Minimizing the Energy Consumption in Wireless Sensor Networks

تمثل الطاقة في شبكات الاستشعار اللاسلكية عاملاً اساسياً في تصميم ومراقبة وتشغيل هذه الشبكات. تعد عملية التقليل من الطاقة المستهلكة في شبكات الاستشعار اللاسلكية وتطبيقاتها مسألة حيوية بالنسبة لفعالية وكفاءة الشبكة من حيث العمر والتكلفة والتشغيل. تم اقتراح وتنفيذ العديد من الخوارزميات والبروتوكولات لتقليل استهلاك الطاقة.  شبكات الاستشعار اللاسلكية تعتمد على اجهزة الاستشعارتعمل ببطارية. بطاريات اجهزة الاستشعار لايمكن اعادة شحنها بسهولة على الرغم من طاقتها محدودة. كثيراً مايحدث الفشل في الشبكة نتيجة عدم كفاية طاقة اجهزة الاستشعار. حققت بروتوكولات مراقبة الدخول المتوسط (MAC) في شبكات الاستشعار اللاسلكية انخفاض في دورة اشتغاله من خلال توظيف مبدا النوم (السكون) والاستيقاض الدوري. حقق بروتوكول مراقبة الدخول المتوسط ذات الاستيقاظ التنبؤئ استفادة من مبدأ دورة الاشتغال غير المتزامن.    يقلل هذا البروتوكول من استهلاك العقدة للطاقة من خلال السماح للمرسلين بالتنبؤ باوقات استيقاظ المستقبلين.  يجب تطبيق شبكة الاستشعار اللاسكلية بطريقة فعالة لتوظيف عقد الاستشعار وطاقتها لضمان انتاجية شبكة كفوءة. ان التنبؤ بعمر شبكة الاستشعار اللاسلكية قبل بناءها يمثل اهتماما كبيرا . لضمان توفر طاقة كفؤءة يجب ضبط دورات تشغيل المستشعرات بدقة مناسبة لتحقيق متطلبات ازدحام الشبكة. كما تم تقدير كمية الطاقة المستهلكة في كل عقدة نتيجة التحول بين حالة النشاط وحالة الخمول. تم افتراض نشر أجهزة الاستشعار بطريقة عشوائية. تهدف هذه الدراسة الى تحسين عمر الشبكة المنشورة عشوائيا العشوائي من خلال جدولة تاثير الارسال والاستقبال وحالات السكون على عمليات استهلاك الطاقة في عقد الاستشعار اللاسلكي. تم دراسة ومناقشة نتائج هذه الحالات باستخدام العديد من مقايسس الاداء.Energy in Wireless Sensor networks (WSNs) represents an essential factor in designing, controlling and operating the sensor networks. Minimizing the consumed energy in WSNs application is a crucial issue for the network effectiveness and efficiency in terms of lifetime, cost and operation. Number of algorithms and protocols were proposed and implemented to decrease the energy consumption. Principally, WSNs operate with battery-powered sensors. Since Sensor's batteries have not been easily recharge.  Therefore, prediction of the WSN represents a significant concern. Basically, the network failure occurs due to the inefficient sensor's energy. MAC protocols in WSNs achieved low duty-cycle by employing periodic sleep and wakeup. Predictive Wakeup MAC (PW-MAC) protocol was made use of the asynchronous duty cycling. It reduces the consumption of the node energy by allowing the senders to predict the receiver′s wakeup time. The WSN must be applied in an efficient manner to utilize the sensor nodes and their energy to ensure effective network throughput. To ensure energy efficiency the sensors' duty cycles must be adjusted appropriately to meet the network traffic demands. The energy consumed in each node due to its switching between the active and idle states was also estimated. The sensors are assumed to be randomly deployed. This paper aims to improve the randomly deployed network lifetime by scheduling the effects of transmission, reception and sleep states on the energy consumption of the sensor nodes. Results for these states with much performance metrics were also studied and discussed.  &nbsp

Centre based evolving clustering framework with extended mobility features for vehicular ad-hoc networks

Vehicular Ad hoc Network (VANET) clustering is an active research area where a group of connected vehicles forms an ad hoc network. A stable cluster is essential for routing and data dissemination in VANET to avoid various issues such as packet loss, broadcast storm, and increased overhead resulting in an unstable clustering. Therefore, clustering is regarded as an essential part of the hierarchy of intelligent transportation systems. The literature contains numerous approaches for VANETs clustering. The majority of the approaches follow heuristic-based protocol combined with various connected phases and processes, such as cluster formation, cluster head selection, and cluster maintenance. Due to the high mobility of vehicles in VANET, it is more attractive to adapt the evolving data clustering to an evolving VANET clustering framework. The inclusion of extended mobility features has not been observed in most of the clustering approaches. The required extended mobility features are essential to overcome the challenges of vehicle movement. Moreover, relying on the non-valid assumptions such as the nature of the spherical cluster and the pre-knowledge about the number of clusters may not be feasible in many cases. In addition, most of VANETs clustering approaches use simple evaluation methodology where most of the approaches disregard a significant issue in the evaluation methodology. This thesis presents VANETs clustering framework called Centre-based Evolving Clustering with Grid Partitioning (CEC-GP). This framework uses an evolving data clustering algorithm by adopting the concept of grid granularity to capture the features of a cluster more efficiently. CEC-GP includes extended mobility features and provides the capability to avoid spherical assumptions for clusters, which is employed in most of the distance-based clustering. Besides, this framework offers high performance even with the challenging and high mobility scenarios related to the variability of mobility behaviour. The developed CEC-GP also includes an integrated approach that combined all clustering tasks such as cluster formation, cluster head selection, and cluster maintenance. Finally, CEC-GP shows a better stability performance compared with "Centre-based Stable Clustering (CBSC)" and "Evolving Data Clustering Algorithm (EDCA)" based on different performance metrics such as the clustering efficiency, the cluster head, and cluster member duration, the cluster head change rate, and the number of created clusters. The performance evaluation results show CEC-GP is better compared with the other two benchmarks in term of stability and consistency

