Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

IoTにおけるリソースの最適化

Recently, there are more than 9 billion things that connected in the Internet of Things (IoT) and the number is exceed more than 24 billion in 2020. It means that numerous data will be generated because of increasing quickly of the number of things. An infrastructure should be developed to manage the connected things in IoT. Moreover, cloud computing will play important role in terms of data storage and analysis for IoT. Therefore, a cloud broker is considered as an intermediary in the infrastructure for managing the connected things. The cloud broker will find the best deal between clients and service providers. However, there are three problems among cloud broker, clients and service providers that are the response time of the request from clients, the energy consumption of the system and the profit of the cloud broker. The three problems are considered as multi-objective optimization problem to maximize the profit of the broker while minimizing the response time of the request and the energy consumption. A multi-objective particle swarm optimization (MOPSO) is proposed to solve the problem. MOPSO is compared with a non-dominated sorting genetic algorithm-II (NSGA-II) and a random search algorithm to show the performance. Since, there are a lot of data including social media and geographic location, generated in IoT. Coupling social media with geographic location has boosted the worth of understanding the real-world situations. Event detection aims to find more specific topic which represents real-world event. However,identification of unusual and seemingly inconsistent patterns in data, called outliers, is necessary. The problem is how to partition a spatio-temporal domain to find a meaningful local outlier pattern. A k-dimensional (KD) tree partitioning is applied to divide a spatio-temporal domain into sub-cells. The optimal partitioning problem in a spatio-temporal domain has been proven as an NP-complete problem. Therefore, a genetic algorithm is proposed to solve the problem. Moreover, the smart grid is strongly related to IoT technologies. It is enabled by IoT to handle big data and reduce the number of communication protocols. The micro-grid is studied because of micro-grids are part of a larger system that makes the smart grid to become reality. The operation management problem and pollutant emission problem are important problems for the micro-grid system. Thus, reducing the total energy expenses and pollutant emission of micro-grid and improving the renewable energy sources (battery energy storage) are considered together with the operation management of the micro-grid system. A fitness-based modified game particle swarm optimization (FMGPSO) algorithm is proposed to minimize the total costs of operation and pollutant emissions in the microgrid and multi-microgrid system. FMGPSO is compared with A non-dominated sorting genetic algorithm-III (NSGA-III), a multi-objective covariance matrix adaptation evolution strategy (MO-CMAES), and a speed-constrained multi-objective particle swarm optimization (SMPSO) to show the performance.最近では、Internet of Things（IoT）に接続されているものは90億件を超え、2020年には240億件を超えている。それは、物事の数が急速に増えるため、多くのデータが生成されることを意味する。IoTで接続されたものを管理するためのインフラストラクチャを開発する必要がある。さらに、クラウドコンピューティングは、IoTのデータストレージと分析の観点から重要な役割を果たしている。したがって、クラウドブローカーは、接続されたものを管理するためのインフラストラクチャの仲介者とみなされる。クラウドブローカーは、クライアントとサービスプロバイダーの間で最良の取引を見つけるだろう。しかし、クラウドブローカー、クライアントおよびサービスプロバイダーには、クライアントからの要求の応答時間、システムのエネルギー消費、クラウドブローカーのプロセスという3つの問題がある。この3つの問題は、要求の応答時間とエネルギー消費を最小限に抑えながら、ブローカーのプロビジョニングを最大化するための多目的最適化問題とみなされる。この問題を解決するために、多目的粒子群最適化（MOPSO）が提案されている。 MOPSOは、非優性選別遺伝的アルゴリズム-II（NSGA-II）およびランダム探索アルゴリズムと比較され、性能が示される。ソーシャルメディアや地理的な場所など、IoTで生成される多くのデータがあるためである。地理的な場所とソーシャルメディアを結び付けることで、現実の状況を理解する価値が高まっている。イベント検出は、実際のイベントを表すより特定のトピックを見つけることを目指している。しかし、異常値と呼ばれる異常なパターンや一見不整合なパターンの同定が必要である。問題は、時空間ドメインを分割して意味のある局所的な奇妙なパターンを見つける方法である。時空間領域をサブセルに分割するために、k次元（KD）ツリー分割が適用される。時空間領域における最適な分割問題は、NP完全な問題として証明されている。したがって、この問題を解決するための遺伝的アルゴリズムが提案されている。さらに、スマートグリッドはIoT技術と強く関連している。 IoTによって大きなデータを処理し、通信プロトコルの数を減らすことができる。マイクログリッドはスマートグリッドを現実化させるより大きなシステムの一部であるため、マイクログリッドが研究されている。運用管理上の問題や汚染物質排出問題は、マイクログリッドシステムにとって重要な問題である。したがって、マイクログリッドシステムの運用管理とともに、マイクログリッドの総エネルギー費用と汚染物質排出量の削減と再生可能エネルギー源の改善（バッテリエネルギー貯蔵）が考慮されている。マイクログリッドおよびマルチマイクログリッドシステムにおける操作および汚染物質排出の総コストを最小限に抑えるため、MGPSO アルゴリズムが提案されている。 FMGPSOは、非優先ソート遺伝的アルゴリズム-III（NSGA-III）、多目的共分散行列適応進化戦略（MO-CMAES）、および性能を示すために速度が制約された多目的粒子群最適化（SMPSO）と比較される。室蘭工業大学 (Muroran Institute of Technology)博士（工学

