Anchor nodes placement for effective passive localization

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

Sensor positioning is a fundamental block in many location-dependent applications of wireless sensor networks. Although the main objective in localization is primarily enhancing the positioning accuracy, the importance of energy consumption and localization accuracy poses new challenges. The localization is usually assisted with some self-known position sensors called anchor nodes. In this letter, optimal power allocation for the anchor nodes in a sense of minimizing the energy consumption considering estimation errors is investigated. To have a better estimation of the relative distance between the anchor and unknown nodes using received signal strength indicator (RSSI), average energy of the received beacon is introduced as a new decision metric. Based on this, a squared position error bound as an accuracy parameter is derived, and an optimization problem is proposed to maximize the localization performance. More specifically, the optimal power allocation policy is first derived for the case that the anchor nodes estimate their own locations with no error. Since there are unavoidable errors in the positions of the anchor nodes, the optimization problem is then modified by including uncertainty in the positions of the anchor nodes. The results show that a substantial reduction in power consumption can be achieved by optimal allocation of the transmission power.

In a ZigBee network, low-cost, low-power sensor nodes are deployed to carry out specific tasks like environment sensing, home automation, human and animal condition monitoring, and robot navigation monitoring etc. Normally the network comprises of numerous sensor nodes deployed in different locations and a coordinator node where the sensor nodes report back the sensed data for possible analysis and further action. For certain applications it is desirable to be able to locate the sensor node within the network. Currently localisation can be achieved using time dependent methods (TOA and TDOA) or received signal strength (RSS). Time dependent approaches require time synchronisation at all times which can be difficult to achieve. RSS based method is simple and cost effective but require at least three anchor nodes for a nodes location to be calculated via triangulation. Such a method can increase the complexity and cost of a network deployment. This paper presents a single anchor based location system which utilizes a differential two-element antenna array to perform node localization. An experimental system is introduced and results demonstrate the effectiveness of the proposed system. The anchor node, which can also be the network gateway, is connected to two dipole antennas separated by ⋋/2. The relative phase of each antenna can be varied using a phase shifter. The combined output of the two antennas can be used to create sum and difference radiation patterns, and to steer the antenna radiation pattern. Experimental validation was performed using seven XBee sensor nodes. Accurate azimuthal localization (relative to the anchor node) can be achieved and range estimation can be made with minimal errors. Adaptive path-loss model estimation can further improve range estimation. Overall, this cost effective localisation system performs well for ZigBee based wireless sensor networks.

Distance Vector-Hop (DV-Hop) localization is a distributed, hop by hop positioning algorithm. Anchor nodes generate the packets of nodes position information to calculate the number of hops between the beacon nodes and the anchor nodes, and these packets are flooded in the respective Wireless Sensor Networks (WSN). The DV-Hop algorithm obtains the distance information from the unknown nodes to the anchor nodes through the network topology calculation rather than the radio wave signals measurement which are used in the range-based algorithm. However, flooding information from all anchors to all beacon nodes will become too expensive for large networks (i.e., cost, energy), even with low anchor fraction. The region of nodes flooding through the network needs to be confined within logical constraints to produce better accuracy to reduce the localization error while reducing the cost. This research proposes and develops an enhanced algorithm that can improve the region area for sensor nodes placement within the specific localization area. This research aims to produce a longer network lifetime by reducing the energy used of the sensor nodes and at the same time minimize the localization error. This research describes the flooding control of the node's placement for the localization of the DV-Hop algorithm. The placement of nodes is limited inside the region of the anchor nodes. The simulation environment setup comes with parameters such as the radius, area, number of nodes, types of nodes, the anchor proportion, and the value of the regions. The result shows an improvement for the localization error, the power consumption, and the accuracy of the nodes positioning.

Estimating the location of sensor nodes in wireless sensor networks is a fundamental requirement in a variety of sensing applications. In large scale dense deployments where the area covered by sensor nodes is very large, it is impossible to localize all sensor nodes using single-hop localization techniques. A solution to this problem is to use a multi-hop localization technique to estimate sensor node positions. In some deployments it is required to maintain the anchor nodes at the edge of the simulated area. In previous work, we introduced a new localization scheme that uses distance measurements to localize sensor nodes using a collinear and non-collinear mobile anchor nodes placed at the edge of the sensed area. A Kalman Filter was then used to improve the location accuracy for each node. In this scheme each SN estimated its location from two independent directions then use such information to improve localization accuracy. In this paper, we extend the work to use side localization using hop measurements and fixed anchor node. We also compare the performance of using side localization for both hop and distance measurement. Through simulation we show that side localization using distance and hop measurements outperform DV-Hop and DV-Distance, which are mainstream localization protocols. The weighted mean hop measurement gives higher localization accuracy than using using distance measurement. However, if Kalman Filter is used distance measurement gives better localization accuracy.

