Voting mechanism for trustworthy localization in wireless sensor networks

Localization is a process of finding the coordinates of the deployed sensor nodes in the sensing area. Localization in wireless sensor networks (WSNs) is important as it results in location-stamped communication. Applications of WSNs like forest fire detection, nuclear or biological attacks, locating survivors post-disasters, etc. require location-aware information for real time and accurate response. Generally, the sensor nodes are not equipped with global positioning system due to its inefficiency for indoor and underwater regions. Other drawbacks include cost and power consumption. Localization in 3D WSNs is a complex task. The complexity increases due to an additional dimension leading to larger neighboring nodes and increase in coverage area (i.e., area coverage of a sensor node changes from circular to spherical). A lot of approaches have been proposed to localize the sensor nodes in 2D and are now being seen in 3D aspect. Although good efforts have been made for research summarization and guidance in 2D localization, it lacked attention by the researchers in 3D scope. This paper provides a comprehensive survey of the algorithms designed for localizing the sensor nodes in terrestrial and underwater regions. These algorithms have been classified based on the nature of anchor nodes. The localization methods have been categorized as static anchor node-based or mobile anchor node-based and further range-free or range-based depending upon the number of anchor nodes availability and distance estimation technique, respectively. The limitations and challenges of the proposed approaches have also been discussed briefly. The paper also tabulates these methods on the basis of attributes like localization process, complexity, number of sensor nodes used, etc.

Wireless sensor and actor networks are fully automated. Actor nodes are inducted to communicate with sensor nodes directly and reduce the communication delay caused by base station or sink nodes. Sometimes, the actor node is directly accessible without the involvement of any control room. The actor node is responsible for taking a prompt action against the reported event by a sensor node. For secure communication, it is essential that sensor and actor nodes be aware of their existing location and the data must be encrypted before transmission. Due to energy constraints, secure localization in wireless sensor networks is a hot issue. To date, the researchers have proposed many approaches for localization of sensor nodes in the network. In this paper, we provide new insights for secure actor directed localization technique in wireless sensor and actor networks. A secure connectivity based localization (CBL) approach for sensor and actor nodes localization is presented. The proposed approach helps to locate a sensor node efficiently and effectively. We have also decreased the possibility of attacks and the registration of attacker nodes with other legitimate nodes in the network. The proposed technique prevents man-in-the-middle attacks and securely delivers data to the destination.

We develop a novel approach to source localization in mobile wireless sensor networks. Standard approaches make explicit assumptions relating to the statistical characteristics of the physical process and propagation environments which result from distributional model assumptions in a likelihood-based inference method. In contrast, we adopt an approach known in statistics as a non-parametric modeling framework which allows one to relax the number of required statistical assumptions, specifically with regard to the distributional properties of the received signal and the physical process. This is achieved via a re-formulation of the problem as a flexible non-parametric regression model via the framework of Gaussian Processes. Coupling this modeling perspective with a Bayesian optimization mechanism, we frame the global optimization objective as a sequential decision problem. We then develop an efficient algorithm to sequentially select the optimal location at which the mobile sensor should obtain observations under communication and mobility constraints. Simulation results demonstrate the efficiency of the algorithm at achieving accurate localization in a wireless sensor network.

In wireless sensor networks (WSNs), location information of sensor nodes (or simply sensors) plays a vital role in both of the management of WNSs and many other applications. Due to the constraints on costs and energy, only a small portion of nodes in a WSN is deployed as anchor nodes or simply anchors with their locations a priori known or determined through certain hardware (e.g. GPS) to localize normal sensor nodes. However, the placement of such anchors has significant influence on the localization performance of sensor nodes. This paper tackles the problem of optimal anchor placement for localization in large-scale WSNs. But, differently from existing studies assuming independent and identically distributed measurement noises, this paper takes into account more practical distance dependent measurement noises. Then, provided that sensors' locations satisfy a homogeneous Poisson Point process, a theoretical analysis based on the average Cramer-Rao Lower Bound (CRLB) proves that it is optimal to place anchors in a regular fashion. In particular, given that each sensor can measure distances to nearby 3 anchors, the optimal anchor placement pattern is the equilateral triangle pattern, which is consistent with the optimal node deployment for 3-coverage and 6-connectivity. This study not only provides the knowledge for guiding the deployment of large-scale WSNs in practice, but also paves the way for building the theory of sensor localization in WSNs.

Localisation in wireless sensor networks (WSNs) not only provides a node with its geographical location but also a basic requirement for other applications such as geographical routing. Although a rich literature is available for localisation in static WSN, not enough work is done for mobile WSNs, owing to the complexity because of node mobility. Most of the existing techniques for localisation in mobile WSNs use Monte Carlo localisation (MCL), which is not only time consuming but also memory intensive. They, consider either the unknown nodes or anchor nodes to be static. In this study, the authors propose a technique called dead reckoning localisation for mobile WSNs (DRLMSN). In the proposed technique all nodes (unknown nodes as well as anchor nodes) are mobile. Localisation in DRLMSN is done at discrete time intervals called checkpoints. Unknown nodes are localised for the first time using three anchor nodes. For their subsequent localisations, only two anchor nodes are used. The proposed technique estimates two possible locations of a node using Bézout's theorem. A dead reckoning approach is used to select one of the two estimated locations. The authors have evaluated DRLMSN through simulation using Castalia simulator, and is compared with a similar technique called received signal strength‐MCL proposed by Wang and Zhu (2008).

Node Localization (NL) in wireless sensor networks (WSNs) is main procedure for defining physical matches such as longitude, latitude, and altitude of Sensor Nodes (SNs) organized in a provided region. Exact NL is very essential for numerous WSN applications like surveillance, asset tracking and environmental monitoring. Localization models involve GPS hardware, anchor nodes with recognized locations or algorithms that influence distance dimensions and connectivity designs amongst SN in order to evaluate their places. Trustworthy NL improves exactness and efficiency of data collection and study in WSNs, finally boosting up the network's performance and quality information it offers. This article introduces a tuna swarm algorithm-based node localization (TSA-RNL) technique in WSN. The major aim of the TSA-RNL model to focus unknown nodes in WSN. TSA-RNL technique is developed for enhancing the localization accuracy in the WSN. The TSA, stimulated from the collective nature of tuna fish, optimizes the localization process by iteratively refining node positions. Over wide simulation and experimentation, we estimate the TSA-RNL model performance and establish its authority in gaining great accurateness node localization in WSNs. The methods provide potential advantages for many applications that based on specific node positioning, environmental monitoring, data fusion and target tracking that contributing to the development of WSN methodology.

In the current global economy, localization in WSNs becomes a hot topic and it is attracting many researchers towards itself. WSN plays an important role in tracking objects in indoor as well as outdoor environments. The sensor data is useless until the location of the reporting node is unknown. The main aim of localization in wireless sensor networks (WSNs) is to determine the coordinates of target nodes in the sensing field by applying different approaches. This can be done either by the global positioning system (GPS) or any other methods. Usually, in localization methods, anchor nodes (GPS-equipped) broadcast beacon signals in the sensing field to help target nodes to localize themselves. The terms localization accuracy, localized nodes and computing time are mainly responsible for the performance of WSNs. The transmission range and density of anchor nodes directly affect the localization error. In this paper, the author surveys the various localization techniques optimized by nature-inspired algorithms and compares the salp swarm optimization algorithm with some well-known existing algorithms.

Localization in wireless sensor networks (WSNs) is a necessity as there is a vital need to have location information combined with the measured quantities. The received signal strength (RSS) based localization is a simple and a cheap model but it provides a low positioning accuracy. However, its accuracy can be enhanced by applying a cooperation mechanism among sensors. In this paper, the performance of RSS, based localization, and its cooperative version are studied, analyzed, and simulated in WSNs which operate in the millimeter wave frequency bands. Moreover, this performance is compared with that of WSNs which operate in radio bands. It can be concluded that, the positioning accuracy is high in WSNs operating in millimeter wave bands. Moreover, their performance is better than that of the corresponding WSNs operating in radio bands especially at low levels of signal to noise ratio.

Localization in wireless sensor networks: Classification and evaluation of techniquesRecent advances in technology have enabled the development of low cost, low power and multi functional wireless sensing devices. These devices are networked through setting up a Wireless Sensor Network (WSN). Sensors that form a WSN are expected to be remotely deployed in large numbers and to self-organize to perform distributed sensing and acting tasks. WSNs are growing rapidly in both size and complexity, and it is becoming increasingly difficult to develop and investigate such large and complex systems. In this paper we provide a brief introduction to WSN applications, i.e., properties, limitations and basic issues related to WSN design and development. We focus on an important aspect of the design: accurate localization of devices that form the network. The paper presents an overview of localization strategies and attempts to classify different techniques. A set of properties by which localization systems are evaluated are examined. We then describe a number of existing localization systems, and discuss the results of performance evaluation of some of them through simulation and experiments using a testbed implementation.

Mobile device localization in wireless sensor networks is a challenging task. It has already been addressed when the WiFi propagation maps of the access points are modeled deterministically or estimated using an offline human training calibration. However, these techniques do not take into account the environmental dynamics. In this paper, the maps are assumed to be made of an average indoor propagation model combined with a perturbation field which represents the influence of the environment. This perturbation field is embedded with a distribution describing the prior knowledge about the environmental influence. The device is localized with Sequential Monte Carlo methods and relies on the estimation of the propagation maps. This inference task is performed online, using the observations sequentially, with a new online Expectation Maximization based algorithm. The performance of the algorithm is illustrated with Monte Carlo experiments using both simulated data and a true data set.

Node localization in mobile wireless sensor networks is a challenging task because of the changing environments and outlying sensor readings. A robust cooperative localization algorithm based on particle filter is proposed in this paper to deal with this non-Gaussian and non-linear estimation problem. Trace prediction, genetic crossover operation and node cooperation methods are used to optimize the particle quality and weight, so as to improve the localization accuracy of the algorithm. Only the inexpensive binary sensors are needed to provide observation information which makes the algorithm suit for almost every type of wireless sensor networks. Simulation results demonstrate that the proposed algorithm outperforms other classical localization methods while with low anchor node density.

Accurate localization in wireless sensor networks is the foundation for many applications, such as geographic routing and position-aware data processing. An important research direction for localization is to develop schemes using connectivity information only. These schemes primary apply hop counts to distance estimation. Not surprisingly, they work well only when the network topology has a convex shape. In this paper, we develop a new Localization protocol based on Approximate Convex Decomposition (ACDL). It can calculate the node virtual locations for a large-scale sensor network with arbitrary shapes. The basic idea is to decompose the network into convex subregions. It is not straight-forward, however. We first examine the influential factors on the localization accuracy when the network is concave such as the sharpness of concave angle and the depth of the concave valley. We show that after decomposition, the depth of the concave valley becomes irrelevant. We thus define concavity according to the angle at a concave point, which can reflect the localization error. We then propose ACDL protocol for network localization. It consists of four main steps. First, convex and concave nodes are recognized and network boundaries are segmented. As the sensor network is discrete, we show that it is acceptable to approximately identify the concave nodes to control the localization error. Second, an approximate convex decomposition is conducted. Our convex decomposition requires only local information and we show that it has low message overhead. Third, for each convex subsection of the network, an improved Multi-Dimensional Scaling (MDS) algorithm is proposed to compute a relative location map. Fourth, a fast and low complexity merging algorithm is developed to construct the global location map. Our simulation on several representative networks demonstrated that ACDL has localization error that is 60%-90% smaller as compared with the typical MDS-MAP algorithm and 20%-30% smaller as compared to a recent state-of-the-art localization algorithm CATL.

To estimate the distance and localization in the wireless sensor networks (WSN), the RSSI (Received Signal Strength Indicator) value is generally assumed to have a linear relationship with the logarithm of distance communication. This is true in the absence of obstacles and wireless interference, as is the case in the free space. In this paper, the principle of nodes localization in wireless sensor networks is analyzed on the basis of RSSI as metric for estimating distances in outdoor environment. The measure of nodes distances and localization process are described. CC2530 RF module is used as a hardware platform of nodes on the location experiment. The results of localization algorithms (Trilateration and Multilateration) in Wireless Sensor Networks are presented. Test results show that the mean errors are around 4.9 m in a real-world environment for the Trilateration algorithm and around 0.5 m for Multilateration algorithm.

Wireless sensor networks (WSNs) localization is a key technology nowadays. Applications and protocols that need localization are called location-aware. One might think of equipping each node with a global position system (GPS) in WSN. In contrast, WSN constraints on power consumption and cost make it infeasible. The anchor nodes are one of the optimistic methods to limit unknown nodes, The anchor nodesequipped with GPS units to broadcast their current locations and used to find the unknown nodes with localization. Moreover, WSN nodes are non-uniformly distributed within the field with vacancies in different uses and different projects. Finally, Euclidean distance calculation between WSNs is giving wrong results with poor accuracy of node localization. Different topologies are possible in WSN localization problems. Concave shape, O shape, and concave shapes are important topologies. The proposed algorithm gives good results for all the topologies. Our results show that the error in the horizontal plane is less than 0.25 m while in the Z-axis is less than 0.5 m.

The main challenge in wireless sensor network is the accuracy and timeliness of event detection and localization. However, for a wireless sensor network, abnormal events are relatively sparse compared with all the events in monitoring area. Compressed sensing theory proposed the idea to recover a sparse signal from a few measurements. In this paper abnormal event detection and localization in wireless sensor network are formulated as a compressed sensing problem. On the assumption that the transmission signal of sensors is binary, the performance of the basis pursuit algorithm combined with alternating direction method of multipliers and other compressed sensing recovery algorithms are analyzed. Simulation results verify the preponderance of the basis pursuit algorithm on accurate detection and localization.