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The transmitting signal of wireless communication system is impaired by the nonlinearity of RF power amplifier (PA) which leads to signal distortion and spectrum spillover, by which the signal transmission quality is affected. Digital predistortion (DPD) is an efficient and economical way to correct the nonlinear effects of power amplifiers. The recursive least square (RLS) recognition algorithm is commonly used to extract the correction coefficients of the DPD model, and the accuracy of the extraction directly affects the system performance. In this paper, a new variable forgetting factor identification algorithm (new variable forgetting factor recursive least square, NVFFRLS) is proposed for recursive least square (RLS) identification algorithm. The 64-QAM signal is combined with a memory polynomial (MP) predistortion model for predistortion system simulation. The experimental results show that, compared with the RLS identification algorithm and two kinds of variable forgetting factor RLS identification algorithms, the algorithm has smaller estimation error, faster convergence, and better tracking capability, stability, and adaptability; the predistortion system based on NVFFRLS identification algorithm can compensate the nonlinear memory effects of power amplifier more effectively. [ABSTRACT FROM AUTHOR]

Joint angle and frequency estimation, one of the key technologies in wireless communication and radar science, has been extensively studied by scholars. For linear arrays, this paper proposes a joint angle and frequency estimation method based on covariance reconstruction and the estimation of signal parameters via rotational invariance techniques (CR-ESPRIT). We first use the received conjugate signal to reconstruct a covariance matrix. Then, we use the least squares-ESPRIT (LS-ESPRIT) algorithm to estimate the desired frequencies. Finally, we estimate the angles according to the reconstructed matrix. The proposed method can estimate signal parameters via automatic pairing and without an additional parameter pairing process under the condition of a uniform or a nonuniform array. Moreover, this method has high estimation accuracy, excellent and stable anti-noise performance, and strong algorithmic robustness. Through a computer simulation analysis, we can confirm the reliability and validity of the proposed parameter estimation method. A comparison with other methods further proves the performance advantages of the developed method. The method in this paper can be easily applied to many signal processing contexts, such as electronic reconnaissance and wireless communication. [ABSTRACT FROM AUTHOR]

The vehicle detection algorithm is an important part of the intelligent transportation system. The accuracy of the algorithm will determine whether accurate vehicle information can be obtained. The system contains several functional modules, including signal amplification, wireless communication, A/D converter, and sensor set/reset functions. To detect all the intersection vehicles, a number of magnetoresistive sensors are connected to the computer system through the wireless communication module, and then, the detected vehicle information will be transferred back to the master host computer. In this paper, two common vehicle detection algorithms, fixed threshold algorithm and adaptive threshold algorithm, were analyzed in the vehicle detection system with magnetoresistive sensors, simultaneously. Finally, an improved multi-intermediate state machine algorithm for vehicle detection was proposed. Using the intermediate state, this algorithm cannot only detect when the vehicle enters the detection area but also decide whether the vehicle leaves the sensor node or not. In this way, it improves the detection accuracy. [ABSTRACT FROM AUTHOR]

Noncircular signals are widely used in the area of radar, sonar, and wireless communication array systems, which can offer more accurate estimates and detect more sources. In this paper, the noncircular signals are employed to improve source localization accuracy and identifiability. Firstly, an extended real-valued covariance matrix is constructed to transform complex-valued computation into real-valued computation. Based on the property of noncircular signals and symmetric uniform linear array (SULA) which consist of dual-polarization sensors, the array steering vectors can be separated into the source position parameters and the nuisance parameter. Therefore, the rank reduction (RARE) estimators are adopted to estimate the source localization parameters in sequence. By utilizing polarization information of sources and real-valued computation, the maximum number of resolvable sources, estimation accuracy, and resolution can be improved. Numerical simulations demonstrate that the proposed method outperforms the existing methods in both resolution and estimation accuracy. [ABSTRACT FROM AUTHOR]

