An Improved Non-ranging Node Localization Method for Wireless Sensor Networks

ABSTRACTIn large‐scale wireless sensor networks, cost‐effective and energy‐efficient localization of sensor nodes is an important research topic. In spite of their coarse accuracy, range‐free (connectivity‐based) localization methods are considered as cost‐effective alternatives to the range‐based localization schemes with specialized hardware requirements.In this paper, we derive closed‐form expressions for the average minimum transmit powers required for the localization of sensor nodes, under deterministic path loss, log‐normal shadowing, and Rayleigh fading channel models. The impacts of propagation environment and spatial density of anchor nodes on the minimum transmit power for node localization are evaluated analytically as well as through simulations. Knowledge of the minimum transmit power requirements for localizability of a sensor node enables improving energy efficiency and prolonging lifetime of the network. We also propose a novel distance metric for range‐free localization in large‐scale sensor networks. The target and anchor nodes are assumed to be positioned according to two statistically independent two‐dimensional homogeneous Poisson point processes. Analytical expression for the average distance from a target node to its kth nearest neighbor anchor node is derived and is used for estimating the target‐to‐anchor node distances for localization. The Cramér–Rao lower bound on the localization accuracy for the new distance estimator is derived. Simulation results show the accuracy of the proposed distance estimate compared with some existing ones for range‐free localization. The results of our investigation are significant for low‐cost, energy‐efficient localization of wireless sensor nodes. Copyright © 2011 John Wiley & Sons, Ltd.

In the wireless sensor network (WSN) localization methods based on Received Signal Strength Indicator (RSSI), it is usually required to determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and an unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the location of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as complex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based on least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model, which positively reduces the computation amount in the estimation process. The experimental results show that the proposed self-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement. Conclusively, the proposed method is of definite practical value.

A three-dimensional (3D) orthogonal frequency division multiplexing (OFDM) system with peak-to-average power ratio (PAPR) reduction method for the wireless sensor networks (WSNs) is presented. The transmit power of wireless nodes in an ad hoc network is strictly limited. Thus, high PAPR of the 3D OFDM system is a possible drawback when it is considered as a physical layer transmission scheme for the WSNs. Here, we propose an improved partial transmit sequence (PTS) technique to reduce PAPR of the OFDM system with 3D modulation formats. In the proposed algorithm, the components of 3D signals are assigned to different subblocks using a diagonal rule (DR), increasing randomness of the signals in the disjoint subblocks forcibly. As a result, the proposed method reduces PAPR of the 3D OFDM system significantly without increase in computational complexity. Hence, the proposed algorithm makes the 3D OFDM system be a possible candidate for a physical layer transmission scheme in future WSNs.

An energy-efficient distributed detection method for wireless sensor networks is considered in this paper. It is assumed that a group of sensors observe the same quantity in independent additive observation noises with possibly different variances. We assume that the links between the nodes and the fusion center are subject to Rayleigh fading wireless channels. At the fusion center, incorporating the effect of fading in the detection process, a decision fusion rule based on the linear minimum mean square error(LMMSE) is investigated. In this paper, two types of constraints are considered: 1) transmission power constraints at the nodes, and 2) the communication channel between the nodes and the decision center. we propose a optimal distributed estimation algorithm in energy-constrained wireless sensor networks. By numerical examples, we illustrate the performance of the proposed serial detection scheme.

In this paper, we propose an energy-efficient data query method for wireless sensor networks (WSNs). Our method is flexible and adaptive. To solve the cluster head coverage problem in our method, we propose virtual grid clustering (VGC) protocol in which the clusters are constructed only in the query area when they are needed. The clusters are destroyed after the query ends. Theoretical analysis and simulations show that our method can save significant amount of energy comparing to routing tree protocol and LEACH.

The transmitting and receiving of data consume the most resources in Wireless Sensor Networks (WSNs). The energy supplied by the battery is the most important resource impacting WSN's lifespan in the sensor node. Therefore, because sensor nodes run from their limited battery, energy-saving is necessary. Data aggregation can be defined as a procedure applied for the elimination of redundant transmissions, and it provides fused information to the base stations, which in turn improves the energy effectiveness and increases the lifespan of energy-constrained WSNs. In this paper, a Perceptually Important Points Based Data Aggregation (PIP-DA) method for Wireless Sensor Networks is suggested to reduce redundant data before sending them to the sink. By utilizing Intel Berkeley Research Lab (IBRL) dataset, the efficiency of the proposed method was measured. The experimental findings illustrate the benefits of the proposed method as it reduces the overhead on the sensor node level up to 1.25% in remaining data and reduces the energy consumption up to 93% compared to prefix frequency filtering (PFF) and ATP protocols.

