Sensing Models and Its Impact on Network Coverage in Wireless Sensor Network

Network coverage of wireless sensor network (WSN) means how well an area of interest is being monitored by the deployed network. It depends mainly on sensing model of nodes. In this paper, we present three types of sensing models viz. Boolean sensing model, shadow-fading sensing model and Elfes sensing model. We investigate the impact of sensing models on network coverage. We also investigate network coverage based on Poisson node distribution. A comparative study between regular and random node placement has also been presented in this paper. This study will be useful for coverage analysis of WSN.Comment: 5 pages, 5 figures, IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, INDIA December 8-10, 200

Impact of Next Generation Cognitive Radio Network on the Wireless Green Eco system through Signal and Interference Level based K Coverage Probability

Land mobile communication is burdened with typical propagation constraints due to the channel characteristics in radio systems.Also,the propagation characteristics vary form place to place and also as the mobile unit moves,from time to time.Hence,the tramsmission path between transmitter and receiver varies from simple direct LOS to the one which is severely obstructed by buildings, foliage and terrain. Multipath propagation and shadow fading effects affect the signal strength of an arbitrary Transmitter-Receiver due to the rapid fluctuations in the phase and amplitude of signal which also determines the average power over an area of tens or hundreds of meters. Shadowing introduces additional fluctuations, so the received local mean power varies around the area –mean. The present paper deals with the performance analysis of impact of next generation wireless cognitive radio network on wireless green eco system through signal and interference level based k coverage probability under the shadow fading effects

Estimation and Improvements of the Fundamental QoS in Networks with Random Topologies

The computer communication paradigm is moving towards the ubiquitous computing and Internet of Things (IoT). Small autonomous wirelessly networked devices are becoming more and more present in monitoring and automation of every human interaction with the environment, as well as in collecting various other information from the physical world. Applications, such as remote health monitoring, intelligent homes, early fire, volcano, and earthquake detection, traffic congestion prevention etc., are already present and all share the similar networking philosophy. An additional challenging for the scientific and engineering world is the appropriateness of the alike networks which are to be deployed in the inaccessible regions. These scenarios are typical in environmental and habitat monitoring and in military surveillance. Due to the environmental conditions, these networks can often only be deployed in some quasi-random way. This makes the application design challenging in the sense of coverage, connectivity, network lifetime and data dissemination. For the densely deployed networks, the random geometric graphs are often used to model the networking topology. This paper surveys some of the most important approaches and possibilities in modeling and improvement of coverage and connectivity in randomly deployed networks, with an accent on using the mobility in improving the network functionality

Estimation and Improvements of the Fundamental QoS in Networks with Random Topologies

The computer communication paradigm is moving towards the ubiquitous computing and Internet of Things (IoT). Small autonomous wirelessly networked devices are becoming more and more present in monitoring and automation of every human interaction with the environment, as well as in collecting various other information from the physical world. Applications, such as remote health monitoring, intelligent homes, early fire, volcano, and earthquake detection, traffic congestion prevention etc., are already present and all share the similar networking philosophy. An additional challenging for the scientific and engineering world is the appropriateness of the alike networks which are to be deployed in the inaccessible regions. These scenarios are typical in environmental and habitat monitoring and in military surveillance. Due to the environmental conditions, these networks can often only be deployed in some quasi-random way. This makes the application design challenging in the sense of coverage, connectivity, network lifetime and data dissemination. For the densely deployed networks, the random geometric graphs are often used to model the networking topology. This paper surveys some of the most important approaches and possibilities in modeling and improvement of co verage and connectivity in randomly deployed networks, with an accent on using the mobility in improving the network functionality

TỐI ƯU HÓA VÙNG PHỦ SÓNG CỦA MẠNG CẢM BIẾN KHÔNG DÂY BẰNG THUẬT TOÁN VORONOI TRONG MÔI TRƯỜNG 3D

In recent years, wireless sensor networks (WSN) has appeared interesting to many authors. Some methods to optimize the coverage of wireless sensor networks is proposed to improve the efficiency of deploying sensor networks, thus increasing the coverage; howerver, most are built on 2D model, which are often hard to implement in reality. In this paper we extend Voronoi algorithm to deploy sensors in 3D environments where there are obstacles which affect the ability of coverage of wireless sensor networks.Trong những năm gần đây mạng cảm biến không dây (WSN) được nhiều nhóm tác giả quan tâm. Một số phương pháp tối ưu hóa vùng phủ sóng của mạng cảm biến không dây được đề xuất để nâng cao hiệu quả triển khai mạng cảm biến do đó làm tăng độ phủ sóng, nhưng hầu hết được xây dựng trên mô hình 2D, mà thường xa rời với thực tế. Trong bài báo này chúng tôi mở rộng thuật toán Voronoi để triển khai các cảm biến trong môi trường 3D mà ở đó có nhiều vật cản làm ảnh hưởng đến khả năng phủ sóng của mạng cảm biến không dây