Minimizing Multiple Objective Function for Scheduling Machine Problems

The multi-criteria problem of scheduling n jobs on a single machine was considered in this paper. The criteria belong to minimize total completion times, total tardiness and total late work and minimize total completion times, total tardiness and maximum late work by using some exact and local search methods. The proposed methods for solving these minimization problems were seemed to be helpful in finding the set of all efficient solutions. This set of all efficient solutions is not easy to find, therefore, it could be preferable to have an approximation to that set in a reasonable amount of time. Therefore branch and bound (BAB) technique was proposed as an exact approach, while the Genetic algorithm (GA) and Particle Swarm Optimization (PSO) methods were also proposed as local search methods. Keywords: Multiple objective Scheduling, Branch and Bound, Pareto Optimal Solutions, Genetic Algorithm, Particle Swarm Optimization

A Novel Stable Clustering Approach Based On Gaussian Distribution And Relative Velocity In VANETs

Vehicles in Vehicular Ad-hoc Networks (VANETs) are characterized by their high dynamic mobility (velocity). Changing in VANET topology is happened frequently which caused continuous network communication failures. Clustering is one of the solutions applied to reduce the VANET topology changes. Stable clusters are required and Indispensable to control, improve and analyze VANET. In this paper, we introduce a new analytical VANET's clustering approach. This approach aims to enhance the network stability. The new proposed grouping process in this study depends on the vehicles velocities mean and standard deviation. The principle of the normal (Gaussian) distribution is utilized and emerged with the relative velocity to propose two clustering levels. The staying duration of vehicles in a cluster is also calculated and used as an indication. The first level represents a very high stabile cluster. To form this cluster, only the vehicles having velocities within the range of mean ± standard deviation, collected in one cluster (i.e. only 68% of the vehicles allowed to compose this cluster). The cluster head is selected from the vehicles having velocities close to the average cluster velocity. The second level is to create a stable cluster by grouping about 95% of the vehicles. Only the vehicles having velocities within the range of mean ± 2 standard deviation are collected in one cluster. This type of clustering is less stable than the first one. The analytical analysis shows that the stability and the staying duration of vehicles in the first clustering approach are better than their values in the second clustering approach

Impacto de la sociedad sin dinero en efectivo en el crecimiento económico de Malasia

For every economy, money is a critical medium of exchange. Money emerges as an intermediary and store of values to improve a barter system. Cashless finance endures utilization generally digital money or plastic without money or currency in papery appearance. The moderate of regulating economic activity endure possibly the massive motivator toward advance the digital economy. This research scrutinizes the progress of embrace cashless payment on economic development and growth of Malaysia. Due to rapid growth in the digital economy, electronic money transactions in Malaysia have increased significantly from in the last fifteen years. There is a positive trend in electronic money usage. The increases caused by the awareness of the society and the government that encourages in using electronic money. At the same time, the velocity of paper money in Malaysia tends to decrease during the same period. While there is a negative trend in the velocity of money, the money supply (M1) has increased in order to maintain an economic growth of the country.Esta investigación examina el progreso de la adopción de pagos sin efectivo en el desarrollo económico y el crecimiento de Malasia. Debido al rápido crecimiento de la economía digital, las transacciones de dinero electrónico en Malasia han aumentado significativamente en los últimos quince años. Hay una tendencia positiva en el uso del dinero electrónico. Los aumentos causados ​​por la conciencia de la sociedad y el gobierno que fomenta el uso del dinero electrónico. Al mismo tiempo, la velocidad del papel moneda en Malasia tiende a disminuir durante el mismo período. Si bien hay una tendencia negativa en la velocidad del dinero, la oferta de dinero (M1) ha aumentado para mantener el crecimiento económico del país

Sentimental classification analysis of polarity multi-view textual data using data mining techniques

The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats. Besides, in the case of the textual data exists as multi-view or unstructured data formats, the hybrid and integrated analysis efforts of text data mining algorithms are vital to get helpful results. The objective of this research is to enhance the knowledge discovery from sentimental multi-view textual data which can be considered as unstructured data format to classify the polarity information documents in the form of two different categories or types of useful information. A proposed framework with integrated data mining algorithms has been discussed in this paper, which is achieved through the application of X-means algorithm for clustering and HotSpot algorithm of association rules. The analysis results have shown improved accuracies of classifying the sentimental multi-view textual data into two categories through the application of the proposed framework on online polarity user-reviews dataset upon a given topics

