The performance evaluation of different logical topologies and their respective protocols for wireless sensor networks

Wireless sensor networks (WSNs) are at present a standout amongst the most, guaranteeing areas in the field of information and communication technologies (ICT). This new technology has boundless potential for various applications in distinctive regions, including environmental research, medical application, military, transportation, stimulation, emergency administration, security, and smart spaces. However, several constraints of the sensor nodes are the principal obstacles in planning efficient protocols for WSNs. The major challenges of WSNs include energy dissipation; prolong the network lifetime and throughput. This thesis explores logical topologies in WSN. Logical topologies play the most significant role in the overall performance of the network, including its lifetime, routing efficiency, energy dissipation and overheads. A number of logical topologies have been proposed for WSNs including flat topology, cluster-distributed topology, cluster-centralized topology, and chain topology along with their corresponding routing protocols. In addition, the outcome of the study should definitely performed an important of those parameters of concerned. The simulation experiments are done by using NS-2.34 program for the logical topologies considered are cluster–distributed, chain-based, cluster–centralized and flat with the corresponding protocols of LEACH, PEGASIS, LEACH-C and MTE respectively, while MATLAB is used to plot the graphs. The performance metrics studied are the network lifetime, energy dissipation and aggregate data received at the base station. From the results it can be deduced, that the chain topology (PEGASIS) gives a better performance (network lifetime, energy dissipation and throughput at the base station) overall topologies (LEACH, LEACH-C and MTE)

An efficient real-time data collection model for multivariate sensors in internet of things (IOT) applications

In the applications of the Internet of Things (IoT), sensor board depends on a battery that has a limited lifetime to function. Furthermore, the IoT sensor board with multivariate sensors influences the battery lifetime since there is additional data transmissions that must be supported by the board causing it to drain the battery much faster than the sensor board with one sensor. The main aim of this thesis is to increase the battery life of the IoT sensor node. To do so, a number of proposals are presented. First, an updating data strategy denoted as an efficient data collection and dissemination (EDCD) is proposed. EDCD aims to save the energy consumption of the IoT sensor board with multiple sensors by means of reducing the number of transmission packets, if no significant change is reported by the payload sensing block; second is proposed a validity of the measuring sensor reading at node level (VSNL) algorithm. VSNL aims to avoid transmitting any incorrect data, which will help in saving the energy consumption; third, an adaptive threshold and new metric for multivariate data reduction models such as principal component analysis – based (PCA-B) and multiple linear regression – based (MLR-B) have been proposed. In addition, proposed a payload data reduction algorithm (APRS). APRS aims to reduce the transmitted packet size for each sensed payload, which that will help in saving the energy of the IoT sensor board. This work provides an extensive analysis for the design and performance evaluation of real-time data collection model for multivariate sensors in IoT applications. Finally, an efficient real-time data collection model for multivariate sensors in IoT applications (RDCM). RDCM integrated EDCD, VSNL, PCA-B/MLR-B and APRS and the ability to prolong sensor board battery lifetime, which that satisfied by reducing number of transmissions and payload packet size, and also increase the accuracy of data validation. Performance of the proposed algorithms was evaluated through simulation by utilising various real-time datasets. The average of the total percentage of energy saved by applied RDCM to real-time data sets injected with various percentage of errors for all nodes is 98%

An efficient algorithm to improve oil-gas pipelines path

Oil-gas pipeline is a complex and high-cost system in terms of materials, construction, maintenance, control, and monitoring in which it involves environmental, economic and social risk. In the case study of Iraq, this system of pipelines is above the ground and is liable to accidents that may cause environmental disaster, loss of life and money. Therefore, the aim of this study is to propose a new algorithm to obtain the shortest path connecting oil-gas wells and addressing obstacles that may appear on the path connecting any two wells. In order to show the efficiency of the proposed algorithm, comparison between ant colony optimization (ACO) algorithm and a real current meth-od of linking is used for this purpose. Result shows that the new proposed algorithm outperformed the other methods with higher reduc-tion in operational cost by 16.4% for a number of 50 wells. In addition, the shortest path of connecting oil-gas wells are able to overcome all the addressed obstacles in the Rumaila north field, which is located in the city of Basra in southern Iraq

