Multiregional secure localization using compressive sensing in wireless sensor networks

Security and accuracy are two issues in the localization of wireless sensor networks (WSNs) that are difficult to balance in hostile indoor environments. Massive numbers of malicious positioning requests may cause the functional failure of an entire WSN. To eliminate the misjudgments caused by malicious nodes, we propose a compressive‐sensing–based multiregional secure localization (CSMR_SL) algorithm to reduce the impact of malicious users on secure positioning by considering the resource‐constrained nature of WSNs. In CSMR_SL, a multiregion offline mechanism is introduced to identify malicious nodes and a preprocessing procedure is adopted to weight and balance the contributions of anchor nodes. Simulation results show that CSMR_SL may significantly improve robustness against attacks and reduce the influence of indoor environments while maintaining sufficient accuracy levels.     

Multi‐interface cognitive radio for enhanced routing in multi‐hop cellular networks

Multi‐hop cellular network (MCN) is a wireless communication architecture that combines the benefits of conventional single‐hop cellular networks and multi‐hop ad hoc relaying networks. The route selection in MCN depends on the availability of intermediate nodes and their neighborhood connectivity. Cognitive radio (CR) is an emerging communication paradigm that exploits the available radio frequencies opportunistically for the effective utilization of the radio frequency spectrum. The incorporation of CR and mobile ad hoc network routing protocols in MCN could potentially improve the spectrum utilization and the routing performance of MCN. This paper firstly presents the proposed model for the multi‐interface CR mobile node with transceiver synchronization and then investigates its opportunistic spectrum utilization and routing performance in MCN. Copyright © 2015 John Wiley & Sons, Ltd.     

Mobile location estimation for DS‐CDMA systems using self‐organizing maps

In this paper, a self‐organizing map (SOM) scheme for mobile location estimation in a direct‐sequence code division multiple access (DS‐CDMA) system is proposed. As a feedforward neural network with unsupervised or supervised and competitive learning algorithm, the proposed scheme generates a number of virtual neurons over the area covered by the corresponding base stations (BSs) and performs non‐linear mapping between the measured pilot signal strengths from nearby BSs and the user's location. After the training is finished, the location estimation procedure searches for the virtual sensor which has the minimum distance in the signal space with the estimated mobile user. Analytical results on accuracy and measurement reliability show that the proposed scheme has the advantages of robustness and scalability, and is easy for training and implementation. In addition, the scheme exhibits superior performance in the non‐line‐of‐sight (NLOS) situation. Numerical results under various terrestrial environments are presented to demonstrate the feasibility of the proposed SOM scheme. Copyright © 2006 John Wiley & Sons, Ltd.     

Comparative node selection‐based localization technique for wireless sensor networks: A bilateration approach

Wireless sensor networks find extensive applications, such as environmental and smart city monitoring, structural health, and target location. To be useful, most sensor data must be localized. We propose a node localization technique based on bilateration comparison (BACL) for dense networks, which considers two reference nodes to determine the unknown position of a third node. The mirror positions resulted from bilateration are resolved by comparing their coordinates with the coordinates of the reference nodes. Additionally, we use network clustering to further refine the location of the nodes. We show that BACL has several advantages over Energy Aware Co‐operative Localization (EACL) and Underwater Recursive Position Estimation (URPE): (1) BACL uses bilateration (needs only two reference nodes) instead of trilateration (that needs three reference nodes), (2) BACL needs reference (anchor) nodes only on the field periphery, and (3) BACL needs substantially less communication and computation. Through simulation, we show that BACL localization accuracy, as root mean square error, improves by 53% that of URPE and by 40% that of EACL. We also explore the BACL localization error when the anchor nodes are placed on one or multiple sides of a rectangular field, as a trade‐off between localization accuracy and network deployment effort. Best accuracy is achieved using anchors on all field sides, but we show that localization refinement using node clustering and anchor nodes only on one side of the field has comparable localization accuracy with anchor nodes on two sides but without clustering.     

