Lightweight Localization Using Trilateration for Sensor Networks

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.

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.

À Load-balancing and Self-adaptation Clustering for Lifetime Prolonging in Large Scale Wireless Sensor Networks

Localization of mobile nodes in wireless sensor network gets more and more important, because many applications need to locate the source of incoming measurements as precise as possible. Many previous approaches to the location-estimation problem need know the theories and experiential signal propagation model and collect a large number of labeled samples. So, these approaches are coarse localization because of the inaccurate model, and to obtain such data requires great effort. In this paper, a semi-supervised manifold learning is used to estimate the locations of mobile nodes in a wireless sensor network. The algorithm is used to compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled data and a large amount of unlabeled data. This mapping function can be used online to determine the location of mobile nodes in a sensor network based on the signals received. We use independent development nodes to setup the network in metallurgical industry environment, outdoor and indoor. Experimental results show that we can achieve a higher accuracy with much less calibration effort as compared with RADAR localization systems.

In an ad hoc sensor network clock synchronization is a serious issue as nodes in the sensor network have different clock drifts. One of the common solutions for the time synchronization among these nodes is the active transmission of time stamp messages. However this method consumes some resources and channel bandwidth, and is not suitable for resource constrained nodes in the sensor network. This work presents a novel method with minimum overhead that reduces clock drift effects in the synchronization of nodes in sensor networks using relative time-slot referencing TDMA based MAC protocol.

This paper presents the feasibility assessment for the adoption of a rectenna with basic structure elements as energy source in wireless sensor networks (WSN) nodes. For that matter, a rectenna that harvests energy from 2.45 GHz was designed by the application of a tuning technique on the dimensions of its elements. In the design, the CST Microwave Studio and a SPICE based software were used. The DC-DC boost converter was selected among several commercial options. The limitations of the rectenna were evaluated using the power consumption of different nodes. Results show that the total maximum conversion efficiency for the rectenna was 34.08% at 20 dBm. The distance ranges between the designed rectenna and the wireless energy source were from 0.03 m to 0.20 m in the indoor scenario and from 0.26 m to 2.03 m in the outdoor scenario. The rectenna can supply the required power for the nodes CC2538 and CC1350 in the full range of distances, but for the nodes Z1 and Sky the range of distances is reduced.

Vision measuring, involving the measurement of the three-dimensional shape and color of large objects using stereo vision-based methods, is a challenging task. In this study, a freely adjustable vision measurement system for large objects is developed by connecting a camera to a total station telescope via a fixed mount. In this system, a camera is focused on the target object to be measured in far-range scenarios. A high-precision calibration method is proposed based on non-parametric camera models. In addition, a point cloud-based three-dimensional reconstruction method is developed by integrating with the non-parametric camera models. The experimental results showed that the proposed system has high precision and robustness. When measuring objects from a distance of 8m in an indoor scenario, the error in the maximum spatial distance measurement between two corner points on the calibration board is less than 0.24mm. The maximum relative measurement error is less than 0.09%. When measuring an object 20m away in an outdoor scenario, the average error in the spatial distance measurement between two arbitrary points in an 11 m×4 m testing region is 5.6mm. The average relative error in the distance measurement is 0.12%.

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.

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.

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.

For wireless sensor networks, sensing data without location information are useless for future processing and decision-making systems. A wireless localization method can provide critical position information for the sensing data in the coverage field. Trilateration localization is applied widely in many systems due to its low cost and widespread availability. However, there are many negative factors that lead to low localization accuracy. Many improved versions of trilateration localization, including the weighted trilateration localization, the trilateration centroid localization, and other related methods, have been proposed to improve localization accuracy. However, these methods do not consider the influence mechanism of the negative factors, and the localization results are unsatisfactory. In this paper, considering the uncertainty propagation mechanism during trilateration localization, we propose an improved trilateration localization method with minimum uncertainty propagation and optimized selection of anchor nodes (ITL-MEPOSA). In this method, we consider the uncertainty propagation from distance estimation to localization calculation and evaluate the uncertainty propagation of each distance estimation result. Then, we utilize a single scan-sliding window with an optimized algorithm to select anchor nodes with minimum uncertainty propagation. Finally, based on the selected anchor nodes and their corresponding distance estimation results, an accurate localization result can be obtained through the least square criterion. The simulations and experimental results show that the proposed method can obtain high localization accuracy with acceptable efficiency.

