Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Secondary Users (SU) are guided by Cognitive Radio Device in identifying a channel licensed to Primary Users (PU) when it is free. Whenever PU arrives, SU shall vacate and search for the alternate band by a conditional handoff. Existing handoff methods reduce the network’s efficiency. The proposed stochastic approach under hybrid Spectrum Hand-off is (SHO) employing the Invasive Weed Optimization (IWO) algorithm thus increasing the SHO efficiency in CR Networks. The proposed Centralized Cognitive Device monitors load balancing, minimizes handoff delay as well as the SU’s service time, conserves energy, speeds up the data transmission, supports the Internet of Things, multimedia applications including audio, live video streaming and still images over resource constrained WSNs. This algorithm is compared with the existing Genetic algorithm and Particle Swarm Optimization. The channel selection accuracy of the proposed IWO method is found to be 97.8% and it outperforms conventional methods. Besides, the pre-emptive resume priority M/G/1 queuing model is also utilized. This paper presents a complete system model along with a detailed study of its parameters proving the technique’s effectiveness.

Department of Smart Computing

School of Information Technology and Engineering

Vellore Campus

Department of Computer Applications

Telecommunication Systems

Stochastic approach for channel selection in cognitive radio networks using optimization techniques

Devices of cognitive Radio (CR) help Secondary Users (SU) to locate a vacant channel licensed to Primary Users (PU). SU vacates the channel by doing a handoff on condition when PU enters that channel. Now SU seeks a different channel to proceed further. This paper narrates an algorithm of an evolutionary type called Invasive Weed Optimization (IWO) that solves the issue of Spectrum Handoff (SHO). The fixed and Probabilistic method of hybrid spectrum handoff utilizes the algorithm of IWO. The prime limitation of SUs’ prolonging the time in data delivery gets minimal by optimizing the period in the network of CR. The centralized Cognitive Device (CCD) is minimizing delay in handoff, monitoring the balancing of the load as well as improving efficiency. The proposed method is verified and validated with the existing Genetic algorithm (GA) and Particle Swarm Optimization (PSO) methods. Apart from that the pre-emptive resume priority MG/1 model for queuing too is employed. This leads the IWO method’s accuracy of channel selection to an improvement of 97.6%. There is a greater reduction in handoff delay. This excels in the schemes of traditional type in practice now. © 2020, World Academy of Research in Science and Engineering. All rights reserved.

International Journal of Advanced Trends in Computer Science and Engineering

Probabilistic approach for channel selection in centralized cognitive scheme utilizing invasive weed optimization

This paper utilizes the cognitive radio (CR) spectrum to the fullest extent for extended applications requiring discretion. The CR technology provides various supports for cognitive radio networks (CRNs). The latter has CR nodes that sense free channels. Then, the CRN allocates the unused channels to secondary users (SUs) or unlicensed users. This allocation is termed the spectrum handoff. In this paper, by considering the identical channels in CR networks, a novel machine learning algorithm (the support vector machine—SVM) is employed. In addition, the queuing model of the preemptive resume priority M/M/1 is used. The proposed spectrum handoff algorithm selects the best possible CR network channel. The spectrum handoff algorithm uses the stated SVM algorithm scheme, which covers the transmitted and received power, the minimum service time, the data rate and the maximum vacancy time for the SU, to attain the maximum throughput. However, in multi-user greedy channel selection (GCS), only two parameters are considered. The proposed spectrum handoff algorithm based on the SVM scheme enhances the performance, and the SU throughput is improved to 68.7%. This approach is better than the GCS channel selection scheme. Additionally, this approach decreases the number of spectrum handoffs. As a result, the training accuracy of the SVM method is 97.6%, and it outperforms conventional methods. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Circuits, Systems, and Signal Processing

Machine Learning-Based Algorithm for Channel Selection Utilizing Preemptive Resume Priority in Cognitive Radio Networks Validated by NS-2

