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Optimization of wireless sensor network deployment for electromagnetic situation monitoring
Abstract
Wireless sensor networks are an increasingly popular tool for monitoring various environmental parameters. They can also be used for monitoring the electromagnetic spectrum. Wireless sensors, due to their small size, typically have simplified radio receivers with reduced sensitivity and use small antennas. As a result, their effective performance area is similarly limited. This is especially important in urban areas where there are various kinds of adverse propagation phenomena related to area coverage. The aim of this paper is to present the phenomena in the wireless sensor networks and propose criteria and methods to optimize their deployment to ensure maximizing the probability of detection of emissions, minimization of unmonitored areas, and to provide the necessary hardware redundancy in the priority areas. Influence of detection parameters, number of sensors and range constraints between sensors on received outcomes are also presented.
... Synchronous data acquisition is crucial, for example, for structural health monitoring [7][8][9][10][11][12] and many applications of Wireless Body Area Networks (WBANs) [13][14][15][16]. Significant challenges in WBANs are described in [17][18][19][20] and include requirements for low-power technology, small sensor dimensions, multi-device synchronous measurements, and the recording of physiological parameters, data fusion, and dissemination. ...
This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ −4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant.
... As a hardware platform SDR USRP B200 mini with frequency range from 70 MHz to 6 GHz and 20 MHz bandwidth was selected. Integration with UAV platform (DJI Mavic 3 Cine) and microcomputer (Raspberry Pi4) is proposed for a complete radiomonitoring sensor (Fig. 5) as a flexible device that can increase mobility and spectrum monitoring range/coverage area [22]. From WINLAS architecture point of view the radiomonitoring sensor as a "Sensing Component" is of the managed sensor type and can be static or dynamic and manned or unmanned. ...
Situational awareness of armed forces acting in an urban environment is a key factor determining the success of military operations. In 2021 the European Defence Agency established the project on Wireless Sensor Networks for Urban Local Areas Surveillance. The main goal of the project is to assess how the situational awareness in an urban environment can be enhanced with the application of heterogeneous, autonomous and reconfigurable sensors. The paper presents a novel comprehensive approach that takes into account modelling and management of heterogeneous sensors, energy harvesting techniques, planning and management of the communication backbone, network security for data transfer and authorization for secure information exchange. The architecture of the system and the information flow are presented. The topology aspects are discussed and the sensing part is described. The paper finally highlights new essential enhancements of C2 with particular emphasis on mission planning, data fusion and threat prediction.
... Finally, proposed models, which are expected to be used in applications such as the Internet of Things, can be effective with WSN in controlling the pebbles laid on the ground at home and garden design and in monitoring rural areas using pebbles. In addition to these scenarios and also many other cases [31][32][33], anticipated applications of WSN and mobile network is constantly evolving and changing. This reveals the importance of accurate channel features. ...
Path loss prediction is quite important for the network performance of the wireless sensors, quality of cellular communication-based link budget, and optimization of coverage planning in mobile networks. With the development of 5G technology, even though different log-distance path loss models are generated for these, new-developed methods are required to make models more flexible and accurate for complex environments. In this study, for different coastal terrains (air-dry sand, wet sand, small pebble, big pebble) and various vegetable areas (pine, orange, cherry, and walnut), the principle and procedure of deep learning-based path loss prediction are provided in 3.5 GHz, 3.8 GHz, and 4.2 GHz in the 5G frequency zone, as a novelty. For this, recurrent neural network (RNN) and long short-term memory (LSTM) methods are proposed. The test sample number is 240 since 20% of all datasets (1200) are test data. In general, path loss for coastal terrains is higher than path loss for vegetation areas with an average of 5 dB. For both coastal terrains and vegetation areas, the recurrent neural network method predicts better than the long short-term memory method. Consequently, for both coastal terrains and vegetation areas, RNN models with R² values of 0.9677 and 0.9042, respectively, are preferred.