The problem of find the location of a node in wireless sensor networks has been a research interest in recent years. However, traditional range-based localization methods for wireless sensor networks usually can not achieve high localization accuracy, when the number of labor anchor is less than three. In this paper, we propose a novel range-based distributed localization algorithm based on genetic algorithm. The algorithm is divided into two stages: use sampling method to obtain initial location and refines the initial location based on genetic algorithm. Simulation results show our algorithm achieves high localization accuracy when anchor ratio is less. Compared with similar method, our algorithm obtains higher localization accuracy.

In order to solve the problem of angular effects and reduced positioning accuracy caused by rapid speed changes in position tracking and positioning methods in wireless sensor networks, as well as the difficulty of improving positioning accuracy with a single solution, the author proposes a research on improving node positioning accuracy in wireless sensor networks based on computer vision and deep learning optimization. The author proposes a tracking and localization method using Kalman filtering (KF) and visual assistance on the TI CC2431 ZPS platform. On the basis of normalized cross-correlation, visual assistance calibration technology is used to extract the position of reference nodes as landmarks using visual assistance methods. Then, the KF method is used to calibrate the position estimation, which randomly generates virtual nodes for neural network training. Then, the priority positioning node is located and used as the anchor node for the next positioning, and the wireless loop is used for positioning calculation. The experimental results show that both the TI ZPS method and the KF based method have an estimated position error distance of over 55%, which is less than 2.2m and 1.8m, respectively, The proposed tracking and positioning method has an estimated position error distance of over 55% less than 1.4m. The method proposed by the author effectively avoids uncertainty caused by system errors in actual dynamic environments, reduces angular effects in position estimation systems, and improves positioning accuracy.

The localization of sensor nodes is a significant issue in wireless sensor networks (WSNs) because many applications cannot provide services without geolocation data, especially during disaster management. In recent years, a promising unknown-nodes positioning method has been developed that localizes unknown nodes, employing a GPS-enabled mobile anchor node moving in the network, and broadcasting its location information periodically to assist localization. In contrast to most studies on path planning that assume infinite energy of the mobile anchor node, the anchor node in this study, consumes different amounts of energy during phases of startup, turning, and uniform motion considering the aftermath of disasters. To enable a trade-off between location accuracy and energy consumption, a path-planning algorithm combining a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) and SCAN algorithm (SLMAT) is proposed. SLMAT ensures that each unknown node is covered by a regular triangle formed by beacons. Furthermore, the number of corners along the planned path is reduced to save the energy of the mobile anchor node. In addition, a series of experiments have been conducted to evaluate the performance of the SLMAT algorithm. Simulation results indicate that SLMAT outperforms SCAN, LMAT, HILBERT, and Z-curve in terms of localization accuracy and energy consumption.

In recent years, with the development of the Internet of things industry, wireless sensor networks, as one of the most important technologies in its sensing layer, have been further studied and applied. However, the security threats it faces are becoming more and more diverse, and the security needs are becoming more and more urgent. However, wireless sensor network nodes are mutually exclusive, which leads to low accuracy of attack path prediction when malicious attacks occur. In order to ensure the security of wireless sensor networks, a hidden Markov based malicious attack path prediction method for wireless sensor networks is designed. The method of authentication coding is introduced to realize data integrity authentication. Extract the malicious software gene sequence, define the constraint conditions of the state transition probability matrix, build the attack means deduction model based on Hidden Markov, and realize the malicious attack path prediction. Experimental results: the average accuracy of the proposed method in four malicious attack scenarios is 73.583%, 55.674%, 32.923% and 85.014%, respectively. The results also verify that the proposed method improves the accuracy of malicious attack path prediction in wireless sensor networks based on Hidden Markov model and has good feasibility. It provides an important basis for wireless sensor network administrators to implement defense strategies in time.