Wireless sensor network (WSN) contains spatially distributed independent sensor nodes to cooperatively monitor physical or environmental conditions. There are several research challenges in WSN, among them Localization and Security are more prominent. To spot location of the sensor node, each node can make the installation of GPS, but GPS is costlier and cannot provide exact location of node in an indoor environment. Manually fixing position reference on each sensor node is additionally difficult within the case of deep network. To beat the above drawbacks, in recent years most of the localization techniques make use of beacon nodes (anchor nodes) where only the present position of the beacon nodes must be known. While transformation of knowledge from one node to a different node, we would like our data to be secured from unauthorized person. So for this issue we would like to think about security aspects like encryption and decryption by using several trust key management techniques where each node is trusted by providing the safety keys. In our proposed research work, by considering localization and securing aspects, we develop location-based energy efficient trust aware localized key management protocol and hence during this paper we discuss about the localization techniques and security solutions utilized in wireless sensor networks.

In the various applications of wireless sensor networks (WSN), the event occurrence information or data sensed by the sensor node has no significance until the location of an event/sensor node is not known. Therefore, localization is an important parameter in WSN for tracking these deployed sensor nodes in the sensing area. To localize these sensor nodes, there are various existing localization models in available detailed literature. One solution to this problem is to deploy a set of static anchor nodes (GPS equipped) in the sensing area, that broadcast their coordination information to help unknown nodes to localize themselves. Another way is to use a single mobile anchor node that will follow a specific path and localize unknown nodes. But a mobile anchor node must follow an optimal path that helps to achieve high accuracy and able to localize a large number of unknown nodes in less computational time. In this paper, the author design and developed a localization algorithm in which a GPS equipped single mobile anchor node follows a new path trajectory i.e. spiral pentagon. Once the target node falls under the communication diameter of the virtual node, six virtual anchor nodes are projected in the shape of a ring around the anchor node and localize all the unknown nodes in the sensing area by using the application of Salp Swarm Algorithm (SSA) for optimization. The hypothesis of six virtual anchor nodes is to avoid the problem of Line of Sight. This proposed method assured that each target node can estimate their node coordinates at least once with a low localization error.

Knowing the position of sensor nodes in an environmental monitoring is useful to identify the location of events. However deploying GPS receivers or other anchor sensors is expensive, since the role of anchor nodes ends after localizing sensor nodes' positions and they are transferred into ordinary sensor nodes. In this paper, we introduce a new localization scheme for a wireless sensor network that can localize sensor nodes using a collinear and non-collinear mobile anchor node. This scheme benefits from the estimated distance between neighbor nodes and additional information provided by the anchor node about the flow direction of the message. Each node localizes it's position from two independent directions. A Kalman Filter is then used to improve the location accuracy for each node. Through simulation studies, we show that the scheme using a Kalman Filter decreases the estimation errors than using single direction by 31% and 16% better than using weighted averages. As well, our new scheme overcomes the collinearity problem that appears from using mobile anchor nodes.

SummaryThis paper deals with the problem of sensor node localization in the presence of uncertainty in anchor node location. Aqueous environments are prone to adverse effects of underwater currents. This adversity causes non‐negligible mobility to the anchor nodes deployed under water. Localization in the presence of uncertainty in the anchor node location is quite challenging. Also, the authors consider the ray‐bending property of underwater medium due to depth dependent sound speed, to furnish the accurate position estimate of the target node. Standard ray equations are used to model the path followed by acoustic rays in water. Maximum likelihood estimation is proposed to estimate the location of target node with uncertainty in anchor node positions and is compared with the scheme with exact knowledge of anchor node positions, and the results are reported. Monte Carlo simulation is used to assess the performance of the proposed method. Also, the Cramer‐Rao lower bound with uncertainty in anchor nodes is derived and described. Simulation results of the proposed algorithm outperform the existing algorithm with known anchor location by up to 49.4%, and hence, accuracy is improved in the proposed method.