... bardzo gęstych sieci radiowych UDN (Ultra-Dense Network). Z drugiej strony, efektywne wykorzystanie dostępnych zasobów radiowych, wymaga ciągłego prowadzenia monitoringu widma [1][2][3]. Monitoring taki może być prowadzony na potrzeby użytkowników cywilnych, np. w celu kognitywnego wykorzystania dostępnych zasobów radiowych (w tym tzw. ...
... A popular platform compatible with the GNU Radio programming environment is the Universal Software Radio Peripheral (USRP) DOI: 10.2478/kbo-2022-0093 platform [6]. Although there are many initiatives to build such monitoring networks using SDR sensors [7,8,9], very few address the possibility of remote control of them, especially for sensors that have implemented supporting applications in the GNU Radio programming environment [10]. A flexible way to achieve remote access to the monitoring system using the Internet can be implemented by establishing a VPN connection. ...
In this article, we present the implementation of a Software Defined Radio (SDR) remote sensor management solution for electromagnetic spectrum monitoring using web interfaces. The proposed sensor is developed on an SDR platform - USRP-2932 with LTE standard specific signal monitoring capabilities. Two solutions have been implemented for remote control and visualization of the sensor parameters: by using VNC (Virtual Network Computing) software for full remote access and by implementing in GNU Radio the Bokeh python library, which allows visualization of graphs and charts directly through a web GUI. For accessing the sensor via the backbone network of an Internet Service Provider (ISP), in both cases a Virtual Private Network (VPN) was set up using the Point-to-Point Tunnelling Protocol (PPTP) solution.
... A popular platform compatible with the GNU Radio programming environment is the Universal Software Radio Peripheral (USRP) DOI: 10.2478/kbo-2022-0093 platform [6]. Although there are many initiatives to build such monitoring networks using SDR sensors [7,8,9], very few address the possibility of remote control of them, especially for sensors that have implemented supporting applications in the GNU Radio programming environment [10]. A flexible way to achieve remote access to the monitoring system using the Internet can be implemented by establishing a VPN connection. ...
In this article, we present the design, implementation, and testing of a virtual reality-based training solution for military personnel. The aim of using virtual reality instead of traditional training processes is to simulate battlefield conditions as realistically as possible and to save financial resources that could be directed to other educational processes. The system was implemented using the Virtual Battlespace 4 (VBS4) virtual simulation software application, interconnected with Virtual Reality Headset and Controllers, to provide an enhanced simulation training experience. A wireless network has also been set up and tested for increased flexibility and quick setup during multiplayer simulation.
... Considering the previous works [21,22] and the solution from [23], a cluster-based MANET architecture is proposed. MANET is divided into groups of nearly located nodes, called clusters. ...
This paper presents a solution for building awareness of the electromagnetic situation in cognitive mobile ad hoc networks (MANET) using the cooperative spectrum sensing method. Signal detection is performed using energy detectors with noise level estimation. Based on the evidence theory, the fusion center decides on the particular channel occupancy, which can process incomplete and unambiguous input data. Next, a reinforced machine learning algorithm estimates the usefulness of particular channels for the MANET transmission and creates backup channels list that could be used in case of interferences. Initial simulations were performed using the MATLAB environment, and next an OMNET-based MAENA high fidelity simulator was used. Performed simulations showed a significant increase in sensing efficiency compared to sensing performed using simple data fusion rules.
... Finally, proposed models, which are expected to be used in applications such as the IoT, can be effective with WSN in controlling the pebbles laid on the ground at home and garden design and in the monitoring of rural areas using pebbles. In addition to these scenarios and also many other cases [21][22][23], the anticipated applications of WSN and mobile network is constantly evolving and changing. This reveals the importance of accurate channel features. ...
