A fast, adaptive, and energy-efficient multi-path-multi-channel data collection protocol for wireless sensor networks

Energy consumption, traffic adaptability, fast data collection, etc are the major issues in wireless sensor networks (WSNs). Most existing WSN protocols are able to handle one or two of the above issues with the other(s) being compromised. In order to reduce the energy consumption of wireless sensor nodes while having fast data collection under different traffic generating rates, this paper proposes a fast, adaptive, and energy-efficient multi-path-multi-channel (FAEM) data collection protocol. FAEM makes use of the Basketball Net Topology proposed in the literature, in which a multi-parent-multi-child connection table is pre-established at each node; each node is also pre-assigned a receiving channel which is different from those of the neighboring nodes so as to eliminate the transmission interference. During data transmission, time is divided into duty cycles, and each consists of two phases, namely distributed iterative scheduling phase and slot-based packet forwarding phase. The former is to match parents and children of the entire WSN in a distributed manner in order to determine whether a node should be in upload (to which parent), download (from which child), or sleep mode in a particular slot; while the latter is for nodes to take action according to the schedule. Simulation shows that our protocol is able to achieve lower energy consumption, data reliability and low latency even during a high traffic load

Genome-wide conserved consensus transcription factor binding motifs are hyper-methylated.

BACKGROUND: DNA methylation can regulate gene expression by modulating the interaction between DNA and proteins or protein complexes. Conserved consensus motifs exist across the human genome ("predicted transcription factor binding sites": "predicted TFBS") but the large majority of these are proven by chromatin immunoprecipitation and high throughput sequencing (ChIP-seq) not to be biological transcription factor binding sites ("empirical TFBS"). We hypothesize that DNA methylation at conserved consensus motifs prevents promiscuous or disorderly transcription factor binding. RESULTS: Using genome-wide methylation maps of the human heart and sperm, we found that all conserved consensus motifs as well as the subset of those that reside outside CpG islands have an aggregate profile of hyper-methylation. In contrast, empirical TFBS with conserved consensus motifs have a profile of hypo-methylation. 40% of empirical TFBS with conserved consensus motifs resided in CpG islands whereas only 7% of all conserved consensus motifs were in CpG islands. Finally we further identified a minority subset of TF whose profiles are either hypo-methylated or neutral at their respective conserved consensus motifs implicating that these TF may be responsible for establishing or maintaining an un-methylated DNA state, or whose binding is not regulated by DNA methylation. CONCLUSIONS: Our analysis supports the hypothesis that at least for a subset of TF, empirical binding to conserved consensus motifs genome-wide may be controlled by DNA methylation.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

A fusion-based machine learning approach for the prediction of the onset of diabetes

A growing portfolio of research has been reported on the use of machine learning-based architectures and models in the domain of healthcare. The development of data-driven applications and services for the diagnosis and classification of key illness conditions is challenging owing to issues of low volume, low-quality contextual data for the training, and validation of algorithms, which, in turn, compromises the accuracy of the resultant models. Here, a fusion machine learning approach is presented reporting an improvement in the accuracy of the identification of diabetes and the prediction of the onset of critical events for patients with diabetes (PwD). Globally, the cost of treating diabetes, a prevalent chronic illness condition characterized by high levels of sugar in the bloodstream over long periods, is placing severe demands on health providers and the proposed solution has the potential to support an increase in the rates of survival of PwD through informing on the optimum treatment on an individual patient basis. At the core of the proposed architecture is a fusion of machine learning classifiers (Support Vector Machine and Artificial Neural Network). Results indicate a classification accuracy of 94.67%, exceeding the performance of reported machine learning models for diabetes by ~1.8% over the best reported to date

Deep learning for diabetic retinopathy analysis : a review, research challenges, and future directions

Deep learning (DL) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple levels of abstraction. In the recent past, the use of deep learning has been proliferating, yielding promising results in applications across a growing number of fields, most notably in image processing, medical image analysis, data analysis, and bioinformatics. DL algorithms have also had a significant positive impact through yielding improvements in screening, recognition, segmentation, prediction, and classification applications across different domains of healthcare, such as those concerning the abdomen, cardiac, pathology, and retina. Given the extensive body of recent scientific contributions in this discipline, a comprehensive review of deep learning developments in the domain of diabetic retinopathy (DR) analysis, viz., screening, segmentation, prediction, classification, and validation, is presented here. A critical analysis of the relevant reported techniques is carried out, and the associated advantages and limitations highlighted, culminating in the identification of research gaps and future challenges that help to inform the research community to develop more efficient, robust, and accurate DL models for the various challenges in the monitoring and diagnosis of DR

Cognitive wireless sensor networks (CogWSNs)