Multi‐target localization in wireless sensor networks: a compressive sampling‐based approach

This paper considers the problem of localizing a group of targets whose number is unknown by wireless sensor networks. At each time slot, to save energy and bandwidth resources, only part of sensor nodes are scheduled to activate to remain continuous monitoring of all the targets. The localization problem is formulated as a sparse vector recovery problem by utilizing the spatial sparsity of targets’ location. Specifically, each activated sensor records the RSS values of the signals received from the targets and sends the measurements to the sink node where a compressive sampling‐based localization algorithm is conducted to recover the number and locations of targets. We decompose the problem into two sub‐problems, namely, which sensor nodes to activate, and how to utilize the measurements. For the first subproblem, to reduce the effect of measurement noise, we propose an iterative activation algorithm to re‐assign the activation probability of each sensor by exploiting the previous estimate. For the second subproblem, to further improve the localization accuracy, a sequential recovery algorithm is proposed, which conducts compressive sampling on the least squares residual of the previous estimate such that all the previous estimate can be utilized. Under some mild assumptions, we provide the analytical performance bound of our algorithm, and the running time of proposed algorithm is given subsequently. Simulation results demonstrate the effectiveness of our algorithms.Copyright © 2013 John Wiley & Sons, Ltd.     

A roadside unit‐based localization scheme for vehicular ad hoc networks

Vehicular Ad Hoc Networks (VANETs), designed to ensure the safety and comfort of passengers via the exchange of information amongst nearby vehicles or between the vehicles and Roadside Units (RSUs), have attracted particular attention. However, the success of many VANET applications depends on their ability to estimate the vehicle position with a high degree of precision, and thus, many vehicle localization schemes have been proposed. Many of these schemes are based on vehicle‐mounted Global Positioning System (GPS) receivers. However, the GPS signals are easily disturbed or obstructed. Although this problem can be resolved by vehicle‐to‐vehicle communication schemes, such schemes are effective only in VANETs with a high traffic density. Accordingly, this paper presents a VANET localization scheme in which each vehicle estimates its location on the basis of beacon messages broadcast periodically by pairs of RSUs deployed on either side of the road. In addition, three enhancements to the proposed scheme are presented for the RSU deployment, RSU beacon collisions, and RSU failures. Overall, the ns‐2 simulation results show that the localization scheme achieves a lower localization error than existing solutions on the basis of vehicle‐to‐vehicle communications and is robust toward changes in the traffic density and the vehicle speed. Copyright © 2012 John Wiley & Sons, Ltd.     

A new range‐free PCA‐based localization algorithm in wireless sensor networks

Range‐free localization algorithms in wireless sensor networks have been an interesting field for researchers over the past few years. The combining of different requirements such as storage space, computational capacities, communication capabilities, and power efficiency is a challenging aspect of developing a localization algorithm. In this paper, a new range‐free localization algorithm, called PCAL, is proposed using soft computing techniques. The proposed method utilizes hop‐count distances as the data to train and build a neural network. Before feeding the data into the neural network for the purpose of training, the dimensionality of data is reduced by principal component analysis algorithm. The performance of the proposed algorithm is evaluated using simulation. The obtained results show that the proposed algorithm has a better performance in contrast to other algorithms based on storage space, communication overhead, and localization accuracy. Furthermore, the effect of various parameters on the PCAL algorithm is studied.     

On multi‐cell admission control in CDMA networks

We consider a multi‐cell (MC) code division multiple access (CDMA) system that supports multiple service classes, including peak rate allocated and elastic ones. Peak rate allocated sessions—when admitted into the system—transmit at a constant bit rate, while elastic sessions can be slowed down at the expense of increasing their residency time. Admitted sessions cause an instantaneous bit rate‐dependent interference in neighbour cells. In this rather general setting, we propose a method to calculate the class‐wise blocking probabilities as the functions of the estimated so‐called inter‐cell coupling factors. In the paper this coupling factor is the ratio between the uplink path gains to different Node‐B:s (that can be easily obtained in a CDMA system from pilot measurement reports), but our model could include other coupling measures as well. We find that when these coupling factors are underestimated, the system may get into false states (FSs) or false rate states (FRSs) that lead to violating the noise rise threshold. As traffic becomes increasingly elastic, the probability of FSs decreases, but the probability of FRSs increases. Based on numerical results, we make the point that as the traffic becomes more elastic, avoiding the underestimation of these coupling factors as well as exercising MC admission control plays an increasingly important role in guaranteeing proper service quality. Copyright © 2007 John Wiley & Sons, Ltd.     

