Accurate Energy-Efficient Localization Algorithm for IoT Sensors

Automatic localization is one of the major issues in Wireless Sensor Networks (WSN). DV-hop algorithm is a well-known localization algorithm in WSN but with limited localization accuracy. In this paper, an improved DV-hop localization algorithm in hybrid optical wireless sensor networks is proposed based on the optimization of the parameters in WSN. Various factors that affect the localization accuracy of the DV-hop algorithm in WSN are investigated, including the communication radius of the node, the number of beacon nodes and the number of the total nodes. As the DV-hop algorithm is applied into hybrid optical sensor and WSNs (O-WSN) with rectangular topology, different parameters have to be optimized accordingly. Simulation results show that the square topology outperforms the rectangle topology more than 45 % under the same network parameters using the improved DV-hop algorithm. Therefore another improved DV-hop called Sub-Square Weighted DV-hop (SSW DV-hop) is proposed for the rectangle topology. Both simulation and experiment results demonstrate that applying the SSW DV-hop algorithmin O-WSNs could significantly improve the localization accuracy.

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 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.

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.

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.

This paper presents a three-dimension localization algorithm for wireless sensor network (WSN) nodes based on support vector machine (SVM). First, distances between nodes to all anchor nodes in sensor network are estimated. Distance vectors are constructed in each node. Distance vectors in the anchor nodes are considered as training feature vectors for SVM and that in unknown nodes are regarded as testing feature vectors. The trained SVM is applied to class the distance vectors and locate cubic units for unknown nodes. Then, the unit centroids are used as the estimated locations of nodes. The experimental results indicate the algorithm proposed has small localization error and is insusceptible to the proportion of the anchor nodes and range error. Hence, the algorithm proposed is robust and stable, which can be applied in network environment where anchor nodes are sparse and range error is large.

A node self-localization algorithm based on virtual central node (VCN) and another CF_OSSDL method which stems from classic DV-Hop propagation algorithm are proposed respectively in our papers before. In this paper comprehensive analysis of the two algorithms are listed. In VCN algorithm, by deducing a 3D special node, which is called the virtual central node, unknown nodes can compute their own positions automatically. Differently in CF_OSSDL algorithm, by computing optimum space step distance between unknown nodes and anchor nodes in which sole correction factor of each unknown node is added (CF_OSSDL), all nodes with no position coordinates can realize self-localizing. The localization mechanism has been implemented in Matlab simulation software. Simulating experiments are made to explore the two range-free localization algorithms. The effectiveness and robustness of algorithms are clearly demonstrated through extensive simulation results.

Localization technology is the basis of practical applications of wireless sensor networks (WSNs). An improved localization algorithm for wireless sensor networks was proposed to reduce hop distance error and solve the premature convergence problem for the hybrid localization algorithm of DV-Hop and Bat (BADV-Hop). Based on the vector routing localization mechanism, an optimization method was designed to enhance the average hop distance accuracy. The linear weight strategy was used to correct searching velocity of bats. In addition, tendentious random disturbance was established to optimize location information. Experimental results showed that the localization accuracy of improved algorithm is 13% greater than that of DV-Hop algorithm and 3% greater than that of BADV-Hop algorithm. Moreover, the improved algorithm effectively reduced the maximum iterations and avoided premature convergence without any additional energy consumption compared to BADV-Hop algorithm.

According to the classical DV-HOP localization algorithm in wireless sensor networks with low precision, we put forward an improved algorithm,which adopted a new way to calculate the distance between unknown nodes and anchor nodes, then by using the weighted least squares method to calculate nodes coordinates. Matlab experimental simulation results show that under the same network environment, the algorithm can calculate the distance to reduce localization errors effectively, and improve the localization accuracy. Keywords-wireless sensor networks; node localization; DV-HOP algorithm

In many applications of wireless sensor networks, location is very important information. Conventional location information comes from manual setting or GPS device. However, manual location setting requires huge cost of human time, and GPS location setting requires expensive device cost and large device size. Furthermore, GPS can not operate in indoor environment. Both approaches are not applicable to localization task of wireless sensor networks. In this paper, an accurate and efficient localization algorithm based on multidimensional scaling (AIDS) is proposed in hierarchical network environment. Through localized computation of multidimensional scaling within a cluster, the computation overhead can be distributed to each cluster. On the other hand, MDS- based localization approach requires the estimation of multihop distance. Through restricting estimation of multihop distances within a cluster, the proposed localization algorithm achieves better accuracy and can operate in non-convex network environment. Experimental results reveal that the proposed HMDS localization algorithm outperforms MDSMAP algorithm in terms of accuracy.

