Power‐aware range‐free wireless sensor network localization using neighbor distance distribution

This chapter describes one of the major challenges in technology advancement of Wireless Sensor Networks (WSNs), i.e., Localization in WSNs. In recent years, sensor node localization is an emerging research area in WSNs. The sensor data become useless, if we do not know the location of the reporting node. Coordinates determination of the sensor node is a challenging problem and it is referred as localization problem. The nodes which has unknown coordinates is termed as target Nodes. Various localization methods can be utilized to find out the location of sensor nodes, those coordinates are not known in a system/network. Efficient WSN localization can be treated as multi-dimensional optimization problem which can be addressed through population based stochastic techniques, which involves the minimization of a function of differences between Euclidean and measured distance between sensor nodes. In this chapter various connectivity, range information and mobility based localization algorithms have been discussed. For optimizing the results of these algorithms, various computational intelligence (CI) based optimizing algorithms like Particle Swarm Optimization, Biogeography Based Optimization, Firefly Algorithm and Genetic Algorithm have been discussed. A choice between these algorithm is influenced by the localization accuracy expected to be and convergence rate.

Wireless sensor node's localization is a funda-mental technology in Wireless Sensor Networks.There are only quite a few study on three-dimensional (3D) localization which is suffered in slow progress, actually, is one of the main difficulties in WSN localization.Based on the study of the existing two-dimensional positioning algorithm and the application of terrain modeling, localization algorithm for sensor nodes in (3D) condition has been focus on as well as the application of terrain model.Using the idea proposed by representative algorithm--APS multi-hop AOA (Angle of Arrival), this paper proposed a new algorithm named Multi-hop Three Dimensional AOA With Space-based Angle Trans-mission (MSAT3D AOA).Using this technology, target nodes can use information of anchor nodes which are more than one hop away form.This paper also combined MSAT3D AOA algorithm with Delaunay triangulation algorithm for terrain modeling.

Within the growing Internet of Things (IoT) paradigm, a Wireless Sensor Network (WSN) is a critical component. In a WSN, sensor node localization is typically utilized to identify the target node’s current location at the sink node (SN). This allows local data to be analysed, making it more meaningful. However, there exists an intrinsic problem with node localization and energy efficiency, as identified in the literature, which has led to poor performance, namely, poor estimation, transmission, and detection of the network. This intrinsic problem also directly affects energy efficiency in a WSN, resulting in energy loss and poor node distribution in the WSN. There seems to be no lasting and reliable solution to this intrinsic node localization problem in WSNs. Hence, this research study proposed hyper-heuristic optimization algorithms to improve node localization and energy efficiency in WSNs. This research adopts the Design Research (DR) methodology and the Theory of Modelling and Simulation as the theoretical frameworks of the study. The hyper-heuristic model designed, was considered the conceptual framework of the study. To validate the novel technique, different sizes of sensor networks, namely: - 100 sensor nodes; 100 to 1 500 nodes and 200 to 450 sensor nodes with 20 anchor nodes were simulated in an area measuring 100m x 100m. The novel hyper-heuristic model was implemented in a MATLAB R2020a environment. The hyper-heuristic optimization algorithm’s substantial simulated experiment results were benchmarked utilizing state-of-the-art (modern) techniques to solve challenges related to node localization error, total energy consumed, average consumed packet energy, network throughput, shortest path, dead nodes, packets dispatched to the base station (BS), and the probability of error within the entire network dependent on size. The Data Energy Efficiency Clustering-Gaussian (DEEC-GAUSS) method was used to provide solutions to challenges related to energy efficiency in WSNs. In addition, this research study explored the use of the novel DEEC-GAUSS Gradient Distance Elimination Algorithm (DGGDEA) as the hyper-heuristic optimisation model for localization of nodes in WSNs. DEEC-GAUSS and DGGDEA were valuable additions to the body of knowledge. The results showed that the novel DEEC-GAUSS was the most energy efficient algorithm for 100 sensor nodes and 1000 to 1500 sensor nodes when compared to other stateof-the-art algorithms. Furthermore, the results showed that the novel DGGDEA was able to drastically minimize the node estimation error for sensor nodes. Reliability, accuracy and convergence using hyper-heuristic models to enhance the communication within WSNs has been simulated with evidence that DEEC-GAUSS and DGGDEA has outperformed other stateof-the-art approaches

The localization of wireless sensor nodes is one of the key technologies in wireless sensor networks (WSN). Recently, a distributed localization method has been proposed based on a novel concept of angle of departure (AOD) estimation, which can be employed by e.g., the single antenna sensors to estimate the angle information and achieve localization. Based on the AOD estimation, in this paper, we introduce a synthetic uniform circular array (SUCA) synthesized by a rotary antenna on anchor to get the distributed localization of the sensors in multi-path environments. The rotated operation of antenna could create the SUCA with a larger aperture to deal with AOD of multipath components (MPCs) and relax the condition that array aperture should be greater than the number of MPCs. This new localization method only relies on radio transceivers without requiring extra measuring equipments, and the sensor nodes conduct localization in a completely distributed manner instead of centralized processing, which is suitable for large-scale WSN. Meanwhile, the influence of carrier frequency offset (CFO) between the transceivers is also considered and it is eliminated by utilizing the signal emitted from an auxiliary antenna.