Propagation modeling of small/big pebbles and air-dry/wet sand environments for wireless sensor networks has not been extensively studied in the 5G frequency band. This study is necessary for the proper coverage planning and efficient operation of wireless sensors in various applications such as monitoring summer sporting activities, and environmental/ground surveillance on coastal pebble/sand environments, or tracking pebble mobility and including the rescue of the flood-type avalanche in Gravel-Bed Rivers. In this study, empirical path loss models are proposed for wireless sensor networks in pebble/sand environments at two discrete frequencies, namely 3.5 and 4.2 GHz. The theoretical models and proposed models are compared to indicate the accuracy of proposed models in predicting the path loss in these environments. Additionally, R -squared and RMSE values of eight different generated models are calculated in the range of 0.931–0.877 and 2.284–2.837, respectively. These comparisons indicate that empirical model parameters have a significant effect on the path loss model.
... Two basic sensing approaches may be used in the proposed solution: local spectrum monitoring or cooperative sensing of spatially distributed radio network nodes. The problem of optimizing the placement of sensing elements is considered, inter alia, in [11] and [12]. The use of cooperative spectrum monitoring allows to reduce the severity of the problem of the so-called hidden nodes [13]. ...
This paper presents an algorithm that supports the dynamic spectrum access process in cognitive radio networks by generating a sorted list of best radio channels or by identifying those frequency ranges that are not in use temporarily. The concept is based on the reinforcement learning technique named Q-learning. To evaluate the utility of individual radio channels, spectrum monitoring is performed. In the presented solution, the epsilon-greedy action selection method is used to indicate which channel should be monitored next. The article includes a description of the proposed algorithm, scenarios, metrics, and simulation results showing the correct operation of the approach relied upon to evaluate the utility of radio channels and the epsilon-greedy action selection method. Based on the performed tests, it is possible to determine algorithm parameters that should be used in this proposed deployment. The paper also presents a comparison of the results with two other action selection methods
... For the radio range assessment, simpler and less accurate deterministic models are usually used than those based on the ray-tracing. In the case of evaluating the real situation between the MANET nodes, results of spectrum sensing [8][9][10][11][12][13], implemented in modern cognitive networks [14][15][16][17][18] may be utilized. These solutions are used in so-called radio environment maps (REMs) [19][20][21][22][23][24]. ...
Radio environment maps (REMs) are beginning to be an integral part of modern mobile radiocommunication systems and networks, especially for ad-hoc, cognitive, and dynamic spectrum access networks. The REMs will use emerging military systems of tactical communications. The REM is a kind of database used at the stage of planning and management of the radio resources and networks, which considers the geographical features of an area, environmental propagation properties, as well as the parameters of radio network elements and available services. At the REM, for spatial management of network nodes, various methods of propagation modeling for determining the attenuation and capacity of wireless links and radio ranges are used. One method of propagation prediction is based on a numerical solution of the wave equation in a parabolic form, which allows considering, i.a., atmospheric refraction, terrain shape, and soil electrical parameters. However, the determination of a current altitudinal profile of atmospheric refraction may be a problem. If the propagation-prediction model uses a fixed refraction profile, then the calibration of this model based on empirical measurements is required. We propose a methodology for calibrating the analyzed model based on an example empirical research scenario. The paper presents descriptions of the propagation model, test-bed and scenario used in measurements, and obtained signal attenuation results, which are used for the initial calibration of the model.
... The purpose of this paper is to present an innovative application of SDF in a cooperative system of locating the emission sources in the urban areas. Elements of this system are arranged on several unmanned aerial vehicles (UAV) and operated as a wireless sensor network (WSN) [15,16]. This solution provides an increase of monitoring area, accuracy and speed of location process. ...
Efficient and precise location of emission sources in an urbanized environment is very important in electronic warfare. Therefore, unmanned aerial vehicles (UAVs) are increasingly used for such tasks. In this paper, we present the cooperation of several UAVs creating a wireless sensor network (WSN) that locates the emission source. In the proposed WSN, the location is based on spectrum sensing and the signal Doppler frequency method. The paper presents the concept of the system. Simulation studies are used to assess the efficiency of the cooperative WSN. In this case, the location effectiveness for the WSN is compared to the single UAV.
