A Review on Sensor Network Issues and Robotics <s> Localization. <s> Wireless ad-hoc sensor networks have recently emerged as a premier research topic. They have great long-term economic potential, ability to transform our lives, and pose many new system-building challenges. Sensor networks also pose a number of new conceptual and optimization problems. Some, such as location, deployment, and tracking, are fundamental issues, in that many applications rely on them for needed information. We address one of the fundamental problems, namely coverage. Coverage in general, answers the questions about quality of service (surveillance) that can be provided by a particular sensor network. We first define the coverage problem from several points of view including deterministic, statistical, worst and best case, and present examples in each domain. By combining the computational geometry and graph theoretic techniques, specifically the Voronoi diagram and graph search algorithms, we establish the main highlight of the paper-optimal polynomial time worst and average case algorithm for coverage calculation. We also present comprehensive experimental results and discuss future research directions related to coverage in sensor networks. <s> BIB001 </s> A Review on Sensor Network Issues and Robotics <s> Localization. <s> Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions. <s> BIB002 </s> A Review on Sensor Network Issues and Robotics <s> Localization. <s> The ability of a sensor node to determine its physical location within a network (Localization) is of fundamental importance in sensor networks. Interpretating data from sensors will not be possible unless the context of the data is known; this is most often accomplished by tracking its physical location. Existing research has focused on localization in static sensor networks where localization is a one-time (or low frequency) activity. In contrast, this paper considers localization for mobile sensors: when sensors are mobile, localization must be invoked periodically to enable the sensors to track their location. The higher the frequency of localization, the lower the error introduced because of mobility. However, localization is a costly operation since it involves both communication and computation. In this paper, we propose and investigate adaptive and predictive protocols that control the frequency of localization based on sensor mobility behavior to reduce the energy requirements for localization while bounding the localization error. We show that such protocols can significantly reduce the localization energy without sacri ficing accuracy (in fact, improving accuracy for most situations). Using simulation and analysis we explore the tradeoff between energy efficiency and localization error due to mobility for several protocols. <s> BIB003 </s> A Review on Sensor Network Issues and Robotics <s> Localization. <s> Previous work on the coverage of mobile sensor networks focuses on algorithms to reposition sensors in order to achieve a static configuration with an enlarged covered area. In this paper, we study the dynamic aspects of the coverage of a mobile sensor network that depend on the process of sensor movement. As time goes by, a position is more likely to be covered; targets that might never be detected in a stationary sensor network can now be detected by moving sensors. We characterize the area coverage at specific time instants and during time intervals, as well as the time it takes to detect a randomly located stationary target. Our results show that sensor mobility can be exploited to compensate for the lack of sensors and improve network coverage. For mobile targets, we take a game theoretic approach and derive optimal mobility strategies for sensors and targets from their own perspectives. <s> BIB004 </s> A Review on Sensor Network Issues and Robotics <s> Localization. <s> Localization is a fundamental operation in mobile and self-configuring networks such as sensor networks and mobile ad hoc networks. For example, sensor location is often critical for data interpretation. Existing research focuses on localization mechanisms: algorithms and infrastructure designed to allow the sensors to determine their location. In a mobile environment, the underlying localization mechanism must be invoked repeatedly to maintain accurate location information. We propose and investigate adaptive and predictive protocols that control the frequency of localization based on sensor mobility behavior to reduce the energy requirements for localization while bounding the localization error. In addition, we evaluate the energy-accuracy tradeoffs. Our results indicate that the proposed protocols reduce the localization energy significantly without sacrificing accuracy. <s> BIB005 </s> A Review on Sensor Network Issues and Robotics <s> Localization. <s> Localization schemes for wireless sensor networks can be classified as range-based or range-free. They differ in the information used for localization. Range-based methods use range measurements, while range-free techniques only use the content of the messages. None of the existing algorithms evaluate both types of information. Most of the localization schemes do not consider mobility. In this paper, a Sequential Monte Carlo Localization Method is introduced that uses both types of information as well as mobility to obtain accurate position estimations, even when high range measurement errors are present in the network and unpredictable movements of the nodes occur. We test our algorithm in various environmental settings and compare it to other known localization algorithms. The simulations show that our algorithm outperforms these known range-oriented and range-free algorithms for both static and dynamic networks. Localization improvements range from 12% to 49% in a wide range of conditions. <s> BIB006 </s> A Review on Sensor Network Issues and Robotics <s> Localization. <s> Localization is an important issue for Wireless Sensor Networks in a wireless sensor network. A mobile sensor may change its position rapidly and thus require localization calls frequently. It is important to control the number of localization calls, as it is rather expensive. The existing schemes for reducing the frequency of localization calls for