DESIGN OF MOBILE DATA COLLECTOR BASED CLUSTERING ROUTING PROTOCOL FOR WIRELESS SENSOR NETWORKS

Wireless Sensor Networks (WSNs) consisting of hundreds or even thousands of nodes, canbe used for a multitude of applications such as warfare intelligence or to monitor the environment. A typical WSN node has a limited and usually an irreplaceable power source and the efficient use of the available power is of utmost importance to ensure maximum lifetime of eachWSNapplication. Each of the nodes needs to transmit and communicate sensed data to an aggregation point for use by higher layer systems. Data and message transmission among nodes collectively consume the largest amount of energy available in WSNs. The network routing protocols ensure that every message reaches thedestination and has a direct impact on the amount of transmissions to deliver messages successfully. To this end, the transmission protocol within the WSNs should be scalable, adaptable and optimized to consume the least possible amount of energy to suite different network architectures and application domains. The inclusion of mobile nodes in the WSNs deployment proves to be detrimental to protocol performance in terms of nodes energy efficiency and reliable message delivery. This thesis which proposes a novel Mobile Data Collector based clustering routing protocol for WSNs is designed that combines cluster based hierarchical architecture and utilizes three-tier multi-hop routing strategy between cluster heads to base station by the help of Mobile Data Collector (MDC) for inter-cluster communication. In addition, a Mobile Data Collector based routing protocol is compared with Low Energy Adaptive Clustering Hierarchy and A Novel Application Specific Network Protocol for Wireless Sensor Networks routing protocol. The protocol is designed with the following in mind: minimize the energy consumption of sensor nodes, resolve communication holes issues, maintain data reliability, finally reach tradeoff between energy efficiency and latency in terms of End-to-End, and channel access delays. Simulation results have shown that the Mobile Data Collector based clustering routing protocol for WSNs could be easily implemented in environmental applications where energy efficiency of sensor nodes, network lifetime and data reliability are major concerns

The journey from 5G towards 6G

This paper gives an overview of the journey from 5G towards 6G evolution. The 5G has been built across three main application verticals as defined by ITU, namely: Enhanced Mobile Broadband, Massive Machine Type Communications and Ultra-reliable Low Latency Communications (URRLC). To support these verticals, 5G has defined the following enablers: Massive MIMO, cloudification of network infrastructure, network automation, network slicing and edge cloud computing. It is expected that 5G will provide flexibility in terms of openness, mobility, programmability and agility and robustness in a standardized manner. The journey towards 6G will describe the limitations of 5G technologies and outlines the technology enablers for 6G. These enablers include smooth integration and interworking of Non-Terrestrial Networking technologies (NTN), use of Reconfigurable Intelligent Surfaces (RIS) and use of AI to orchestrate network and cloud resources. Additionally, the paper will give an overview of 6G research initiatives at both regional and international level

Old U.S. Highway 80 area plan

abstract: The Old U.S. Highway 80 Area Plan is an entirely new plan which removes portions of the State Route 85 Area Plan and the Tonopah/Arlington Area Plan. It is important to note that this Plan is not a document that represents final buildout as many municipal general plans typically do. Rather, it prepares for and accommodates growth over the next ten to fifteen years, but will be reexamined and updated as necessary to reflect current conditions and changes. While not a complete solution, the Plan helps address the effects of growth and development by enhancing cooperation between government agencies, citizens, and other affected interests, and by considering regional implications.Issued as part of Maricopa County 2020 Eye to the Future, the Maricopa County General Plan

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

Habitat Distribution Modeling and Estimating Minimum Viable Area for Population Persistence for Three Arachnids of Conservation Interest in Gauteng Province

Faculty of Science; School of Animal, Plant and Environmental Sciences; MSC Research ReportThree arachnid species, the rock scorpion Hadogenes gunningi, the burrowing scorpion Opistophthalmus pugnax and the baboon spider Harpactira hamiltoni have been identified as species of conservation interest for inclusion in a bioregional systematic conservation planning project by the provincial conservation authority in Gauteng province, South Africa. The systematic conservation planning procedure requires information on the spatial distribution and an estimate of the minimum viable area (MVA) required to support a population for species of conservation interest. The purpose of this report is to provide this information for these three arachnid species. 47 sites were sampled on a regular grid across Gauteng province where data were collected for habitat distribution modeling and density estimation for MVA calculation. Sites were sampled by two field workers. Distance sampling methodology was used for the estimation of density and the genetic algorithm for rule set production (GARP) was used for habitat distribution modeling. Analysis of distance data comprised fitting several alternative models to both continuous and interval data, and data for each field worker were analyzed both separately and pooled. To calculate MVA from estimates of population density a minimum viable population size of 2000 adult individuals was assumed. Based on composite models fitted to continuous data collected by both field workers mean MVA for Opistophthalmus pugnax was 431.57 ha (279.44 ha to 666 ha, 95% confidence interval), while that for Harpactira hamiltoni was 909.09 ha (518.00 ha to 1594.90 ha, 95% confidence interval). Insufficient data were collected for the estimation of population density for Hadogenes gunningi, but based on encounter rate relative to the other two species a subjective estimate of MVA between 380 ha and 570 ha is presented. Habitat distribution modeling was conducted at two grains of predictor variable data. As GARP produces highly variable results models were selected according to the criteria of having less than 5% omission and less than 10% non-prediction. Selected models were stacked and predictions of presence and absence summed for each map pixel across all models. The resultant maps of proportion of positive predictions per pixel were multiplied to obtain a final composite map of probability of occurrence. Accuracy of the coarse, fine and composite maps was assessed using receiver operating characteristic analysis. Mean AUC for models for Hadogenes gunningi were 0.893, 0.857 and 0.886. For Opistophthalmus pugnax and Harpactira hamiltoni mean AUC values were 0.831, 0.790, 0.856 and 0.783, 0.765, 0.805 respectively. Probabilities of occurrence were converted to presence absence at the threshold where false positive and false negative prediction rates were equivalent. Hadogenes gunningi is predicted to occur on most ridges within the province, while Opistophthalmus pugnax and H. hamiltoni are predicted to have a patchy distribution in the southern two thirds of the province. The results presented are a significant improvement on the data previously available for these species and it is recommended that their conservation status be revised in light of the results. Concerns regarding the utility of GARP in conservation planning and suggestions for further research are outlined