Wireless sensor networks (WSNs) have been highly utilized and defensible technology in diverse application areas for data gathering from remote and hard-to-approach regions. Wireless Sensor Networks are substantially important for the real-world applications such as environmental monitoring, surveillance, and smart infrastructure. Network coverage, connectivity and energy savings are significant factors in the WSN deployment. Wireless sensor networks (WSNs) undergo a great deal of crucial challenges such as minimize energy consumption, maximize coverage, and network lifetime improvement. Sensor nodes are energy constrained and deployed in resource-constrained environments for many real-world applications. Low energy usage is hence crucial to prolong network life. Meanwhile, to guarantee the performance of a WSN, it is crucial to ensure data transmission with less energy consumption and full coverage. These challenges are the central focus of this work, requiring scalable and efficient deployment strategies. In this paper, a complete survey study on optimization technique for deployment of WSN to improve network performance and resource utilization is offered. The paper also suggests a new algorithm named as Fractal Clustering Based Firefly Deployment Algorithm which is particularly designed for the deployment of sensor nodes deployed in WSNs. The proposed hybridize method uses the principles of fractal clustering and firefly optimization algorithm to make light-weight, energy efficient and enhanced optimized deployment strategy. To start with, the algorithm makes use of a fractal clustering technique to partition an area of interest into regions that have similar attributes. This clustering determines the areas that are needed to have higher sensor node density requirements — regions where events requiring a critical response or data traffic are high. The algorithm represents each cluster by a virtual firefly. The firefly algorithm is a biologically-inspired swarm intelligence optimization technique, inspired by the flashing behavior of fireflies which stochastically moves through input parameter space to find favorable deployment configurations. In this paper, the efficiency of the algorithm is verified by simulating the proposed algorithm using MATLAB2020 and comparing it with other deployment strategies. This analysis shows promising results.
Modern wireless communication systems use various technological solutions to increase the efficiency of created radio networks. This efficiency also applies to radio resources. Currently, the utilization of a radio environment map (REM) is one of the directions allowing to improve radio resource management. The REM is increasingly used in emerging mobile ad-hoc networks (MANETs), in particular military tactical networks. In this case, the use of new technologies such as software-defined radio and network, cognitive radio, radio sensing, and building electromagnetic situational awareness made it possible to implement REM in tactical MANETs. Propagation attenuation maps (PAMs) are crucial REM elements that allow for determining the ranges of radio network nodes. In this paper, we present a novel algorithm for PAM based on a parabolic equation method (PEM). The PEM allows determining the signal attenuation along the assumed propagation direction. In this case, we consider terrain topography to obtain a more realistic analysis. Then, we average the adjacent attenuation profiles defined for the selected directions in places where attenuation has not been calculated. To this aim, linear regression is applied. Finally, we define several metrics that allow for the accuracy assessment of determining the PAM as a function of its dimensions.
The paper presents the design and implementation of an electromagnetic field monitoring sensor for the measurement of the Terrestrial Truncked Radio (TETRA) signals using low-cost software defined radio (SDR) platforms. The sensor includes: an SDR platform, a Global Positioning System (GPS) module and a hardware control module. Several SDR platforms having different resolutions of the analog–digital converters were tested in the first phase. The control module was implemented in two variants: a fixed one, using a laptop, and a mobile one, using a Raspberry Pi. The tests demonstrate the following achieved performances: instantaneous acquisition band of 5.12 MHz; dynamic range of the input signal level of (−100 to −30) dBm; frequency resolution of 2.5 kHz; portability and flexibility for use in outdoor environments. The sensor allows complete reporting through amplitude-time-frequency-location descriptors, and in the case of the mobile version, the system performs correctly even at a maximum speed of displacement of 120 km/h. The price of the mobile sensor system variant is approximately EUR 320.