A Review On IoT-Based Healthcare Monitoring Systems For Patient In Remote Environments

The Internet of Things (IoT) is a powerful technology that can enable users’ smart devices and sensors to link each other and access the internet for daily usage. It is a platform that can be used by end users to access many smart application fields, such as smart city, smart home, and smart healthcare. In recent years, the healthcare monitoring systems are becoming the major development field that use the IoT as advanced technology enabler. This review is discussing more about the design, functions, and the main uses of the IoT-based healthcare monitoring applications and systems for remote patient environments. Therefore, a deep review has been made to identify the usage, efficiency, and acceptability for the current applications and systems to become more efficient to patients. Different healthcare monitoring systems have employed the IoT to integrate the different wireless interfaces with the cloud-based healthcare services. These services are including the sensing, gathering, processing, storing, and learning more among the patients’ data. The various IoT healthcare applications will help to develop useful and effective solutions by using these systems in practice. From this review it can be proof that the IoT-based healthcare monitoring applications are growing faster to build more healthcare solutions that useful for different healthcare conditions and environment

Clustering Approach In Wireless Sensor Networks Based On K-Means: Limitations And Recommendations

Clustering approach in wireless sensor network is very important, the structure of cluster and how to improve it is a first challenge that faced the developers, because of it represent as a base for design the cluster-based routing protocol. One of most popular cluster algorithms that utilizing into organize sensor nodes is K-means algorithm. This algorithm has beneficial in construct the clusters for various real-world applications of WSN.K-means algorithm suffering from many drawbacks that hampering his work.The lack of adequate studies that investigates in the limitations of this algorithm and seek to propose the solutions motivated us to do this study. In this paper the limitations of K-means and some suggestions are proposed. These suggestions can improve the performance of K-means, which will be reflected on saving the energy forsensor nodes and consequently maximize the lifetime of the wireless sensor networks

Clustering Based Affinity Propagation In Vanets : Taxonomy And Opportunity Of Research

Vehicular communication networks received good consideration and focusing on diverse researchers in the latest years. Vehicular Adhoc Networks (VANETs) represents a developed type of an effective communication technology to facilitate the process of information dissemination among vehicles. VANETs established the cornerstone to develop the Intelligent Transport Systems (ITS). The great challenging task in routing the messages in VANETs is related to the different velocities of the moving vehicles on the streets in addition to their sparse distribution. Clustering approach is broadly used to report this challenge. It represents the mechanism of the alliance the vehicles based on certain metrics such as velocity, location, density, direction and lane position. This paper is to investigate and analyze several challenges and their present solutions which based on different developed clustering approaches based on the affinity propagation algorithm. This paper isaim to present a complete taxonomy on vehicles clustering and analyzing the existing submitted proposals in literature based on affinity propagation. Presenting and analyzing the submitted proposals will provide these domain researchers with a good flexibility to select or apply the suitable approach to their future application or research activities. To prepare this paper in a systematic manner, a total of 1444 articles concerning the Affinity Propagation in clustering published in the era of 2008 to 2019 were collected from the reliable publishing sources namely (ScienceDirect, IEEE Xplore, and SCOPUS). Due to their relevance, applicability, generality level and comprehensiveness, only nineteen articles among the collected articles were assigned and eventually analyzed in a systematic review method.A considerable success has been achieved in revealing the essential challenges and necessities for clustering based affinity Propagation in VANETs to guide the researchers in their upcoming investigations. This paper also contributes in dealing with open problems issues, challenges and guidelines for the upcoming investigations

Data Dissemination Based Clustering Techniques For Vanets : A Review

Exploration on Vehicular Ad hoc Networks (VANETs) is still having great consideration by most of the interest researchers.VANET are characterized by its high mobility and various vehicles distributions on roads among other wireless communication networks.The essential purpose of VANET is to exchange safety and entertainment messages among vehicles.VANET environment topology is changing repeatedly due to vehicles high velocity.Such feature will effectively break the communication links between vehicles.It can also rise the routing overhead and reduce the life time of the network.The supreme feasible solution to solve this difficulty is by applying clustering.Clustering is to group the vehicles into sets according to some conditions,rules or characteristics.It also represents an effective methodology to recover the networking protocol scalability.Forming and maintaining suitable cluster to achieve VANETs purposes is creating a big exciting challenge.A stable cluster is indispensable to ensure effective communication among the road vehicles in a dynamic environment.Cluster stability can be achieved by maintaining the Cluster Head (CH) for long possible time,letting the Cluster Members (CM) staying for long duration in the same cluster and reducing the number of CH changes.To improve the cluster stability, Literatures show that mobility-based clustering is one of the best approaches.Certain level of stability can be achieved by taking vehicles density,direction,distances and velocities into consideration.This paper highlights the updated clustering approaches and sheds the light over the related relation between clustering and data dissemination in order to achieve reliable communication among vehicles in VANET’s environment