A Survey of Localization in Wireless Sensor Network <s> Scheduling the Sensor Node to Optimize the Tradeoff between Localization Performance and Energy Consumption. <s> Sensor Positioning is a fundamental and crucial issue for sensor network operation and management. In the paper, we first study some situations where most existing sensor positioning methods tend to fail to perform well, an example being when the topology of a sensor network is anisotropic. Then, we explore the idea of using dimensionality reduction techniques to estimate sensors coordinates in two (or three) dimensional space, and we propose a distributed sensor positioning method based on multidimensional scaling technique to deal with these challenging conditions. Multidimensional scaling and coordinate alignment techniques are applied to recover positions of adjacent sensors. The estimated positions of the anchors are compared with their true physical positions and corrected. The positions of other sensors are corrected accordingly. With iterative adjustment, our method can overcome adverse network and terrain conditions, and generate accurate sensor position. We also propose an on demand sensor positioning method based on the above method. <s> BIB001 </s> A Survey of Localization in Wireless Sensor Network <s> Scheduling the Sensor Node to Optimize the Tradeoff between Localization Performance and Energy Consumption. <s> It is often useful to know the geographic positions of nodes in a communications network, but adding GPS receivers or other sophisticated sensors to every node can be expensive. MDS-MAP is a recent localization method based on multidimensional scaling (MDS). It uses connectivity information - who is within communications range of whom - to derive the locations of the nodes in the network, and can take advantage of additional data, such as estimated distances between neighbors or known positions for certain anchor nodes, if they are available. However, MDS-MAP is an inherently centralized algorithm and is therefore of limited utility in many applications. In this paper, we present a new variant of the MDS-MAP method, which we call MDS-MAP(P) standing for MDS-MAP using patches of relative maps, that can be executed in a distributed fashion. Using extensive simulations, we show that the new algorithm not only preserves the good performance of the original method on relatively uniform layouts, but also performs much better than the original on irregularly-shaped networks. The main idea is to build a local map at each node of the immediate vicinity and then merge these maps together to form a global map. This approach works much better for topologies in which the shortest path distance between two nodes does not correspond well to their Euclidean distance. We also discuss an optional refinement step that improves solution quality even further at the expense of additional computation. <s> BIB002 </s> A Survey of Localization in Wireless Sensor Network <s> Scheduling the Sensor Node to Optimize the Tradeoff between Localization Performance and Energy Consumption. <s> Localization is one of the substantial issues in wireless sensor networks. Several approaches, including range-based and range-free, have been proposed to calculate positions for randomly deployed sensor nodes. With specific hardware, the range-based schemes typically achieve high accuracy based on either node-to-node distances or angles. On the other hand, the range-free mechanisms support coarse positioning accuracy with the less expense. This paper describes a range-free localization scheme using mobile anchor points. Each anchor point equipped with the GPS moves in the sensing field and broadcasts its current position periodically. The sensor nodes obtaining the information are able to compute their locations. With the scheme, no extra hardware or data communication is needed for the sensor nodes. Moreover, obstacles in the sensing fields can be tolerated. The localization mechanism has been implemented in the network simulator ns-2. The simulation results show that our scheme performed better than other range-free mechanisms. <s> BIB003 </s> A Survey of Localization in Wireless Sensor Network <s> Scheduling the Sensor Node to Optimize the Tradeoff between Localization Performance and Energy Consumption. <s> Energy efficiency and positional accuracy are often contradictive goals. We propose to decrease power consumption without sacrificing significant accuracy by developing an energy-aware localization that adapts the sampling rate to target's mobility level. In this paper, an energy-aware adaptive localization system based on signal strength fingerprinting is designed, implemented, and evaluated. Promising to satisfy an application's requirements on positional accuracy, our system tries to adapt its sampling rate to reduce its energy consumption. The contribution of this paper is three-fold. (1) We have developed a model to predict the positional error of a real working positioning engine under different mobility levels of mobile targets, estimation error from the positioning engine, processing and networking delay in the location infrastructure, and sampling rate of location information. (2) In a real test environment, our energy-saving method solves the mobility estimation error problem by utilizing additional sensors on mobile targets. The