This paper considers the problem of conserving energy in a single node in a wireless sensor network by turning off the node's radio for periods of a fixed time length. While packets may continue to arrive at the node's buffer during the sleep periods, the node cannot transmit them until it wakes up. The objective is to design sleep control laws that minimize the expected value of a cost function representing both energy consumption costs and holding costs for backlogged packets. Large number of very small size, low cost, low power multifunctional sensor nodes constitute the WSN. These sensors way the understanding of the physical world by sensing, processing and transmitting the data. The above functions are highly influenced by factors such as power, energy and lifetime of the sensor nodes. This technical paper explores various low power design architectures like reconfigurable hardware, sleep walker, variable dual VDD, modular architecture and folded tree architecture by presenting a comprehensive survey concerning passive and active power control mechanisms in WSN with investigation of the existing solution and evaluation. © 2006-2016 Asian Research Publishing Network (ARPN). All rights reserved.

World University root node

Department of Digital Communications

ARPN Journal of Engineering and Applied Sciences

Energy efficient consumption for quality based sleep scheduling in wireless sensor networks

Objectives: Consolidating any two dissimilar networks leads to a formation of a heterogeneous wireless network. Vision to achieve distinct networks to get converged such that characterizing of the upcoming wireless networks becomes a reality. This in turn ushers in vertical handoff such that handoff among different technologies is efficient. And at the same time it is so smooth that naming it as seamless is justifiable. Pragmatic consideration of the network characteristics and the dynamics are very essential to choose one of the best available network handoff decisions. Methods: Stating conventionally, when a mobile client is roaming, a single criterion- for example, received signal strength is employed to realize the vertical handoff. But, single criterion consideration is not sufficient and taking into account of other parameters is needed for a proper handoff decision. The present paper puts forth a strong load balancing algorithm M-OPTF that is based on dynamic weight assignment technique for allocation of fresh calls and handoff calls. Findings: In the present scheme assignment of weight factor is done to attachment points basing on the distribution of load on them. In addition it contains a process for handoff decision that takes into account the velocity of mobile node to do triggering of handoff. The put forth M-OPTF algorithm is compared along with Remote Sensing Systems (RSS) and OPT-F algorithm in this paper. Application/Improvements: The simulation results confirm the fact that M-OPTF algorithm apart from balancing the network load it can effectively decrease congestion in network, reduce the number of handoffs that are unnecessary, and increases the battery life time, thus improve the overall performance of the system.&nbsp;

Department of Communication Engineering

School of Electronics Engineering

Fulltext

Indian Journal of Science and Technology

Dynamic Weight Assignment based Vertical Handoff Algorithm for Load Optimization

The main focus of this paper is on Big Data and Data Visualization techniques which together make the usage of data analytics more efficient and valuable. The term ‘Big Data’, is a phenomenon that is characterized by the rapid expansion of raw data. Big Data is often voluminous and tends to rapidly change, making it challenging to get a handle on and difficult to access. The majority of tools available to work with Big Data are complex, and most enterprises don’t have the sufficient experts to perform the required data analysis. Thus Data visualization techniques simplify this challenge and create an opportunity in analyzing and controlling the data in much more efficient way. © Research India Publications.

International Journal of Applied Engineering Research

Visualization: Enhancing big data more adaptable and valuable

The main objective of the paper is effective use of Vertical handoff for a heterogeneous vehicular network. To minimize the data transmission time or alternatively the cost of transmitting (data), taking into account the velocity of the vehicle are the goals too. The problems arise during these process will also be addressed. If a handoff is required, with the help of available technologies, considering the velocity of vehicle, a handoff between WLAN and Cellular will be done. In depth analysis shows that to minimize cost of both parameters at lower velocities VHO is suitable, while at higher velocities cellular suits. On ensuring a handoff using Max-Opt, the best Attachment Point is found. For optimized VHO decision, it is compared with other methods like the one, where Strongest Signal First (SSF) was used. We demonstrate in Max-Opt, apart from handoff collective battery lifetime and load balancing is also better, compared to SSF method. Results obtained justify that functioning of optimal VHO decision making while considering (as it also takes care of) the network selection parameters as well as user mobility profile.Final section of this article discussed the survey of various possible alternative algorithms deciding vertical handoff. © 2016, Research India Publications.