Constrained decentralized algorithm for the relative localization of wearable wireless sensor nodes

Wireless Body Area Networks (WBAN) are endowed with relatively raw but intrinsic motion capture capabilities through radiolocation, which may be of interest for home activity monitoring, large-scale postural rehabilitation or gaming applications. In this context, we propose a solution to localize wearable wireless nodes relatively to a body-strapped Local Coordinate System (LCS). More particularly, we consider adapting a Constrained Distributed Weighted Multi-Dimensional Scaling algorithm (CDWMDS) that asynchronously estimates nodes' locations under fixed-length constraints. This algorithm is fed by inter-node range measurements based on e.g. Impulse Radio - Ultra Wideband (IR-UWB) Time Of Arrival (TOA) estimation. Several new enhancements to the nominal CDWMDS, including nodes censoring and updates scheduling for nodes' locations, are herein put forward to mitigate errors propagation and harmful effects caused by fast moving nodes. Simulation results are provided to illustrate the gains observed on the average location error per node under moderate pedestrian mobility, relying on a realistic biomechanical model.

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.

DV-Hop is a typical node localization algorithm without ranging of wireless sensor network. Because of the shortcoming that this algorithm's accuracy for node positioning is not high, a sensor node localization algorithm based on improved wolves algorithm is proposed. The algorithm is based on the classic DV-Hop algorithm, converts the wireless sensor node localization problem into a multi-constrained optimization problem, and uses improved wolves algorithm to solve this problem, then obtains the optimal solution of unknown node coordinates. Matlab simulation show that, this algorithm can significantly improve the positioning accuracy of the node than other wireless sensor network node localization algorithm.

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.

The miniaturization of biomedical sensor nodes has paved the way for remote patient monitoring using wearable wireless sensor nodes. The network of such invasive and non-invasive sensor nodes is most commonly known as Wireless Body Sensor Network (WBSN). The healthcare applications of WBSN are delay sensitive and of critical nature, therefore, it require reliable and timely dissemination of patients’ critical data to the remote location to ensure Quality of Service (QoS). However, poor link quality (LQ) may affect QoS and result in higher transmission delay, loss of critical data packets data corruption, packet retransmissions, thereby, degrading network performance and compromising the patient’s privacy and confidentiality. Furthermore, the in-vivo wireless sensor node produces electromagnetic radiations which are absorbed by the human body thereby, causing temperature rise around the implanted sensor node. The prolonged communication of such in-vivo nodes creates a hotspot node that results in sensitive tissue damage. Therefore, ensuring hotspot-free communication is a necessity in WBSN. Considering such issues, we propose a Reliable, Link Quality and Temperature aware routing protocol (RLTD) for delay-sensitive application of WBSNs that route data through reliable nodes and good quality links to the destination node. The efficacy of the proposed (RLTD) routing protocol is confirmed by comparing results with the well-known thermal-aware protocols for WBSN.

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.

Due to rapid growth in urban population and advances in the automotive industry, the number of vehicles is increasing exponentially, posing the parking challenges. Automated parking systems provide efficient and optimal parking solution so that the drivers can have hassle free and quick parking. One of the demanding requirements is the design of smart parking systems, not only for comfort but also of economic interest. With the advancements in the Internet of Things (IoT), wireless sensors-based parking systems are the promising solutions for the deployment. Optimal positioning of IoT enabled wireless sensor nodes in the parking area is a crucial factor for the efficient parking model with the lower cost. In this paper, we propose a novel multi-objective grey wolf optimization technique for node localization with an objective to minimize a localization error. Two objective functions are considered for distance and geometric topology constraints. The proposed algorithm is compared with other node localization algorithms. Our algorithm outperforms the existing algorithms. The result shows that localization error is reduced up to 17% in comparison with the other algorithms. The proposed algorithm is computationally efficient due to the choice of fast converging parameters.

Node localization is the basis of a variety of applications as an important technology in wireless sensor networks. This paper studies the existing localization algorithms, and puts forward a sensor node localization algorithm based on particle swarm algorithm, this algorithm uses the fitness value of the particle to adjust the inertia weight value, in order to achieve , and fast and accurate positioning of the target node. The simulation results show that the algorithm is an effective refinement algorithm in nodes localization.

For most wireless sensor network (WSN) applications, the positions of the sensor nodes need to be known. Global positioning systems have not fitted into WSNs very well owing to their price, power consumption, accuracy and limitations in their operating environment. Hence, the last decade has brought about a large number of proposed methods for WSN node localization. They show tremendous variation in the physical phenomena they use, the signal properties they measure, the resources they consume, as well as in their accuracy, range, advantages and limitations. This paper provides a high-level, comprehensive overview of this very active research area.

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.

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.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Accurate localization of nodes in a sensor network is a crucial step before the sensor network can be utilized for various applications. This paper proposes a successive estimation method to self-localize the sensor nodes using time of arrival (TOA) measurements, through the aid of a few anchor nodes whose positions are known <emphasis emphasistype="italic">a priori</emphasis>. The successive nature of the proposed algorithm makes it attractive for distributed computation in a resource constrained environment. The proposed technique uses subsets of TOAs to obtain coarse sensor node estimates. Depending on available computation and transmission resources, the node positions are refined to improve their accuracies. The solution of the proposed node localization algorithm is algebraic and closed-form, which simplifies computation and avoids possible local convergence or divergence problems as in the traditional iterative approaches. A main benefit of the proposed algorithm is that although it is closed-form and performs successive estimation of the node locations, it is able to reach the CramÉr–Rao lower bound (CRLB) accuracy under white Gaussian measurement noise with sufficient SNR. This is confirmed by the theoretical analysis and corroborated by simulations. </para>

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 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.

SummaryDespite the close of a tumultuous 2020 and the start of 2021, connected devices will continue to shape the future of numerous industries, and businesses are confident that the Internet of Things (IoT) will play a key role in the future success of their trade. The growing Internet of Things (IoT) is connecting devices to a variety of sensors, applications, and other IoT elements to automate business processes and support human efficiencies in business and the home. WSN along with node localization algorithms can play a critical role in IoT applications. Nevertheless, in IoT applications, the context of real‐time location‐based services is gaining an overwhelming interest. To do this, several approaches are proposed in the recent literature based mainly on computational intelligence algorithms. This paper proposes a node localization algorithm based on swarm intelligence algorithms, that is, a hybrid Harris Hawks optimization based on differential evolution (HHODE).HHODE algorithm relies on Euclidian Distance as objective function to evaluate best‐fit coordinates of sensor nodes in a wireless sensor network. Moreover, several experimentations are performed depending on the network size, communication range of sensors, geographical distribution, and the beacon nodes' density to demonstrate the efficiency of the HHODE algorithm. Compared to Standard DE, HOO, PSO, and Bat Algorithm, HHODE shows higher performance with regard to node localization.