Energy efficient algorithm for swarmed sensors networks

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In this work we are presenting the design of an intelligent hybrid optimization algorithm which is based on Evolutionary Computation and Swarm Intelligence to increase the life time of mobile wireless sensor networks (WSNs). It is composed of two phases; Phase-1 is designed to divide the sensor nodes into independent clusters by using Genetic Algorithms (GAs) to minimise the overall communication distance between the sensor-nodes and the sink-point. This will decrease the energy consumption for the entire network. Phase-2 which is based on Particle Swarm Optimization (PSO) is designed to keep the optimum distribution of sensors while the mobile sensor network is directed as a swarm to achieve a given goal. One of the main strengths in the presented algorithm is that the number of clusters within the sensor network is not predefined, this gives more flexibility for the nodes’ deployment in the sensor network. Another strength is that sensors’ density is not necessary to be uniformly distributed among the clusters, since in some applications constraints, the sensors need to be deployed in different densities depending on the nature of the application domain. Although traditionally Wireless Sensor Network have been regarded as static sensor arrays used mainly for environmental monitoring, recently, its applications have undergone a paradigm shift from static to more dynamic environments, where nodes are attached to moving objects, people or animals. Applications that use WSNs in motion are broad, ranging from transport and logistics to animal monitoring, health care and military. These application domains have a number of characteristics that challenge the algorithmic design of WSNs

ENAMS: Energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence.

Although traditionally Wireless Sensor Network (WSNs) have been regarded as static sensor arrays used mainly for environmental monitoring, recently, its applications have undergone a paradigm shift from static to more dynamic environments, where nodes are attached to moving objects, people or animals. Applications that use WSNs in motion are broad, ranging from transport and logistics to animal monitoring, health care and military. These application domains have a number of characteristics that challenge the algorithmic design of WSNs. Firstly, mobility has a negative effect on the quality of the wireless communication and the performance of networking protocols. Nevertheless, it has been shown that mobility can enhance the functionality of the network by exploiting the movement patterns of mobile objects. Secondly, the heterogeneity of devices in a WSN has to be taken into account for increasing the network performance and lifetime. Thirdly, the WSN services should ideally assist the user in an unobtrusive and transparent way. Fourthly, energy-efficiency and scalability are of primary importance to prevent the network performance degradation. This thesis contributes toward the design of a new hybrid optimization algorithm; ENAMS (Energy optimizatioN Algorithm for Mobile Sensor networks) which is based on the Evolutionary Computation and Swarm Intelligence to increase the life time of mobile wireless sensor networks. The presented algorithm is suitable for large scale mobile sensor networks and provides a robust and energy- efficient communication mechanism by dividing the sensor-nodes into clusters, where the number of clusters is not predefined and the sensors within each cluster are not necessary to be distributed in the same density. The presented algorithm enables the sensor nodes to move as swarms within the search space while keeping optimum distances between the sensors. To verify the objectives of the proposed algorithm, the LEGO-NXT MIND-STORMS robots are used to act as particles in a moving swarm keeping the optimum distances while tracking each other within the permitted distance range in the search space

Environmental Application with Multi Sensor Network

This paper aimed to monitor temperature, humidity, and CO gas level using environmental application with multi sensor network (MSN). This system was applied in real life and real time, to be able to obtain data and information through mobile devices and other on internet network. In this research, environmental application is monitored remotely using displays on the web and sensors as device. This research obtained data in outdoor and indoor parking area also with obstacles and without obstacles, so it obtained the results from each of the different environmental conditions.   &nbsp