SUMMARY Computational capability of wireless sensor network (WSN) significantly facilitates application of dense sensory arrays, which is increasingly important in health monitoring of large-scale structural systems. As the wireless sensor technology improves, more complicated tasks can be assigned to sensing units, and the communication between sensing nodes and their base station can be minimized, utilizing in-network processing. This strategy should be used to address WSN challenges, such as limited communication bandwidth and prohibitive power consumption, associated with wireless communication and battery power. An iterative modal identification algorithm is proposed in this paper, which uses the on-board processors for estimation of system parameters through iteration cycles. The iterative algorithm was originally developed such that each individual sensor, having an initial estimate of the system parameters, its local measurement, and the excitation signal, updates the estimated model and passes it through the network until convergence. This study further improves the algorithm to eliminate its limitations in need for availability of excitation load and initial estimate of the system parameters. As a result, the algorithm is applicable for modal identification of structural systems under ambient loading without need for prior information about the system parameters. The development of the algorithm is presented in this paper and validated through implementation on a numerically simulated example and a laboratory experiment. Furthermore, its performance is evaluated using data from an ambient vibration test of the Golden Gate Bridge using a WSN. Results of these implementations verify the functionality of the algorithm in monitoring of real-life structural systems. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Voice over Internet Protocol (VoIP) allows communication of voice and/or data over the internet in less expensive and reliable manner than traditional ISDN systems. This solution typically allows flexible interconnection between organization and companies on any domains. Cloud VoIP solutions can offer even cheaper and scalable service when virtualized telephone infrastructure is used in the most efficient way. Scheduling and load balancing algorithms are fundamental parts of this approach. Unfortunately, VoIP scheduling techniques do not take into account uncertainty in dynamic and unpredictable cloud environments. In this paper, we formulate the problem of scheduling of VoIP services in distributed cloud environments and propose a new model for biobjective optimization. We consider the special case of the on-line nonclairvoyant dynamic bin-packing problem and discuss solutions for provider cost and quality of service optimization. We propose twenty call allocation strategies and evaluate their performance by comprehensive simulation analysis on real workload considering six months of the MIXvoip company service. [ABSTRACT FROM AUTHOR]

This paper assumes a set of identical wireless hosts, each one aware of its location. The network is described by a unit distance graph whose vertices are points on the plane two of which are connected if their distance is at most one. The goal of this paper is to design local distributed solutions that require a constant number of communication rounds, independently of the network size or diameter. This is achieved through a combination of distributed computing and computational complexity tools. Starting with a unit distance graph, the paper shows: 1. How to extract a triangulated planar spanner; 2. Several algorithms are proposed to construct spanning trees of the triangulation. Also, it is described how to construct three spanning trees of the Delaunay triangulation having pairwise empty intersection, with high probability. These algorithms are interesting in their own right, since trees are a popular structure used by many network algorithms; 3. A load balanced distributed storage strategy on top of the trees is presented, that spreads replicas of data stored in the hosts in a way that the difference between the number of replicas stored by any two hosts is small. Each of the algorithms presented is local, and hence so is the final distributed storage solution, obtained by composing all of them. This implies that the solution adapts very quickly, in constant time, to network topology changes. We present a thorough experimental evaluation of each of the algorithms supporting our claims. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are a family of wireless networks that usually operate with irreplaceable batteries. The energy sources limitation raises the need for designing specific protocols to prolong the operational lifetime of such networks. These protocols are responsible for messages exchanging through the wireless communications medium from the sensors to the base station (sink node). Therefore, the determination of the optimal location of the sink node becomes crucial to assure both the prolongation of the network’s operation and the quality of the provided services. This paper proposes a novel algorithm based on a Particle Swarm Optimization (PSO) approach for designing an energy-aware topology control protocol. The deliverable of the algorithm is the optimal sink node location within a deployment area. The proposed objective function is based on a number of topology control protocol’s characteristics such as numbers of neighbors per node, the nodes’ residual energy, and how they are far from the center of the deployment area. The simulation results show that the proposed algorithm reveals significant effectiveness to both topology construction and maintenance phases of a topology control protocol in terms of the number of active nodes, the topology construction time, the number of topology reconstructions, and the operational network’s lifetime. [ABSTRACT FROM AUTHOR]

