Radio Irregularity Research Articles

An enhanced node segmentation and distance estimation scheme with a reduced search space boundary and improved PSO for obstacle-aware wireless sensor network localization

This paper proposes an enhanced method for localizing sensor nodes in wireless sensor networks with obstacles. Such environment settings lead to lower localization accuracy because locations are estimated based on detour distances circumventing the obstacles; we, therefore, improve the segmentation technique to address the issue as they divide the whole area into multiple smaller ones, each containing fewer or no obstacles. Nevertheless, when radio transmissions between sensor nodes are obstructed (as simulated by the radio irregularity model), the signal-strength variation tends to be high, reducing localization accuracy; thus, we provide a method for accurately approximating the distances between pairs of an anchor node (whose location is known) and an unknown node by incorporating the related error into the approximation process. Additionally, when the nodes with unknown locations are outside the polygon formed by the anchor nodes, the search area for localization is relatively large, resulting in lower accuracy and a longer search time; we then propose a method for reducing the size of approximation areas by forming boundaries based on the two intersection points between the ranges of two anchor nodes used to localize an unknown node. However, these reduced search areas could still be large; we further increase the accuracy of the PSO location estimation method by adaptively adjusting the number of particles. In addition, with PSO, the accuracy of unknown node location estimation depends on having a properly selected fitness function; therefore, we incorporate appropriate variables to reflect the distance approximation accuracy between each anchor-unknown node pair. In experiments, we measure performance in sensor node deployment areas of three different shapes: C-shaped, with 1 hole, and with 2 rectangular holes. The results show that our method provides higher localization accuracy than others in small-, medium-, and large-scaled WSNs. Specifically, our proposed method is 27.46%, 49.28%, 50.33%, and 74.62% more accurate on average than IDE-NSL, PSO–C, min-max PSO, and niching PSO, respectively.

A Voronoi Diagram and Q-Learning based Relay Node Placement Method Subject to Radio Irregularity

Industrial Wireless Sensor Networks (IWSNs) have been widely used in industrial applications that require highly reliable and real-time wireless transmission. A lot of works have been done to optimize the Relay Node Placement (RNP), which determines the underlying topology of IWSNs and hence impacts the network performance. However, existing RNP algorithms use a fixed communication radius to compute the deployment result at once offline, while ignoring that the radio environment may vary drastically across different locations, also known as radio irregularity. To address this limitation, we propose a Voronoi diagram and Q-learning based RNP (VQRNP) method in this article. Instead of using a fixed communication radius, VQRNP employs the Q-learning algorithm to dynamically update the radio environment of measured areas, uses a Voronoi diagram based method to estimate the radio environment of unmeasured areas, and proposes a coverage extension location selection algorithm to place RNs so as to extend the coverage of the deployed network based on the results estimated by Voronoi diagram based Graph Generating (VGG). In this way, the VQRPN method can adapt itself well to the variation of radio environment and largely speed up the deployment process. Extensive simulations verify that VQRNP significantly outperforms existing RNP algorithms in terms of reliability.

Localization for Wireless Sensor Networks Assisted by Two Mobile Anchors with Improved Grey Wolf Optimizer

Localization is crucial to wireless sensor networks. Among the recently proposed localization algorithms, the mobile anchor-assisted localization (MAL) algorithm seems promising. A MAL algorithm using a single mobile anchor has low energy consumption but a high localization error. Conversely, a MAL algorithm with three or more mobile anchors has minor localization errors but high energy consumption. By balancing energy consumption and localization accuracy, our study developed a localization algorithm assisted by two mobile anchors. A mobile anchor traverses the network along a double anchor SCAN (DASCAN) path, which divides the deployment region into grids and requires the two mobile anchors to traverse different horizontal lines in a zigzag pattern. Sensor nodes estimate their locations using a multiple-disturbance strategy grey wolf optimization (MDS-GWO) algorithm, which improves optimization by introducing a nonlinearly decreasing weight, a random perturbation of grey wolves and a mirror grey wolf. Using MATLAB, DASCAN was compared with GTURN, GSCAN, PP-MMAN, H-Curves, M-Curves, and SCAN paths by their energy consumption and localization rates. The localization error of MDS-GWO was compared with trilateration, PSO, WOA, and GWO. The impacts of radio irregularity, radio radius, and fading effect on MDS-GWO with different paths were also analyzed. The simulation results showed that the energy consumption of DASCAN was, on average, 30.1% less than GSCAN, GTURN, and PP-MMAN, but they had almost the same localization accuracy. The energy consumption of DASCAN was an average of 18.67% more than M-Curves, H-Curves, and SCAN, but the localization error of DASCAN was average of 32.3% less than SCAN, H-Curves, and M-Curves. The localization error of MDS-GWO was average of 25.5% less than trilateration, PSO, WOA, and GWO. Moreover, the performance of the proposed algorithm was less affected by different setups than the compared methods.