Most of the current RSS (Received Signal Strength)-based localization algorithms in Wireless Sensor Networks (WSNs) rely on isotropic radio propagation model to infer the distance between a pair of nodes from received signal strength, which however has been proved to be quite unreliable in recent research work. Performance analysis is important to evaluate the applicability of a localization algorithm, however little work has been done on evaluating the existing localization algorithms in simulated realistic settings. This paper firstly presents the motivation and detailed implementation of a proposed Link-State Based Annulus (LSBA) localization algorithm, and then gives a panorama of performance comparison among LSBA and other four localization algorithms in terms of estimation error, convergence speed, computational complexity and communication cost in the simulated realistic environment. Simulation results show that LSBA achieves the best tradeoff among all the four metrics in WSNs with moderate number of anchors, and has good adaptability to irregular node deployment as well.

The development of various applications of wireless sensor networks has aroused great interest in using these types of networks in various fields. These networks, without infrastructure and self‐organization, are easily deployed in most environments and collect information about environmental phenomena for analysis and proper response to accidents and send them to the basic centers. They do. Wireless sensor networks are made up of some sensor nodes that both act as sensors and act as relay nodes concerning to each other. On the other hand, the lack of infrastructure in these networks has limited resources so that the nodes of the battery are fed with limited energy. Due to the location of networks in difficult and impassable areas, it is not possible to recharge or replace the node battery. Therefore, saving energy consumption in this type of network is one of the most important challenges. Since the rate of energy consumption when sensing information and receiving data packets from another node is a fixed value, so sensor nodes have the highest energy consumption when sending data. Therefore, routing methods try to reduce energy consumption based on systematic approaches. One of the most promising solutions to reduce energy consumption in wireless sensor networks is to cluster the nodes and select the threaded node based on the data transfer parameters so that the average energy consumption in the nodes is reduced and the network lifetime is increased. Therefore, in this research, a new optimization approach using multiobjective genetic algorithm and cuckoo algorithm for clustering wireless sensor networks is presented. In this study, in order to select clustered nodes from a multiobjective genetic algorithms based on reducing intracluster distances and reducing energy consumption in cluster member nodes and near‐optimal routing based on cuckoo optimization algorithm to transfer information between nodes have been used in the direction of the cavity. The implementation results show that considering the evolutionary capabilities of the multiobjective genetic algorithm and the cuckoo optimization algorithm, the proposed method in terms of energy consumption, efficiency, delivery rate, and packet transmission latency, compared to previous methods, has improved.

Wireless sensor networks (WSNs) is emerging as a key tool for data acquisition in many applications, and localization algorithms of the sensors have a vital importance due to the intrinsic characteristics of this type of networks. The objective of this paper is to propose an algorithm of range-free localization for WSNs using a mobile robot equipped with a rotary directional antenna. This algorithm, denominated as the azimuthally defined area localization algorithm (ADAL), is executed in each sensor node and is based only on the analysis of the information received from the mobile robot, therefore is energy efficient and contributes to extend the lifetime of the sensor network. Additionally the proposed algorithm has the advantage of being simple and economical. The simulation results show that the proposed algorithm is a practical, effective and accurate method to localize sensor nodes in a WSN.

Abstract—Many applications in wireless sensor networks require sensor nodes to obtain their absolute or relative geographical positions. Although various localization algorithms have been recently proposed, most of them require nodes to be equipped with range-determining hardware to obtain distance information. In this paper, we propose a concentric anchor beacon (CAB) localization algorithm for wireless sensor networks. CAB is a range-free approach and uses a small number of anchor nodes. Each anchor emits beacons at different power levels. From the information received by each beacon heard, nodes can determine in which annular ring they are located within each anchor. Each node uses the approximated center of intersection of the rings as its position estimate. We also propose two heuristics, namely CAB with Equal Area and CAB with Equal Width, to determine the transmitting power levels of the beacons. Simulation results show that the estimation error is reduced by half when anchors transmit beacons at two different power levels instead of at a single power level. CAB also gives a lower estimation error than some other range-free localization schemes (e.g., Centroid and Approximated Point-In-Triangulation) when the anchor-to-node range ratio is less than 4. Index Terms—Localization, position estimation, wireless sensor networks. I.

The TDOA localization algorithm for wireless sensor networks can help us measure the time differences of an unknown node to more anchor nodes, which establish nonlinear equation of the differential distance between unknown node and anchor node. Newton iteration is the crucial method for nonlinear equation numerical solutions, so using the Newton iterative method can better achieve nonlinear optimization in TDOA location. The simulation results indicate that the algorithm can meet the requirement of localization and possesses the preferable localization precision.