A novel concept of metal glasses frame antenna is proposed and relevant investigations are conducted to estimate its performance. A prototype antenna is designed based on a pair of glasses and then measured for demonstration and verification. It is indicated by the simulations and the measurements that the width of the ring rim has only a small impact on the performance. However, the investigation shows that the shape of the frame and even the material of the lens could impose significant influence on the resonance frequency points and the matching performance. The main lobe would shift away from the direct front if the feeding location is moved on the central pole connecting the two rings or the excitation is put on other points of the spectacles frame. The difference between the semi-rimmed glasses and the one with closed rings primarily lies in the beam width of the main lobe. It can be seen that a fractional bandwidth of 14.5 realized at 5.8 GHz, falling into the ISM (Industrial, Scientific, and Medical) band. In addition, the maximum value of the simulated Specific Absorption Rate (SAR) is 1.56 W/kg within this frequency band. The measurement results are well consistent with those of the simulations, indicating that it is a potential candidate serving as an efficient tool for sensing and communications for the applications in Internet of Things, especially body-centric wireless networks.
Channel bonding (CB) is a proven technique to increase bandwidth and reduce delays in wireless networks. It has been applied in traditional wireless networks such as cellular networks and wireless local area networks along with the emerging cognitive radio networks. This paper first focuses on providing a survey of CB schemes for traditional wireless networks such as cellular networks, wireless local area networks, and wireless sensor networks, and then provides a detailed discussion on the CB schemes proposed for cognitive radio networks. Finally, we highlight a number of issues and challenges regarding CB in cognitive radio sensor networks and also provide some guidelines on using CB schemes in these futuristic networks.
Cognitive Radio (CR) usually performs an analysis of the existing environment, enabling collection of all information on current spectrum use and available resources, to decide on its own transmission parameters to optimize communication performance. From practical point of view, the principal task of spectrum sensing is an efficient detection of different types of signals. This paper presents behavior of Sensing Client (SC) as a logical block of Supervisor (SP) in CR node, to obtain information about backup channels' ranking list. Methodology of creation, refreshing the ranking list of backup channels and change of used channel by SP are the main goals of this research. The ranking list of backup channels is based on spectrum sensing results collected by all nodes according to predefined sensing policy, enabling different sensing schemes like detector type, number of slots for sensing, sensing period etc. Channels with the highest positions on the ranking list are potential backup channels, used in case of jamming, interferences from legacy systems, decreasing link quality caused by nodes positions changes etc. To analyze behavior of SC, several simulation scenarios were developed. All tests were performed for CR network topology with presence of jammers and interferences from other users. Presented results are based on CORASMA simulator.
Apart from efficient use of assigned spectral resources as a primary user, Cognitive Radio (CR) has the ability to act as a secondary user, utilizing available radio resources, which are not used by primary users for a certain period of time. This paper refers to mobile CR network management, based on multi-stage machine method. The article shows the idea of the Cognitive Manager (CM), consisting of three logical modules: Clustering (CL), Clusters Graph Coloring (CGC) and Supervisor (SP) with Sensing Client (SC). SP is responsible for management of all other cognitive manager blocks. This paper describes the SP structure and its cooperation with other CM modules. Prepared simulation scenarios take into account the propagation phenomena and presence of other users and jammers, that can use the same frequency channels as CR network. The events like lack of communication between nodes, bad links quality and presence of jamming, cause the appropriate reaction of SP. In such cases, SP performs analysis of current situation, and if necessary, it changes actual used frequency (color) using backup channels list, which is permanently updated. The presented idea has been implemented in the CORASMA (COgnitive RAdio for dynamic Spectrum MAnagement) simulator.
Latest regulations on TV white space communications and emerging trends of spectrum access through geolocation databases relax the regulatory constraints on Cognitive Radios (CRs). Geolocation databases are designed to store information related to incumbents, and CRs are envisioned to consult this database before spectrum access. Spectrum occupancy and related environment information can be constructed using these geolocation databases. In that regard, Radio Environment Map (REM) is a promising tool that provides a practical means for the realization of cognitive radio networks (CRNs). It constructs a comprehensive map of the CRN by utilizing multi-domain information from geolocation databases, characteristics of spectrum use, geographical terrain models, propagation environment, and regulations. REMs contribute to cognition engines by building long-term knowledge via processing spectrum measurements collected from sensors to estimate the state of locations where there is no measurement data. In addition, REM utilizes feedback from the CRs and can apply various learning tools. The vision is to design CRNs such that CRs, though being simple devices without advanced cognitive functionalities, can become cognitive via REMs and operate in an efficient manner. In this paper, an overview of the REM concept is presented in various dimensions ranging from its architecture and stored information to REM construction techniques as well as REM quality metrics.