A Study of an Equatorial Coronal Hole Observed at the First Parker Solar Probe Perihelion

Abstract In this study, we present an observational analysis of a coronal hole (CH) observed on 2018 November 1 and solar wind (SW) that originated from it, using the Solar Dynamics Observatory, the Parker Solar Probe (PSP) observations at 68 solar radii, ACE and WIND data at 1 au, and interplanetary scintillation (IPS) observations from 0.2 to 1 au. The CH-originated SW stream was observed by L1 on 2018 November 4 and by PSP on 2018 November 15. We examined the CH for nine Carrington Rotations (CR) and find that the SW stream to reach L1 varied from one CR to other. We find that the pressure, temperature, and magnetic fields increase as the speed of the SW increases and the density decreases with distance. We noticed suprathermal particle enhancement at and after the stream interaction region in both PSP and L1 observations, but the enhancement lasted longer in PSP compared to measurements made at L1. The multiple-rotation observations of the CH imply that any differences in observations between PSP and spacecraft at L1 are due to the radial evolution of the solar wind stream rather than of the CH or the source plasma itself. In addition, IPS measured the radio signal irregularities driven by the SW. Furthermore, we employed a standard analytical model to extrapolate the magnetic field at larger heights. We find that the extrapolated magnetic field at 68 R ⊙ and 1 au matches well with the magnetic field measured by PSP and OMNI.

Coordinated sensing coverage optimisation in sensor networks using RSSI

We present a coordinated algorithm for sensors' effective deployment, which aims to optimise coverage and surveillance in distributed sensor networks. In this paper, the easy accessed received signal strength indication (RSSI) is adopted to form virtual force, the virtual force algorithm for coverage optimisation will encounter oscillation problems when it has nearly reached an optimised position. We propose a new method which combines virtual force algorithm and the whale swarm algorithm (WSA) according to the RSSI value. When the number of continuous oscillations using a virtual force algorithm is greater than the threshold we set in advance, we optimise the cooperative position of adjacent multi-nodes with the whale swarm algorithm. The regular signal transmission model and radio irregularity model (RIM) are used to simulate the ideal environment and the actual environment, and simulation results demonstrate that the coverage ratio is above 95%, which verifies the effectiveness and feasibility of this algorithm.

DV-Hop localization methods for displaced sensor nodes in wireless sensor network using PSO

Localization of sensor nodes is one of the major challenge in wireless sensor networks. It helps in identifying the geographically area where an event occurred. Most of the cases it is one time process wherein nodes are localized once in network life time. However, in real time scenario nodes may get dislocated within the network due to land slide or any event occurrence. In such events, localization of these sensor nodes is required. In this paper, we have proposed a new framework of localization for displaced sensor nodes. The proposed framework consider DV-Hop localization methods using particle swarm optimization (PSO). Radio irregularity model is also considered to show the applicability of proposed algorithm in anisotropic network. Simulation results show that our proposed localization framework effectively minimizes the elapsed time, localization error and energy consumption as compared to traditional framework of PSO DV-Hop.

DV-Hop-based range-free localization algorithm for wireless sensor network using runner-root optimization

In this paper, we proposed an improved DV-Hop localization using runner-root algorithm to overcome the existing disadvantage of traditional DV-Hop. In proposed algorithm, we have introduced a correction factor in order to modify the hop size of the anchor nodes. The proposed algorithm lessens the communication between unknown and anchor nodes by calculating hop size of all anchors at unknown nodes. To show the applicability of proposed algorithm in anisotropic network, radio irregularity model is also considered. Simulations have been carried out on MATLAB, and comparison is made for our proposed algorithm with traditional DV-Hop, genetic algorithm-based DV-Hop and particle swarm optimization-based DV-Hop. Simulation results confirm that our proposed localization algorithm minimizes the localization error, localization error variance and computational time in comparison with existing localization techniques.