Localization of sensor node is imperative in Wireless Sensor Networks (WSN) as data gathered at the user end become meaningless without location information of the node. In this work, impact of anchor position errors on WSN localization using Mobile Anchor Positioning (MAP) Algorithm has been investigated by evaluating two deployment strategies. In deployment I sensor nodes are randomly spread in the entire chosen area (1000 χ 1000) whilst deployment II has nodes spread in the central region of the chosen area. Firstly, MAP Algorithm employing three anchor nodes is investigated for the two deployments. To study the impact of mobile anchors on MAP, anchor number is increased to four and the two deployments are investigated again. Further, this work also investigates the effect of anchor position errors on localization accuracy of sensor nodes. Error term is modelled using Additive White Gaussian Noise (AWGN) Model. Performance metrices considered for validation are Average Root Mean Square Error and Execution time.

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.

Node localization is a fundamental research problem in wireless sensor networks, and the implementation of many applications requires the accurate location of sensor nodes. Currently, among the existing localization algorithms, non-ranging algorithms are a class of algorithms that localize sensor nodes through multi-hop connectivity information between them, and the DV_Hop algorithm is one of the most frequently used algorithms. Although the implementation of DV_Hop algorithm is relatively simple, its location accuracy is low, which is difficult to meet the higher requirements of the application. For this reason, this paper proposes an improved non-ranging node localization method for wireless sensor networks. The proposed algorithm adopts the minimum mean square error criterion to calculate the average hop distance of anchor nodes. On this basis, the distance estimation from unknown node to anchor node is replaced. Finally, a nonlinear iterative approach is used to perform the multilateral localization process. The experimental results show that the localization accuracy and stability of the proposed algorithm are also significantly improved.

Localization is one of the significant field of research in wireless sensor networks (WSNs). It broadly refers to estimating the location of unidentified nodes in a WSN using anchor nodes (nodes whose location is known). Localization, along with the mobility of nodes in the network, makes this field more potent for practical purposes. Therefore, in this paper, we propose an algorithm for localization in WSNs using a mobile anchor and subordinate nodes (LWMS). In the proposed algorithm, we use a mobile anchor node to localize all the other unidentified nodes in the network. The mobile anchor node localizes the nodes in its path using RSSI technique, and these localized nodes are called the static anchor nodes. Using these static anchor nodes, the algorithm localizes all other nodes around them. The nodes localised using static anchor nodes are called subordinate anchor nodes. There are still some nodes left in the network to be localized due to holes or a small percentage of anchor nodes. Further, these subordinate nodes are used to localize all the remaining nodes in the network. We have also used a method of solving the system of distance equations, which reduces the propagation of error. The simulation results corroborate that LWMS performs better than the other two algorithms used for comparison.

A wireless sensor network consist of spatially distributed autonomous sensor nodes for data acquisition. Accurate localization of sensor nodes is a strong requirement in a wide area of applications. This paper describes the localization of sensor nodes using range measurements and trilateration with the Extended Kalman Filter. The commercially available nanoLOC sensor network is utilized for range measurements. Experimental results show, that a localization accuracy better than 0.5 m can be achieved in most cases.

Underwater acoustic communication is a method of sending and receiving messages beneath the water’s surface that has been extensively researched for long-distance communication and localization. While, in this paper we will focus on medium range optical communication localization and short range Magneto Inductive (MI) communication localization. Both optical wireless sensor networks (OWSNs) and magneto inductive wireless sensor networks (MIWSNs) are investigated for localization of sensor nodes using a received signal strength indicator (RSSI) based localization technique for underwater environment/medium. Location information for MI nodes is obtained directly from mutual coupling measurement with a proposed localization scheme. Where as the localization of optical sensor nodes is done by exploiting the Lambert cosine law which gives values for luminous intensity in different distances and orientations. A simulation environment for both underwater optical wireless sensor networks (UOWSNs), and underwater magneto inductive wireless sensor networks (UMIWSNs) consisting of eight Tx anchor nodes and ten Rx sensor nodes is created to elaborate the localization estimation results.