In‐band bootstrapping in database‐driven multi‐hop cognitive radio networks

Database‐driven approach has emerged recently as an alternative or supplement for spectrum sensing for cognitive radio networks (CRNs). Within database‐driven CRNs, master devices obtain spectrum information by direct connection to a spectrum database, while slave devices can only access spectrum information indirectly via masters. The in‐band approach completely based on primary spectrum channels can be used, which eliminates the need for out‐of‐band connections and eases the adoption of the database‐driven spectrum sharing. In this paper, we study the in‐band bootstrapping process for database‐driven multi‐hop CRNs, where master/slave devices form a multi‐hop networks, and slaves need multi‐hop communications to obtain spectrum information from the master during bootstrapping. We start with the basic design of in‐band bootstrap protocol, whose performance is unsatisfactory of protocol overhead and bootstrap time. Then we propose 2 enhancements: first, we incorporates the recursive fractional spectrum information query scheme to reduce protocol overhead; then we propose the prefetch scheme to reduce the bootstrap time. According to the analysis and simulation results, our proposed protocols can greatly improve the performance: the recursive fractional spectrum information query enhancement reduces up to 40% of the overhead, the prefetch enhancement reduces more than 20% of the bootstrap time.     

A study of multi‐hop cellular networks

The number of cellular communication subscribers continues to grow, attesting to the great success of this technology. However, cellular networks have inherent limitations on cell capacity and coverage and shortcomings such as the dead spot and the hot spot problems. Multi‐hop cellular networks (MCNs) help enhance the cell capacity and coverage, while, at the same time, alleviating the dead spot and hot spot problems, increasing the utilization of radio resource, and reducing the power consumption of mobile terminals. In the past decade, more than a dozen of MCN architectures were proposed. In this paper, we study various types of MCN proposals. We identify and discuss the design decision factors and use these factors to classify most existing MCN proposals. Future research directions, including studies of capacity and energy consumption, and approaches addressing design issues such as cell size, routing, channel assignment, load balancing for MCNs are discussed. Copyright © 2010 John Wiley & Sons, Ltd.     

Range‐free intersecting chord‐based geometric localization scheme for wireless sensor networks

Recent advancement in wireless sensor network has contributed greatly to the emerging of low‐cost, low‐powered sensor nodes. Even though deployment of large‐scale wireless sensor network became easier, as the power consumption rate of individual sensor nodes is restricted to prolong the battery lifetime of sensor nodes, hence the heavy computation capability is also restricted. Localization of an individual sensor node in a large‐scale geographic area is an integral part of collecting information captured by the sensor network. The Global Positioning System (GPS) is one of the most popular methods of localization of mobile terminals; however, the use of this technology in wireless sensor node greatly depletes battery life. Therefore, a novel idea is coined to use few GPS‐enabled sensor nodes, also known as anchor nodes, in the wireless sensor network in a well‐distributed manner. Distances between anchor nodes are measured, and various localization techniques utilize this information. A novel localization scheme Intersecting Chord‐Based Geometric Localization Scheme (ICBGLS) is proposed here, which loosely follows geometric constraint‐based algorithm. Simulation of the proposed scheme is carried out for various communication ranges, beacon broadcasting interval, and anchor node traversal techniques using Omnet++ framework along with INET framework. The performance of the proposed algorithm (ICBGLS), Ssu scheme, Xiao scheme, and Geometric Constraint‐Based (GCB) scheme is evaluated, and the result shows the fact that the proposed algorithm outperforms the existing localization algorithms in terms of average localization error. The proposed algorithm is executed in a real‐time indoor environment using Arduino Uno R3 and shows a significant reduction in average localization time than GCB scheme and similar to that of the SSU scheme and Xiao scheme.     

Hybrid area exploration–based mobility‐assisted localization with sectored antenna in wireless sensor networks

In common practice, sensor nodes are randomly deployed in wireless sensor network (WSN); hence, location information of sensor node is crucial in WSN applications. Localization of sensor nodes performed using a fast area exploration mechanism facilitates precise location‐based sensing and communication. In the proposed localization scheme, the mobile anchor (MA) nodes integrated with localization and directional antenna modules are employed to assist in localizing the static nodes. The use of directional antennas evades trilateration or multilateration techniques for localizing static nodes thereby resulting in lower communication and computational overhead. To facilitate faster area coverage, in this paper, we propose a hybrid of max‐gain and cost‐utility–based frontier (HMF) area exploration method for MA node's mobility. The simulations for the proposed HMF area exploration–based localization scheme are carried out in the Cooja simulator. The paper also proposes additional enhancements to the Cooja simulator to provide directional and sectored antenna support. This additional support allows the user with the flexibility to feed radiation pattern of any antenna obtained either from simulated data of the antenna design simulator, ie, high frequency structure simulator (HFSS) or measured data of the vector network analyzer (VNA). The simulation results show that the proposed localization scheme exhibits minimal delay, energy consumption, and communication overhead compared with other area exploration–based localization schemes. The proof of concept for the proposed localization scheme is implemented using Berkeley motes and customized MA nodes mounted with indigenously designed radio frequency (RF) switch feed network and sectored antenna.     