An adaptive opposition-based learning selection: the case for Jaya algorithm

Over the years, opposition-based Learning (OBL) technique has been proven to effectively enhance the convergence of meta-heuristic algorithms. The fact that OBL is able to give alternative candidate solutions in one or more opposite directions ensures good exploration and exploitation of the search space. In the last decade, many OBL techniques have been established in the literature including the Standard-OBL, General-OBL, Quasi Reflection-OBL, Centre-OBL and Optimal-OBL. Although proven useful, much existing adoption of OBL into meta-heuristic algorithms has been based on a single technique. If the search space contains many peaks with potentially many local optima, relying on a single OBL technique may not be sufficiently effective. In fact, if the peaks are close together, relying on a single OBL technique may not be able to prevent entrapment in local optima. Addressing this issue, assembling a sequence of OBL techniques into meta-heuristic algorithm can be useful to enhance the overall search performance. Based on a simple penalized and reward mechanism, the best performing OBL is rewarded to continue its execution in the next cycle, whilst poor performing one will miss cease its current turn. This paper presents a new adaptive approach of integrating more than one OBL techniques into Jaya Algorithm, termed OBL-JA. Unlike other adoptions of OBL which use one type of OBL, OBL-JA uses several OBLs and their selections will be based on each individual performance. Experimental results using the combinatorial testing problems as case study demonstrate that OBL-JA shows very competitive results against the existing works in term of the test suite size. The results also show that OBL-JA performs better than standard Jaya Algorithm in most of the tested cases due to its ability to adapt its behaviour based on the current performance feedback of the search process

Deep Pipeline Architecture for Fast Fractal Color Image Compression Utilizing Inter-Color Correlation

Fractal compression technique is a well-known technique that encodes an image by mapping the image into itself and this requires performing a massive and repetitive search. Thus, the encoding time is too long, which is the main problem of the fractal algorithm. To reduce the encoding time, several hardware implementations have been developed. However, they are generally developed for grayscale images, and using them to encode colour images leads to doubling the encoding time 3× at least. Therefore, in this paper, new high-speed hardware architecture is proposed for encoding RGB images in a short time. Unlike the conventional approach of encoding the colour components similarly and individually as a grayscale image, the proposed method encodes two of the colour components by mapping them directly to the most correlated component with a searchless encoding scheme, while the third component is encoded with a search-based scheme. This results in reducing the encoding time and also in increasing the compression rate. The parallel and deep-pipelining approaches have been utilized to improve the processing time significantly. Furthermore, to reduce the memory access to the half, the image is partitioned in such a way that half of the matching operations utilize the same data fetched for processing the other half of the matching operations. Consequently, the proposed architecture can encode a 1024×1024 RGB image within a minimal time of 12.2 ms, and a compression ratio of 46.5. Accordingly, the proposed architecture is further superior to the state-of-the-art architectures.©2022 The Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

Biomedical engineering applications: cell engineering

This book comprises of four chapters demonstrating the studies which involve cell culture with bioinstrumentation experimental work identifying the potential solutions for wound healing applications via exploitation of high voltage electric field or micro second pulse. The high voltage exposure on cells have shown interesting findings which gives us an idea on how to develop a dug free wound healing method in the nearest future. The book also consists of two chapters that will present about the investigation of bone microstructures acquired from human samples. In this study there will be combination of bone histomorphology, imaging processing and computational method analysis thus also a new biomedical engineering application. Traditionally, the investigation of bone microstructures was performed by forensic officer by looking through microscope and their expert estimation by years of experience. However, by having the engineers joining this investigation we could help the forensic officer to perform the analysis through computational method and automated analysis. Therefore the accomplishment of the six chapters will give an idea for the reader on what to expert in the field of research the so called “Cell Engineering”

An Efficient Approach for Uncertain Event Detection in RFID Complex Event Processing

