An assessment of power sector reforms and utility performance to strengthen consumer self-confidence towards private investment

Localization plays an important role in the field of Wireless Sensor Networks (WSNs) and robotics. Currently, localization is a very vibrant scientific research field with many potential applications. Localization offers a variety of services for the customers, for example, in the field of WSN, its importance is unlimited, in the field of logistics, robotics, and IT services. Particularly localization is coupled with the case of human-machine interaction, autonomous systems, and the applications of augmented reality. Also, the collaboration of WSNs and distributed robotics has led to the creation of Mobile Sensor Networks (MSNs). Nowadays there has been an increasing interest in the creation of MSNs and they are the preferred aspect of WSNs in which mobility plays an important role while an application is going to execute. To overcome the issues regarding localization, the authors developed a framework of three algorithms named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Particle Filter (PF) Localization algorithms. In our previous study, the authors only focused on EKF-based localization. In this paper, the authors present a modified Kalman Filter (KF) for localization based on UKF and PF Localization. In the paper, all these algorithms are compared in very detail and evaluated based on their performance. The proposed localization algorithms can be applied to any type of localization approach, especially in the case of robot localization. Despite the harsh physical environment and several issues during localization, the result shows an outstanding localization performance within a limited time. The robustness of the proposed algorithms is verified through numerical simulations. The simulation results show that proposed localization algorithms can be used for various purposes such as target tracking, robot localization, and can improve the performance of localization.

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A Localization Based on Unscented Kalman Filter and Particle Filter Localization Algorithms

This paper concerns about the radio propagation models used for the upcoming 4 th Generation (4G) of cellular networks known as Long Term Evolution (LTE). The radio wave propagation model or path loss model plays a very significant role in planning of any wireless communication systems. In this paper, a comparison is made between different proposed radio propagation models that would be used for LTE, like Stanford University Interim (SUI) model, Okumura model, Hata COST 231 model, COST Walfisch-Ikegami & Ericsson 9999 model. The comparison is made using different terrains e.g. urban, suburban and rural area.SUI model shows the lowest path lost in all the terrains while COST 231 Hata model illustrates highest path loss in urban area and COST Walfisch-Ikegami model has highest path loss for suburban and rural environments.

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Comparison of Radio Propagation Models for Long Term Evolution (LTE) Network

Wireless Sensor Network (WSN) are of paramount significance since they are responsible for maintaining the routes in the network, data forwarding, and ensuring reliable multi-hop communication. The main requirement of a wireless sensor network is to prolong network energy efficiency and lifetime. Researchers have developed protocols Low Energy Adaptive Clustering Hierarchy (LEACH) and Power-Efficient Gathering in Sensor Information Systems (PEGASIS) for reducing energy consumption in the network. However, the existing routing protocols experience many shortcomings with respect to energy and power consumption. LEACH features the dynamicity but has limitations due to its cluster-based architecture, while PEGASIS overcomes the limitations of LEACH but lacks dynamicity. In this paper, we introduce PEGASIS-LEACH (P-LEACH), a near optimal cluster-based chain protocol that is an improvement over PEGASIS and LEACH both. This protocol uses an energy-efficient routing algorithm to transfer the data in WSN. To validate the energy effectiveness of P-LEACH, we simulate the performance using Network Simulator (NS2) and MATLAB.

P-LEACH: Energy efficient routing protocol for Wireless Sensor Networks

During the previous few years, Wireless Sensor Network (WSN) appears as an active research domain due to the wide use of this technology in several applications, such as military, health, automation, and so forth. WSN supervises physical attributes where human activity is difficult or impossible. The WSN is a set of multiple sensor nodes that are haphazardly deployed in a specific space. These nodes sense data and record values continuously and send these data to the Base Station (BS) through other sensor nodes. Several issues are encountered in the WSNs, including energy consumption, deployment of sensor nodes, routing algorithms, energy efficiency, Cluster-Head (CH) selection, robustness, etc. Numerous routing protocols have been developed by researchers to resolve these constraints, and several techniques of optimization are proposed to define the optimal road between the transmitter and the receiving node. Hierarchical routing protocols increase the network lifetime by enhancing its performance. The most popular hierarchical method is LEACH (Low Energy Adaptive Clustering Hierarchy) that grouped sensor nodes into clusters. Each cluster consists of normal nodes and is controlled by a CH, which is elected by cluster members to collect their data and send it to the BS. The LEACH protocol decreased the energy consumption in WSNs. In this survey, we provide a comprehensive study of LEACH descendant clustering protocols. This survey is the first study to classify LEACH-based routing protocols into CH selection, data transmission, and both CH selection and data transmission techniques. This survey is compared to other actual surveys. To evaluate these protocols, we look at many metrics, such as the CH selection method, communication method, the scalability, energy efficiency, mobility, localization of nodes, and so forth. According to these metrics, we propose a comparative analysis of these clustering routing protocols. This survey discusses also the strengths and limitations of each LEACH-variant protocol. Conclusively, the paper terminates with recommendations on future research fields in WSN.

A comprehensive survey on LEACH-based clustering routing protocols in Wireless Sensor Networks

Economic analysis and impact on national grid by domestic photovoltaic system installations in Pakistan

Wireless sensor networks (WSNs) are achieving importance with the passage of time. Out of massive usage of wireless sensor networks, few applications demand quick data transfer including minimum possible interruption. Several applications give importance to throughput and they have not much to do with delay. It all rest on the applications desires that which parameter is more favourite. The knowledge of network structure and routing protocol is very important and it should be appropriate for the requirement of the usage. In the end a performance analysis of different routing protocols is made using a WLAN and a ZigBee based Wireless Sensor Network.

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Routing Protocols for Wireless Sensor Networks (WSNs)

Wind power is very different from other sources due to its volatile and stochastic nature. The forecasting of wind energy generation is a very important aspect for the reliability and challenges regarding balancing the supply and demand in power systems. In this paper, Support Vector Machine (SVM) based regression algorithm is used for one day ahead prediction of wind energy production in Estonia. The proposed algorithm is then compared with the results of the prediction algorithm used by the Estonian energy regulatory organization. The results indicate that our proposed algorithms give better forecasting and the lowest Root Mean Square Error (RMSE) values.

Forecasting Short Term Wind Energy Generation using Machine Learning

Load forecasting is a vital factor in electrical power systems due to technical and economic implications. Its forecasting is, however, a very challenging task due to many variable factors like occupant behavior, household size, loads present in the household and many other factors. In this article, three different machine learning algorithms have been applied for load forecasting on a load dataset of an Estonian household which was measured for a whole month. The simulation results of the Support Vector Machine (SVM) based forecasting model gives best results when compared with the real data. The validation results indicate that it has the lowest Root Mean Square Error (RMSE) value of 187.73 which is 28% and twice less than tree based and linear regression, respectively.

Noman Shabbir

Papers

Comparison of Radio Propagation Models for Long Term Evolution (LTE) Network

Economic analysis and impact on national grid by domestic photovoltaic system installations in Pakistan

Routing Protocols for Wireless Sensor Networks (WSNs)

Forecasting Short Term Wind Energy Generation using Machine Learning

Comparison of Machine Learning Based Methods for Residential Load Forecasting