Advanced Air and Missile Defense under Spatial Grasp Technology

A novel control ideology and technology for solving tasks in large distributed networked systems will be briefed. Based on active scenarios self-navigating and self-matching distributed spaces in a highly organized super-virus mode, it can effectively establish global control over large systems of any natures. The technology can use numerous scattered and dissimilar facilities in an integral and holistic way, allow-ing them to work together in goal-driven supercomputer mode. The approach can be useful for advanced air and missile defense in a variety of ways, some of which described and explained in this paper. The re-lated material has been accepted for presentation at The 12th 3AF Integrated Air and Missile Defence in-ternational conference, June 27–29, Stockholm, Sweden, http://3af-integratedairmissiledefence.com.Коротко викладено нову ідеологію та технологію керування для вирішення задач у великих мережевих системах. Запропонований підхід, який базується на активних сценаріях, що здійснюють самонавігацію і самосинхронізацію в розподілених просторах у режимі організованого супервіруса, може встановлювати глобальний контроль над системами довільної природи. Технологія дозволяє ефективно інтегрувати безліч розрізнених та неоднорідних об’єктів, дозволяючи їм працювати разом у цілеспрямованому суперкомп’ютерному режимі. Вона може бути корисною для перспективної протиповітряної та протиракетної оборони різними способами; деякі з них викладені та пояснені у цій статті. Відповідний матеріал прийнятий для презентації на 12-й Міжнародній конференції з Інтегрованої протиповітряної і протиракетної оборони, 27–29 червня в Стокгольмі, Швеція, http://3af-integratedairmissiledefence.com.Кратко изложены новая идеология и технология управления для решения задач в больших сетевых системах. Предложенный подход, основанный на активных сценариях, осуществляющих самонавигацию и самосинхронизацию в распределенных пространствах в режиме организованного супервируса, может устанавливать глобальный контроль над системами любой природы. Технология позволяет эффективно интегрировать множество разрозненных и разнородных объектов, позволяя им работать совместно в целенаправленном суперкомпьютерном режиме. Она может быть полезна для перспективной противовоздушной и противоракетной обороны разными способами; некоторые из них изложены и объяснены в этой статье. Соответствующий материал принят для представления на 12-й Международной конференции по интегрированной противовоздушной и противоракетной обороне, 27–29 июня в Стокгольме, Швеция, http://3af-integratedairmissiledefence.com

Providing an Efficient Model for Wireless Sensor Networks Using the Scenario of the Variable Sink Counts Based on the Particle Swarm Algorithm

Introduction. A wireless sensor network is a set of independent sensor nodes, which are dispersed in a distributed manner to monitor and collect data in a geographic environment. One of these problems is the manner of node division in a set of multi-sink sensors. Problem Statement. In fact, the main issue in this area is related to the division of sensor nodes between sinks so that re duced energy consumption and increased network life survival will be resulted. In this study, a solution has been provided to partition a multi-sink sensor network. Due to the nature of the problem of partitioning a multi-sink sensor network, the search space is very extensive and, on the other hand, proving that this issue is classified as NP-hard problems has made the presentation of a definitive solution very difficult. Purpose. To develop a solution for distribution of sensor network with a few sinks. Materials and Methods. Thus, given the broad search space of the problem ahead, particle swarm algorithm has been selected. In order to evaluate the proposed approach, MATLAB programming language has been applied. Results. The proposed approach has been developed using the criteria of hop counts to the sink and also the number of cluster heads plus the power of particle search in particle swarm algorithm. Conclusions. Study of these results in the form of two criteria of hop counts and the number of cluster heads using the scenario of the variable sink counts demonstrate that in the desired scenario, the proposed approach has been able to improve hop counts relative to the base method by 17% and the number of cluster heads by 59%.Вступ. Бездротова сенсорна мережа — це набір незалежних сенсорних вузлів, які розподілені певним чином для моніторингу та збору даних в географічному середовищі. Одним з їхніх функціональних завдань є спосіб розподілу вузлів у наборі датчиків з декількома стоками. Проблематика. Основна проблема в цій галузі пов’язана з розділенням вузлів датчиків між стоками, що дозволить знизити споживання енергії та збільшити термін служби мережі. У зв’язку з природою проблеми розбиття сенсорної мережі з декількома стоками, пошуковий простір є надто великим і, з іншого боку, доведення того, що ця задача є NP-складною проблемою, зробило представлення остаточного рішення дуже складним. Мета. Розробка рішення розподілу сенсорної мережі з декількома стоками. Матеріали й методи. З огляду на широкий простір пошуку, в роботі використано алгоритм рою частинок. Для оцінки запропонованого підходу застосовано мову програмування MATLAB. Результати. Запропонований підхід було розроблено з використанням критеріїв підрахунку кількості транзитних ділянок до стоку, а також кількості головок кластера сумарно з потужністю пошуку частинок в алгоритмі рою частинок. Висновки. Вивчення отриманих результатів у вигляді двох критеріїв підрахунку кількості транзитних ділянок та кількості головок кластера з використанням сценарію змінної кількості стоків свідчить, що запропонований підхід дозволив поліпшити кількість транзитних ділянок відносно базового методу на 17 %, а кількість головок кластера — на 59 %.Введение. Беспроводная сенсорная сеть — это набор независимых сенсорных узлов, которые распределены определенным образом для мониторинга и сбора данных в географической среде. Одной из их функциональных задач является способ распределения узлов в наборе датчиков с несколькими стоками. Проблематика. Основная проблема в этой области связана с разделением узлов датчиков между стоками, что позволит снизить потребление энергии и увеличить срок службы сети. В связи с природой проблемы разбиения сенсорной сети с несколькими стоками, область поиска является слишком большой и, с другой стороны, доказательства того, что эта задача является NP-сложной проблемой, сделало представление окончательного решения очень сложным. Цель. Разработка решения распределения сенсорной сети с несколькими стоками. Материалы и методы. Учитывая обширную область поиска, в работе использован алгоритм роя частиц. Для оценки предложенного подхода применены язык программирования MATLAB. Результаты. Предложенный подход был разработан с использованием критериев подсчета количества транзитных участков к стоку, а также количества головок кластера суммарно с мощностью поиска частиц в алгоритме роя частиц. Выводы. Изучение полученных результатов в виде двух критериев подсчета количества транзитных участков и количества головок кластера с использованием сценария переменного количества стоков свидетельствует, что предложенный подход позволил улучшить количество транзитных участков относительно базового метода на 17 %, а количество головок кластера — на 59 %