Exploiting Radio Irregularity for Location Verification in Sparse MANETs

Traditional distance-based location verification system (LVS) is ineffective for some attacks in sparse mobile ad hoc networks, e.g., the similar distance-based malicious (SDM) attack. In this letter, we propose to exploit radio irregularity to build a novel LVS. Our system detects attack based on the estimated difference of radio irregularity coefficients and the claimed locations of the malicious node and an assistant node. To the best of our knowledge, our system is the first-of-its-kind that can detect SDM attack and pollution attack with only the received signal strength indicator information transmitted by a detecting node to the suspicious malicious node and an assistant node. Simulation results demonstrate that our proposed system can detect the attacks that cannot be detected by traditional distance-based methods.

Performance analysis of clustering-based fingerprinting localization systems

Localization is highly required to develop the smart-phone based pervasive computing applications. Because of very poor signal strength of global positioning system in indoor areas, various indoor localization systems have been proposed in literature. Among these, received signal strength (RSS) based fingerprinting localization systems are very popular. However, these localization systems at first, need to construct a fingerprint database by collecting RSS patterns at a set of known training locations and then determine the location of an object by comparing the currently observed RSS pattern with all the RSS patterns stored in the fingerprint database. Thus, such localization systems can provide better positioning accuracy by including large number of training data, which in turn, increase the searching overhead. To resolve this issue, several clustering strategies, which restrict the search within a smaller subset of the whole fingerprint database for such localization systems, have been proposed in the literature over the past decade. This paper presents an extensive comparative performance analysis of various clustering-based fingerprinting localization systems to demonstrate their effectiveness on the large-scale positioning system in the presence of radio irregularities and wall attenuation in the wireless environment.

Cross-Layer Greedy position-based routing for multihop wireless sensor networks in a real environment

It has been shown that the maximum distance routing strategy which works well on a Unit Disk Graph (UDG), performs poorly when it is executed on a Non Unit Disk Graph (N-UDG) which reflects the radio irregularity phenomenon. This latter arises from multiple factors, such as antenna and medium type, and is accentuated by environmental factors such as obstacles (e.g., buildings, hills, mountains) and weather conditions. In this paper, we propose a Cross-Layer Greedy Routing algorithm (CL-GR) which enables a correct position-based routing on a N-UDG. It provides two novel greedy routing strategies, termed respectively, Progress towards the sink node through Symmetrical links that experience the lowest Path Loss (PSPL) and progress through symmetrical links, combining the Maximum Distance forwarding strategy and the PSPL (MDPSPL). We compare our CL-GR to an Enhanced version of the Greedy algorithm of the Greedy Perimeter Stateless Routing protocol (GPSR), that we call E-GR and which can be executed on a N-UDG, and to COP_GARE algorithm. The simulation results show that both PSPL and MDPSPL enable higher Packet Delivery Ratio (PDR) and energy efficiency. In terms of end-to-end delay, while the PSPL strategy significantly increases this metric, the MDPSPL strategy enables a satisfactory end-to-end delay, comparatively to E-GR and COP_GARE.

Analysis of Cross-Layer Design of Quality-of-Service Forward Geographic Wireless Sensor Network Routing Strategies in Green Internet of Things

Wireless sensor networks suffer from some limitations such as energy constraints and the cooperative demands essential to perform multi-hop geographic routing for Internet of things (IoT) applications. Quality of Service (QoS) depends to a great extent on offering participating nodes an incentive for collaborating. This paper presents a mathematical model for a new-generation of forwarding QoS routing determination that enables allocation of optimal path to satisfy QoS parameters to support a wide range of communication-intensive IoT’s applications. The model is used to investigate the effects of multi-hop communication on a traffic system model designed with a Markov discrete-time $M/M/1$ queuing model, applicable to green deployment of duty-cycle sensor nodes. We present analytical formulation for the bit-error-rate, and a critical path-loss model is defined to the specified level of trust among the most frequently used nodes. Additionally, we address the degree of irregularity parameter for promoting adaptation to geographic switching with respect to two categories of transmission in distributed systems: hop-by-hop and end-to-end retransmission schemes. The simulations identified results for the average packet delay transmission, the energy consumption for transmission, and the throughput. The simulations offer insights into the impact of radio irregularity on the neighbor-discovery routing technique of both schemes. Based on the simulation results, the messages en-coded with non-return-to-zero have more green efficiency over multi-hop IoT (without loss of connectivity between nodes) than those encoded with the Manchester operation. The findings presented in this paper are of great help to designers of IoT.