Sensor node localization is a crucial aspect of many location-related applications that utilize wireless sensor networks. Among the many studies in the literature, multidimensional scaling-based localization techniques have been proven to be efficient, obtaining high accuracy with lower information requirements. However, when applied to large-scale wireless sensor networks with coverage holes, which are common in many scenarios, such as underground mines, the transmission path can become deviated, degrading the localization performance of this type of connectivity-based technique. Furthermore, in such complex wireless environments, non-line-of-sight reference objects, the presence of obstacles and signal fluctuations change the communication range and make it difficult to obtain an accurate position. In this article, we present a anchor-free localization scheme for large-scale wireless sensor networks called the ranging and multidimensional scaling–based localization scheme. We use ranging and non-line-of-sight error mitigation techniques to estimate accurate distances between each node pair and attempt to find inflection nodes using a novel flooding protocol to correct transmission paths that have become deviated by a coverage hole. Moreover, we replace the singular value decomposition with an iterative maximum gradient descent method to reduce the computational complexity. The results of the simulations and experiments show that our scheme performs well on wireless sensor networks with different coverage holes and is robust to varying network densities.

The effective localization of sensor nodes can ensure stable operation of sensor networks. The sensor node localization in traditional method does not considers the produced sparse of sensor channel due to the uneven distribution of nodes, resulting in much meaningless zero tap estimation in the positioning process, thereby reducing the efficiency of localization. In this paper, an improved node localization algorithm based on DV-Hop is proposed. In this method, the traditional DV-Hop localization algorithm is improved. Using particle swarm algorithm to optimize the positioning process, the positioning results of sensor nodes are finally obtained. The experimental results show that the improved algorithm can improve the positioning accuracy of sensor nodes, which are satisfactory.

Distributed sensing, computing and communication capabilities of wireless sensor networks require, in most situations, an efficient node localization procedure. In the case of random deployments in harsh or hostile environments, a general localization process within global coordinates is based on a set of anchor nodes able to determine their own position using GPS receivers. In this paper we propose another anchor node localization technique that can be used when GPS devices cannot accomplish their mission or are considered to be too expensive. This novel technique is based on the fusion of video and compass data acquired by the anchor nodes and is especially suitable for video- or multimedia-based wireless sensor networks. For these types of wireless networks the presence of video cameras is intrinsic, while the presence of digital compasses is also required for identifying the cameras' orientations.

Wireless Sensor Networks have been proposed for use in many location-dependent applications. Most of these need to identify the locations of sensor nodes, a challenging task because of severe constraints on cost, energy and effective range of sensor devices. To overcome limitations in existing solutions, we present a Multi-Sequence Positioning (MSP) method for large-scale stationary sensor node localization in outdoor environments. The novel idea behind MSP is to reconstruct and estimate two-dimensional location information for each sensor node by processing multiple one-dimensional node sequences, easily obtained through loosely guided event distribution. Starting from a basic MSP design, we propose four optimizations that work together to increase localization accuracy. We address several interesting issues such as incomplete (partial) node sequences and sequence flip, found in the Mirage test-bed we built. We have evaluated the MSP system through theoretical analysis, extensive simulation as well as two physical systems (an indoor version with 46 MICAz motes and an outdoor version with 20 MICAz motes). Evaluation demonstrates that MSP can achieve an accuracy within one foot, requiring neither additional costly hardware on sensor nodes nor precise event distribution. In fact, it provides a nice tradeoff between physical cost (anchors) and soft cost (events) while maintaining localization accuracy.

Motion capture might become one key feature of future Wireless Body Sensor Networks (WBSN), allowing new applications such as home activity monitoring, nomadic postural rehabilitation or sportive gesture recording through standard on-body communications. In this context we present herein a stand-alone solution that enables the localization of wearable wireless nodes relatively to a body-strapped Local Coordinate System (LCS). In particular, we consider adapting a Distributed Weighted Multi-Dimensional Scaling (DWMDS) algorithm fed by cooperative inter-node range measurements obtained through e.g., Time Of Arrival (TOA) estimation, where estimated nodes' locations are asynchronously updated based on their neighborhood information. Exploiting further the presence of constant-length radio links, a Constrained solution (CDWMDS) is thus proposed to improve localization accuracy, while reducing traffic and power consumption. Another novelty lies in the initialization step, which somehow benefits from the space-time correlation of nodes' locations under body mobility. Relying on a realistic biomechanical model, we provide preliminary simulation results to illustrate the relative gains observed in comparison with a nominal algorithm setting.