This paper describes the concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are also discussed.
In this paper we study the dynamic aspects of the coverage of a mobile sensor
network resulting from continuous movement of sensors. As sensors move around,
initially uncovered locations are likely to be covered at a later time. A
larger area is covered as time continues, and intruders that might never be
detected in a stationary sensor network can now be detected by moving sensors.
However, this improvement in coverage is achieved at the cost that a location
is covered only part of the time, alternating between covered and not covered.
We characterize area coverage at specific time instants and during time
intervals, as well as the time durations that a location is covered and
uncovered. We further characterize the time it takes to detect a randomly
located intruder. For mobile intruders, we take a game theoretic approach and
derive optimal mobility strategies for both sensors and intruders. Our results
show that sensor mobility brings about unique dynamic coverage properties not
present in a stationary sensor network, and that mobility can be exploited to
compensate for the lack of sensors to improve coverage.
In this tutorial article we review different multicarrier communication methods for the physical layer of cognitive radio systems. There, secondary users need to dynamically and reliably determine spectral holes, and transmit data in these resources without interfering with other parts of the frequency band. To satisfy the first, each SU has to be equipped with a spectrum analyzer. To satisfy the second, it is widely accepted that a multicarrier modulation technique should be adopted. Moreover, to maximize efficiency, it has been recognized that the side-lobes of each subcarrier band must be minimized. Much of the attention in the present literature emphasizes on the use of conventional OFDM, exploiting the fact that fast Fourier transform (FFT) as part of the OFDM modulator can also be used for channel sensing. Herein, we discuss the performance of OFDM, and also introduce filterbanks for multicarrier communication and spectral analysis in a CR setting. Moreover, the multitaper method has been proposed as an effective method for spectrum analysis. Our article provides an insight into the pros and cons of these technologies.
This applied engineering reference covers a wide range of wireless communication design techniques; including link budgets, error detection and correction, adaptive and cognitive techniques, and system analysis of receivers and transmitters. Digital modulation and demodulation techniques using phase-shift keyed and frequency hopped spread spectrum systems are addressed. The title includes sections on broadband communications and home networking, satellite communications, and global positioning systems (GPS). Various techniques and designs are evaluated for modulating and sending digital signals, and the book offers an intuitive approach to probability plus jammer reduction methods using various adaptive processes. This title assists readers in gaining a firm understanding of the processes needed to effectively design wireless digital communication systems. © 2009, 2014 by SciTech Publishing, Edison, NJ. All rights reserved.
Group Average defines cluster proximity to be the average pairwise length of edges from different clusters. All the Secondary Users(SUs)in the clusters send the source information to their Cluster Head(CH) through the available common channel at this location. Each cluster in the CHs transmit decision to the common receiver. the proposed method provide dynamical Hierarchical clustering scheme with bandwidth constraints according to the inter and intra user cluster channel conditions with reduce overhead and delay of sensing. Clusters based Channel Sensing provides preferable stability and scalability because of its low complexity under dynamic Primary Users(PUs) activity.
We consider an unreliable wireless sensor grid-network with n nodes placed in a square of unit area. We are interested in the coverage of the region and the connectivity of the network. We first show that the necessary and sufficient conditions for the random grid network to cover the unit square region as well as ensure that the active nodes are connected are of the form p(n)r2(n) ∼ log(n)/n, where r(n) is the transmission radius of each node and p(n) is the probability that a node is "active" (not failed). This result indicates that, when n is large, even if each node is highly unreliable and the transmission power is small, we can still maintain connectivity with coverage. We also show that the diameter of the random grid (i.e., the maximum number of hops required to travel from any active node to another) is of the order √n/log(n). Finally, we derive a sufficient condition for connectivity of the active nodes (without necessarily having coverage). If the node success probability p(n) is small enough, we show that connectivity does not imply coverage.