Sensors have a strong connection to the real world, which sets them apart from conventional networks in a major way. Through the use of a variety of sensing devices as well as the processing of raw data, sensor networks may detect & monitor physical phenomena occurring in the locations/regions where even the sensors are located or deployed. Physical phenomena linked with geographic locations/regions are much more important to users of wireless sensor networks over raw data from individual sensor nodes. Geospatial information is becoming more important in sensor networks and applications, and sensor nodes having GPS signal receivers become more widely available. Sensor nodes may also be located without GPS using a variety of localization methods. A location-aware sensor network is now possible thanks to these recent technological advancements. To address the issue of estimating the location and position among wireless sensor nodes, new approaches, techniques, and algorithms must be created. In order to achieve this goal, In this Paper, we are proposing a Distance Vector Hop (DV-Hop) algorithm that Significantly decreases the average of localization error of sensor nodes and which is a solution for localizing nodes having few adjacent anchors. simulation results prove that the proposed algorithm substantially reduces the average of localization error of sensor nodes

이동 사용자의 위치 정보를 제공하는 위치인식서비스는 센서네트워크가 제공하는 대표적인 서비스이다. 그동안 이동 사용자의 위치정보를 획득하기 위한 다양한 기법들이 제시되어 왔다. 하지만 대부분의 기법들은 단일 사용자인 경우만 고려하여 연구되어 이를 다중 사용자인 경우로 확장하기에는 문제가 있다. 여러 이동 사용자들이 한 지역에서 동시에 위치인식 작업을 수행하는 경우에 센서노드들이 발생시키는 라디오 주파수나 초음파 등이 서로 간에 간섭을 발생시킬 수 있다. 본 논문에서는 여러 이동 사용자들이 동시 다발적으로 위치확인 작업을 수행하고자 하는 경우에 발생 가능한 간섭을 최소화 하는 적응적 파워 조절에 기반한 위치인식 기법인 APL(Adaptive Power Control based Resource Allocation Technique for Efficient Localization Technique)기법을 제안한다. APL기법은 센서노드가 위치인식 작업을 수행하기 전에 주위에 있는 앵커노드를 선점하여 점유함으로써 노드간의 간섭을 방지한다. 이를 위해 IEEE 802.11에 정의된 RTS(Ready To Send)패킷을 자원 선점을 요청하는데 사용하며 CTS(Clear To Send)패킷을 자원 선점을 확인하는데 사용한다. 반대로 이미 앵커노드가 다른 노드에 의해 선점하여 위치인식 작업을 방해할 가능성이 있는 경우 해당 앵커노드들을 임계영역으로 정의한다. 임계영역 처리를 위한 NTS(Not To Send) 패킷을 새로이 정의하여 노드 간에 간섭이 발생하지 않도록 한다. 추가적으로 거리측정 작업시 앵커노드간 동기화를 위한 STS(Start to Send)패킷을 새로이 정의한다. 최종적으로 센서노드의 전송 파워를 적응적으로 조절하여 작업이 영향을 미치는 영역을 최소화하도록 한다. 실험을 통하여 이동 사용자가 다수일 경우 노드 간에 간섭이 많이 발생함을 보이며 제안하는 APL기법이 위치인식 작업시 간섭을 방지함을 보인다. Given the increased interest in ubiquitous computing, wireless sensor network has been researched widely. The localization service which provides the location information of mobile user, is one of important service provided by sensor network. Many methods to obtain the location information of mobile user have been proposed. However, these methods were developed for only one mobile user so that it is hard to extend for multiple mobile users. If multiple mobile users start the localization process concurrently, there could be interference of beacon or ultrasound that each mobile user transmits. In the paper, we propose APL(Adaptive Power Control based Resource Allocation Technique for Efficient Localization Technique), the localization technique for multiple mobile nodes based on adaptive power control in mobile wireless sensor networks. In APL, collision of localization between sensor nodes is prevented by forcing the mobile node to get the permission of localization from anchor nodes. For this, we use RTS(Ready To Send) packet type for localization initiation by mobile node and CTS(Clear To Send) packet type for localization grant by anchor node. NTS(Not To Send) packet type is used to reject localization by anchor node for interference avoidance and STS(Start To Send) for synchronization between 모anchor nodes. At last, the power level of sensor node is controled adaptively to minimize the affected area. The experimental result shows that the number of interference between nodes are increased in proportion to the number of mobile nodes and APL provides efficient localization.

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.

The k nearest neighbor (KNN) query, an essential query for information processing in sensor networks, has not received sufficient attention in the research community of sensor networks. In this paper, we examine in-network processing of KNN queries by proposing two alternative algorithms, namely the GeoRouting Tree (GRT) and the KNN Boundary Tree (KBT). The former is based on a distributed spatial index structure and prunes off the irrelevant nodes during query propagation. The latter is based upon ad-hoc geographic routing and first obtains a region within which at least k nearest sensor nodes are enclosed and then decides the k nearest nodes to the query point. We provide an extensive performance evaluation to study the impact of various system factors and protocol parameters. Our results show that GRT yields a good tradeoff between energy consumption and query accuracy in static scenarios. On the other hand, KBT achieves better energy efficiency while being more tolerant to network dynamics.