result is that we can improve the prediction accuracy by as much as 37.01%. (3) We implemented our energy-saving methods inside a working localization infrastructure and conducted performance evaluation in a real office environment. Our performance results show as much as 49.76 % reduction in power consumption <s> BIB004 </s> A Survey of Localization in Wireless Sensor Network <s> Scheduling the Sensor Node to Optimize the Tradeoff between Localization Performance and Energy Consumption. <s> Determining node positions is essential for many next-generation network functionalities. Previous localization algorithms lack correctness guarantees or require network density higher than required for unique localizability. In this paper, we describe a class of algorithms for fine-grained localization called Sweeps. Sweeps correctly finitely localizes all nodes in bilateration networks. Sweeps also handles angle measurements and noisy measurements. We demonstrate the practicality of our algorithm through extensive simulations on a large number of networks, upon which it consistently localizes one-thousand-node networks of average degree less than five in less than two minutes on a consumer PC. <s> BIB005 </s> A Survey of Localization in Wireless Sensor Network <s> Scheduling the Sensor Node to Optimize the Tradeoff between Localization Performance and Energy Consumption. <s> The persistent nature of physical events makes it possible to detect events using lowly duty-cycled sensors. However, it raises a serious over-detection problem when every sensor blindly wakes up in each cycle, in particular, if sensor density is high. In this paper, we propose an innovative probabilistic wakeup protocol for energy-efficient event detection in wireless sensor networks. The central idea is to reduce the duty cycle of every sensor via probabilistic wakeup, exploiting the dense deployment of sensor networks. A distinctive feature is that the system ensures that the detection delay of any event occurring anywhere in the sensing field is statistically bounded. In addition, the algorithm exposes a convenient interface for users to define the requirement on detection latency, thereby tuning the intrinsic tradeoff between energy efficiency and event detection performance. The algorithm is a lightweight and completely localized protocol, and introduces minimal communication overhead. Extensive experiments have been conducted and results demonstrate that this algorithm significantly prolongs the system lifetime. <s> BIB006 </s> A Survey of Localization in Wireless Sensor Network <s> Scheduling the Sensor Node to Optimize the Tradeoff between Localization Performance and Energy Consumption. <s> The paper develops DILOC, a \emph{distributive}, \emph{iterative} algorithm that locates M sensors in $\mathbb{R}^m, m\geq 1$, with respect to a minimal number of m+1 anchors with known locations. The sensors exchange data with their neighbors only; no centralized data processing or communication occurs, nor is there centralized knowledge about the sensors' locations. DILOC uses the barycentric coordinates of a sensor with respect to its neighbors that are computed using the Cayley-Menger determinants. These are the determinants of matrices of inter-sensor distances. We show convergence of DILOC by associating with it an absorbing Markov chain whose absorbing states are the anchors. We introduce a stochastic approximation version extending DILOC to random environments when the knowledge about the intercommunications among sensors and the inter-sensor distances are noisy, and the communication links among neighbors fail at random times. We show a.s. convergence of the modified DILOC and characterize the error between the final estimates and the true values of the sensors' locations. Numerical studies illustrate DILOC under a variety of deterministic and random operating conditions. <s> BIB007 </s> A Survey of Localization in Wireless Sensor Network <s> Scheduling the Sensor Node to Optimize the Tradeoff between Localization Performance and Energy Consumption. <s> Power scheduling and localization play important roles in network topology management. Distributed power scheduling is an efficient way to construct a reliable and energy-efficient network topology. Localization provides geographical information for topology management. However, during the course of transmission power control, localization based on the received signal strength (RSS) is a challenging problem because of the inconsistent RSS indication (RSSI) measurements in wireless sensor networks. This paper presents an algorithm that takes advantage of the information from the ongoing wireless communication links to calculate the estimated position of sensor nodes. Considering the existing transmit-power-aware medium access control (MAC) protocols, we propose a localization algorithm based on particle filtering for sensor networks assisted by multiple transmit-power information. Therefore, the primary contribution of this paper is the elegant strategy on how to incorporate the practical multiple transmit-power information into the process of particle filtering. Furthermore, a general message-passing framework of the transmit-power-aware MAC is seamlessly integrated with the tracking service. The proposed particle-filtering-based localization algorithm uses the RSS information from