Optimizing RPL performance based on the selection of best route between child and root node using E-MHOF method

IETF has proposed the routing protocol for low power and lossy networks (RPL) for IOT as view as light weight routing protocol. In RPL, the objective function (OF) is used to select the best route between child and root node. Several researches have been conducted in order to, enhance OF according to number parameters such as number of hops, remaining energy and expected number of transmissions (ETX), without a consideration to other challenges such as congestion node problem and latency. So, to overcome these challenges a new technique called “Enhance-Minimum Rank with Hysteresis Objective Function (MHOF)” is proposed in this paper, to select the ideal path between the child and root node. The technique is consisted of three layers: parent selection layer in which parent is selected based on three parameters (ETX, RSSI and nodes’ residual energy), path selection layer in which the best route is chosen according to the minimum of (average ETX value) and maximum of (average remaining energy value) of all nodes in the selected route. The last layer is child node minimization, which utilized to solve the congestion node energy problem by using two parameters (RSSI reference and threshold value). The proposed method has been implemented and evaluated by using Cooja simulator software. The simulation results have shown that selected path with E-MHOF is increased the network lifetime and reduced latency in comparison with MHOF

Adaptive Energy Saving and Mobility Support IPv6 Routing Protocol in Low-Power and Lossy Networks for Internet of Things and Wireless Sensor Networks

Internet of Things (IoT) is an interconnection of physical objects that can be controlled, monitored and exchange information from remote locations over the internet while been connected to an Application Programme Interface (API) and sensors. It utilizes low-powered digital radios for communication enabling millions and billions of Low-power and Lossy Network (LLN) devices to communicate efficiently via a predetermined routing protocol. Several research gaps have identified the constraints of standardised versions of IPv6 Routing Protocol for Low Power and Lossy Networks evidently showing its lack of ability to handle the growing application needs and challenges. This research aims to handle routing from a different perspective extending from energy efficiency, to mobility aware and energy scavenging nodes thereby presenting numerous improvements that can suit various network topologies and application needs. Envisioning all the prospects and innovative services associated with the futuristic ubiquitous communication of IoT applications, we propose an adaptive Objective Function for RPL protocol known as Optimum Reliable Objective Function (OR-OF) having a fuzzy combination of five routing metrics which are chosen based on system and application requirements. It is an approach which combines the three proposed implemented Objective Functions within this thesis to enable the OR-OF adapt to different routing requirements for different IoT applications. The three proposed OFs are Energy saving Routing OF, Enhanced Mobility Support Routing OF and Optimized OF for Energy Scavenging nodes. All proposed OFs were designed, implemented, and simulated in COOJA simulator of ContikiOS, and mathematical models were developed to validate simulated results. Performance Evaluation indicated an overall improvement as compared with the standardised versions of RPL protocols and other related research works in terms of network lifetime with an average of 40%, packet delivery ratio of 21%, energy consumption of 82% and End-to-End Delay of 92%

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Application of PLS-SEM for small-scale survey: an empirical example of SMEs

Recent developments in Structural Equation Modelling (SEM) have been claimed to add some sophistication onto quantitative research methods' usage in terms of their research versatility, efficiency and practicality in a range of disciplines including Information Systems, Marketing, and People Management research. Although covariance based SEM (CB-SEM) is most prominent, application of partial least square structural equation modelling (PLS-SEM) is an attractive alternative. This paper examines and applies the characteristics of PLS-SEM onto SMEs to see whether the efficiency, practicality and versatility assumptions, as claimed, do actually contribute to SMEs' business entrepreneurship in practice. The research question is therefore 'Do the embedded PLS-SEM assumptions of research versatility, practicality and efficiency actually translate into practical reality in SMEs operating in an emerging economy context?' We used a quantitative method data analysis technique as a precursor to help us identify the types of challenges faced by SMEs at both the micro and macro levels of analysis. Primary survey data from 212 Bangladeshi SMEs located at various geographic districts provide the study's population. We assess the application of the technique as a research methodological tool and its limitations provided the basis for us to develop and validate a partial least square based structural equation model (PLS-SEM) as part of a small scale survey-based research on SMEs. These methodological insights then led to a successful framing of SMEs in a model that contributes to a process of identifying which types of challenges are more critical for SMEs' growth. Our results show that for SMEs to be competitive, the business and research benefits of our modelling and methodological technique should be given foreseeable attention by both academics and business practitioners. This methodological perspective is yet to gain researchers and professional practitioners' attention from SMEs' business perspective. By applying the statistical PLS technique to Business and Management Studies research we are contributing to a deeper understanding and knowledge creation in examining the assumptions, the design and application of a sophisticated research tool for the development of People Management, Business and SME theory and practice with a focus on an emerging economy

Proceedings of the 9th MIT/ONR workshop on C3 Systems, held at Naval Postgraduate School and Hilton Inn Resort Hotel, Monterey, California June 2 through June 5, 1986

GRSN 627729"December 1986."Includes bibliographical references and index.Sponsored by Massachusetts Institute of Technology, Laboratory for Information and Decision Systems, Cambridge, Mass., with support from the Office of Naval Research. ONR/N00014-77-C-0532(NR041-519) Sponsored in cooperation with IEEE Control Systems Society, Technical Committee on C.edited by Michael Athans, Alexander H. Levis

Multicriteria Parent Selection Using Cognitive Radio for RPL in Smart Grid Network

To maintain reliability of advanced metering infrastructure network in smart grid, data sent from a smart meter must reach a data concentrator unit efficiently. Parent selecting mechanism in routing protocol for low-power and lossy (RPL) is a key to maintain the reliability by balancing workload of meters in the network. In this paper, a parent selecting mechanism with three criteria including expected transmission count, residual energy, and expected transmission time is proposed to improve workload balancing and lifetime differences of all meters. A meter selects an immediate parent based on three factors. From simulation results, parents’ workload is better balanced and the lifetime of all meters in the network is depleted nearly at the same time. Moreover, a simulation with cognitive radio enabled meters, where data can be transmitted on a licensed channel opportunistically when the channel is not utilized, shows an improvement in the packet delivery ratio

European Distance and E-Learning Network (EDEN). Conference Proceedings

Erasmus+ Programme of the European UnionThe powerful combination of the information age and the consequent disruption caused by these unstable environments provides the impetus to look afresh and identify new models and approaches for education (e.g. OERs, MOOCs, PLEs, Learning Analytics etc.). For learners this has taken a fantastic leap into aggregating, curating and co-curating and co-producing outside the boundaries of formal learning environments – the networked learner is sharing voluntarily and for free, spontaneously with billions of people.Supported by Erasmus+ Programme of the European Unioninfo:eu-repo/semantics/publishedVersio