Three dimensional compressed sensing for wireless networks‐based multiple node localization in multi‐floor buildings

In wireless network‐based node localization, the received signals are hampered by complex phenomena, such as shadowing, noise, and multi‐path fading. In this work, the localization is stated as an ill‐posed problem that can be solved by compressed sensing (CS) technique. A three dimensional (3D)‐CS approach using the ratio of received signal strength (R2S2) and the time difference of arrival metrics was proposed to improve the localization accuracy of multiple target nodes in 3D wireless networks, and to reduce deployment complexity and processing time. Simulation and experimental tests were conducted in a large multi‐floors building using the strength of the received signals and the radio map of the localization area. The results indicated that the 3D‐CS approach is reliable for identifying the floor number and estimating the horizontal position. The localization precision is less affected by the propagation medium variation than the conventional 2D‐CS method. The localization mean error is lower when the number of access points increases, and the radio map spacing decreases. In addition, the accuracy of the 3D‐CS approach was assured as well as the building material characteristics, position of access points, and wireless‐terminal real transmission power are unknown. Copyright © 2015 John Wiley & Sons, Ltd.     

MANCL: a multi‐anchor nodes collaborative localization algorithm for underwater acoustic sensor networks

Localization is an essential and major issue for underwater acoustic sensor networks (UASNs). Almost all the applications in UASNs are closely related to the locations of sensors. In this paper, we propose a multi‐anchor nodes collaborative localization (MANCL) algorithm, a three‐dimensional (3D) localization scheme using anchor nodes and upgrade anchor nodes within two hops for UASNs. The MANCL algorithm divides the whole localization process into four sub‐processes: unknown node localization process, iterative location estimation process, improved 3D Euclidean distance estimation process, and 3D DV‐hop distance estimation process based on two‐hop anchor nodes. In the third sub‐process, we propose a communication mechanism and a vote mechanism to determine the temporary coordinates of unknown nodes. In the fourth sub‐process, we use two‐hop anchor nodes to help localize unknown nodes. We also evaluate and compare the proposed algorithm with a large‐scale localization algorithm through simulations. Results show that the proposed MANCL algorithm can perform better with regard to localization ratio, average localization error, and energy consumption in UASNs. Copyright © 2014 John Wiley & Sons, Ltd.     

Cross‐layer scheduling for multi‐users in cognitive multi‐radio mesh networks

A wireless mesh network has been popularly researched as a wireless backbone for Internet access. However, the deployment of wireless mesh networks in unlicensed bands of urban areas is challenging because of interference from external users such as residential access points. We have proposed Urban‐X, which is a first attempt towards multi‐radio cognitive mesh networks in industrial, scientific, and medical bands. Urban‐X first controls network topology with a distributed channel assignment to avoid interference in large timescale. In such a topology, we develop a new link‐layer transmission‐scheduling algorithm together with source rate control as a small‐timescale approach, which exploits receiver diversity when receivers of multi‐flows can have different channel conditions because of varying interference. For this purpose, mesh nodes probe the channel condition of received mesh nodes using group Request to Send and group Clear to Send. In this study, we establish a mathematical Urban‐X model in a cross‐layer architecture, adopting a well‐known network utility maximization framework. We demonstrate the feasibility of our idea using a simulation on the model. Simulation results show improved network throughput from exploiting receiver diversity and distributed channel assignment under varying external user interference. Copyright © 2012 John Wiley & Sons, Ltd.     

基于改进接收信号强度指示的室内定位算法

为了提高室内环境节点定位精度,针对传统定位算法的不足,提出一种改进接收信号强度指示的室内定位算法。首先通过神经网络对各锚节点接收信号强度的权值进行拟合,得到路径损耗模型的参数值,然后利用最大似然法对未知节点进行定位,最后采用仿真实验测试其性能。结果表明,相对其它室内定位算法,本文算法提高了室内定位的精度,降低了平均定位误差,可以满足室内定位的实时性要求。     

Efficient localization of target in large scale farmland using generalized regression neural network