Indoor localization, an emerging technology in location based service (LBS), is now playing a more and more important role both in commercial and in civilian industry. Global position system (GPS) is the most popular solution in outdoor localization field, and the accuracy is around 10 meter error in positioning. However, with complex obstacles in buildings, problems rise in the "last mile" of localization field, which encourage a momentum of indoor localization. The traditional indoor localization system is either range-based or fingerprinting-based, which requires a lot of time and efforts to do the predeployment. In this paper, we present a 3-dimensional on-demand indoor localization system (3D-ODIL), which can be fingerprint-free and deployed rapidly in amultistorey building. The 3D-ODIL consists of two phases, vertical localization and horizontal localization. On vertical direction, we propose multistorey differential (MSD) algorithm and implement it to fulfill the vertical localization, which can greatly reduce the number of anchors deployed. We use enhanced field division (EFD) algorithm to conduct the horizontal localization. EFD algorithm is a range-free algorithm, the main idea of which is to dynamically divide the field within different signature area and position the target. The accuracy and performance have been validated through our extensive analysis and systematic experiments. [ABSTRACT FROM AUTHOR]

This paper considers a joint optimal design of admission control and resource allocation for multimedia services delivery in high-speed railway (HSR) wireless networks. A stochastic network optimization problem is formulated which aims at maximizing the system utility while stabilizing all transmission queues under the average power constraint. By introducing virtual queues, the original problem is equivalently transformed into a queue stability problem, which can be naturally decomposed into three separate subproblems: utility maximization, admission control, and resource allocation. A threshold-based admission control strategy is proposed for the admission control subproblem. And a distributed resource allocation scheme is developed for the mixed-integer resource allocation subproblem with guaranteed global optimality. Then a dynamic admission control and resource allocation algorithm is proposed, which is suitable for distributed implementation. Finally, the performance of the proposed algorithm is evaluated by theoretical analysis and numerical simulations under realistic conditions of HSR wireless networks. [ABSTRACT FROM AUTHOR]

Cognitive radio technology has received wide attention for its ability to sense and use idle frequency. IEEE 802.22WRAN, the first to follow the standard in cognitive radio technology, is featured by spectrum sensing and wireless data transmission. As far as wireless transmission is concerned, the availability and implementation of a mature and robust physical layer algorithm are essential to high performance. For the physical layer of WRAN using OFDMA technology, this paper proposes a synchronization algorithm and at the same time provides a public platform for the improvement and verification of that new algorithm. The simulation results show that the performance of the platform is highly close to the theoretical value. [ABSTRACT FROM AUTHOR]

As an important application domain of wireless sensor networks (WSN), wireless home sensor network (WHSN) can be built as a traditional WSN. However, when we consider its own character that plug-in sensors are fixed with AC power supply while mobile sensors are battery powered, traditional WSN techniques do not match well. In this paper, we propose a smart wireless home sensor network architecture with improved routing protocols. It is a hierarchical architecture in which AC-powered sensors act as backbone nodes for data retransmission, while battery-powered sensors act as leaves that only transmit data relevant to themselves. Each sensor is assigned with a prime number as its location identifier. For our routing algorithm, the LID is used as routing address and is decomposed to a sequence of prime numbers that indicates the route towards its destination. In addition, we improve existing routing algorithms such as SPIN, LEACH, and DD to incorporate traditional WSN routing algorithms into our smart WHSN architecture, and comparable efficiencies are made between them. Moreover, we propose a network path recovery algorithm for failures that are caused by node mobility or backbone node failures. All our algorithms have been provided with faithful simulations to verify the feasibility and efficiency. [ABSTRACT FROM AUTHOR]

Node position information is one of the important issues in many wireless sensor networks' usages. In this paper, based on path planning, a location predicting method (PPLP) for indoor mobile target localization is proposed. We first establish the path planning model to constrain the movement trajectory of the mobile target in indoor environment according to indoor architectural pattern. Then, one certain localization result can be obtained using MLE algorithm. After that, based on the path-planning model and some previous localization results, the most likely position of the target in the next time interval can be predicted with the proposed predicting approach. Finally, the MLE result and prediction result are weighted to obtain the final position. The simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Random mobility and energy constraint are two main factors affecting system performance in mobile sensor networks, which cause many difficulties to system design. It is necessary to develop high-efficiency algorithms and protocols for mobile sensor networks to adapt to dynamic network environment and energy limitation. In this paper, a new clustering algorithm based on residual energy difference ratio is presented to improve system performance. Firstly, it is an energy-efficient algorithm. The residual energy of sensor nodes and average residual energy of system are considered in the residual energy difference ratio, which effectively avoid the nodes with low residual energy being selected as cluster heads. An energy-optimal scheme is used in cluster formation phase to minimize energy consumption. Secondly, it is a dynamic algorithm. The system dynamically clusters the sensor nodes according to the data transmission delays. It makes the whole system adapt to the random mobility of sensor nodes. The NS2 software is used to simulate the new clustering algorithm. The simulation experiments can verify the validity of the proposed theory. [ABSTRACT FROM AUTHOR]