mobile sensors uses the technique of extrapolation which involves simple arithmetic calculations. We propose a technique to control the localization that gives much better result. The proposed method involves very low arithmetic computation overheads. We find analytical expressions for the estimated error if the rate of localizations is specified. Simulation studies are carried out to compare the performances of the proposed method with the methods proposed by Tilak et al. <s> BIB007 </s> A Review on Sensor Network Issues and Robotics <s> Localization. <s> Mobile wireless sensors need to know their localizations in many control and monitoring applications. Among all sensors, some know their exact position (i.e., they are equipped with GPS or they are positioned by human intervention). These sensors are called anchors. Some sensors can have different capabilities allowing them to calculate either distances or angles when they receive messages from others nodes. So, they only use anchor positions to obtain an estimated position. However, when sensors are mobile they cannot continuously calculate their position because of the energy constraints. This paper concerns the localization problem in the case where all nodes in the network (anchors and others sensors) are mobile. The authors propose three techniques following the capabilities of nodes. Thus, each node obtains either an exact position or an approximate position with the knowledge of the maximal error born. Also, the authors adapt the periods where nodes invoke their localization. Simulation results show the performances of our methods in term of accuracy and determinate the technique the more adapted related to the network configurations. <s> BIB008 </s> A Review on Sensor Network Issues and Robotics <s> Localization. <s> Many wireless sensor network applications require knowledge of node placement in order to make sense of sensor data in a spatial context. Networks of mobile sensors require position updates for navigation through the sensing region. The global positioning system is able to provide localization information, however in many situations it cannot be relied on, and alternative localization methods are required. We propose a technique for the localization and navigation of a mobile robot that uses the Doppler-shift in frequency observed by stationary sensor nodes. Our experimental results show that, by using observed RF Doppler shifts, a robot is able to navigate through a sensing region with an average localization error of 1.68 meters. <s> BIB009 </s> A Review on Sensor Network Issues and Robotics <s> Localization. <s> One interesting issue in recent sensor network research is the application of sensor nodes to mobile objects, such as people or animals, to gather non-stationary information. Apparently, locating mobile nodes is essential in many of the mobile sensor applications. Although several localization techniques have been proposed to locate mobile nodes, the mechanisms are usually based on many impractical assumptions, and mobility pattern of the mobile object is seldom considered. In this paper, we propose a practical and yet simple mobile node localization system using nearby anchor information which contains absolute time and positions. A history of anchor information is used to characterize the mobility of mobile devices. The unknown node then calculates its position with the archived anchor using a regression model. The simulation results show that the proposed algorithm outperforms previous methods under realistic conditions. We have also implemented the algorithm on real hardware, and diverse experiments were conducted to validate the feasibility of the proposed approach. <s> BIB010 </s> A Review on Sensor Network Issues and Robotics <s> Localization. <s> This paper proposes a new location algorithm based on MCB(Monte-Carlo Localization Boxed),that is,the Weighted-MCB.The MCB algorithm adopts the first-hop anchors and second-hop anchors in prediction step and filtering step,and uses no location information of neighboring nodes.In addition to the first-hop anchors and second-hop anchors,the samples(estimated locations) in the sample set of the first-hop nodes are also exploited in the Weighted-MCB,and thus the Weighted-MCB improves the localization accuracy by 13%～18% as compared with MCB. <s> BIB011 </s> A Review on Sensor Network Issues and Robotics <s> Localization. <s> More and more researches focus on the development of mobile wireless sensor networks (MWSNs) due to the favorable advantages and applications of MWSNs. However, there is not a comprehensive survey about the research issues on MWSNS for the state of art. In this paper, we survey the communication and data management issues on MWSNs and provide extensible research directions of MWSNs. <s> BIB012
Recently, there has been much focus on building mobile sensors, and we have seen the development of small-profile sensing devices that are quite capable of controlling their own movement. Mobility has become an important area of research for mobile sensor networks. Mobility enables sensor nodes to target and track moving phenomena such as vehicles, chemical clouds, and packages BIB009 . One of the most significant challenges for mobile sensor nodes is the need for localization. Localization is the ability of sensor nodes to find out its physical coordinates, and localization on mobile sensors is performed for navigational and tracking purposes. Localization is required in many applications in wireless sensor networks such as health, military, and industry. Extensive research has been done so far on localization. Many location discovery schemes have proposed to eliminate the need of GPS on every sensor node BIB011 . GPS is commonly considered to be a good solution for outdoor localization. However, GPS is still expensive and hence insufficient to be used for large number of devices in WSN. Some of the problems with GPS are as follows. These are some situations in which GPS will not work reliably because GPS receiver needs line of sight to the multiple satellites and it does not work well in the indoor environment. And GPS receivers are available for mote scale devices only. They are still expensive and undesirable for many applications. Even if GPS receivers became cheaper