Energy-Efficient RSSI-Based Localization for 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.

As the geographical location of sensors is of great importance in the applications of a wireless sensor network, localization attracts significant research interest. Among the localization methods, received signal strength indicator (RSSI) localization algorithm is widely studied due to its low-cost. However, the radio signal transmission is affected by the factors of channel attenuation and noise, etc. Therefore, the actual location is impossible to estimate. To alleviate the effect of the RSSI measure errors, we proposed a novel error modified method for RSSI location algorithm based on neural network, short for RSSI-NN. First, we use the fitting parameters of the monitoring area to determine the parameters of RSSI model. Second, the distance between anchor node i and the unknown node Liis calculated. Then, the error modified algorithm based on neural network is proposed. Finally, we can obtain the modified value of the output layer data. Simulations demonstrate that RSSI-NN achieves lower localization error compared with conventional RSSI-based localization algorithm.

To improve the performance of location accuracy for wireless sensor network, a new location algorithm based on mobile anchor node and modified hop count is proposed. Firstly, we set different communication powers for all nodes to make them have different communication ranges. This makes the relationship between the hop count and real distance more accurate. Secondly, the unknown node computes the mean distance per hop between it and the three anchor nodes that are the nearest to the unknown node and uses the mean value as the mean distance per hop. Finally, we suppose that some anchor nodes can move. Once the position of some anchor nodes changes, we recalculate the positions of unknown nodes and use the mean value of recorded positions as position of unknown nodes. Simulation results show that the proposed method has lower location error than other methods.

In the wireless sensor network (WSN) localization methods based on Received Signal Strength Indicator (RSSI), it is usually required to determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and an unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the location of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as complex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based on least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model, which positively reduces the computation amount in the estimation process. The experimental results show that the proposed self-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement. Conclusively, the proposed method is of definite practical value.

Wireless sensor network (WSN) indoor location systems are based on effective distance estimation between the target node and anchors to find the node's precise location. Almost all low-cost wireless devices use the received signal strength indicator (RSSI) metric for estimating distance. This study aims to minimise the error in the estimated distance between the anchor nodes and the target node by proposing a model based on an Artificial Neural Network (ANN) for determining the distance using the RSSI. The proposed model was used to apply 3D indoor localisation. ANN is implemented in an Esp32 embedded system to make it compatible with WSN and IoT applications by using the TinyML approach for designing and implementing an efficient base station. The results show that the time required for the indoor localisation process does not exceed 40 ms, which is a very promising result for real-time localisation applications.

In many applications, the exact location of the sensor nodes is unknown after deployment. Localization is a process used to locate sensor nodes' positional coordinates, which is vital information. The localization is generally assisted by anchor nodes that are also sensor nodes but with known locations. Anchor nodes generally are expensive and need to be optimally placed for effective localization. Passive localization is one of the localization techniques where the sensor nodes silently listen to the global events like thunder sounds, seismic waves, lighting, etc. According to previous studies, the ideal location to place anchor nodes was on the perimeter of the sensor network. This may not be the case in passive localization, since the function of anchor nodes here is different than the anchor nodes used in other localization systems. We do extensive studies on positioning anchor nodes for effective localization. Several simulations are run in dense and sparse networks for proper positioning of anchor nodes. We show that, for effective passive localization, the optimal placement of the anchor nodes is at the center of the network in such a way that no three anchor nodes share linearity. The more the non-linearity, the better the localization. The localization for our network design proves better when we place anchor nodes to form right angles.

Due to the un-consideration of random distribution of DV-Hop algorithm in wireless sensor network, a big error caused by using of hop count between unknown and anchor nodes and the nearest anchor nodes’ average hop distance instead of the average hop distance of all the anchor nodes. An improved DV-HOP localization algorithm is proposed based on the traditional DVHOP localization algorithm aimed to reduce the localization error in this paper. The proposed algorithm introduce RSSI technique to modify the Hop count of DV-Hop and use the original DV-HOP algorithm to estimate the positions of anchor nodes which are already known, the error between estimation and real distance of anchor nodes so obtained are then to modify the Hop size. The simulation result shows that the proposed algorithm has a better performance in localization precision.

Wireless sensor network localization is an important area that attracted significant research interest. Recently, received signal strength indicator (RSSI)-based range measurement technology is widely used in sensor network for its low-cost. But the radio signal transmission is influenced by the factors of channel attenuation and shielding obstacles, etc. Therefore, the accuracy of RSSI-based localization algorithm is not high. In this paper, a new localization algorithm which is based on the probability distribution model is proposed. Firstly, the anchor node which is nearest to the unknown node is made as the reference node in order to correct the RSSI-based ranging distance. Secondly, the common communication area where the unknown nodes may exist is divided into a number of spatial grids. And then, the confidence degree of each grid is calculated out according to the probability model of radio signal space transmission. Finally, based on the centroids of spatial grids, the coordinates of the unknown nodes are calculated out. Through this algorithm, the localization accuracy is improved greatly compared with conventional RSSI-based localization algorithms, which has been demonstrated by the simulating results.