The optimal distributed tracking control algorithms over nonlinear cooperative wireless sensor networks (WSNs) are presented in this paper. In order to solve transfer delay and packet loss problem, the architecture of wireless active sensor (WAS) is employed, where a state estimator is embedded, which can provide the needed state information. Furthermore, the optimal distributed tracking control algorithm is proposed. By solving the matrix equations and the adjoint difference equations, the optimal control law can be obtained easily, in which an increment integral regulator is designed to implement tracking target without steady-state error and a nonlinearity compensation term is designed to compensate for the effect produced by system nonlinearity Moreover, the observer-based dynamical algorithm is given considering the physically unrealizable disturbance states and the unavailable sensor states. Finally, computer simulations are carried out with application to two nonlinear pendulums, which prove that the algorithm is effective and easy to implement, and the system achieves the desired performance based on tradeoff between tracking error and control energy consumption. [ABSTRACT FROM AUTHOR]

Low data delivery efficiency and high energy consumption are the inherent problems in Underwater Wireless Sensor Networks (UWSNs) characterized by the acoustic channels. Existing energy-efficient routing algorithms have been shown to reduce energy consumption of UWSNs to some extent, but still neglect the correlation existing in the local data of sensor nodes. In this paper, we present a Multi-population Firefly Algorithm (MFA) for correlated data routing in UWSNs. We design three kinds of fireflies and their coordination rules in order to improve the adaptability of building, selecting, and optimization of routing path considering the data correlation and their sampling rate in various sensor nodes. Different groups of fireflies conduct their optimization in the evolution in order to improve the convergence speed and solution precision of the algorithm. Moreover, after the data packets are merged during the process of routing path finding, MFA can also eliminate redundant information before they are sent to the sink node, which in turn saves energy and bandwidth. Simulation results have shown that MFA achieves better performance than existing protocols in metrics of packet delivery ratio, energy consumption, and network throughput. [ABSTRACT FROM AUTHOR]

Recent communications in wireless sensor networks (WSNs) have much new energy-efficient protocols specifically designed, where energy awareness is an essential consideration. In WSNs, large numbers of tiny sensor nodes are used as an effective way of data gathering in various environments. Since the sensor nodes operate on battery of limited power, it is a great challenging aim to design an energy-efficient routing protocol, which can minimize the delay while offering high-energy efficiency and long span of network lifetime. Low energy adaptive clustering hierarchy (LEACH) is one of the clustering key routing techniques used to reduce energy consumption. In this paper, we propose an energy-efficient node scheduling reclustering algorithm in clusters and rotating cluster-head positions to evenly distribute the energy load among all of the nodes by the node scheduling concept of node active and sleep system in both homogeneous and heterogeneous methods to get energy-saving result. Our proposed model is enhanced from LEACH protocol by adapting node scheduling scheme (active and sleep) in homogeneous and heterogeneous node topologies of WSNs, which yield better performance in energy saving and increased network lifetime. This node scheduling scheme is properly implemented, and simulation results show that our proposed model is giving improved energy efficiency and prolongs the lifetime of WSNs. [ABSTRACT FROM AUTHOR]

Cognitive Wireless Sensor Network (CWSN) is a new paradigm which integrates cognitive features in traditional Wireless Sensor Networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in Cognitive Wireless Sensor Networks is an important problem because these kinds of networks manage critical applications and data. Moreover, the specific constraints of WSN make the problem even more critical. However, effective solutions have not been implemented yet. Among the specific attacks derived from new cognitive features, the one most studied is the Primary User Emulation (PUE) attack. This paper discusses a new approach, based on anomaly behavior detection and collaboration, to detect the PUE attack in CWSN scenarios. A nonparametric CUSUM algorithm, suitable for low resource networks like CWSN, has been used in this work. The algorithm has been tested using a cognitive simulator that brings important results in this area. For example, the result shows that the number of collaborative nodes is the most important parameter in order to improve the PUE attack detection rates. If the 20% of the nodes collaborates, the PUE detection reaches the 98% with less than 1% of false positives. [ABSTRACT FROM AUTHOR]