Distance measurement correction based on feedback filter for RSSI localisation technique in WSNs

Localisation methods based on the received signal strength indicator (RSSI) are widely used in wireless sensor networks (WSNs). They offer an energy reduced and cost-effective solutions for wireless localisation. Due to radio irregularity, RSSI localisation is vulnerable to multipath, shadowing, interference and environmental effects, which reduces the accuracy of localisation results. In this paper, a new distance measurement correction algorithm is presented, which is based on feedback filter and hardware characteristic. It implements the feedback filter with consideration of historical values of RSSI. This enables to predict possible values of RSSI and thus nodes positions. Hardware characteristics, such as multiplication and summation capabilities are explored to improve the localisation accuracy. Real experiments were carried out on panStamp sensor nodes at two different temperatures. The new proposed RSSI technique shows a reduced localisation error compared to the simple RSSI method.

Distance measurement correction based on feedback filter for RSSI localisation technique in WSNs

Localisation methods based on the received signal strength indicator (RSSI) are widely used in wireless sensor networks (WSNs). They offer an energy reduced and cost-effective solutions for wireless localisation. Due to radio irregularity, RSSI localisation is vulnerable to multipath, shadowing, interference and environmental effects, which reduces the accuracy of localisation results. In this paper, a new distance measurement correction algorithm is presented, which is based on feedback filter and hardware characteristic. It implements the feedback filter with consideration of historical values of RSSI. This enables to predict possible values of RSSI and thus nodes positions. Hardware characteristics, such as multiplication and summation capabilities are explored to improve the localisation accuracy. Real experiments were carried out on panStamp sensor nodes at two different temperatures. The new proposed RSSI technique shows a reduced localisation error compared to the simple RSSI method.

Modified Energy-Efficient Range-Free Localization Using Teaching–Learning-Based Optimization for Wireless Sensor Networks

ABSTRACTThis paper presents an energy-efficient Modified Distance Vector Hop algorithm using Teaching–Learning-Based Optimization, viz. MDV-TLBO, which is range-free, distributed localization algorithm for wireless sensor network (WSN). In the proposed algorithm, the hop size of anchor node is modified by adding correction factor. The concept of collinearity is introduced in this paper to reduce location errors caused by anchor nodes which are collinear. TLBO is used to enhance the localization accuracy, which is parameter-free, efficient optimization technique. Target nodes estimate their final coordinates after location upgradation procedure. In MDV-TLBO, anchor nodes communicate only one time with target nodes to broadcast their location. Hop size modification, optimal selection of anchor nodes, location optimization, and location upgradation are done at target node level, resulting considerable reduction in communication between nodes, due to which energy consumption of the nodes has been significantly reduced. To show the applicability of proposed algorithm in real scenarios, radio irregularity model is considered. Simulation results show that our proposed algorithm achieves better localization accuracy, high positioning coverage with less energy consumption in comparison of the existing improved DV-Hop algorithms.

A 3-D Radio Irregularity Model (3DRIM) for Wireless Sensor Network

Localization in wireless sensor network will be adversely effected due to radio irregularity. This makes the localization procedure more complex and as a result most of the localization techniques are based on the regular radio patterns and isotropic propagation model. In order to address this problem, we propose a three dimensional radio irregularity model, that takes into account non-isotropic radio model along with radio irregularity and also considers the three dimensional aspects. For this proposed model three dimensional degree of irregularity (3-D DOI) is calculated by forming a practical test bed and finally with the simulations are carried out to validate the theoretical model and effect of increase in distance on 3-D DOI.

실제 네트워크를 고려한 베이지안 필터 기반 이동단말 위치 추적

연결정보만을 이용하는 range-free 측위 기법의 성능은 이동성을 갖는 무선 단말 움직임에 취약한 문제점이 있다. 본 논문은 실제 전파 환경을 고려한 실내 네트워크에서 베이지안 필터를 사용하여 실시간으로 움직이는 무선장치를 추적하는 두 가지 알고리즘을 제안하였다. 제안하는 알고리즘은 측정 모델의 선형성에 따라 Kalman filter 와 Markov Chain Monte Carlo (MCMC) particle filter를 적용하였다. Kalman과 MCMC particle filter 기반 알고리즘은 각각 무선단말 간 연결정보를, 이동 단말의 한 홉 간격 내 단말로부터 수신하는 신호의 세기 (RSS: received signal strength)와 연결정보를 혼합한 융합정보를 측정 모델로 사용하였다. 정확한 시뮬레이션을 위해 실내 쇼핑몰 지도를 구현한 네트워크 지형, 그리고 라디오 불규칙도 모델을 적용하였다. 또한, 장애물 존재 여부에 따라 라디오 불규칙도를 분류하였다. 성능평가를 위해 MATLAB 시뮬레이션을 수행하였으며, 기존 range-free 측위 기법보다 향상된 위치정확도를 확인하였다. The range-free localization using connectivity information has problems of mobile tracking. This paper proposes two Bayesian filter-based mobile tracking algorithms considering a propagation scenario. Kalman and Markov Chain Monte Carlo (MCMC) particle filters are applied according to linearity of two measurement models. Measurement models of the Kalman and MCMC particle filter-based algorithms respectively are defined as connectivity between mobiles, information fusion of connectivity information and received signal strength (RSS) from neighbors within one-hop. To perform the accurate simulation, we consider a real indoor map of shopping mall and degree of radio irregularity (DOI) model. According to obstacles between mobiles, we assume two types of DOIs. We show the superiority of the proposed algorithm over existing range-free algorithms through MATLAB simulations.

