Study on Different Topology Manipulation Algorithms in Wireless Sensor Network

Wireless sensor network (WSN) comprises of spatially distributed autonomous sensors to screen physical or environmental conditions and to agreeably go their information through the network to a principle area. One of the critical necessities of a WSN is the efficiency of vitality, which expands the life time of the network. At the same time there are some different variables like Load Balancing, congestion control, coverage, Energy Efficiency, mobility and so on. A few methods have been proposed via scientists to accomplish these objectives that can help in giving a decent topology control. In the piece, a few systems which are accessible by utilizing improvement and transformative strategies that give a multi target arrangement are examined. In this paper, we compare different algorithms' execution in view of a few parameters intended for every target and the outcomes are analyzed. DOI: 10.17762/ijritcc2321-8169.15029

A network-aware framework for energy-efficient data acquisition in wireless sensor networks

Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN

A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing

QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms

Copyright @ Elsevier Ltd. All rights reserved.In this paper, two bio-inspired Quality of Service (QoS) multicast algorithms are proposed in IP over dense wavelength division multiplexing (DWDM) optical Internet. Given a QoS multicast request and the delay interval required by the application, both algorithms are able to find a flexible QoS-based cost suboptimal routing tree. They first construct the multicast trees based on ant colony optimization and artificial immune algorithm, respectively. Then a dedicated wavelength assignment algorithm is proposed to assign wavelengths to the trees aiming to minimize the delay of the wavelength conversion. In both algorithms, multicast routing and wavelength assignment are integrated into a single process. Therefore, they can find the multicast trees on which the least wavelength conversion delay is achieved. Load balance is also considered in both algorithms. Simulation results show that these two bio-inspired algorithms can construct high performance QoS routing trees for multicast applications in IP/DWDM optical Internet.This work was supported in part ny the Program for New Century Excellent Talents in University, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1, the National Natural Science Foundation of China under Grant no. 60673159 and 70671020, the National High-Tech Reasearch and Development Plan of China under Grant no. 2007AA041201, and the Specialized Research Fund for the Doctoral Program of Higher Education under Grant no. 20070145017

A COMMUNICATION FRAMEWORK FOR MULTIHOP WIRELESS ACCESS AND SENSOR NETWORKS: ANYCAST ROUTING & SIMULATION TOOLS

The reliance on wireless networks has grown tremendously within a number of varied application domains, prompting an evolution towards the use of heterogeneous multihop network architectures. We propose and analyze two communication frameworks for such networks. A first framework is designed for communications within multihop wireless access networks. The framework supports dynamic algorithms for locating access points using anycast routing with multiple metrics and balancing network load. The evaluation shows significant performance improvement over traditional solutions. A second framework is designed for communication within sensor networks and includes lightweight versions of our algorithms to fit the limitations of sensor networks. Analysis shows that this stripped down version can work almost equally well if tailored to the needs of a sensor network. We have also developed an extensive simulation environment using NS-2 to test realistic situations for the evaluations of our work. Our tools support analysis of realistic scenarios including the spreading of a forest fire within an area, and can easily be ported to other simulation software. Lastly, we us our algorithms and simulation environment to investigate sink movements optimization within sensor networks. Based on these results, we propose strategies, to be addressed in follow-on work, for building topology maps and finding optimal data collection points. Altogether, the communication framework and realistic simulation tools provide a complete communication and evaluation solution for access and sensor networks

Integrated Access and Backhaul for 5G and Beyond (6G)

Enabling network densification to support coverage-limited millimeter wave (mmWave) frequencies is one of the main requirements for 5G and beyond. It is challenging to connect a high number of base stations (BSs) to the core network via a transport network. Although fiber provides high-rate reliable backhaul links, it requires a noteworthy investment for trenching and installation, and could also take a considerable deployment time. Wireless backhaul, on the other hand, enables fast installation and flexibility, at the cost of data rate and sensitivity to environmental effects. For these reasons, fiber and wireless backhaul have been the dominant backhaul technologies for decades. Integrated access and backhaul (IAB), where along with celluar access services a part of the spectrum available is used to backhaul, is a promising wireless solution for backhauling in 5G and beyond. To this end, in this thesis we evaluate, analyze and optimize IAB networks from various perspectives. Specifically, we analyze IAB networks and develop effective algorithms to improve service coverage probability. In contrast to fiber-connected setups, an IAB network may be affected by, e.g., blockage, tree foliage, and rain loss. Thus, a variety of aspects such as the effects of tree foliage, rain loss, and blocking are evaluated and the network performance when part of the network being non-IAB backhauled is analysed. Furthermore, we evaluate the effect of deployment optimization on the performance of IAB networks.First, in Paper A, we introduce and analyze IAB as an enabler for network densification. Then, we study the IAB network from different aspects of mmWave-based communications: We study the network performance for both urban and rural areas considering the impacts of blockage, tree foliage, and rain. Furthermore, performance comparisons are made between IAB and networks of which all or part of small BSs are fiber-connected. Following the analysis, it is observed that IAB may be a good backhauling solution with high flexibility and low time-to-market. The second part of the thesis focuses on improving the service coverage probability by carrying out topology optimization in IAB networks focusing on mmWave communication for different parameters, such as blockage, tree foliage, and antenna gain. In Paper B, we study topology optimization and routing in IAB networks in different perspectives. Thereby, we design efficient Genetic algorithm (GA)-based methods for IAB node distribution and non-IAB backhaul link placement. Furthermore, we study the effect of routing in the cases with temporal blockages. Finally, we briefly study the recent standardization developments, i.e., 3GPP Rel-16 as well as the\ua0Rel-17 discussions on routing. As the results show, with a proper planning on network deployment, IAB is an attractive solution to densify the networks for 5G and beyond. Finally, we focus on improving the performance of IAB networks with constrained deployment optimization. In Paper C, we consider various IAB network models while presenting different algorithms for constrained deployment optimization. Here, the constraints are coming from either inter-IAB distance limitations or geographical restrictions. As we show, proper network planning can considerably improve service coverage probability of IAB networks with deployment constraints