Wireless Sensor Networks (WSNs) are becoming very common technology which combine sensing, processing, and wireless multi-hop networking. This paper provides a wide review of the present state about WSNs at the time of its writing. Following a top-down approach, WSNs concept, definition and applications is provided. Furthermore, an overview of WSNs constrains and judgment metrics such as lifetime and latency is given. Then, the communication protocol stack for WSNs is described, and protocols developed for each layer are discussed. Finally, this paper provides review and comparisons of current simulation programs All of these features make the paper valuable for an extensive variety of possible readers, researchers in WSNs, students stating research in WSNs, specialists wanting to offer WSN solutions, and WSN application designers.

Wireless Sensor Networks (WSNs) are becoming very common technology which combine sensing, processing, and wireless multi-hop networking. This paper provides a wide review of the present state about WSNs at the time of its writing. Following a top-down approach, WSNs concept, definition and applications is provided. Furthermore, an overview of WSNs constrains and judgment metrics such as lifetime and latency is given. Then, the communication protocol stack for WSNs is described, and protocols developed for each layer are discussed. Finally, this paper provides review and comparisons of current simulation programs All of these features make the paper valuable for an extensive variety of possible readers, researchers in WSNs, students stating research in WSNs, specialists wanting to offer WSN solutions, and WSN application designers.

ABSTRACT Cyclostationary feature detection is one of the most powerful spectrum sensing techniques used for cognitive radio (CR) systems. This is because of its robustness against noise uncertainties. However, this technique needs high sampling rates, which is limited by the state-of the-art analog to digital converters (ADCs), especially in wideband regime. Comressive sensing (CS) was used by many researchers for solving this problem via sub-Nyquist sampling rates. However CS solves the high sampling rate problem, but it does ...

ABSTRACT Cyclostationary feature detection is one of the most powerful spectrum sensing techniques used for cognitive radio (CR) systems. This is because of its robustness against noise uncertainties. However, this technique needs high sampling rates, which is limited by the state-of the-art analog to digital converters (ADCs), especially in wideband regime. Comressive sensing (CS) was used by many researchers for solving this problem via sub-Nyquist sampling rates. However CS solves the high sampling rate problem, but it does ...

ABSTRACT Cyclostationary feature detection is one of the most powerful spectrum sensing techniques used for cognitive radio (CR) systems. This is because of its robustness against noise uncertainties. However, this technique needs high sampling rates, which is limited by the state-of the-art analog to digital converters (ADCs), especially in wideband regime. Comressive sensing (CS) was used by many researchers for solving this problem via sub-Nyquist sampling rates. However CS solves the high sampling rate problem, but it does ...

Abstract—In this paper, cooperative compressive spectrum sens-ing is considered to enable accurate sensing of the wide-band spectrum. The proposed algorithm is based on compressive sensing theory and aims to reduce the hardware complexity of the cognitive radio receiver by distributing the sensing work among groups of sensing nodes. The proposed algorithm classifies the cooperated sensing nodes into different sensing groups depending on the quality of the reporting channel between the sensing node and the ...

Energy consumption and energy modeling are important issues in designing and implementing of Wireless Sensor Networks (WSNs), which help the designers to optimize the energy consumption in WSN nodes. Good knowledge of the sources of energy consumption in WSNs is the first step to reduce energy consumption. Therefore, an accurate energy model is required for the evaluation of communication protocols. In this paper, we provide an energy model for WSNs considering the physical layer and MAC layer parameters by determining the energy consumed per payload bit transferred without error over AWGN channel. We show how the transmission power must be chosen in order to achieve energy-efficient communications over AWGN channel. We also find that, for each modulation scheme, there are optimal transmission power at which the energy consumption is minimized. Moreover, we investigated the energy saving gained from optimizing the constellation size.

Energy consumption and energy modeling are important issues in designing and implementing of Wireless Sensor Networks (WSNs), which help the designers to optimize the energy consumption in WSN nodes. Good knowledge of the sources of energy consumption in WSNs is the first step to reduce energy consumption. Therefore, an accurate energy model is required for the evaluation of communication protocols. In this paper, we provide an energy model for WSNs considering the physical layer and MAC layer parameters by determining the energy consumed per payload bit transferred without error over AWGN channel. We show how the transmission power must be chosen in order to achieve energy-efficient communications over AWGN channel. We also find that, for each modulation scheme, there are optimal transmission power at which the energy consumption is minimized. Moreover, we investigated the energy saving gained from optimizing the constellation size.

Energy consumption and energy modeling are important issues in designing and implementing of Wireless Sensor Networks (WSNs), which help the designers to optimize the energy consumption in WSN nodes. Good knowledge of the sources of energy consumption in WSNs is the first step to reduce energy consumption. Therefore, an accurate energy model is required for the evaluation of communication protocols. In this paper, we provide an energy model for WSNs considering the physical layer and MAC layer parameters by determining the energy consumed per payload bit transferred without error over AWGN channel. We show how the transmission power must be chosen in order to achieve energy-efficient communications over AWGN channel. We also find that, for each modulation scheme, there are optimal transmission power at which the energy consumption is minimized. Moreover, we investigated the energy saving gained from optimizing the constellation size.