Data broadcast is a fundamental operation in wireless sensor networks (WSNs). The existence of wireless interference makes it nontrivial to design a minimum-latency broadcast scheme, which is known to be NP-hard. Existing works all assume strict time synchronization and provide centralized TDMA scheduling algorithms. However, WSNs in practice are more likely to be distributed asynchronous systems. In this paper, we investigate the problem of data broadcast with minimum latency for distributed asynchronous WSNs. To this end, we propose a Distributed Asynchronous Broadcast (DAB) algorithm which crucially leverages an elaborately optimized carrier-sensing range together with collision-backoff schemes to coordinate the transmissions among the nodes on a predetermined broadcast backbone. Theoretical analysis shows that DAB is order-optimal and achieves constant factor approximation to the optimal delay. We then conduct extensive simulations to evaluate the practical capability of DAB in asynchronous WSNs and the results corroborate our theoretical analysis. [ABSTRACT FROM AUTHOR]

Localization is the key supporting technology for wireless sensor networks (WSNs). Security and accuracy are the premise of the localization application. Real-world applications of wireless sensor networks are often subject to a variety of adverse circumstances interference, and the localization performance is seriously affected. In this paper, we propose a BRS-based robust secure localization (BRSL) algorithm in order to reduce the impact of the malicious attackers in WSNs. The BRSL method includes two phases. In the first stage, the trust evaluation framework is established on the basis of beta reputation system. In the second phase, we employ the weighted Taylor-series least squares method to estimate the coordinates of sensor nodes. Simulation results demonstrate that the proposed algorithm is robust and effective. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANETs) are gaining popularity in recent years due to their flexibility, the proliferation of smart computing devices, and developments in wireless communications. Due to the mobile nature of the network, creating reliable clusters that can sustain for long period of time is a challenging problem. In this paper, we use probabilistic analysis to guide our clustering algorithm towards more reliable clusters. We also use scatter search to perform clustering while considering various performancemetrics. Experiment results show that our clustering approach produces more reliable clusters than prior approaches. [ABSTRACT FROM AUTHOR]

With the development of wireless technologies, mobile communication applies more and more extensively in the various walks of life. The social network of both fixed andmobile users can be seen as networked agent system. At present, kinds of devices and access network technology are widely used. Different users in this networked agent system may need different coding rates multimedia data due to their heterogeneous demand. This paper proposes a distributed flow rate control algorithmto optimizemultimedia data transmission of the networked agent systemwith the coexisting various coding rates. In this proposed algorithm, transmission path and upload bandwidth of different coding rate data between source node, fixed and mobile nodes are appropriately arranged and controlled. On the one hand, this algorithm can provide user nodes with differentiated coding rate data and corresponding flow rate. On the other hand, it makes the different coding rate data and user nodes networked, which realizes the sharing of upload bandwidth of user nodes which require different coding rate data. The study conducts mathematical modeling on the proposed algorithmandcompares the systemthat adopts theproposedalgorithmwith the existingsystembasedon the simulationexperiment andmathematical analysis. The results show that the system that adopts the proposed algorithmachieves higher upload bandwidth utilization of user nodes and lower upload bandwidth consumption of source node. [ABSTRACT FROM AUTHOR]

Control channel is used to transmit protocol or signal information between wireless network nodes and is a key component of wireless network. Compared with data information, protocol or signal information is usuallymuch less, so the spectrumbandwidth requirement of control channel is also much less than that of data channel. In order to optimize the usage of the limited spectrum resources, this paper focuses on the issue of control channel selection. We propose a greedy algorithm which minimizes the total spectrum bandwidth of the set of control channels. The oretical analysis proves that the proposed algorithm can achieve the optimal set of control whose sum of the spectrum bandwidth is the minimum. Simulation results also show that the proposed algorithm consumes less spectrum resources than other algorithms in the same wireless network environment. [ABSTRACT FROM AUTHOR]

Positioning of the location information of wireless sensor network nodes is one of the key issues in wireless sensor network applications. The traditional node positioning method based on least-square algorithm heavily depends on the ranging accuracy, and therefore cannot guarantee high precision. This paper presents a new method for wireless sensor network node positioning based on nonlinear weighting least-square algorithm. Regarding ranging equation error-weighted sum as a whole, this method starts with the initial iteration point of stepwise refinement to explore the optimal solution and further reduces the positioning computational complexity by the simplification of the Taylor equation. Experimental results demonstrate that promising results have been achieved by using this method. [ABSTRACT FROM AUTHOR]

