Bodacious-instance coverage mechanism for wireless sensor network

Copyright © 2020 Shahzad Ashraf et al. Due to unavoidable environmental factors, wireless sensor networks are facing numerous tribulations regarding network coverage. These arose due to the uncouth deployment of the sensor nodes in the wireless coverage area that ultimately degrades the performance and confines the coverage range. In order to enhance the network coverage range, an instance (node) redeployment-based Bodacious-instance Coverage Mechanism (BiCM) is proposed. The proposed mechanism creates new instance positions in the coverage area. It operates in two stages; in the first stage, it locates the intended instance position through the Dissimilitude Enhancement Scheme (DES) and moves the instance to a new position, while the second stage is called the depuration, when the moving distance between the initial and intended instance positions is sagaciously reduced. Further, the variations of various parameters of BiCM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius have been explored, and the optimized parameters are identified. The performance metric has been meticulously analyzed through simulation results and is compared with the state-of-the-art Fruit Fly Optimization Algorithm (FOA) and, one step above, the tuned BiCM algorithm in terms of mean coverage rate, computation time, and standard deviation. The coverage range curve for various numbers of iterations and sensor nodes is also presented for the tuned Bodacious-instance Coverage Mechanism (tuned BiCM), BiCM, and FOA. The performance metrics generated by the simulation have vouched for the effectiveness of tuned BiCM as it achieved more coverage range than BiCM and FOA

Automatic triangulation positioning system for wide area coverage from a fixed sensors network

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn a wide area that many Transmitters (TRs) operate, systems of Fixed Sensors (FS) might be used in order to detect them and find TRs position. The detection and the accurate location of a new TR entering in the area frequently can be missed if the system fails to triangulate accurately the relative readings and analyze the changes in the received data. Additionally, there are cases that a Triangulation Station Network (TSN) can detect the heading as well as the transmitter’s position wrong. This thesis presents the design of a Sensors Network (FSN) system which is able to interact with a user, and exploit the relative data of the Sensors (SRs) in real time. The system performs localization with triangulation and the SRs are detect only TRs bearing data (range free). System design and algorithms are also explained. Efficient algorithms were elaborated and the outcomes of their implementation were calculated. The system design targets to reduce system errors and increase the accuracy and the speed of detection. Synchronously and through interaction with the user and changes of relative settings and parameters will be able to offer the user accurate results on localization of TRs in the area minimizing false readings and False Triangulations (FTRNs). The system also enables the user to apply optimization techniques in order to increase the system detection rate and performance and keep the surveillance in the Field of Interest (FoI) on a high level. The optimization methodology applied for the system proves that the FSN system is able to operate with a high performance even when saturation phenomena appear. The unique outcome of the research conducted, is that this thesis paves the way to enhance the localization via Triangulation for a network of Fixed Sensors with known position. The value of this thesis is that the FSN system performs bearing only detection (Range free) with a certain accuracy and the Area of Interest (AOI) is covered efficiently

HEDCOS: High Efficiency Dynamic Combinatorial Optimization System using Ant Colony Optimization algorithm