and are used in every node, the nodes cannot actively use GPS in mobile sensor networks. Typically GPS node consumes more energy than sensors and low-power transceivers. The problem of using GPS in a real environment also exists in GPS itself. GPS shows 10∼20 m of error when used in normal outdoor environments unless it uses a costly mechanism such as differential GPS. Deploying a large number of GPS in mobile sensor network has both limits and possibilities BIB010 . There are two types of localization algorithms, namely, centralized and distributed algorithms BIB011 . These centralized location techniques depend on sensor nodes transmitting data to a central location, where computation is performed to find out the location of each node BIB002 . Distributed algorithms do not need a central base station and for determining its location it relays on each node with only limited communication with nearby nodes BIB011 . Localization algorithms in MWSNs can be categorized into (1) range-based method, (2) range-free method, (3) mobility based method BIB012 . All of these methods vary in the information used for localization purposes. Range-based methods use range measurements while rangefree techniques only use the content of messages BIB006 . Rangebased methods also require expensive hardware to measure signal arrival time and angle of signal arrival. As compared to range-free methods these methods are expensive because of their pricey hardware BIB012 . Range-based approaches have also utilized time of arrival, received signal strength, time difference of arrival of two different signals (TDOA), and the angle of arrival. Though they can reach the fine resolution, either the required hardware is expensive or the results depend on impractical assumptions about signal propagation BIB002 . While range-free methods use local and hop count techniques, for range-based approaches these methods are very cost effective. Many localization algorithms have been proposed so far such as elastic localization algorithm (ELA) and mobile geographic distributed localization algorithm; both of these algorithms assume nonlimited storage in sensor nodes BIB012 . For sensor networks BIB002 two types of range-free algorithms have been proposed: local techniques and hop count techniques. Local techniques rely on high speed on a high density of seeds so that every node can hear several seeds and hop count techniques rely on a flooding network. Each node in centroid method estimates its location by calculating the center of the seeds locations it hears. Location error can be reduced if seeds are well positioned but in ad hoc deployments this is impossible. The APIT method separates the environment into triangular regions between beaconing nodes and to calculate the maximum area it uses the grid algorithm in which a node will likely reside BIB002 . Hop count techniques propagate the location estimation throughout the network where the seed density is low. In mobility-based methods, to improve accuracy and precision of localization method sequential Monte Carlo localization (SML) was proposed BIB004 without additional hardware except for GPS BIB012 . And without decreasing the nonlimited computational ability many techniques using SML are also being proposed. In order to achieve the accurate localization researchers proposed many algorithms, using the principles of Doppler shift and radio interferometry to achieve the accurate localization has also been used BIB012 . The three phases typically used in localization are (1) Coordination, (2) measurement, and (3) position estimation BIB009 (Figure 7 ). To initiate the localization a group of nodes coordinate first, a signal is then emitted by some nodes, and then some property of the signal is observed by some other nodes. By transforming the signal measurements into position estimates node position is then determined. To find the positions of sensors in order to reduce the frequency of localization, three techniques were proposed: static fixed rate (SFR), dynamic velocity monotonic (DVM) and mobility aware dead reckoning driven (MADRD) BIB005 . (1) Static fixed rate (SFR): the performance of this protocol varies with the mobility of sensors. In this base protocol each sensor invokes its localization periodically with a fixed time period . In this technique, the error will be high if the sensor is moving quickly and if it is moving slowly the error will be low BIB003 : (2) Dynamic velocity monotonic: this is an adaptive protocol; with the mobility of sensors localization is called adaptively in DVM; the higher the observed velocity is, the faster the node should localize to maintain the same level of error. It computes the node velocity when it localizes by dividing the distance it has moved since the last localization point by the time that elapsed since localization. The next localization point is scheduled based on the velocity at the time when a prespecified distance will be travelled if the node continues with the same velocity BIB007 . (3) Mobility aware dead reckoning driven (MADRD): to predict the future mobility this protocol computes the mobility pattern of the sensors. When the expected difference between the predicted mobility and expected mobility reaches the error threshold the localization should be triggered BIB008 : Mobility aware interpolation (MAINT) was proposed to estimate the current position with better trade-off between energy consumption and accuracy BIB007 . Their method uses interpolation which gives better estimation in most cases: BIB001 .4 networking protocol for fast point-to-multipoint or peer-to-peer networking. Its low-power consumption limits transmission distances to 10-100-meter line of sight though, depending on power output and environmental characteristics [40] . It operates in unlicensed ISM bands, so it is prone to interference from a wide range of signal types using the same frequency which can disrupt the radio frequency. From RSSI values the distance between two Zigbee nodes is calculated; based on the distance calculated from Zigbee modules many researchers found it suitable for indoor localization. It comes with modules, though orientation is the problem in many modules because most of its modules are without omnidirectional antenna ( Figure 8 ).