Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

If you want to get Handbook of Internet Computing pdf eBook copy write by good Handbook of Wireless Networks and Mobile Computing Google Books. Mobile Computing General. Handbook of Algorithms for Wireless Networking and Mobile Computing by Azzedine Boukerche (Editor). Call Number: TK 5103.2. CITS4419 Mobile and Wireless Computing software projects related to wireless networks, (2) write technical reports and documentation for complex computer.

Handbook of wireless networks and mobile computing

More particularly, calculations are developed and examined to reduce the entire quantity of Wireless access points as well as their locations in almost any given atmosphere while with the throughput needs and the necessity to ensure every place in the area can achieve a minimum of k APs. This paper concentrates on using Wireless for interacting with and localizing the robot. We've carried out thorough studies of Wireless signal propagation qualities both in indoor and outside conditions, which forms the foundation for Wireless AP deployment and communication to be able to augment how human operators communicate with this atmosphere, a mobile automatic platform is developed. Gas and oil refineries could be a harmful atmosphere for various reasons, including heat, toxic gasses, and unpredicted catastrophic failures. When multiple Wireless APs are close together, there's a possible for interference. A graph-coloring heuristic can be used to find out AP funnel allocation. Additionally, Wireless fingerprinting based localization is developed. All of the calculations implemented are examined in real life situations using the robot developed and answers are promising. For example, within the gas and oil industry, during inspection, maintenance, or repair of facilities inside a refinery, people might be uncovered to seriously high temps to have a long time, to toxic gasses including methane and H2S, and also to unpredicted catastrophic failures.

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Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds

Internet of Things is smartly changing various existing research areas into new themes, including smart health, smart home, smart industry, and smart transport. Relying on the basis of “smart transport,” Internet of Vehicles (IoV) is evolving as a new theme of research and development from vehicular ad hoc networks (VANETs). This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects. Specifically, following the background on the evolution of VANETs and motivation on IoV an overview of IoV is presented as the heterogeneous vehicular networks. The IoV includes five types of vehicular communications, namely, vehicle-to-vehicle, vehicle-to-roadside, vehicle-to-infrastructure of cellular networks, vehicle-to-personal devices, and vehicle-to-sensors. A five layered architecture of IoV is proposed considering functionalities and representations of each layer. A protocol stack for the layered architecture is structured considering management, operational, and security planes. A network model of IoV is proposed based on the three network elements, including cloud, connection, and client. The benefits of the design and development of IoV are highlighted by performing a qualitative comparison between IoV and VANETs. Finally, the challenges ahead for realizing IoV are discussed and future aspects of IoV are envisioned.

https://ir.nctu.edu.tw:443/bitstream/11536/145531/1/3ecec2a2f6e38b0824ccca63f66259e9.pdf

Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects

The wireless sensor networks have long been an attractive field to the researchers and scientists for its ease in deployment and maintenance. In this research, we focus on the maximization of network lifetime which has become a critical issue in sensor networks. Clustered organization of nodes with aggregation of data at the cluster head becomes one of the significant means to extend life expectancy of the network. This paper proposes Particle Swarm Optimization (PSO) approach for generating energy-aware clusters by optimal selection of cluster heads. The PSO eventually reduces the cost of locating optimal position for the head nodes in a cluster. In addition, we have implemented the PSO-based approach within the cluster rather than base station, which makes it a semi-distributed method. The selection criteria of the objective function are based on the residual energy, intra-cluster distance, node degree and head count of the probable cluster heads. Furthermore, influence of the expected number of packet retransmissions along the estimated path towards the cluster head is also reflected in our proposed energy consumption model. The performance evaluation of our proposed technique is carried out with respect to the well-known cluster-based sensor network protocols, LEACH-C and PSO-C respectively. Finally, the simulation clarifies the effectiveness of our proposed work over its comparatives in terms of network lifetime, average packet transmissions, cluster head selection rounds supported by PSO and average energy consumption.