Cognitive Wireless Sensor Networks (CogWSNs) are an adaptive learning based wireless sensor network relying on cognitive computational processes to provide a dynamic capability in configuring the network. The network is formed by sensor nodes equipped with cognitive modules with awareness of their operating environment. If the performance of the sensor network does not meet requirements during operation, a corrective action is derived from stored network knowledge to improve performance. After the action is implemented, feedback on the action taken is evaluated to determine the level of improvement. Example functions within CogWSNs can be as simple as to provide robust connectivity or as complex as to negotiate additional resources from neighbouring network groups with the goal of forwarding application-critical data. In this work, the concept of CogWSNs is defined and its decision processes and supporting architecture proposed. The decision role combines the Problem Solving cognitive process from A Layered Reference Model of the Brain and Polya Concept, consisting of Observe, Plan, Implement, and Evaluate phases. The architecture comprises a Transceiver, Transducer, and Power Supply virtual modules coordinated by the CogWSN's decision process together with intervention, if necessary, by a user. Three types of CogWSN modules are designed based on different implementation considerations: Rule-based CogWSN, Supervised CogWSN, and Reinforcement CogWSN. Verification and comparison for these modules are executed through case studies with focus on power transmission and communication slot allocation. Results show that all three modules are able to achieve targeted connectivity and maintain utilisation of slots at acceptable data rates.Cognitive Wireless Sensor Networks (CogWSNs) are an adaptive learning based wireless sensor network relying on cognitive computational processes to provide a dynamic capability in configuring the network. The network is formed by sensor nodes equipped with cognitive modules with awareness of their operating environment. If the performance of the sensor network does not meet requirements during operation, a corrective action is derived from stored network knowledge to improve performance. After the action is implemented, feedback on the action taken is evaluated to determine the level of improvement. Example functions within CogWSNs can be as simple as to provide robust connectivity or as complex as to negotiate additional resources from neighbouring network groups with the goal of forwarding application-critical data. In this work, the concept of CogWSNs is defined and its decision processes and supporting architecture proposed. The decision role combines the Problem Solving cognitive process from A Layered Reference Model of the Brain and Polya Concept, consisting of Observe, Plan, Implement, and Evaluate phases. The architecture comprises a Transceiver, Transducer, and Power Supply virtual modules coordinated by the CogWSN's decision process together with intervention, if necessary, by a user. Three types of CogWSN modules are designed based on different implementation considerations: Rule-based CogWSN, Supervised CogWSN, and Reinforcement CogWSN. Verification and comparison for these modules are executed through case studies with focus on power transmission and communication slot allocation. Results show that all three modules are able to achieve targeted connectivity and maintain utilisation of slots at acceptable data rates

A master plan for Kuala Lumpur

Thesis (Undergrad) -- University of Melbourne, Faculty of ArchitectureIt is the record of a personal battle waged on behalf of better planning for Kuala Lumpur, the capital city of Malaysia between the years 1960 - 1970 -- page

Energy-Efficient Routing Algorithms For Wireless Sensor Network

In this project, the author has carried out a study on the performance of various routing algorithms applied to a two-dimensional grid topology wireless sensor network. The study focuses on collecting sensor data from the sensor nodes to a base station with minimum energy consumption and minimum network delay in a flat-tree network. To achieve the objectives, the author proposes two routing algorithms, called the Tree-Based Routing Algorithm and the Information Selection Branch Grow Routing Algorithm (ISBG)

A Store-and-delivery Based MAC Protocol for Air-ground Collaborative Wireless Networks for Precision Agriculture

Due to rapid population growth, the demand for food is also elevating, which inspires farmers to embrace precision agriculture to increase production by exploiting predictive analytics on relevant real-time data. The exactitude of a prediction is vital to decide the next course of actions to be taken to compensate current demands, which again relies on a competent data acquisition technique. The Media Access Control (MAC) protocols have significant contribution in designing data acquisition technique. In this paper, we propose a new Storeand-Delivery base MAC (SD-MAC) protocol for Air-Ground Collaborative Wireless Networks (AGCWNs) to acquire data efficiently from the sensing devices which are deployed in the agricultural field. Our proposed protocol takes into consideration of the factors of network architecture and transforms them into advantages to attain higher throughput. The performance of the proposed protocol is evaluated using simulations and involving another such protocol, where the proposed protocol outperforms the other protocol

A comprehensive analysis on data hazard for RISC32 5-Stage pipeline processor

This paper describes the verification plan on data hazard detection and handling for a 32-bit MIPS ISA (Microprocessor without Interlocked Pipeline Stages Instruction Set Architecture) compatible 5-stage pipeline processor, RISC32. Our work can be used as a reference for RISC32 processor developers to identify and resolve every possible data hazard that might arise during execution phase within the range of the basic MIPS core instruction set. The techniques used to resolve data hazard in this paper are data forwarding and pipeline stages stalling. When data hazard arises, it is first resolve by using data forwarding. If the problem persists, we use pipeline stages stalling then only follow by another data forwarding to resolve the data hazard. This combination will reduce the impact of data hazard on the processor throughput, instead of only using the pipeline stages stalling. This paper delivers a comprehensive analysis and the development of the data hazard resolving blocks that are able to resolve data hazard arises

Energy Efficient Routing for Wireless Sensor Networks with Grid Topology

Agricultural monitoring using wireless sensor networks has gained much popularity recently. In this paper, we review five existing flat-tree routing algorithms and proposed a new algorithm suitable for applications such as paddy field monitoring using wireless sensor network. One of the popular data collection methods is the data aggregation approach, where sensor readings of several nodes are gathered and combined into a single packet at intermediate relay nodes. This approach decreases the number of packets flowing and minimizes the overall energy consumption of the sensor network. However, most studies in the past do not consider the network delay in this context, which is an essential performance measure in real-time interactive agricultural monitoring through Internet and cellular network. We propose an algorithm called Information Selection Branch Grow Algorithm (ISBG), which aims to optimize the network in achieving higher network lifetime and shortening the end-to-end network delay. The performance of this algorithm is assessed by computer simulation and is compared with the existing algorithms used for data aggregation routing in wireless sensor networks