Ant Colony Algorithms for the Resolution of Semantic Searches in P2P Networks

Tesis por compendio[EN] The long-lasting trend in the field of computation of stress and resource distribution has found its way into computer networks via the concept of peer-to-peer (P2P) connectivity. P2P is a symmetrical model, where each network node is enabled a comparable range of capacities and resources. It stands in a stark contrast to the classical, strongly asymmetrical client-server approach. P2P, originally considered only a complimentary, server-side structure to the straightforward client-server model, has been shown to have the substantial potential on its own, with multiple, widely known benefits: good fault tolerance and recovery, satisfactory scalability and intrinsic load distribution. However, contrary to client-server, P2P networks require sophisticated solutions on all levels, ranging from network organization, to resource location and managing. In this thesis we address one of the key issues of P2P networks: performing efficient resource searches of semantic nature under realistic, dynamic conditions. There have been numerous solutions to this matter, with evolutionary, stigmergy-based, and simple computational foci, but few attempt to resolve the full range of challenges this problem entails. To name a few: real-life P2P networks are rarely static, nodes disconnect, reconnect and change their content. In addition, a trivial incorporation of semantic searches into well-known algorithms causes significant decrease in search efficiency. In our research we build a solution incrementally, starting with the classic Ant Colony System (ACS) within the Ant Colony Optimization metaheuristic (ACO). ACO is an algorithmic framework used for solving combinatorial optimization problems that fits contractually the problem very well, albeit not providing an immediate solution to any of the aforementioned problems. First, we propose an efficient ACS variant in structured (hypercube structured) P2P networks, by enabling a path-post processing algorithm, which called Tabu Route Optimization (TRO). Next, we proceed to resolve the issue of network dynamism with an ACO-compatible information diffusion approach. Consequently, we attempt to incorporate the semantic component of the searches. This initial approximation to the problem was achieved by allowing ACS to differentiate between search types with the pheromone-per-concept idea. We called the outcome of this merger Routing Concept ACS (RC-ACS). RC-ACS is a robust, static multipheromone implementation of ACS. However, we were able to conclude from it that the pheromone-per-concept approach offers only limited scalability and cannot be considered a global solution. Thus, further progress was made in this respect when we introduced to RC-ACS our novel idea: dynamic pheromone creation, which replaces the static one-to-one assignment. We called the resulting algorithm Angry Ant Framework (AAF). In AAF new pheromone levels are created as needed and during the search, rather than prior to it. The final step was to enable AAF, not only to create pheromone levels, but to reassign them to optimize the pheromone usage. The resulting algorithm is called EntropicAAF and it has been evaluated as one of the top-performing algorithms for P2P semantic searches under all conditions.[ES] La popular tendencia de distribución de carga y recursos en el ámbito de la computación se ha transmitido a las redes computacionales a través del concepto de la conectividad peer-to-peer (P2P). P2P es un modelo simétrico, en el cual a cada nodo de la red se le otorga un rango comparable de capacidades y recursos. Se trata de un fuerte contraste con el clásico y fuertemente asimétrico enfoque cliente-servidor. P2P, originalmente considerado solo como una estructura del lado del servidor complementaria al sencillo modelo cliente-servidor, ha demostrado tener un potencial considerable por sí mismo, con múltiples beneficios ampliamente conocidos: buena tolerancia a fallos y recuperación, escalabilidad satisfactoria y distribución de carga intrínseca. Sin embargo, al contrario que el modelo cliente-servidor, las redes P2P requieren de soluciones sofisticadas a todos los niveles, desde la organización de la red hasta la gestión y localización de recursos. Esta tesis aborda uno de los problemas principales de las redes P2P: la búsqueda eficiente de recursos de naturaleza semántica bajo condiciones dinámicas y realistas. Ha habido numerosas soluciones a este problema basadas en enfoques evolucionarios, estigmérgicos y simples, pero pocas han tratado de resolver el abanico completo de desafíos. En primer lugar, las redes P2P reales son raramente estáticas: los nodos se desconectan, reconectan y cambian de contenido. Además, la incorporación trivial de búsquedas semánticas en algoritmos conocidos causa un decremento significativo de la eficiencia de la búsqueda. En esta investigación se ha construido una solución de manera incremental, comenzando por el clásico Ant Colony System (ACS) basado en la metaheurística de Ant Colony Optimization (ACO). ACO es un framework algorítmico usado para búsquedas en grafos que encaja perfectamente con las condiciones del problema, aunque no provee una solución inmediata a las cuestiones mencionadas anteriormente. En primer lugar, se propone una variante eficiente de