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDynamic combinatorial optimization is gaining popularity among industrial practitioners due to the ever-increasing scale of their optimization problems and efforts to solve them to remain competitive. Larger optimization problems are not only more computationally intense to optimize but also have more uncertainty within problem inputs. If some aspects of the problem are subject to dynamic change, it becomes a Dynamic Optimization Problem (DOP). In this thesis, a High Efficiency Dynamic Combinatorial Optimization System is built to solve challenging DOPs with high-quality solutions. The system is created using Ant Colony Optimization (ACO) baseline algorithm with three novel developments. First, introduced an extension method for ACO algorithm called Dynamic Impact. Dynamic Impact is designed to improve convergence and solution quality by solving challenging optimization problems with a non-linear relationship between resource consumption and fitness. This proposed method is tested against the real-world Microchip Manufacturing Plant Production Floor Optimization (MMPPFO) problem and the theoretical benchmark Multidimensional Knapsack Problem (MKP). Second, a non-stochastic dataset generation method was introduced to solve the dynamic optimization research replicability problem. This method uses a static benchmark dataset as a starting point and source of entropy to generate a sequence of dynamic states. Then using this method, 1405 Dynamic Multidimensional Knapsack Problem (DMKP) benchmark datasets were generated and published using famous static MKP benchmark instances as the initial state. Third, introduced a nature-inspired discrete dynamic optimization strategy for ACO by modelling real-world ants’ symbiotic relationship with aphids. ACO with Aphids strategy is designed to solve discrete domain DOPs with event-triggered discrete dynamism. The strategy improved inter-state convergence by allowing better solution recovery after dynamic environment changes. Aphids mediate the information from previous dynamic optimization states to maximize initial results performance and minimize the impact on convergence speed. This strategy is tested for DMKP and against identical ACO implementations using Full-Restart and Pheromone-Sharing strategies, with all other variables isolated. Overall, Dynamic Impact and ACO with Aphids developments are compounding. Using Dynamic Impact on single objective optimization of MMPPFO, the fitness value was improved by 33.2% over the ACO algorithm without Dynamic Impact. MKP benchmark instances of low complexity have been solved to a 100% success rate even when a high degree of solution sparseness is observed, and large complexity instances have shown the average gap improved by 4.26 times. ACO with Aphids has also demonstrated superior performance over the Pheromone-Sharing strategy in every test on average gap reduced by 29.2% for a total compounded dynamic optimization performance improvement of 6.02 times. Also, ACO with Aphids has outperformed the Full-Restart strategy for large datasets groups, and the overall average gap is reduced by 52.5% for a total compounded dynamic optimization performance improvement of 8.99 times

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

Langattomien anturiverkkojen sotilas-, agroteknologia- ja energiatutkimussovelluksia

The physical quantities nowadays are widely measured by using electronic sensors. Wireless sensor networks (WSNs) are low-cost, low-power electronic devices capable of collecting data using their onboard sensors. Some wireless sensor nodes are equipped with actuators, providing the possibility to change the state of the physical world. The ability to change the state of a physical system means that WSNs can be used in control and automation applications. This research focuses on appropriate system design for four different wireless measurement and control cases. The first case provides a hardware and software solution for camera integration to a wireless sensor node. The images are captured and processed inside the sensor node using low power computational techniques. In the second application, two different wireless sensor networks function in cooperation to overcome seeding problems in agricultural machinery. The third case focuses on indoor deployment of the wireless sensor nodes into an area of urban crisis, where the nodes supply localization information to friendly assets such as soldiers, firefighters and medical personnel. The last application focuses on a feasibility study for energy harvesting from asphalt surfaces in the form of heat.Fysikaaliset suureet mitataan nykyisin elektronisten anturien avulla. Langattomat anturiverkot ovat kustannustasoltaan edullisia, matalan tehonkulutuksen elektronisia laitteita, jotka kykenevät suorittamaan mittauksia niissä olevilla antureilla. Langattomat anturinoodit voidaan myös liittää toimilaitteisiin, jolloin ne voivat vaikuttaa fyysiseen ympäristöönsä. Koska langattomilla anturi- ja toimilaiteverkoilla voidaan vaikuttaa niiden fysikaalisen ympäristön tilaan, niiden avulla voidaan toteuttaa säätö- ja automaatiosovelluksia. Tässä väitöskirjaty össä suunnitellaan ja toteutetaan neljä erilaista langattomien anturi- ja toimilaiteverkkojen automaatiosovellusta. Ensimmäisenä tapauksena toteutetaan elektroniikka- ja ohjelmistosovellus, jolla integroidaan kamera langattomaan anturinoodiin. Kuvat tallennetaan ja prosessoidaan anturinoodissa vähän energiaa kuluttavia laskentamenetelmiä käyttäen. Toisessa sovelluksessa kahdesta erilaisesta langattomasta anturiverkosta koostuvalla järjestelmällä valvotaan siementen syöttöä kylvökoneessa. Kolmannessa sovelluksessa levitetään kaupunkiympäristössä kriisitilanteessa rakennuksen sisätiloihin langaton anturiverkko. Sen anturinoodit välittävät paikkatietoa rakennuksessa operoiville omille joukoille, jotka voivat tilanteesta riippuen olla esimerkiksi sotilaita, palomiehiä tai lääkintähenkilökuntaa. Neljännessä sovelluksessa toteutetaan langaton anturiverkko, jonka keräämää mittausdataa käytetään arvioitaessa lämpöenergian keräämismahdollisuuksia asfalttipinnoilta.fi=vertaisarvioitu|en=peerReviewed