/pdf/a-novel-energy-aware-cluster-head-selection-based-on-4dd3da93ll.pdf

A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks

A broad variety of location dependent services will become feasible in the near future due to the use of the Global Positioning System (GPS), which provides location information (latitude, longitude and possibly height) and global timing to mobile users. Routing is a problem of sending a message from a source to a destination. Geocasting is a problem of sending a message to all nodes located within a region (e.g. circle or square). Recently, several localized GPS based routing and geocasting protocols for a mobile ad hoc network were reported in literature. In directional (DIR) routing and geocasting methods, node A (the source or intermediate node) transmits a message m to all neighbors located between the two tangents from A to the region that could contain the destination. It was shown that memoryless directional methods might create loops in routing process. In two other proposed methods (proven to be loop-free), geographic distance (GEDIR) or most forward progress within radius (MFR) routing, node A forwards the message to its neighbor who is closest to destination, or has greatest progress towards destination (respectively). In this paper, we propose a general algorithm (based on a unified framework for both routing and geocasting problems), in which message is forwarded to exactly those neighbors, which may be best choices for a possible position of destination (using the appropriate criterion). We then propose and discuss new V-GEDIR and CH-MFR methods and define R-DIR, modified version of existing directional methods. In V-GEDIR method, these neighbors are determined by intersecting the Voronoi diagram of neighbors with the circle (or rectangle) of possible positions of destination, while the portion of the complex hull of neighboring node is analogously used in the CH-MFR method. Routing and geocasting algorithms differ only inside the circle/rectangle. We propose memoryless and past traffic memorization variants of each scheme. The proposed methods may be also used for the destination search phase allowing the application of different routing schemes after the exact destination of position is discovered. Memoryless V-GEDIR and CH-MFR algorithms are loop-free, and have smaller flooding rate (with similar success rate) compared to directional method. Simulations involving the proposed and some known algorithms are in progress and confirm our expectations.

Voronoi diagram and convex hull based geocasting and routing in wireless networks

In emerging sensor network applications, localization in wireless sensor network is a recent area of research. Requirement of its applications and availability of resources need feasible localization algorithm with lower cost and higher accuracy. In this paper, we propose an Advanced DV-Hop localization algorithm that reduces the localization error without requiring additional hardware and computational costs. The proposed algorithm uses the hop-size of the anchor (which knows its location) node, from which unknown node measures the distance. In the third step of Advanced DV-Hop algorithm, inherent error in the estimated distance between anchor and unknown node is reduced. To improve the localization accuracy, we use weighted least square algorithm. Furthermore, location of unknown nodes is refined by using extraneous information obtained by solving the equations. By mathematical analysis, we prove that Advanced DV-Hop algorithm has lesser correction factor in the distance between anchor and the unknown node compared with DV-Hop algorithm, improved DV-Hop algorithm (Chen et al. 2008) and improved DV-Hop algorithm (Chen et al. in IEICE Trans Fundam E91-A(8), 2008), which is cause of better location accuracy. Simulation results show that the performance of our proposed algorithm is superior to DV-Hop algorithm and improved DV-Hop algorithms in all considered scenarios.

An Advanced DV-Hop Localization Algorithm for Wireless Sensor Networks

In wireless sensor network, data fusion is considered an essential process for preserving sensor energy. Periodic data sampling leads to enormous collection of raw facts, the transmission of which would rapidly deplete the sensor power. In this paper, we have performed data aggregation on the basis of entropy of the sensors. The entropy is computed from the proposed local and global probability models. The models provide assistance in extracting high precision data from the sensor nodes. We have also proposed an energy efficient method for clustering the nodes in the network. Initially, sensors sensing the same category of data are placed within a distinct cluster. The remaining unclustered sensors estimate their divergence with respect to the clustered neighbors and ultimately join the least-divergent cluster. The overall performance of our proposed methods is evaluated using NS-2 simulator in terms of convergence rate, aggregation cycles, average packet drops, transmission cost and network lifetime. Finally, the simulation results establish the validity and efficiency of our approach.

https://link.springer.com/content/pdf/10.1186%2F2192-1962-3-13.pdf

D. K. Lobiyal

Papers

A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks

Voronoi diagram and convex hull based geocasting and routing in wireless networks

An Advanced DV-Hop Localization Algorithm for Wireless Sensor Networks

Performance evaluation of data aggregation for cluster-based wireless sensor network

Bèzier curves based on Lupaş (p,q)-analogue of Bernstein functions in CAGD