Localization is extensively used in Wireless Sensor Networks (WSNs) to identify the current location of the sensor nodes. A WSN consist of thousands of nodes that make the installation of GPS on each sensor node expensive and moreover GPS will not provide exact localization results in an indoor environment. Manually configuring location reference on each sensor node is also not possible in the case of dense network. This gives rise to a problem where the sensor nodes must identify its current location without using any special hardware like GPS and without the help of manual configuration. Localization techniques makes the deployment of WSNs economical. Most of the localization techniques are carried out with the help of anchor node or beacon node, which knows its present location. Based on the location information provided by the anchor node or beacon node, other nodes localize themselves. In this paper we present a succinct survey on the localization techniques used in wireless sensor networks covering its problems and research gap.

<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>

In wireless sensor networks (WSNs), node’s locations play a critical role in many applications. Having a GPS receiver on every sensor node is costly. In this paper, we propose an adaptive mobile anchor localization algorithm based on ant colony optimization, firstly, some virtual anchor nodes are distributed in the area, second, ant colony was used which has the maximum of transition probabilities to obtain the optimal path, last, the centroid-weighted localization algorithm was proposed to locate the position of unknown nodes. Simulation results show that the localization accuracy of the proposed algorithm is better than the traditional centroid algorithm, the more number of anchor nodes, namely, the density is bigger, the position errors is smaller. Under the same anchor nodes, the more number of unknown nodes, the position errors is smaller and the overall trend is downward.

ABSTRACTIn Mobile Wireless Sensor Networks (MWSNs), localization is one of the key technologies since it plays a critical role for many location-aware protocols and applications. Existing range-free localization algorithms for MWSNs are usually based on the Sequential Monte Carlo localization algorithm. They suffer from low sampling efficiency, high communication computation cost or require high beacon density to achieve high localization accuracy. In this paper, we propose a novel algorithm called ‘Power Adjusting Anchors’ that outperforms in terms of accuracy. In this algorithm, mobile sensor node localizes its position based on the received power levels transmitted by the neighbouring anchors and by constructing a refined prediction area with the hop-distance estimations between elected anchor nodes. This area will be divided into square grids, and sensor node selects all the grids which owns the highest elections, and then, computes the centroid of such grids. The calculated centroid is considered as the estimated location of the sensor node. In addition, the algorithm addresses the information state provided by the radio communication between sensor nodes and anchor nodes to the success of the localization. Simulation results show that the algorithm is very competitive by achieving high localization accuracy with better localization success.

For transmission and collection of sensed data, it is essential that the connectivity among deployed sensor nodes in WSNs. The maintenance of network connectivity is a challenging task in harsh environmental conditions when participating nodes’ failures lead to the network’s disjoint partitions. To improve the connectivity and coverage with energy efficiency for the partitioned network, optimal positioning of sensor nodes has been performed based on the moth flame optimization algorithm (OPS-MFO). In the anchor node, the relay nodes have exploited in the proposed model—two phases involved in the proposed model, such as the inter-partition phase and intra-partition phase. For intra-partitioning and inter-partitioning, all sensor nodes and relay nodes’ positions have been estimated using the moth flame optimization algorithm for better connectivity. The proposed model is outperformed based on the experimental analysis and evaluation by comparing them with the existing algorithms.

On the premise of guaranteeing high-quality measurement data, active wireless localization (A-WL) locates the target by at least three anchor nodes (ANs) with known coordinates, while passive wireless localization (P-WL) requires more ANs. In addition, P-WL requires a cumbersome training process to maintain the robustness in application scenarios as much as possible. However, the optimal deployment of ANs is not an easy task, and the application scenario will not remain unchanged. Therefore, a lightweight localization system that satisfies the localization accuracy requirements and can be quickly deployed should be one of the preferred solutions for WL. In this paper, we propose a single-anchor localization system based on continuous time-of-arrival (TOA) measurement. Specifically, we fix the AN on the blade with uniform rotation, construct the time-length distribution map by using the cyclically varying TOA caused by blade rotation, and intercept it according to the rotation period. We extract the change angle and distance information from the intercepted data in real time to achieve single-anchor localization. Meanwhile, we also construct a support vector machine classifier to identify the line-of-sight state of each interception period to assist the angle estimation in harsh environments and further improve the adaptability of the system. Experimental results show that the line-of-sight recognition accuracy of higher than 92% under continuous movement, and the average localization error of less than 0.6m. The average localization error is less than 0.3m in the mixed motion state.