ACS para redes P2P estructuradas (con estructura de hipercubo) permitiendo el postprocesamiento de las rutas, al que hemos denominado Tabu Route Optimization (TRO). A continuación, se ha tratado de resolver el problema del dinamismo de la red mediante la difusión de la información a través de una estrategia compatible con ACO. En consecuencia, se ha tratado de incorporar el componente semántico de las búsquedas. Esta aproximación inicial al problema ha sido lograda permitiendo al ACS diferenciar entre tipos de búsquedas através de la idea de pheromone-per-concept. El resultado de esta fusión se ha denominado Routing Concept ACS (RC-ACS). RC-ACS es una implementación multiferomona estática y robusta de ACS. Sin embargo, a partir de esta implementación se ha podido concluir que el enfoque pheromone-per-concept ofrece solo escalabilidad limitada y que no puede ser considerado una solución global. Por lo tanto, para lograr una mejora a este respecto, se ha introducido al RC-ACS una novedosa idea: la creación dinámica de feromonas, que reemplaza la asignación estática uno a uno. En el algoritmo resultante, al que hemos denominado Angry Ant Framework (AAF), los nuevos niveles de feromona se crean conforme se necesitan y durante la búsqueda, en lugar de crearse antes de la misma. La mejora final se ha obtenido al permitir al AAF no solo crear niveles de feromona, sino también reasignarlos para optimizar el uso de la misma. El algoritmo resultante se denomina EntropicAAF y ha sido evaluado como uno de los algoritmos más exitosos para las búsquedas semánticas P2P bajo todas las condiciones.[CA] La popular tendència de distribuir càrrega i recursos en el camp de la computació s'ha estès cap a les xarxes d'ordinadors a través del concepte de connexions d'igual a igual (de l'anglès, peer to peer o P2P). P2P és un model simètric on cada node de la xarxa disposa del mateix nombre de capacitats i recursos. P2P, considerat originàriament només una estructura situada al servidor complementària al model client-servidor simple, ha provat tindre el suficient potencial per ella mateixa, amb múltiples beneficis ben coneguts: una bona tolerància a errades i recuperació, una satisfactòria escalabilitat i una intrínseca distribució de càrrega. No obstant, contràriament al client-servidor, les xarxes P2P requereixen solucions sofisticades a tots els nivells, que varien des de l'organització de la xarxa a la localització de recursos i la seua gestió. En aquesta tesi s'adreça un dels problemes clau de les xarxes P2P: ser capaç de realitzar eficientment cerques de recursos de naturalesa semàntica sota condicions realistes i dinàmiques. Existeixen nombroses solucions a aquest tema basades en la computació simple, evolutiva i també basades en l'estimèrgia (de l'anglès, stigmergy), però pocs esforços s'han realitzat per intentar resoldre l'ampli conjunt de reptes existent. En primer lloc, les xarxes P2P reals són rarament estàtiques: els nodes es connecten, desconnecten i canvien els seus continguts. A més a més, la incorporació trivial de cerques semàntiques als algorismes existents causa una disminució significant de l'eficiència de la cerca. En aquesta recerca s'ha construït una solució incremental, començant pel sistema clàssic de colònia de formigues (de l'anglés, Ant Colony System o ACS) dins de la metaheurística d'optimització de colònies de formigues (de l'anglès, Ant Colony Optimization o ACO). ACO és un entorn algorísmic utilitzat per cercar en grafs i que aborda el problema de forma satisfactòria, tot i que no proveeix d'una solució immediata a cap dels problemes anteriorment mencionats. Primer, s'ha proposat una variant eficient d'ACS en xarxes P2P estructurades (en forma d'hipercub) a través d'un algorisme de processament post-camí el qual s'ha anomenat en anglès Tabu Route Optimization (TRO). A continuació, s'ha procedit a resoldre el problema del dinamisme de les xarxes amb un enfocament de difusió d'informació compatible amb ACO. Com a conseqüència, s'ha intentat incorporar la component semàntica de les cerques. Aquest enfocament inicial al problema s'ha realitzat permetent a ACS diferenciar entre tipus de cerques amb la idea de ''feromona per concepte'', i s'ha anomenat a aquest producte Routing Concept ACS o RC-ACS. RC-ACS és una implementació multi-feromona robusta i estàtica d'ACS. No obstant, s'ha pogut concloure que l'enfocament de feromona per concepte ofereix només una escalabilitat limitada i no pot ser considerada una solució global. En aquest respecte s'ha realitzat progrés posteriorment introduint una nova idea a RC-ACS: la creació dinàmica de feromones, la qual reemplaça a l'assignació un a un de les mateixes. A l'algorisme resultant se l'ha anomenat en anglès Angry Ant Framework (AAF). En AAF es creen nous nivells de feromones a mesura que es necessiten durant la cerca, i no abans d'aquesta. El progrés final s'ha aconseguit quan s'ha permès a AAF, no sols crear nivells de feromones, sinó reassignar-los per optimitzar la utilització de feromones. L'algorisme resultant s'ha anomenat EntropicAAF i ha sigut avaluat com un dels algorismes per a cerques semàntiques P2P amb millors prestacions.Krynicki, KK. (2016). Ant Colony Algorithms for the Resolution of Semantic Searches in P2P Networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/61293TESISPremios Extraordinarios de tesis doctoralesCompendi