The globalization of manufacturing has increased the risk of counterfeiting as the demand grows, the production flow increases, and the availability expands. The intensifying counterfeit issues causing a worriment to companies and putting lives at risk. Companies have ploughed a large amount of money into defensive measures, but their efforts have not slowed counterfeiters. In such complex manufacturing processes, decision-making and real-time reactions to uncertain situations throughout the production process are one way to exploit the challenges. Detecting uncertain conditions such as counterfeit and missing items in the manufacturing environment requires a specialized set of technologies to deal with a flow of continuously created data. In this paper, we propose an uncertain detection algorithm (UDA), an approach to detect uncertain events such as counterfeit and missing items in the RFID distributed system for a manufacturing environment. The proposed method is based on the hashing and thread pool technique to solve high memory consumption, long processing time and low event throughput in the current detection approaches. The experimental results show that the execution time of the proposed method is averagely reduced 22% in different tests, and our proposed method has better performance in processing time based on RFID event streams

TEMSEP: threshold-oriented and energy-harvesting enabled multilevel SEP protocol for improving energy-efficiency of heterogeneous WSNs

Energy-saving in WSN-based monitoring systems has drawn considerable interest lately. Further investigations and real efforts are needed to reduce the rapid energy consumption in such networks that commonly use battery-operated nodes. In this paper, we propose TEMSEP (Threshold-oriented and Energy-harvesting enabled Multi-level Stable Election Protocol) for improving the energy of large-scale WSNs. TEMSEP is a reactive protocol basing on hierarchical clustering, energy-harvesting relay nodes, and multilevel sensor nodes' heterogeneity that supports unlimited levels of battery initial energy. Instead of continuous data transmission, the network nodes in TEMSEP send their data only when it is necessary by responding reactively to the changes in relevant parameters or events of interest. We introduce a new thresholding model that provides an ideal mechanism for such reactive behaviour in detecting events, based on the values of heterogeneous thresholds and the sliding window formulated. This efficiently regulates the data reporting frequency, and hence, directly achieves significant reductions in the network traffic-load, optimizes the energy consumption of battery-powered nodes, and maximizes the network lifetime. The extensive simulations show that TEMSEP highly improves the network performance by reducing up to 53% of the network traffic-load and save up to 73% of the total dissipated energy, on average. The stability period and overall network lifetime are increased, at least, by 69% and 56% respectively, compared to other tested protocols

An enhanced energy efficient protocol for large-scale IoT-based heterogeneous WSNs

There is increasing attention, recently, to optimizing energy consumption in IoT-based large-scale networks. Extending the lifetime of battery-powered nodes is a key challenge in such systems and their various application scenarios. This paper proposes a new zone-based and event-driven protocol for saving energy in large-scale heterogeneous WSNs called TESEES (Threshold Enabled Scalable and Energy Efficient Scheme). The proposed protocol is designed to support network scenarios deploying higher levels of heterogeneity with more than three types of sensor nodes (i.e., four, five, and more). TESEES is a reactive version of the proactive SEES protocol, in which we leverage a novel state-of-the-art thresholding model on the zone-based hierarchical deployments of heterogeneous nodes to regulate the data reporting process, avoiding unnecessary frequent data transmission and reducing the amount of energy dissipation of the sensing nodes and the entire system. With this model, we present a general technique for formulating distinct thresholds for network nodes in each established zone. This mechanism allows for individually configuring the nodes with transmission settings tailored to their respective roles, independent of the heterogeneity levels, total node count, or initial energy. This approach ensures that each node operates optimally within the network. In addition, we present an improved hybrid TMCCT (Threshold-based Minimum Cost Cross-layer Transmission) algorithm that operates at the node level and ensures effective data transmission control by considering current sensor values, heterogeneous event thresholds, and previous data records. Instead of periodical data transmission, this hybridization mechanism, integrated with a grid of energy-harvesting relay nodes, keeps the zone member nodes in the energy-saving mode for maximum time and allows for reactive data transmission only when necessary. This results in a reduced data-reporting frequency, less traffic load, minimized energy consumption, and thus a greater extension of the network’s lifetime. Moreover, unlike the traditional cluster-head election in the weighted probability-based protocols, TESEES relies on an efficient mechanism for zone aggregators’ election that runs at the zone level in multiple stages and employs various static and dynamic parameters based on their generated weights of importance. This leads to selecting the best candidate nodes for the aggregation task and, hence, fairly rotating the role among the zones’ alive nodes. The simulation results show significant improvements in the total energy saving, the lifetime extension, and the transmitted data reduction, reaching 29%, 68%, and 26% respectively, compared to the traditional SEES protocol. Also, the average energy consumption per single round has decreased by 36%