We proposed a localization algorithm named LSARSSI for mobile node based on RSSI (received signal strength indicator) between locating sensor node with inertia module built-in and the single anchor. Instead of directly mapping RSSI values into physical distance, contrasting RSSI values received from anchor in different visited locations, LSARSSI utilizes the geometric relationship of perpendicular intersection to compute node positions. Given that the values of RSSI among two visited locations are equal, we regard that their distances to anchor node are equal. After obtaining several sets of such visited locations, the relative location of mobile node and anchor node can be calculated. Because of the limitations of LSARSSI, we put forward an improved algorithm named ILSARSSI. Our scheme uses only one location-known anchor which is useful in low density environment without using additional hardware. The simulations show that LSARSSI achieves high accuracy and ILSARSSI performs high stability and feasibility.

Wireless sensor network (WSN) is usually deployed in harsh environments for collecting and delivering the data to the remotely located base station. Due to the recent achievements in WSN, tiny and expensive sensors are used with capability of sensing large information and to propagate over longer distances. In range-based localization algorithms, anchor node whose location is known plays an important role. In order to resolve the challenges of traditional localization algorithms, an anchor-based node localization algorithm is proposed for WSN. The distance between anchor or known node and unknown node is measured by optimizing anchors and creating database of optimized anchors. Using trilateration method unknown nodes are located and are used as new anchors which will reduce localization algorithm’s dependency on anchors. The objective of the proposed work is to maximize the life time of the network by minimizing the energy consumption. From the simulation results, we show that the proposed algorithm increases lifetime of wireless sensor network.

Wireless Sensor Networks (WSNs) applications have attracted attention in Internet of Things (IoT) as a novel networking paradigm consisting of billions of small sensor nodes. These sensors collect environmental information and communicate with each other to provide solutions for real time IoT applications' requirements. Since the majority of applications require location-based services, it is necessary to improve the accuracy of localization algorithms. DV-Hop is one of the most attractive range-free localization algorithms in wireless sensor networks and several works have been undertaken to improve its accuracy, however, since sensor nodes have limited power resources, the energy consumption of nodes should be also considered. In this paper, we propose a method based on DV-Hop to improve both accuracy and power consumption. Each unknown node calculates the Hopsize of each anchor node according to the limited information it has from the network topology; therefore there is no need to broadcast the Hopsize from anchor nodes, and in this way energy can be saved. In the next step, we use Shuffled Frog Leaping Algorithm (SFLA) as an evolutionary algorithm to improve the accuracy of estimated Hopsizes and a hybrid Genetic-PSO algorithm is applied to the third step of DV-Hop to achieve more accurate values for unknown nodes' positions. Simulation results show that our proposed method decreases the localization error significantly by jointly considering the energy consumption of sensors and is overall 44% more accurate than DV-Hop.

In some wireless sensor networks (WSN) applications (e.g. fire lighting scattering wireless nodes from the plane) nodes positions needs to be resolved. In WSN we are trying to keep the price of a node as low as possible and for that reason we are trying to avoid implementing additional hardware (e.g. GPS module) to the node. One of possible localization methods using standard WSN 802.15.4 radio chip is use of Received Signal Strength Indicator (RSSI) parameter and radio propagation model. In the article we are describing the implementation and experiments of localization using RSSI and Log-distance path model. Measurements and visualization of the distances was successfully realized online and in real time.

Localization plays an important role in wireless sensor network management. Currently, the approaches mainly fall into two categories: range-based and range-free. In this paper, the proposed work combines the advantages of the two types of methods (range-based and range-free) to introduce the near and far field characteristic of RSSI(NFC), which is 1) in the near field, there is a relatively stable relationship between RSSI and distance. 2) In the far field, a max range of the signal radiation exists. NFC provides a new method of distance measurement and node positioning. A new localization technique (NFCB) based on NFC is also proposed. In this algorithm, anchor nodes transmit beacons at different power levels, which can divide geographical regions into the rings with multiple near and far field combination. From the information received by each beacon heard, nodes can determine its position, which depends on the property of intersections of near-fields. NFCB can turn the nodes with high-precisions into anchor nodes. Without increasing the hardware, it can raise the density of anchor nodes, which greatly improve positioning accuracy. Simulation results demonstrate that the estimation error is lower than CAB APIT.

Because the RSSI value greatly changes, the direct use of RSSI value has more errors in the positioning process as the basis to calculate the position of anchor nodes. This paper proposes a RSSI node localization algorithm based on constraint particle swarm optimization (PSO-RSSI). In the algorithm, particle swarm optimization is used to select anchor nodes set which are near the unknown node. The algorithm takes an element in the set, and measure distance between it and the other elements in the set. Then, the maximum likelihood method is used to calculate the coordinates. According to the difference between the calculated coordinates and the actual coordinates of the anchor node, the obtain coordinate of unknown node is corrected. When all the elements in the set perform such operation, the statistical methods are used to determine the coordinates of the unknown node. The algorithm embodies all the reference points influence on positioning, corrects the error problem on a single reference node positioning in the past. The simulation results show that the effect of the PSO-RSSI algorithm is more excellent.

In this paper, a computational function based on the CORDIC algorithm for location estimation is proposed. Position awareness is a desirable feature for many applications of Wireless Sensor Networks. The Received Signal Strength Indicator (RSSI) values provide a feasible way for relative position estimation. The relationship of precision to bit length for IEEE 754 is discussed. Finally, the hardware implementation for the computation of RSSI based on the CORDIC algorithm is demonstrated.