In wireless healthcare monitoring systems, bandwidth allocation is an efficient solution to the problem of scarce wireless bandwidth for the monitoring of patients. However, when the central unit cannot access the exact channel state information (CSI), the efficiency of bandwidth allocation decreases, and the system performance also decreases. In this paper, we propose an algorithm to reduce the negative effects of imperfect CSI on system performance. In this algorithm, the central unit can predict the current CSI by previous CSI when the current CSI is not available. We analyze the reliability of the proposed algorithm by deducing the standard error of estimated CSI with this algorithm. In addition, we analyze the efficiency of the proposed algorithm by discussing the system performance with this algorithm. [ABSTRACT FROM AUTHOR]

A channel simulator is an essential component in the development and accurate performance evaluation of wireless systems. A key technique for producing statistically accurate fading variates is to shape the flat spectrum of Gaussian variates using digital filters. This paper addresses various challenges when designing real and complex spectrum shaping filters with quantized coefficients for efficient realization of both isotropic and nonisotropic fading channels. An iterative algorithm for designing stable complex infinite impulse response (IIR) filters with fixed-point coefficients is presented. The performance of the proposed filter design algorithm is verified with 16-bit fixed-point simulations of two example fading filters. [ABSTRACT FROM AUTHOR]

The proportional loss rate differentiation (PLD) model was proposed to provide controllable and predictable loss rate for different classes of wired network connections. However, these algorithms cannot be directly applied to wireless networks, because of the location-dependent and time-varying wireless channel capacity. This paper proposes a novel packet dropper for fuzzy controlling of the proportional loss rate differentiation in a wireless network with multistate channel. The proposed dropper, fuzzy proportional loss rate dropper (FPLR), prefers to drop the small packets destined to a poor condition channel to improve the network performance. The loss rate debts of the poor channel will be compensated later to keep PLD. From simulation results, FPLR does achieve accurate loss rate proportion, lower queuing delay and loss rate, and higher throughput, compared with other methods in the wireless environment. [ABSTRACT FROM AUTHOR]

Appropriate wireless access point deployment (APD) is essential for ensuring seamless user communication. Optimal APD enables good telecommunication quality, balanced capacity loading, and optimal deployment costs. APD is a typical NP-complex problem because improving wireless networking infrastructure has multiple objectives (MOs). This paper proposes a method that integrates a goal-programming-driven model (PM) and a genetic algorithm (GA) to resolve the MO-APD problem. The PM identifies the target deployment subject of four constraints: budget, coverage, capacity, and interference. The PM also calculates dynamic capacity requirements to replicate real wireless communication. Three experiments validate the feasibility of the PM. The results demonstrate the utility and stability of the proposed method. Decision makers can easily refer to the PM-identified target deployment before allocating APs. [ABSTRACT FROM AUTHOR]

In wireless channel assignment, control channels are often needed for coordination among wireless devices. In this paper, we propose an algorithm for finding the optimal set of control channels given a number of wireless devices and the ranges of channels these devices can access. [ABSTRACT FROM AUTHOR]

The V-BLAST wireless communication architecture, space-time block code (STBC), and space-frequency block code (SFBC) techniques are strong candidates for achieving very high data rates in 4G broadband wireless communications. This paper extends the efficient Active Constellation Extension Smart Gradient-Project (ACE-SGP) peak-to-average power (PAPR) reduction method to STBC, SFBC, and V-BLAST systems. Simulation results show 4.19 and 3.57 dB of PAPR reduction for the Alamouti STBC and SFBC, respectively. [ABSTRACT FROM AUTHOR]