The purpose of a localization system is to estimate the coordinates of the geographic location of a mobile device. The accuracy of wireless localization is influenced by nonline-of-sight (NLOS) errors in wireless sensor networks. In this paper, we present an improved time of arrival (TOA)-based localization method for wireless sensor networks. TOA-based localization estimates the geographic location of a mobile device using the distances between a mobile station (MS) and three or more base stations (BSs). However, each of the NLOS errors along a distance measured from an MS (device) to a BS (device) is different because of dissimilar obstacles in the direct signal path between the two devices. To accurately estimate the geographic location of a mobile device in TOA-based localization, we propose an optimized localization method with a BS selection scheme that selects three measured distances that contain a relatively small number of NLOS errors, in this paper. Performance evaluations are presented, and the experimental results are validated through comparisons of various localization methods with the proposed method.

Aiming at the problem of poor encryption effect of the existing wireless sensor network privacy node encryption method, a wireless sensor network privacy node encryption method is designed based on edge computing. Firstly, the wireless sensor network structure and protocol stack structure are clarified, the composition structure of privacy nodes and the set of neighbour nodes are determined, and then the privacy node sensitivity is obtained through the adjacency matrix, and the privacy node location of wireless sensor network is realised by the trilateral positioning method. Finally, the edge computing method is introduced to aggregate the number of edge nodes of privacy nodes, complete the division of privacy node datasets, set terminal encryption keys for them, and realise wireless sensor network privacy node encryption. The experimental results show that the positioning error of this method is up to 0.9%, which can effectively improve the positioning accuracy of privacy nodes and improve the security of the network.

The wireless sensor network (WSN) has been among the fast-growing areas in recent years in different fields, like health applications, military applications, the operations of disaster relief, etc. In an unattended, even hostile environment, nodes in WSNs are commonly deployed. What is worse, these nodes are fitted with minimal resources for communication, computation, storage and battery. It is also impossible to guarantee the lifespan of WSNs without reducing their network performance. A massive volume of application-specific data is produced by WSNs. Such data must be processed and sent to the base station by sensor nodes, which is an expensive matter. As the resources of sensor nodes are constrained, the key challenges facing WSNs are effective data processing and energy conservation. To tackle those challenges, data aggregation can be used, which is a popular approach that filters redundant data in-network and speeds up the extraction of knowledge. In this paper, we propose an energy-saving data aggregation method for WSNs based on the extraction of extrema points (DAEP) to greatly minimize the energy consumption of each node and boost the higher lifespan of WSN. The efficiency of the proposed method was measured by performing multiple simulation experiments based on real sensor readings collected in Intel Berkeley laboratories and comparing the results achieved with some work in the literature. The results show the ability of the proposed method to reduce the load on the sensor nodes in terms of reducing the amount of transmitted data by up to 69-80%, energy consumed by 73-77% while maintaining an acceptable level of accuracy compared to some existing works.

In recent years, the wireless sensor network has become a fundamental research area in smart farm and smart irrigation applications. In smart farming applications, the sensors can be scattered randomly via an aircraft, or an airplane,to detect and collect a set of climatic and environmental parameters. The data collected by the sensor nodes don't matter if their locations are unknown. Therefore, it is necessary to determine the location of sensor nodes for correctly interpreting the detected information of communication between nodes. In this case, the location of the sensor nodes is carried out by the use of a set of localization systems such as GPS, GSM, and UWB. These last ones produce a major drawback in terms of huge energy consumption, high cost, and low accuracy in some zone. To resolve these problems, extensive research is being carried out to integrate the range-based or range-free localization algorithm into nodes sensors to improve the localization accuracy. In this paper, we present the performance evaluation of some improved versions of the DV-HOP localization algorithm existing in the literature. Thus, we study the impact of variation in the number of anchor nodes, the number of unknown nodes, and the communication range on the minimum mean square error(MMSE) of these algorithms. In order to make a comparison between the DV HOP algorithm and these versions, we extract a more practical, innovative version.

A wireless sensor network (WSN) is a large collection of sensor nodes with limited power supply and constrained computational capability. Clustering routing method in wireless sensor networks has been considered as an important field of research recently to prolong the network lifetime of WSNs. We present a novel clustering method that can balance the energy consumption and extend the lifetime of WSN. Network nodes can be divided into densely connected subgroups through the algorithm of detecting community structure in complex networks. Moreover, the role of cluster-head is scheduled among the cluster members according to the residual energy of nodes, and then the cluster heads send the data to the sink directly. Based on the community clustering strategy, a novel routing protocol, called community structure clustering routing protocol (CSCR), has been raised for WSN. Performance evaluation has shown that the proposed method can achieve improvement compared with LEACH and SEP.