An enhanced wireless sensor network localization scheme for radio irregularity models using hybrid fuzzy deep extreme learning machines

Localization is one of the key challenges facing wireless sensor networks (WSNs), particularly in the absence of global positioning equipment such as GPS. However, equipping WSNs with GPS sensors e...

Communication Range Dynamics and Performance Analysis for a Self-Adaptive Transmission Power Controller.

The deployment of the nodes in a Wireless Sensor and Actuator Network (WSAN) is typically restricted by the sensing and acting coverage. This implies that the locations of the nodes may be, and usually are, not optimal from the point of view of the radio communication. Additionally, when the transmission power is tuned for those locations, there are other unpredictable factors that can cause connectivity failures, like interferences, signal fading due to passing objects and, of course, radio irregularities. A control-based self-adaptive system is a typical solution to improve the energy consumption while keeping good connectivity. In this paper, we explore how the communication range for each node evolves along the iterations of an energy saving self-adaptive transmission power controller when using different parameter sets in an outdoor scenario, providing a WSAN that automatically adapts to surrounding changes keeping good connectivity. The results obtained in this paper show how the parameters with the best performance keep a k-connected network, where k is in the range of the desired node degree plus or minus a specified tolerance value.

Geographic Routing in Duty-Cycled Industrial Wireless Sensor Networks With Radio Irregularity

Industrial wireless sensor networks (IWSNs) are required to provide highly reliable and real-time transmission. Moreover, for connected K-neighborhood (CKN) sleep scheduling-based duty-cycled IWSNs in which the network lifetime of IWSNs can be prolonged, the two-phase geographic greedy forwarding (TPGF) geographic routing algorithm has attracted attention due to its unique transmission features: multi path, shortest path, and hole bypassing. However, the performance of TPGF in CKN-based duty-cycled IWSNs with radio irregularity is not well investigated in the literature. In this paper, we first evaluate the impact of radio irregularity on CKN-based duty-cycled IWSNs. Furthermore, we investigate the routing performance of TPGF in CKN-based duty-cycled IWSNs with radio irregularity, in terms of the number of explored routing paths as well as the lengths of the average and shortest routing paths. Particularly, we establish the upper bound on the number of explored routing paths. The upper bound is slightly relaxed with radio irregularity compared with without radio irregularity; however, it is bounded by the number of average 1-hop neighbors in always-on IWSNs. With extensive simulations, we observe that the cross-layer optimized version of TPGF (i.e., TPFGPlus) finds reliable transmission paths with low end-to-end delay, even in CKN-based duty-cycled IWSNs with radio irregularity.

Range-free 3D node localization in anisotropic wireless sensor networks

In this paper, we propose two computationally efficient ‘range-free’ 3D node localization schemes using the application of hybrid-particle swarm optimization (HPSO) and biogeography based optimization (BBO). It is considered that nodes are deployed with constraints over three layer boundaries, in an anisotropic environment. The anchor nodes are randomly distributed over the top layer only and target nodes distributed over the middle and bottom layers. Radio irregularity factor, i.e., an anisotropic property of propagation media and heterogenous properties of the devices are considered. To overcome the non-linearity between received signal strength (RSS) and distance, edge weights between each target node and neighboring anchor nodes have been considered to compute the location of the target node. These edge weights are modeled using fuzzy logic system (FLS) to reduce the computational complexity. The edge weights are further optimized by HPSO and BBO separately to minimize the location error. Both the proposed applications of the two algorithms are compared with the earlier proposed range-free algorithms in literature, i.e., the simple centroid method and weighted centroid method. The results of our proposed applications of the two algorithms are better as compared to centroid and weighted centroid methods in terms of error and scalability.