The automatic placement of multiple indoor antennas using Particle Swarm Optimisation

In this thesis, a Particle Swarm Optimization (PSO) method combined with a ray propagation method is presented as a means to optimally locate multiple antennas in an indoor environment. This novel approach uses Particle Swarm Optimisation combined with geometric partitioning. The PSO algorithm uses swarm intelligence to determine the optimal transmitter location within the building layout. It uses the Keenan-Motley indoor propagation model to determine the fitness of a location. If a transmitter placed at that optimum location, transmitting a maximum power is not enough to meet the coverage requirements of the entire indoor space, then the space is geometrically partitioned and the PSO initiated again independently in each partition. The method outputs the number of antennas, their effective isotropic radiated power (EIRP) and physical location required to meet the coverage requirements. An example scenario is presented for a real building at Loughborough University and is compared against a conventional planning technique used widely in practice

Environmental Application with Multi Sensor Network

This paper aimed to monitor temperature, humidity, and CO gas level using environmental application with multi sensor network (MSN). This system was applied in real life and real time, to be able to obtain data and information through mobile devices and other on internet network. In this research, environmental application is monitored remotely using displays on the web and sensors as device. This research obtained data in outdoor and indoor parking area also with obstacles and without obstacles, so it obtained the results from each of the different environmental conditions

Spatial Sensor Network Based Target Tracking By Classification

The wide use of sensor networks in the day to day communication in recent trends made tracking a significant feature in monitoring systems. The automated systems capable of detection and tracking of targets is a desirable application in many fields. Firstly, deploy a sensor network with appropriate space between sensors and then introduce targets into the network. As the sensors detect the targets, each sensor communicates with neighborhood sensor nodes and one of those sensors are elected as cell-head which will calculate the position of target from the data and transmit that to sink. This process is repeated iteratively to track the moving target. Feature extraction methods and classification techniques have been studied to classify targets by their type. For the challenging task of Multi-target tracking, the methods of sequential Bayesian filtering and Sequential Monte Carlo-Particle Hypothesis Density filters are sought. Accurate algorithms have been simulated for Localization and tracking of target using the data of sensor strengths which are collaboratively communicated among the sensors. Direction of moving target inside a cell was estimated. Algorithm for Hierarchical multi-hop communication model was established

Challenges in `seeing' through particulate materials

In this perspective article, we discuss the challenges of imaging granular particles in three dimensions. Starting with a brief motivation for investigating particulate materials, we provide an overview of selected experimental approaches developed for studying static or dynamic granular systems. We then list some of the challenges and solutions associated with X-ray tomography, one of the standard methods to study static packings. Subsequently, we discuss new techniques such as `smart' tracer and radar tracking for granular dynamics. We close by giving our personal view on the outstanding problems and potential solutions in the future.Comment: 17 pages, 5 figure

PERANCANGAN WIRELESS SENSOR NETWORK MENGGUNAKAN TEKNOLOGI MULTISENSOR SEBAGAI SISTEM MONITORING KUALITAS UDARA

Along with the current development, air quality is very vulnerable to being polluted. Factors that affect air quality are an increase in infrastructure development, factories,  motor vehicle fumes and any other human activity. Based on these factors, an air quality monitoring system that is integrated into a Wireless Sensor Network (WSN) system is needed. In this study, a Wireless Sensor Network will be designed using Multisensor Network technology that works in real time to measure pollutant gas levels using the TGS 2442 sensor as a measure of carbon monoxide (CO) , MG811 sensor as a measure of carbon dioxide (CO2) , TGS 2611 sensor as a measure of HydroCarbon (HC), the DHT-11 sensor as a temperature and humidity meter and the SHARPGP2Y1010 sensor as a measure of particulate levels in the air (PM10). The data from this sensor reading is sent to the server using a Raspberry pi microprocessor. Furthermore, the data will be processed until it becomes information that can be used by users or the general public