In this paper, we consider the parameters estimation of a model of superimposed exponential signals in multiplicative and additive noise when some observations are missing randomly. The least squares estimators (LSEs) and asymptotic Cramer-Rao low bound (ACRLB) for the considered model are studied and the asymptotic distributions of the LSEs for parameters of frequencies, phases and amplitudes of the considered model are also derived and obtained. An adaptive and computationally efficient iterative algorithm is proposed to estimate the frequencies of the considered model. It can be seen that the iterative algorithm works quite well in terms of biases and mean squared errors and the refined estimators by three iterations are observed to be asymptotically unbiased and consistent. The statistics for iteration are designed to change adaptively according to different missing distributions of time points so as to keep the estimators of frequencies to be asymptotically unbiased. Moreover, the proposed estimators attain the same convergence rate and asymptotic distribution as those of LSEs which are used to obtain the confident intervals and coverage probabilities of the frequencies for finite sample. Since the iterative algorithm needs only three iterations to work, it saves much computation time. So the proposed estimators are LSEs equivalent while avoid the heavy computation cost of LSEs. Finally, several simulation experiments are performed to verify the effectiveness of the proposed algorithm. To examine the robustness of the proposed algorithm, we also test the algorithm on the dual tone multi-frequency (DTMF) signal with observations missing in block and symmetric α-stable (SaS) noise condition, as well as on sinusoidal frequency modulated signals.
A wireless sensor network (WSN) has important applications such as remote environmental monitoring and target tracking. This has been enabled by the availability, particularly in recent years, of sensors that are smaller, cheaper, and intelligent. These sensors are equipped with wireless interfaces with which they can communicate with one another to form a network. The design of a WSN depends significantly on the application, and it must consider factors such as the environment, the application’s design objectives, cost, hardware, and system constraints. The goal of our survey is to present a comprehensive review of the recent literature since the publication of [I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, IEEE Communications Magazine, 2002]. Following a top-down approach, we give an overview of several new applications and then review the literature on various aspects of WSNs. We classify the problems into three different categories: (1) internal platform and underlying operating system, (2) communication protocol stack, and (3) network services, provisioning, and deployment. We review the major development in these three categories and outline new challenges.
Wireless sensor networks are an emerging technology for low-cost, unattended monitoring of a wide range of environments. Their importance has been enforced by the recent delivery of the IEEE 802.15.4 standard for the physical and MAC layers and the forthcoming ZigBee standard for the network and application layers. The fast progress of research on energy efficiency, networking, data management and security in wireless sensor networks, and the need to compare with the solutions adopted in the standards motivates the need for a survey on this field.
In many branches of science, particularly astronomy and
geophysics, power spectra of the form f-β, where β
is a positive power-law exponent, are common. This form of spectrum is
characterized by a sharp increase in the spectral density as the
frequency f decreases toward zero. A power spectrum analysis method that
has proven very powerful wherever the spectrum of interest is detailed
and/or varies rapidly with a large dynamic range is the multitaper
method. With multitaper spectral estimation, a set of orthogonal tapers
are applied to the time series, and the resulting direct spectral
estimators (“eigenspectra”) are averaged, thus, reducing the
variance. One class of processes with spectra of the power-law type are
fractionally differenced Gaussian processes that are stationary and can
model certain types of long-range persistence. Spectral decay
f-β can be modeled for 0<β<1. Estimation of
the spectral slope parameter by regression on multitaper spectral
ordinates is examined for this class of processes. It is shown that
multitapering, or using sine or Slepian tapers, produces much better
results than using the periodogram and is attractive compared with other
competing methods. The technique is applied to a geophysical estimation
problem
Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: · highly reliable communication whenever and wherever needed; · efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.