Although simple to implement, the traditional trilateration technique is generally associated with significant location estimation errors because of highly nonlinear relationship between Received Signal Strength Indicator (RSSI) and distance. In case of agricultural farmland, there is always noise uncertainty in the RSSI measurements because of signal propagation issues such as NLOS, multipath propagation, and reflection. In the context of such environmental dynamicity, the localization algorithm must be efficient in terms of Localization Accuracy and Execution Speed to provide real‐time performance. The Generalized Regression Neural Network (GRNN) is a noniterative highly parallel neural architecture with the capability to get trained quickly using very few training samples. This paper introduces a range free GRNN localization algorithm as an alternative to the traditional range‐based trilateration technique for a large scale wheat farmland. This paper also presents the modified Optimal Fitted Parametric Exponential Decay Model (OFPEDM)‐based signal path loss model to deal with the issue of environmental dynamicity. The evaluation of localization performance of the trilateration and the proposed GRNN‐based approaches is carried out with the help of Wireless Sensor Network (WSN) using three path loss models, namely, Log Normal Shadow Fading (LNSM), Original OFPEDM, and proposed Modified OFPEDM. For all these implementations, the proposed GRNN algorithm demonstrates superior localization performance (localization accuracy of the order of few centimeters) over traditional trilateration irrespective of nonlinear system dynamics, path loss model, and environmental dynamicity. The execution speed of the proposed algorithm is of the order of few milliseconds.     

Aligned and non-aligned double JPEG detection using convolutional neural networks

Due to the wide diffusion of JPEG coding standard, the image forensic community has devoted significant attention to the development of double JPEG (DJPEG) compression detectors through the years. The ability of detecting whether an image has been compressed twice provides paramount information toward image authenticity assessment. Given the trend recently gained by convolutional neural networks (CNN) in many computer vision tasks, in this paper we propose to use CNNs for aligned and non-aligned double JPEG compression detection. In particular, we explore the capability of CNNs to capture DJPEG artifacts directly from images. Results show that the proposed CNN-based detectors achieve good performance even with small size images (i.e., 64 × 64), outperforming state-of-the-art solutions, especially in the non-aligned case. Besides, good results are also achieved in the commonly-recognized challenging case in which the first quality factor is larger than the second one.     

Stability‐aware multi‐metric clustering in mobile ad hoc networks with group mobility

Clustering can help aggregate the topology information and reduce the size of routing tables in a mobile ad hoc network (MANET). The maintenance of the cluster structure should be as stable as possible to reduce overhead and make the network topology less dynamic. Hence, stability measures the goodness of clustering. However, for a complex system like MANET, one clustering metric is far from reflecting the network dynamics. Some prior works have considered multiple metrics by combining them into one weighted sum, which suffers from intrinsic drawbacks as a scalar objective function to provide solution for multi‐objective optimization. In this paper, we propose a stability‐aware multi‐metric clustering algorithm, which can (1) achieve stable cluster structure by exploiting group mobility and (2) optimize multiple metrics with the help of a multi‐objective evolutionary algorithm (MOEA). Performance evaluation shows that our algorithm can generate a stable clustered topology and also achieve optimal solutions in small‐scale networks. For large‐scale networks, it outperforms the well‐known weighted clustering algorithm (WCA) that uses a weighted sum of multiple metrics. Copyright © 2008 John Wiley & Sons, Ltd.     

Futuristic speed prediction using auto‐regression and neural networks for mobile ad hoc networks

In this paper, we propose a speed prediction model using auto‐regressive integrated moving average (ARIMA) and neural networks for estimating the futuristic speed of the nodes in mobile ad hoc networks (MANETs). The speed prediction promotes the route discovery process for the selection of moderate mobility nodes to provide reliable routing. The ARIMA is a time‐series forecasting approach, which uses autocorrelations to predict the future speed of nodes. In the paper, the ARIMA model and recurrent neural network (RNN) trains the random waypoint mobility (RWM) dataset to forecast the mobility of the nodes. The proposed ARIMA model designs the prediction models through varying the delay terms and changing the numbers of hidden neuron in RNN. The Akaike information criterion (AIC), Bayesian information criterion (BIC), auto‐correlation function (ACF), and partial auto‐correlation function (PACF) parameters evaluate the predicted mobility dataset to estimate the model quality and reliability. The different scenarios of changing node speed evaluate the performance of prediction models. Performance results indicate that the ARIMA forecasted speed values almost match with the RWM observed speed values than RNN values. The graphs exhibit that the ARIMA predicted mobility values have lower error metrics such as mean square error (MSE), root MSE (RMSE), and mean absolute error (MAE) than RNN predictions. It yields higher futuristic speed prediction precision rate of 17% to 24% throughout the time series as compared with RNN. Further, the proposed model extensively compares with the existing works.