The backward and forward consecutive mean excision (CME/FCME) algorithms are diagnostic methods for outlier (signal) detection. Since they are computationally simple, they have applications for both narrowband signal detection in cognitive radios and interference suppression. In this paper, a theoretical performance analysis framework of the CME algorithms is presented. The analysis provides simple tests of the detectability of the signals based on their shape in the considered domain (e.g., spectrum). As a consequence, results can be used to quickly check whether the CME/FCME algorithms are usable for a given problem or not without the need to resort to time consuming computer simulations. The computer simulations for random and orthogonal frequency division multiplexing (OFDM) signals show that the presented analysis is able to predict the detectability of signals well. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are one of the most promising technologies and have immense potential in both the military and civil field. WSNs offer a range of challenges for scientists and engineers of today. The biggest challenge among all is the energy constraint of these networks. In this context, various schemes have been presented in order to improve the life time of these networks and to overcome the energy constraint. One of the effective schemes is based on clustering of sensor nodes within a network in order to improve the network life time and decrease communication latency. Clustering algorithms are believed to be the best for wireless sensor networks because they work on the principle of divide and conquer. This paper includes a brief survey of various existing clustering algorithms and present a new clustering algorithm based on nondetermistic finite automata which further divides the communication between cluster heads into multihop by using a few nodes from each cluster. Performance studies indicate that the proposed algorithm is more efficient in terms of energy consumption and network connectivity. [ABSTRACT FROM AUTHOR]

Compared with the small overhead of data payload in sensor networks, the overhead of MAC address is significant from the point of view of energy-saving. A distributed algorithm (Virtual Grid Spatial Reusing-VGSR) for MAC address assignment is presented in this paper, which is a low energy cost algorithm and reduces the size of the fixed MAC address largely based on the mapping of the geographical position. Moreover, VGSR algorithm scales well with the network size and achieves the optimum performance by adjusting the communication range of sensor nodes. In typical scenarios, the MAC address size is 5 bits and the corresponding average size is only 3.86 bits, which outperforms other existing approaches. [ABSTRACT FROM AUTHOR]

This paper is concerned with modeling of time-varying wireless long-term fading channels, parameter estimation, and identification from received signal strength data. Wireless channels are represented by stochastic differential equations, whose parameters and state variables are estimated using the expectation maximization algorithm and Kalman filtering, respectively. The latter are carried out solely from received signal strength data. These algorithms estimate the channel path loss and identify the channel parameters recursively. Numerical results showing the viability of the proposed channel estimation and identification algorithms are presented. [ABSTRACT FROM AUTHOR]

Sensor network research is still in its infancy. There is a large volume of exploratory research. From lack of experimental data and sophisticated models derived from such data, many sensor network publications continue to use data generated from simple models in their algorithm evaluation. It is commonly agreed that data processing algorithms in sensor networks are sensitive to input data. However, no previous efforts have been devoted to quantitatively characterize the range of the algorithm performance when evaluated using different data input.In this paper, we made the first attempt to quantify the algorithm's sensitivity to data. Our evaluation results demonstrated that different data input could change the algorithm performance by as much as an order of magnitude or even change the relative performance order of two alternative algorithms. This pointed out the need to evaluate sensor network systems with data representing a wide range of real-world scenarios. For each algorithm in our case study, we identified a small set of data characteristics essential to the algorithm's performance. This defined a unique feature of our synthetic data generation framework and made both synthetic data generation and evaluation scalable. To support systematic algorithm evaluation and robust algorithm design and deployment, our synthetic data generation toolbox can generate 1. irregular topology data based on empirical models which will maintain important features of the experimental data; and 2. data corresponding to a wide range of parameter values. [ABSTRACT FROM AUTHOR]

This paper reviews some of the recent advances in the development of algorithms for wireless sensor networks. We focus on sensor deployment and coverage, routing, and sensor fusion. [ABSTRACT FROM AUTHOR]

This paper proposes a twice deployment node balance algorithm (TDNB) which guarantees successful detection of the moving target on road networks. Through dividing the deployment of the sensor nodes into two phases instead of deploying all the sensors at one time, TDNB has a better performance. In the former phase, some of the sensors are deployed on the road at random. In the latter phase, the rest of the sensors are deployed on demand to make the number on each path the same. Due to the equivalence of the node's number, the network will have a prolonged lifetime after inserting nodes into the segment according to this algorithm. TDNB extends the network's lifetime remarkably compared with the former algorithms. Furthermore, TDNB enables us to insert nodes to the segment required in the network instead of all the segments, which reduces the workload to a large extent. In short, without increasing the number of sensors in road network, TDNB has a better performance compared to VISA in terms of network lifetime, which meets the demand for persistent monitoring application. [ABSTRACT FROM AUTHOR]