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

ENERGY EFFICIENT WIRED NETWORKING

This research proposes a new dynamic energy management framework for a backbone Internet Protocol over Dense Wavelength Division Multiplexing (IP over DWDM) network. Maintaining the logical IP-layer topology is a key constraint of our architecture whilst saving energy by infrastructure sleeping and virtual router migration. The traffic demand in a Tier 2/3 network typically has a regular diurnal pattern based on people‟s activities, which is high in working hours and much lighter during hours associated with sleep. When the traffic demand is light, virtual router instances can be consolidated to a smaller set of physical platforms and the unneeded physical platforms can be put to sleep to save energy. As the traffic demand increases the sleeping physical platforms can be re-awoken in order to host virtual router instances and so maintain quality of service. Since the IP-layer topology remains unchanged throughout virtual router migration in our framework, there is no network disruption or discontinuities when the physical platforms enter or leave hibernation. However, this migration places extra demands on the optical layer as additional connections are needed to preserve the logical IP-layer topology whilst forwarding traffic to the new virtual router location. Consequently, dynamic optical connection management is needed for the new framework. Two important issues are considered in the framework, i.e. when to trigger the virtual router migration and where to move virtual router instances to? For the first issue, a reactive mechanism is used to trigger the virtual router migration by monitoring the network state. Then, a new evolutionary-based algorithm called VRM_MOEA is proposed for solving the destination physical platform selection problem, which chooses the appropriate location of virtual router instances as traffic demand varies. A novel hybrid simulation platform is developed to measure the performance of new framework, which is able to capture the functionality of the optical layer, the IP layer data-path and the IP/optical control plane. Simulation results show that the performance of network energy saving depends on many factors, such as network topology, quiet and busy thresholds, and traffic load; however, savings of around 30% are possible with typical medium-sized network topologies

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

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

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp