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Nowadays, different types of computer networks such as Wireless sensor networks (WSNs), the Internet of things (IoT), and wireless body area networks (WBANs) transfer information, share resources, and process information. The IoT is a novel network which interconnects various smart devices and can consist of heterogeneous components such as WSNs for monitoring and collecting information. Characterized by specific advantages, the IoT contains different types of nodes, each with few sensors to collect environmental information on agriculture, ecosystem, search and rescue, conflagrations, etc. Despite extensive applications and high flexibility in the modern world, the IoT faces specific challenges, the most important of which include routing, energy consumption and localization. Localization leads to other network challenges and thus can be considered the most important challenge in the IoT. Localization refers to a process aiming at determining the positions and locations of objects lacking global positioning system (GPS) and needing to use the information of network sensors and topology to estimate their own positions and locations. The distance vector hop (DV-Hop) algorithm is a range-free localization technique, in which the major challenge is that the number of hops between two nodes is multiplied by a number that is the same for all nodes leading to a significant reduction in the localization accuracy. In the method proposed in this paper, a network node with no GPS determines the hops from three anchor nodes with GPS. The location of smart objects can be then estimated according to distances from those anchor nodes. Thereafter, a few positions can be created nearby to mitigate the error. Then each position can be regarded as a member of the grasshopper optimization algorithm (GOA) to minimize the localization error. According to the results obtained from implementation of the proposed algorithm, it is characterized by a lower localization error than grasshopper optimization, butterfly optimization, firefly and swarm optimization algorithms. [ABSTRACT FROM AUTHOR]

Nodes localization has been a critical subject in wireless sensor network (WSN) field. As far as existing localization algorithms are concerned, distance vector hop (DV-Hop) has the advantages of no extra hardware and implementation simplicity, however its localization accuracy cannot meet some specific requirements. In order to enhance the accuracy of WSN nodes localization, a DV-Hop based localization algorithm based on nodes negotiation and multi communication radii (NNMCR DV-Hop) is proposed in this paper. Firstly, the hop counts between WSN nodes is modified from an integer to a decimal by changing communication radius of anchor nodes through nodes negotiation. By refining the hop counts, the accuracy of the estimated distance from the unknown to the anchor nodes is improved. Secondly, the calculation of the average hop size of the anchor node is abstracted into a combinatorial optimization problem which is solved by using binary particle swarm optimization (BPSO) to improve the accuracy of the estimated distance which is between the anchor and the unknown node. Finally, when calculating the coordinates of unknown nodes, only the anchor nodes with smaller hop counts are selected to participate in the calculation. Simulation experiments show that compared with the original DV-Hop as well as other improved algorithms based on DV-Hop, NNMCR DV-Hop greatly improves the localization accuracy of unknown nodes without additional hardware. [ABSTRACT FROM AUTHOR]

Detecting pollutants and/or odorants is a challenge in environmental monitoring. Wireless sensor network can sense and gather data pertaining to different environmental parameters. However, data collected by wireless sensor network has no significance if the location of data is unknown. Thus, localization is important in wireless sensor network used in environmental monitoring. Range-free localization like DV-Hop is preferred in many applications due to its simplicity and low cost. However, nodes in wireless sensor network are vulnerable to many attacks. Without proper measure the localization may be affected once nodes are compromised. A location-aware application may fail with such faulty localization. This paper gives a simple but secured localization for environmental monitoring on the top of DV-Hop algorithm utilizing fuzzy logic. Results obtained from simulation demand that the proposed algorithm can prevent attacks in range-free localization. [ABSTRACT FROM AUTHOR]

High localization rigor and low development expense are the keys and pivotal issues in operation and management of wireless sensor network. This paper proposes a neoteric and high efficiency algorithm which is based on new optimization method for locating nodes in an outdoor environment. This new optimization method is non-linear optimization method and is called intelligent water drops (IWDs). It is proposed that the objective function which need to be optimized by using IWDs is the mean squared range error of all neighboring anchor nodes. This paper affirms that received signal strength indicator (RSSI) is used to determine the interior distances between WSNs nodes. IWDs is an elevated performance stochastic global optimization tool that affirms the minimization of objective function, without being trapped into local optima. The proposed algorithm based on IWDs is more attractive to promote elevated localization precision because of a special features that is an easy implementation of IWDs, in addition to non cost of RSSI. Simulation results have approved that the proposed algorithm able to perform better than that of other algorithms based on optimization techniques such as ant colony, genetic algorithm, and particle swarm optimization. This is distinctly appear in some of the evaluation metrics such as localization accuracy and localization rate. [ABSTRACT FROM AUTHOR]

Considering the existing mobile anchor-assisted localization methods generally adopt a traverse strategy for the localization region, leading to an unnecessary increase in path length. The long path will cause long localization time and high energy consumption of mobile anchor. To reduce the long path of mobile anchor, this paper proposes a novel mobile anchor-assisted localization method based on the density of nodes distribution called Division of Regions Nested Equilateral Triangles path planning (DR-NET). Firstly, DR-NET divides the localization region into several small regions according to the density of nodes distribution, thereby reducing the movement in the distribution region of sensorless nodes. Secondly, the shortest path algorithm and genetic algorithm are used to calculate the shortest path of the inter-region and intra-region. Finally, the mobile anchor moves according to the planned path and assists the unknown nodes to locate. Compared with the existing classical methods LMAT, MAALRH, H-Curves and M-Curves, a series of experimental results demonstrate that DR-NET can effectively reduce the path length and energy consumption of mobile anchor by 8.56–30.66%, 4.26–28.59%, respectively. To further verify the validity and adaptability of DR-NET, we conduct experiments on localization regions with different shapes, areas, deployment strategies of nodes and structure types of nodes. The results show that DR-NET still maintains high stability in path length and localization success ratio. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Early prediction of a forest fire is one of the critical research challenges of the wireless sensor network (WSN) to save our ecosystem. In WSN based forest fire detection system, sensor nodes are deployed in the remote forest area for transmitting the sensed data to the base station, which is accessible by the forest department. Though sensor nodes in the forest are localized through GPS connection, the high deployment cost for it motivates the authors of this paper to design a novel localization technique applying the Support Vector Machine. Forest fire prediction in an energy efficient way is another concern of this paper. The semi-supervised classification model is proposed to address this problem by dividing the forest area into different zones [High Active (HA), Medium Active (MA), and Low Active (LA)]. It is designed in such a way that it can be able to predict the state of the (HA, MA, LA) fire zone with 90% accuracy when only one parameter is sensed by sensor nodes due to energy constraints. The greedy forwarding technique is used to transmit the packets from the HA zone to the base station continuously, and the MA zone transmits packets periodically, whereas, LA zone avoids transmitting the sensed data to the base station. This technique of data forwarding enhances network lifetime and reduces congestion during data transmission from the forest area to the base station. [ABSTRACT FROM AUTHOR]

Node localization is one of the key technologies of Underwater Sensor Networks (UWSN). Because of the unique ocean environment conditions, beacon nodes are difficult to deploy precisely and move under the action of ocean currents and tides. Usually the beacon nodes are fixed to the sea floor through anchors and cables, and they can move within a certain range. Most existing localization algorithms do not take such mobility of beacon nodes into account, resulting in large localization errors. In order to overcome this disadvantage, this paper analyzes the characteristics of mobile-constrained beacon nodes and proposes a new localization algorithm in UWSN, namely, Mobile-constrained beacon based localization algorithm. It solves the problem that the position of beacon node is dynamic and imprecise. The unknown node can be located by the geometric relationship between the position of the anchor and the moving radius of the beacon node. In the calculation process, the polygonal area is transformed into a rectangular area, and the three-dimensional (3D) space is mapped into a two-dimensional (2D) space, the calculation process is simplified, and the feasibility of the algorithm is improved. The algorithm can be used both for 2D and 3D localization. Experiment results show that the algorithm proposed in this paper improves the localization accuracy, reduces the error rate of network node location, and has good practicality. [ABSTRACT FROM AUTHOR]

Geographical routing is a prominent area of research in wireless networks where route establishment is based on known locations of wireless nodes. The location may be an exact physical location or virtual location. Many geographical routing protocols based on greedy and face routing approach have been designed for 2D networks, but these protocols may not be suitable in 3D environment like hill area, airborne networks, underground networks, underwater networks and so forth. The objective of this paper is to provide the research issues and challenges of geographical routing in the three-dimensional surface. These routing techniques suffer from many problems like energy efficiency, localization, mobility, load balancing, routing stretch, void node problems, etc. These issues have been addressed in the literature survey. In this paper, the recent research papers related to geographical routing have been discussed, but the main focus is on 3D geographic routing techniques, issues and challenges. [ABSTRACT FROM AUTHOR]

This paper proposes a Smartphone-Assisted Localization Algorithm (SALA) for the localization of Internet of Things (IoT) devices that are placed in indoor environments (e.g., smart home, smart office, smart mall, and smart factory). This SALA allows a smartphone to visually display the positions of IoT devices in indoor environments for the easy management of IoT devices, such as remote-control and monitoring. A smartphone plays a role of a mobile beacon that tracks its own position indoors by a sensor-fusion method with its motion sensors, such as accelerometer, gyroscope, and magnetometer. While moving around indoor, the smartphone periodically broadcasts short-distance beacon messages and collects the response messages from neighboring IoT devices. The response messages contains IoT device information. The smartphone stores the IoT device information in the response messages along with the message's signal strength and its position into a dedicated server (e.g., home gateway) for the localization. These stored trace data are processed offline through our localization algorithm along with a given indoor layout, such as apartment layout. Through simulations, it is shown that our SALA can effectively localize IoT devices in an apartment with position errors less than 20 cm in a realistic apartment setting. [ABSTRACT FROM AUTHOR]

Localization in wireless sensor networks (WSNs) is required to examine the coordinates of the sensor nodes deployed in the sensing field. It is the process that determines the location of the target nodes relative to the location of deployed anchor nodes. The anchor nodes positions are known as the nodes that have GPS unit incorporated with them. All sensor nodes are generally not configured with GPS as it is not suitable for indoor environments and/or underwater areas. A network becomes more expensive and utilizes more energy if all nodes are equipped with GPS that is a major drawback of WSNs. Various localization schemes have been proposed in literature, while most research proposals deal with the study of 2D applications. However, in the 3D applications, the area under observation may have a complexity in the sensing environment. An optimized algorithm is required for the determination of node location in 3D environment. In this paper, we propose an adaptive flower pollination algorithm (AFPA) with enhanced exploration and exploitation capabilities of conventional FPA for the localization of sensor nodes in WSN. To test the performance of AFPA, benchmark functions (CEC 2019) are used to compare it with other meta-heuristics. The results show that proposed AFPA outperforms in terms of convergence speed and provides better results for most of the benchmark functions. Also, the proposed AFPA is tested on WSN Localization problem, it provides least localization error in comparison to other techniques applied in 3D WSN environments. [ABSTRACT FROM AUTHOR]

DV-Hop, a range-free localization algorithm, has been one of the most popular localization algorithm. It is easy and inexpensive to implement. Therefore, in the literature, many improved variants of this algorithm exist. However, poor location accuracy and higher power consumption by DV-Hop algorithm always open new avenues for research on this algorithm and makes it a favorite among the researchers. In this paper, we have proposed an Improved 3-Dimensional DV-Hop algorithm based on the information of nearby nodes (I3D-DVLAIN). In the algorithm, by calculating hopsize at the unknown nodes, we eliminate one communication among the nodes, which reduces power consumption in the network. The hopsize calculation and location estimation is done by using only the nearby anchor nodes, which minimizes the network usage and decreases the computational effort. For the selection of nearby anchor nodes, we introduce a new method. Further, for localization, a novel method is used for solving the system of distance equations that restricts propagation of inherent error in the distance and increases localization accuracy. Furthermore, by mathematically analyzing the propagation of error in solving the system of equations, we prove the superiority of I3D-DVLAIN over other compared algorithms. The results obtained through simulation and complexity analysis of the computation and communication further strengthens our observations about the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Localization for underwater acoustic sensor networks is an active research topic where a large number of techniques have been proposed recently. This paper addresses one of the open research issues, the impact of underwater sound speed variation on the localization accuracy. In this paper, modified versions of stochastic proximity embedding and multi-dimensional scaling localization algorithms customized for underwater application are proposed. The algorithms are found to provide good performance in underwater scenario as they take into account refractive ray bending of acoustic waves. Detailed study of the algorithm performance has been done and the results are reported. Cramer Rao Lower Bound for the problem is also derived. [ABSTRACT FROM AUTHOR]

Location information of a sensor node is the primary concern to process the sensed data in Wireless Sensor Networks (WSNs). The location of the sensor node is used in other domains of sensor network like message routing, node tracking, load balancing. For statically deployed sensor nodes, mobile anchor based localization is an efficient solution. The main challenge in mobile anchor based localization is designing an optimum path for the mobile anchor node considering the coverage, path length and localizability of sensor nodes as the key features. In this paper, we propose a novel path planning approach for mobile anchor based localization called "M-Curves". Our proposed model promises that all the nodes in the network will receive at least three non-collinear beacon messages for localization. Our proposed trajectory assures full coverage, high localization accuracy as compared to other static models. Also, we optimize the localization process by using Dolphin Swarm Algorithm(DSA). The fitness function used for optimization in DSA, minimizes the localization error of the node in the network. [ABSTRACT FROM AUTHOR]

Geographic routing in wireless sensor networks brings numerous inherent advantages, albeit its performance relying heavily on accurate node locations. In mobile networks, localization of the continuously moving nodes is a challenging task and location errors are inevitable and affect considerably routing decisions. Our proposal is in response to the unrealistic assumption widely made by previous geographic routing protocols that the accurate location of mobile nodes can be obtained at any time. Such idealized assumption results in under-performing or infeasible routing protocols for the real world applications. In this paper, we propose INTEGER, a localization method intertwined with a new location-error-resilient geographic routing specifically designed for mobile sensor networks even when these networks are intermittently connected. By combining the localization phase with the geographic routing process, INTEGER can select a relay node based on nodes' mobility predictions from the localization phase. Results show that INTEGER improves the efficiency of the routing by increasing the packet delivery ratio and by reducing the energy consumption while minimizing the number of relay nodes compared to six prevalent protocols from the literature. [ABSTRACT FROM AUTHOR]

WSNs (Wireless sensor networks) are used in multiple applications including IoT (Internet of Things) applications like intelligent control, prediction, tracking, and other communication network services (Internet-of-Things). Due to their limited design for two-dimensional space, high computing costs, or sensitivity to measurement errors, the typical localization frameworks might not perform well in real-world settings. Location information of deployed sensor nodes in their surrounding environments is important for algorithmic three-dimensional localizations. But there are drawbacks to current 3D localization algorithms in many parameters including complexity, positional precisions, and excessive energy consumption. Hence, this work proposes Enhanced 3D-DV-Hop (3D-Distance Vector Hop) localizations based on PSO (Particle Swarm Optimization) and GAs (Genetic algorithms) for the aforementioned issues. To further increase the diversity and accuracy of the DV-Hop outputs, a learning technique is employed. The learning technique leads to an improvement in search effectiveness, convergence speed, and result in quality. The simulation results demonstrate that the suggested strategy can improve positioning coverage while maintaining positioning accuracy. [ABSTRACT FROM AUTHOR]

As localization represents the main core of various wireless sensor network applications, several localization algorithms have been suggested in wireless sensor network research. In this article, we put forward an iterative bounding box algorithm enhanced by a Kalman filter to refine the unknown node's estimated position. In fact, several research efforts are currently in progress to extend the 2D positioning algorithm in WSNs to 3D that reflects reality and the most practical applications. Subsequently, we replace a large number of GPS-equipped anchors with a single mobile anchor. In our studies, we consider the type of range-free sensor network exploiting the wireless sensors connectivity. We assess the performance of our algorithm using exhaustive experiments on several isotropic and anisotropic topologies. Our proposed algorithm can fulfill the joint goals of algorithm transparency and accuracy for various scenarios by evaluating parameters such as localization accuracy whilst changing other simulation parameters such as the effect of communication range, mobile anchor node position and sensor node deployment topology. It has been proven by the results of the experiments that the proposed algorithm effectively reduces the location error without requiring more equipment or increasing the communication cost. [ABSTRACT FROM AUTHOR]

Patch and stitch is a technique used for localization in wireless sensor networks. It combines the accuracy of centralized schemes with the computational efficiency of distributed approaches. The network is partitioned into small overlapping patches, localized and finally merged to form a single global map. However, due to noisy distance measurements, ordinary merging may lead to wrong reflection of patches causing large localization error. In this paper, we present a new patch and stitch algorithm which improves the map construction as well as map stitching phase to increase the accuracy of the algorithm. In map construction phase, each node, along with two-hop neighbors builds a local map using Isomap. In stitching phase, maps are stitched to each other in an incremental style. To restrict error propagation (1). a core map with a highest node degree is selected, (2). a robustness criterion that employs flip detect and discard policy is used to ensure a flip resistant stitching and (3). once the absolute core map is formed estimated positions of anchors are replaced by their true positions. Simulation results show that the proposed algorithm enhances the reliability of the location estimates without compromising with the number of nodes localized considerably and thus outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Localization is one of the most important challenges of wireless sensor networks because the location information is typically used in other domains such as coverage, deployment, routing, and target tracking. There exist some localization algorithms that facilitate the sensor nodes to locate itself using the mobile anchor node position. Some crucial attempts have been made in the past for optimizing the mobile anchor node trajectory with good accuracy. This paper presents a novel path planning scheme, D-connect, which ensures the localization of all the sensor nodes with minimum trajectory length. The performance of the proposed scheme is evaluated through a series of simulations. Experimental results reveal that the shortest path for traversing the whole area can be traced with the minimum localization error using this method. It also shows that D-connect outperforms the existing methods in terms of the anchor node trajectory length as well as the localization error. [ABSTRACT FROM AUTHOR]

Sensing events occur in an area without knowing the events locations, is meaningless. Since there is no priorly knowledge about the locations of most of the sensors which scattered randomly in an area, wireless sensor network localization methods try to find out where sensors are located. A new cooperative and distributed range-free localization algorithm, based on only connectivity information is proposed in this paper. The method first uses convex optimization techniques to find primitive target nodes locations estimation, then nodes cooperate with each other in several iterations to improve the whole network location estimation. CRWSNP converges after a finite number of iterations because of applying two novel heuristic location correction techniques. As well as, results of the algorithm have been compared with six range-free based methods like CPE, DV-hop, APIT; and CRWSNP algorithm provides more accurate results over 50 random topologies for the network, in mean error and maximum error metrics. [ABSTRACT FROM AUTHOR]

While localization is essential to many applications, there are quantities of wireless nodes whose accurate locations remain unknown or hard to tell. As a remedy, this paper is leveraging a GPS-capable Android handset that features portability and sufficient computing capability to collect data and display localization results thereof. Our objective is to locate the unknown nodes under irregular radio coverage. To this end, we first devise a range-free approach that computes the most likely circumcenter from a convex polygon representative of radio coverage. Preliminary simulations corroborate the effectiveness of our development. Then the first approach is simplified by allowing for received signal strengths with the notion of estimative regions, namely rectangular areas enclosing an unknown node, to refine the area of interest for localization. Free from involved computations, our treatment mitigates the errors of estimation resulting from different radio signal propagation scenarios, by taking an additional parameter for ameliorating localization accuracy. Field experiments were conducted to demonstrate performance results overlying Google Maps on the handset and through quantitative comparisons in different settings. Performance results show that our development can get fielded in practice. [ABSTRACT FROM AUTHOR]

Wireless communication is susceptible to radio interference and jamming attacks, which prevent the reception of communications. Most existing anti-jamming work does not consider the location information of radio interferers and jammers. However, this information can provide important insights for networks to manage its resource in different layers and to defend against radio interference. In this paper, we investigate issues associated with localizing jammers in wireless networks. In particular, we formulate the jamming effects using two jamming models: region-based and signal-to-noise-ratio(SNR)-based; and we categorize network nodes into three states based on the level of disturbance caused by the jammer. By exploiting the states of nodes, we propose to localize jammers in wireless networks using a virtual-force iterative approach. The virtual-force iterative localization scheme is a range-free position estimation method that estimates the position of a jammer iteratively by utilizing the network topology. We have conducted experiments to validate our SNR-based jamming model and performed extensive simulation to evaluate our approach. Our simulation results have showed that the virtual-force iterative approach is highly effective in localizing a jammer in various network conditions when comparing to existing centroid-based localization approaches. [ABSTRACT FROM AUTHOR]

The sensor network localization problem is one of determining the Euclidean positions of all sensors in a network given knowledge of the Euclidean positions of some, and knowledge of a number of inter-sensor distances. This paper identifies graphical properties which can ensure unique localizability, and further sets of properties which can ensure not only unique localizability but also provide guarantees on the associated computational complexity, which can even be linear in the number of sensors on occasions. Sensor networks with minimal connectedness properties in which sensor transmit powers can be increased to increase the sensing radius lend themselves to the acquiring of the needed graphical properties. Results are presented for networks in both two and three dimensions. [ABSTRACT FROM AUTHOR]

The existing mobile beacon based range free localization method has two basic problems: (1) the accuracy of position estimation depends on closer beacon broadcasting intervals and (2) varying radio propagation irregularity has a major impact on the localization accuracy. In this paper, we overcome these two problems by proposing a mobile beacon (MOB) based range free localization method for wireless sensor networks, which is based on analytical geometry of an arc. In this scheme, Cramers rule is used, where the intersection point of two perpendicular bisectors of the chords is taken as the estimated position of the sensor node. Simulation results show that our proposed scheme performs better than the existing range free localization algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the number of applications utilizing mobile wireless sensor networks (WSNs) has increased, with the intent of localization for the purposes of monitoring and obtaining data from hazardous areas. Location of the event is very critical in WSN, as sensing data is almost meaningless without the location information. In this paper, two Monte Carlo based localization schemes termed MCL and MSL* are studied. MCL obtains its location through anchor nodes whereas MSL* uses both anchor nodes and normal nodes. The use of normal nodes would increase accuracy and reduce dependency on anchor nodes, but increases communication costs. For this reason, we introduce a new approach called low communication cost schemes to reduce communication cost. Unlike MSL* which chooses all normal nodes found in the neighbor, the proposed scheme uses set theory to only select intersected nodes. To evaluate our method, we simulate in our proposed scheme the use of the same MSL* settings and simulators. From the simulation, we find out that our proposed scheme is able to reduce communication cost-the number of messages sent-by a minimum of 0.02 and a maximum of 0.30 with an average of 0.18, for varying node densities from 6 to 20, while nonetheless able to retain similar MSL* accuracy rates. [ABSTRACT FROM AUTHOR]

In many applications of wireless sensor network, the position of the sensor node is useful to identify the actuating response of the environment. The main idea of the proposed localization scheme is similar with most of the existing localization schemes, where a mobile beacon with global positioning system broadcast its current location coordinate periodically. The received information of the coordinates help other unknown nodes to localize themselves. In this paper, we proposed a localization scheme using mobile beacon points based on analytical geometry. Sensor node initially choose two distant beacon points, in-order to minimize its residence area. Later using the residence area, sensor node approximate the radius and half length of the chord with reference to one of the distant beacon point. Then the radius and half length of the chord are used to estimate the sagitta of an arc. Later, sensor node estimate its position using radius, half length of the chord, and sagitta of an arc. Simulation result shows the performance evaluation of our proposed scheme on various trajectories of mobile beacon such as CIRCLE, SPIRAL, S-CURVE, and HILBERT. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are increasingly being used in remote environment monitoring, security surveillance, military applications, and health monitoring systems among many other applications. Designing efficient localization techniques have been a major obstacle towards the deployment of WSN for these applications. In this paper, we present a novel lightweight iterative positioning (LIP) algorithm for next generation of wireless sensor networks, where we propose to resolve the localization problem through the following two phases: (1) initial position estimation and (2) iterative refinement. In the initial position estimation phase, instead of flooding the network with beacon messages, we propose to limit the propagation of the messages by using a random time-to-live for the majority of the beacon nodes. In the second phase of the algorithm, the nodes select random waiting periods for correcting their position estimates based on the information received from neighbouring nodes. We propose the use of Weighted Moving Average when the nodes have received multiple position corrections from a neighbouring node in order to emphasize the corrections with a high confidence. In addition, in the refinement phase, the algorithm employs low duty-cycling for the nodes that have low confidence in their position estimates, with the goal of reducing their impact on localization of neighbouring nodes and preserving their energy. Our simulation results indicate that LIP is not only scalable, but it is also capable of providing localization accuracy comparable to the Robust Positioning Algorithm, while significantly reducing the number of messages exchanged, and achieving energy savings. [ABSTRACT FROM AUTHOR]

Wireless localization technology has become an important research area of pervasive computing applications. This paper presents $$DE ^{2 }$$ (direction estimation and distance estimation), a wireless localization approach that is responsive to the scenario where only a single beacon is available to locate multiple position-unknown targets. Unlike the previous work which needs a priori knowledge of the scenario during the training phase by manual recording (taking significant human efforts), or uses a certain number of beacons during the localization phase to reach a certain accuracy (leading to high energy consumption and reducing network lifecycle), $$DE ^{2 }$$ leverages a single beacon without many prior human efforts to locate multiple targets. The intuition underlying $$DE ^{2 }$$ is that, direction and distance constraints between an unknown position and the single beacon are adequate to determine the unknown position. When a person rotates around an RF receiver (the single beacon), the human body acts as a signal-blocking obstacle. It causes the signal from a transmitter (position-unknown) to the single receiver to attenuate in a certain scope. The blocking effect caused by human body can be utilized to obtain the direction constraint between the unknown position and the single beacon. And we call the signal strength which is perceived by the receiver during the person's rotation as rotating received signal strength (RSS). Moreover, a corresponding distance constraint is also concluded in the rotating RSS according to the RF propagation model. $$DE ^{2 }$$ pushes the limit of minimum beacons needed for localization without much pre-configuration effort. To demonstrate the utility of $$DE ^{2 }$$ , we implement $$DE ^{2 }$$ in real-world single beacon wireless networks. The results show that these applications can significantly benefit from $$DE ^{2 }$$ . [ABSTRACT FROM AUTHOR]

The received signal strength (RSS) is a common source of information used for estimating the distance between two wireless nodes, whether these nodes are stationary or mobile. Minimum mean squared error distance estimation methods that use the RSS require prior knowledge of both the variance of the noise and, in the case of mobile sensors, the dynamics of the nodes' mobility. In mobile applications, where low computational complexity is important, pseudo-optimal estimations are preferred, as they do not require such information. In this case, the maximum likelihood estimator (MLE) is often used. In this paper, we propose an efficient pseudo-optimal log-power based distance estimation method using RSS under lognormal shadowing, that improves the MLE. It does not require a priori knowledge either of the movement dynamics or of the variance of the noise. The method is based on adaptively minimizing the variance of the prediction error, using a random walk model with correlated increments. It is analytically demonstrated that the distance estimation error variance of the proposed method improves the MLE in both the static and mobile cases. We use a simulated velocity model example to compare its performance with other algorithms in this group, such as the linear mean square filter and the Gauss-Newton search. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks are a set of sensor nodes capable of detecting, measuring, and reporting information about the environment. Many routing protocols have been developed for WSNs. A great attention has been given to geographic routing protocols which rely only on local knowledge of nodes to disseminate data towards the final destination. In this paper, we propose a new energy efficient geographic routing protocol called Energy-aware and delivery Guarantee Geographic Routing protocol (EGGR) for wireless sensor networks. The proposed protocol EGGR, manages energy resources of nodes efficiently during data communication in the network by introducing a mechanism that tries to forward packets towards the shortest path in terms of energy consumption while privileging nodes with greater remaining energy and avoiding holes in the network to guarantee packets delivery. The shortest path between the source and the destination is calculated based only on local knowledge of sensor nodes (neighborhood). Our experimental results show that the proposed protocol EGGR reduces energy consumption and increases delivery rate between sensor nodes and the base station compared with other protocols. [ABSTRACT FROM AUTHOR]

A Wireless Sensor Network (WSN) consists of many sensors that communicate wirelessly to monitor a physical region. Location information is critical essential and indispensable for many applications of WSNs. A promising solution for localizing statically deployed sensors is to benefit from mobile location-aware nodes called beacons. However, the essential problem is to find the optimum path that the mobile beacon should travel in order to improve localization accuracy, time and success as well as energy efficiency. In this paper, we evaluate the performance of five mobile beacon trajectories; Random Way Point, Scan, Hilbert, Circles and Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) based on three different localization techniques such as Weighted Centroid Localization and trilateration with time priority and accuracy priority. This evaluation aims to find effective and essential properties that the trajectory should have. Our simulations show that a random movement cannot guarantee the performance of localization. The results also show the efficiency of LMAT regarding accuracy, success and collinearity while the Hilbert space filling curve has lower energy consumption. Circles path planning can help to localize unknown sensors faster than others at the expense of lower localization precision. [ABSTRACT FROM AUTHOR]

Positioning of things, devices and people is the fundamental technology in ubiquitous computing. However, few literature has discussed the impact of positioning errors due to localization algorithm properties such as ranging noise and deployment of anchors on people's identification of objects. Since several factors such as relative distance, relative angles and grouping of objects are intricately related with each other in such identification, it is not an easy task to investigate its characteristics. In this paper, we propose criteria to assess the 'accuracy' of the estimated positions in identifying the objects. The criteria are helpful to design, develop and evaluate localization algorithms that are used to tell people the location of objects. Augmented reality is a typical example that needs such localization algorithms. To model the criteria without ambiguity, we prove that the Delaunay triangulation well-captures natural human behavior of finding similarity between estimated and true positions. We have examined different localization algorithms to observe how the proposed model quantifies the properties of those algorithms. Subjective testing has also been conducted using questionnaires to justify our quantification sufficiently renders human intuition. [ABSTRACT FROM AUTHOR]

In this paper, we explore the possibility of using directionality of free-space-optical (a.k.a. optical wireless) communications for solving the 3-D localization problem in ad-hoc networking environments. Range-based localization methods either require a higher node density (i.e., at least three other localized neighbors must exist) than required for assuring connectedness or a high-accuracy power-intensive ranging device such as a sonar or laser range finder which exceeds the form factor and power capabilities of a typical ad-hoc node. Our approach exploits the readily available directionality information provided by a physical layer using optical wireless and uses a limited number of GPS-enabled nodes, requiring a very low node density (2-connectedness, independent of the dimension of space) and no ranging technique. We investigate the extent and accuracy of localization with respect to varying node designs (e.g., increased number of transceivers with better directionality) and density of GPS-enabled and ordinary nodes as well as messaging overhead per re-localization. Although denser deployments are desirable for higher accuracy, our method still works well with sparse networks with little message overhead and small number of anchor nodes (as little as 2). We also present a proof-of-concept prototype of our FSO-based localization techniques and show the validity of our approach even with three transceivers per node. [ABSTRACT FROM AUTHOR]

One of the most important tasks in sensor networks is to determine the physical location of sensory nodes as they may not all be equipped with GPS receivers. In this paper we propose a localization method for wireless sensor networks (WSNs) using a single mobile beacon. The sensor locations are maintained as probability distributions that are sequentially updated using Monte Carlo sampling as the mobile beacon moves over the deployment area. Our method relieves much of the localization tasks from the less powerful sensor nodes themselves and relies on the more powerful beacon to perform the calculation. We discuss the Monte Carlo sampling steps in the context of the localization using a single beacon for various types of observations such as ranging, Angle of Arrival (AoA), connectivity and combinations of those. We also discuss the communication protocol that relays the observation data to the beacon and the localization result back to the sensors. We consider security issues in the localization process and the necessary steps to guard against the scenario in which a small number of sensors are compromised. Our simulation shows that our method is able to achieve less than 50% localization error and over 80% coverage with a very sparse network of degree less than 4 while achieving significantly better results if network connectivity increases. [ABSTRACT FROM AUTHOR]

The ongoing ubiquitous computing era with the internet of things (IoT) and wireless sensor networks (WSN) is desperate to realize near-to-truth information by collecting and processing every significant data. It needs precise localization of unknown sensors that are communicating field-sensed data. The significance of location attracts security threats also, like wormhole attacks (WA). The WA compromises network security more simply; however it ruins the localization terribly. Most of the existing WA-combat localization algorithms instead of finding nodes by defining infeasibility, try to identify inappropriate nodes with too narrowly defined parameters to apply to the global visibility of network states. Therefore, the infeasibility defined by Farkas' lemma is implemented in the proposed algorithm i.e. secure localization algorithm using Farkas' lemma (SLAF). SLAF has two steps: WA detection and elimination (WDE), and localization of unknown node (LoU). In WDE, it detects nodes that are inconsistent in terms of distance equations defined between sensor pairs. Subsequently, in LoU, distance is approximated as suggested by DV-Hop. However, distance values are further improved by defining elastic variables. Finally, these distance values are passed to linear optimization for localization. The simulation validates SLAF as consistent, reliable, scalable, and precise in comparison with other recent time contenders. [ABSTRACT FROM AUTHOR]

Precise localization of access points (APs) has become more and more important with the booming of diverse location based services. AP localization accuracy heavily relies on the locations of measurement points. However, collecting abundant information from the APs usually requires tremendous manual efforts, which greatly compromise the efficiency and accuracy of the localization of the APs. Thus, the selection of appropriate measurement points becomes an important issue to achieve a high localization accuracy with a low-cost method, which has not been fully explored in existing research. To this end, we propose a novel approach to select measurement points for AP localization under the assumption that the number of APs to be located is known in advance. Specifically, an initial point is selected randomly and the locations of APs are estimated according to the measured information. Then, subsequent measurement points are determined by locating the intersection of the coverage areas of APs in real time, so that as many APs as possible can be detected at each measurement point. Both simulation and experimental results show that our approach can reduce the number of measurement points while improving the AP localization accuracy. [ABSTRACT FROM AUTHOR]

Indoor device-free localization systems utilizing received signal strength indicator (RSSI) have become mainstream because of their low pricing, low complexity, low effort, low energy, and the framework can be reusable. However, it is reviewed from the existing literature that for paying extra attention to positioning errors mitigation, researchers fail to meet the optimum characteristics requirement for measurements. To overcome such limitations and focus on current challenges in measurements, this article proposes an RSSI fingerprinting positioning algorithm using the K-nearest neighborhood and regression analysis technique such that the localization precisions can be improved. Meanwhile, to get higher location accuracy, some special RSSI values are compared to obtain a suitable one. In the proposed positioning scheme, the indoor site is divided into two parts. Moreover, in each part, the positions of a smartphone at different reference points are localized with at most two access points (APs). This strategy not only reduces the neighboring noises significantly caused by the surrounding wireless signals coming from other electronic devices but also reduces the system management overhead due to the use of a minimum number of APs, which increases the system's ability. The performance shows that the proposed scheme is highly capable of increasing the users' experience and could be a research direction for future positioning systems. [ABSTRACT FROM AUTHOR]

Localisation of a mobile device in Wi-Fi based indoor environments has received much attention and is an important area of research, considering the uptake of devices with built-in Wi-Fi and often blanket coverage in enterprise. Most localisation techniques in this area require an off-line calibration phase where a radio map is manually built using the laborious efforts of administrators. This radio map is then subject to becoming inaccurate with time and requires refreshing as the equipment is upgraded and replaced. We propose a system that can automatically build and calibrate a radio map for use with location fingerprinting techniques to provide indoor and outdoor localisation with Wi-Fi. This system first uses a self-mapping technique that makes use of user roaming patterns to build a weighted undirected graph of Access Point locations. We then combine this initial map with a map built by users of smartphones with built-in Wi-Fi and GPS to generate anchor nodes. We show that the unique combination of these two techniques provides a zero-configuration calibration map for use with location fingerprinting techniques, which not only saves time and effort in the calibration phase, but provides a constantly fine-tuning and self-healing map. [ABSTRACT FROM AUTHOR]

In the majority of wireless sensor network applications, location information is crucial. Numerous localization techniques have been presented in recent years, the majority of them are oriented at two-dimensional applications. Whereas the challenge is more complicated in three-dimensional systems due to the broad range of altitude levels. For these purposes, two-dimensional localization models are unreliable. In this research, we use only one anchor node to identify the location of unknown sensors in a three-dimensional scenario utilizing both range-based and range-free strategies (with fuzzy logic). The middle and lower layers include sensor nodes with uncertain positions, whereas the top layer contains an anchor node. These heterogeneous mobile target nodes are deployed in an anisotropic environment having Degree of Irregularity of 0.01. The simulation results demonstrate that range-based localization techniques are significantly more efficient than range-free techniques by applying a new Adaptive SSA technique and other meta-heuristic algorithms to compute the results of range-based and range-free techniques in terms of localization error, computational time, and number of localized sensor nodes. [ABSTRACT FROM AUTHOR]

Localization is regarded as one of the important challenges in the internet of things and Wireless Sensor Networks. The failure to localize sensors properly causes data loss and inefficiency in their information. Also, using GPS in all sensors causes extra cost. Hence, proper localization with least error is very important. Algorithms according to range- based and range-free are the major methods of location detection, as DV-Hop is one of the examples of range-free algorithm. Its important challenge is the error that is caused by this localization method. For solving the problem of error of GPS-free node localization, we used the new, efficient, simple Beetle Antennae Search algorithm (BAS). At first, we resolved some shortcomings of beetle antennae search algorithm. The results of the implementation on random data in Matlab shows that suggested algorithm is more accurate than firefly algorithm butterfly optimization algorithm (BOA), particle swarm optimization and whale optimization algorithm. And, beetle antennae search algorithm (BAS) does localization with least error and more accuracy. The proposed algorithm offers about 35.23% less error for positioning than the butterfly algorithm (BOA). [ABSTRACT FROM AUTHOR]

To solve the problem of estimating the locations of sensor nodes in wireless sensor networks where most nodes are without an effective positioning device, a novel range-free localization algorithm-weighted centroid localization based on compressive sensing (WCLCS) is proposed. WCLCS makes use of compressive sensing to get decomposition coefficients between each nonbeacon node and beacon nodes. According to these coefficients, WCLCS algorithm decides the weighted value of each beacon node for Centroid and estimates the locations of nonbeacon nodes. The simulation results show that WCLCS has better localization performance than LSVM. [ABSTRACT FROM AUTHOR]

Predominantly, GPS-free localization algorithms are either predictive or probabilistic or range-dependent which outrage energy source and computing power of sensor-nodes. Whereas, comparatively less complex algorithms based on range-independent approaches find GPS-free approaches unfeasible due to their everlasting dependency on GPS like DV-Hop, etc. This gap motivates to propose RiL-DZ, an estimative range-independent absolute localization algorithm for GPS dead zone in mobile wireless sensor networks. RiL-DZ has two steps- ALE, and UL. In ALE, for the dead zone anchor-nodes calculate their location by measuring their respective distance and angular movement. Subsequently in UL, the absolute location of the unknown-nodes is attained by estimating their distances from the anchor-nodes of ALE step using hop parameters. Further, for precise localization, RiL-DZ considers errors in distance estimation between the node-pairs and the imprecise approximation of the location of anchor-nodes caused by error-affected displacement measurements. Furthermore, the error factors of distances calculated in between anchor-nodes and intended unknown-node are minimized using linear programming. The simulation shows RiL-DZ absorbs displacement error notably than other contenders and GPS-assisted DV-Hop throughout. The analysis-of-variance establishes that RiL-DZ depends significantly on communication range and anchor-nodes density only by 40.81% and 29.09% respectively. [ABSTRACT FROM AUTHOR]

Recent trends of using wireless sensor network in various applications have reduced the significance of human intervention greatly. Due to the cost efficiency and energy constraint nature of wireless sensor network, mobile anchor nodes are preferred over the static ones for localization and thus reducing the amount of GPS modules required to be associated within sensor region. Use of mobile anchor node requires traversal path optimization so that average localization error as well the traversed path length is reduced. Specific localization algorithm is used in literature to measure average localization error, which results in different preference of traversal scheme. In order to encounter the problem and to make comparison among the traversal schemes more generalized, a novel evaluation metric namely Inverted Coverability, a variation of ANOVA, is proposed. Besides this, a novel traversing path scheme, Linear-Hexagonal (LH) traversal scheme is proposed. Mathematical analysis and the result shows better performance of the proposed scheme with respect to the total path traversed, number of beacon points, Inverted Coverability and average localization error over other geometric based deterministic path planning schemes like DOUBLE SCAN, CIRCLES, Z-curve, and Polygon approach. [ABSTRACT FROM AUTHOR]

Localization is an important issue in field of Wireless Sensor Networks. Range-free approach is most promising solution used for networks due to its low cost and less power consumption. The main limitation of range-free approach is low accuracy as it is affected by many factors such as node density, coverage and topology diversity. This work proposes a solution that achieves higher accuracy and gives higher coverage for all kinds of network scenarios, even for sparse one. The approach is mainly developed to improve traditional range-free (DV-Hop) method by using a single mobile anchor and mesh based path planning models. The single mobile anchor identifies different reference points in uniform manner to locate nodes in the network region. The performance of proposed methods is evaluated via simulations to demonstrate its effectiveness and simulation results confirm adequacy of the proposed solutions with higher accuracy and higher coverage as compared to traditional one. [ABSTRACT FROM AUTHOR]

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 entails the additional costs of hardware logic and increased power consumption, thereby lowering the lifetime of the sensor, which is normally operated on a non-rechargeable battery. Range-free-based localization schemes have shown promise compared to range-based approaches as preferred and cost-effective solutions. Typical range-free localization algorithms have a key advantage: simplicity. However, their precision must be improved, especially under varying node densities, sensing coverage conditions, and topology diversity. Thus, this work investigates the probable integration of two soft-computing techniques, namely, Fuzzy Logic (FL) and Extreme Learning Machines (ELMs), with the goal of enhancing the approximate localization precision while considering the above factors. In stark contrast to ELMs, FL methods yield high accuracy under low node density and limited coverage conditions. In addition, as a hybrid scheme, extra steps are integrated to compensate for the effects of irregular topology (i.e., noisy signal density due to obstacles). Signal and weight are normalized during the fuzzy states, while the ELM uses a deep learning concept to adjust the signal coverage, including the spring force error estimation enhancement. The performance of our hybrid scheme is evaluated via simulations that demonstrate the scheme’s effectiveness compared with other soft-computing-based range-free localization schemes. [ABSTRACT FROM AUTHOR]

Localization and synchronization are fundamental services for many applications in wireless sensor networks (WSNs), since it is often required to know the sensor nodes' position and global time to relate a given event detection to a specific location and time. However, the localization and synchronization tasks are often performed after the sensor nodes' deployment on the sensor field. Since manual configuration of sensor nodes is usually an impractical activity, it is necessary to rely on specific algorithms to solve both localization and clock synchronization problems of sensor nodes. With this in mind, in this work we propose a joint solution for the problem of 3D localization and time synchronization in WSNs using an unmanned aerial vehicle (UAV). A UAV equipped with GPS flies over the sensor field broadcasting its geographical position. Therefore, sensor nodes are able to estimate their geographical position and global time without the need of equipping them with a GPS device. Through simulation experiments, we show that our proposed joint solution reduces time synchronization and localization errors as well as energy consumption when compared to solutions found in the literature. [ABSTRACT FROM AUTHOR]

Most existing connectivity-based localization algorithms require high node density which is unavailable in many large-scale sparse mobile networks. By analyzing large datasets of real user traces from Dartmouth and MIT, we observe that user mobility exhibits high spatiotemporal regularity and, more importantly, that user mobility is strongly correlated with the user' s social encounters (including so called Familiar Strangers). Motivated by these important observations, we propose a distributed localization scheme called SOMA that is particularly suitable for sparse mobile networks. To exploit the correlation between mobility and social encounters, we formulate the localization process as an optimization problem with the objective of maximizing the probability of visiting a sequence of locations when the user witnesses the given set of social encounters at different time. Employing the Hidden Markov Model, we design an efficient algorithm based on dynamic programming for solving the optimization problem. SOMA is fully distributed, in which each user only makes use of the connectivity information with other users. Since different users may have varying levels of mobility regularity, one critical challenge with SOMA is that a user with weak mobility regularity may result in poor localization accuracy. We introduce the concept of mobility irregularity to distinguish users. Then, one optimization is made to SOMA that allows a user with weak mobility regularity to leverage the locations from the users encounters. Experimental results based on large-scale real traces demonstrate that SOMA achieves much smaller localization error than many state-of-the-art localization schemes, but requires it minimal running time. [ABSTRACT FROM AUTHOR]

Considering energy consumption, hardware requirements, and the need of high localization accuracy, we proposed a power efficient range-free localization algorithm for wireless sensor networks. In the proposed algorithm, anchor node communicates to unknown nodes only one time by which anchor nodes inform about their coordinates to unknown nodes. By calculating hop-size of anchor nodes at unknown nodes one complete communication between anchor node and unknown node is eliminated which drastically reduce the energy consumption of nodes. Further, unknown node refines estimated hop-size for better estimation of distance from the anchor nodes. Moreover, using average hop-size of anchor nodes, unknown node calculates distance from all anchor nodes. To reduce error propagation, involved in solving for location of unknown node, a new procedure is adopted. Further, unknown node upgrades its location by exploiting the obtained information in solving the system of equations. In mathematical analysis we prove that proposed algorithm has lesser propagation error than distance vector-hop (DV-Hop) and other considered improved DV-Hop algorithms. Simulation experiments show that our proposed algorithm has better localization performance, and is more computationally efficient than DV-Hop and other compared improved DV-Hop algorithms. [ABSTRACT FROM AUTHOR]