full duplex active eavesdropper.