the beacons or the neighboring nodes to infer the position of the concerned node without the requirement of any additional hardware instruments. Theoretical analysis and simulation results are presented to demonstrate the performance of the proposed localization method. The simulation results show that the proposed algorithm outperforms the existing algorithms that do not utilize multiple power information. <s> BIB008 </s> A Survey of Localization in Wireless Sensor Network <s> Scheduling the Sensor Node to Optimize the Tradeoff between Localization Performance and Energy Consumption. <s> Existing localization schemes in wireless ad hoc networks rely on redundant measurements from multiple devices with known positions in order to reduce error. However, when node density is high this can result in excessive localization messages with minimal improvement on position accuracy. In this work we present a scheduling algorithm to select a subset of active reference nodes to be used in localization, which has the effect of reducing message overhead, increasing network lifetime, and improving localization accuracy in dense mobile networks. We investigate the Cramer-Rao Lower Bound (CRLB) and existing single-hop localization techniques to determine the optimal average node density to ensure sufficient estimation accuracy. The correctness and effectiveness of the proposed scheme is evaluated through extensive simulation results, which show that in dense networks localization messages are greatly reduced and network lifetimes are more than doubled, while maintaining high estimation accuracy. Furthermore, computational time of localization algorithms is reduced, which effectively decreases accumulated error due to computation latency when locating a mobile device. <s> BIB009
A typical sensor network consists of a large number of small sensors which are deployed randomly. However, a sensor node has limited resources because of battery power and small memory. Therefore, nodes' resource management is compulsory. In typical sensor network applications, nodes are deployed in an unattended environment such as disaster management, habitat monitoring, industrial process control, and object tracking. Enormous event data will be generated for a long sensing time in WSN. Hence, by the methods of nodes resource management, effective usage of the vast amount of data is crucial. In the meanwhile, the scalability of both energy and spatial dimensions in distributed sensor network is a key issue. Sensor networks must track various phenomena at the same time and work within limited communication bandwidth, energy, and processing speed. Therefore, it is critical to distribute the workload across only the "relevant" sensors equally and leave other sensors available for other things. These characteristics of WSN determine the importance of nodes resource management. Energy consumption is one of the most important issues in recent years. Ren and Meng BIB008 proposed a localization algorithm based on particle filtering for sensor networks. Assisted by multiple-transmit-power information, it outperforms the existing algorithms that do not utilize multiplepower information. You et al. BIB004 proposed a specified positional error tolerance, the sensor-enhanced and energyefficient adaptive localization system in an application. This localization system dynamically sets sleep time for the nodes and adapting the sampling rate of target's mobility level. However, the process of error estimation dynamically relies on several factors in the specific environment. Gribben et al. BIB009 proposed a scheduling algorithm that selects a subset of active beacon nodes to be used in localization. It served to reduce the message overhead, increased network lifetime, and improved localization accuracy in dense mobile networks. However, maximizing the nodes' sleep time is much more energy efficient if the nodes never wake up until the reception of wake-up messages. The above algorithms have the same feature that the duty cycle of the sensor nodes is fixed in advance. In BIB006 , the authors proposed an innovative probabilistic wake-up protocol for energyefficient event detection in WSNs. The main idea of it is to reduce the duty cycle of every sensor via probabilistic wakeup through the dense deployment of sensor networks. The problem of unique network localization and a mathematical topic known as rigidity theory have a strong connection. Goldenberg et al. BIB005 proposed a localization method for sparse networks by sweeping techniques. This method is saving all possible positions in each position step and pruning incompatible ones. One drawback of sweeping method is that the possible positions could increase exponentially as long as the number of nodes increased. Other types of localization methods are also available, such as using multidimensional scaling BIB001 BIB002 or mobile anchors BIB003  . However, all the previous works tried to localize 8 International Journal of Distributed Sensor Networks more sensor nodes in a network without guaranteeing all of them. Khan et al. BIB007 introduced a localization method to localize all nodes by the minimal number of anchor nodes. However, they assume that the sensing range of each sensor can be enlarged to guarantee certain triangulation, so that three anchor nodes are enough to localize all sensors.