Sensing Coverage Prediction for Wireless Sensor Networks in Shadowed and Multipath Environment

Sensing coverage problem in wireless sensor networks is a measure of quality of service (QoS). Coverage refers to how well a sensing field is monitored or tracked by the sensors. Aim of the paper is to have a priori estimate for number of sensors to be deployed in a harsh environment to achieve desired coverage. We have proposed a new sensing channel model that considers combined impact of shadowing fading and multipath effects. A mathematical model for calculating coverage probability in the presence of multipath fading combined with shadowing is derived based on received signal strength (RSS). Further, the coverage probability derivations obtained using Rayleigh fading and lognormal shadowing fading are validated by node deployment using Poisson distribution. A comparative study between our proposed sensing channel model and different existing sensing models for the network coverage has also been presented. Our proposed sensing model is more suitable for realistic environment since it determines the optimum number of sensors required for desirable coverage in fading conditions

بهینه سازی جایابی شبکه های سنسور بی سیم با استفاده از الگوریتم های بهینه سازی سراسری و مدل سنجش احتمالی

در سال های اخیر، شبکه­ های حسگر بیسیم[1] در کاربردهای متعددی مورد مطالعه قرار گرفته ­اند. یکی از مسائل مهم مورد مطالعه در این شبکه­ ها، جایابی[2]  بهینه حسگرها به منظور دستیابی به بیشینه­ ی مقدار پوشش[3]  است. از این رو، در اکثر تحقیقات برای رسیدن به پوشش حداکثر از الگوریتم­ های بهینه­ سازی استفاده شده است. در یک رده­ بندی کلی، الگوریتم­ های بهینه­ سازی برای جایابی بهینه حسگر با هدف افزایش پوشش، به دو گروه الگوریتم­ های بهینه­ سازی محلی و سراسری تقسیم می­ شوند. الگوریتم­ های سراسری عموماً از یک روش تصادفی بر اساس یک روند تکاملی استفاده می کنند. در اغلب تحقیقات انجام شده، مدل محیط و بعضاً چیدمان حسگرها در شبکه به صورت کاملاً ساده­ سازی شده در نظر گرفته شده­ اند. در این تحقیق با مدلسازی رستری و برداری محیط در فضاهای دو و سه بعدی، عملکرد الگوریتم­ های بهینه­ سازی سراسری به منظور جانمایی بهینه حسگرها، ارزیابی و مقایسه شده اند و مدل محیط برداری به عنوان مدل دقیق تر استفاده می­ شود. از آنجایی که هدف مقایسه عملکرد و نتایج الگوریتم‌های سراسری بوده است، منطقه مورد مطالعه و شرایط پیاده‌سازی یکسان فرض شده‌اند. در این مقاله، چند روش بهینه‌سازی برای جایابی سنسور، از جمله الگوریتم‌های ژنتیک، L-BFGS، VFCPSO و CMA-ES ،پیاده‌سازی و معیار ارزیابی الگوریتم‌ها برای مسئله جایابی شبکه‌های حسگر بی‌سیم، مقدار پوشش بهینه، دقت پوشش آنها نسبت به مدل محیط و سرعت همگرایی الگوریتم‌ها در نظر گرفته شده است.از سوی دیگر، در این تحقیق مدل احتمالی پوشش[4]  برای هر یک از الگوریتم‌های بهینه‌سازی سراسری پیاده‌سازی شدند. نتایج این پیاده‌سازی‌ها نشان می‌دهد که وجود پارامترهای پیچیده‌تر در مدل محیط و پوشش، نتایج دقیق‌تر و منطبق‌تری با واقعیت را ارائه می‌کند. با این حال ممکن است کارایی زمانی الگوریتم‌ها را کاهش دهد. [1]4- Wireless Sensor Networks [2]5-Deployment [3]6- Coverage [4]7- Probablity coverage mode