A Review on Sensor Network Issues and Robotics <s> Robotic Sensor Network Applications <s> Ubiquitous computing is finally becoming a reality. However, there are many practical issues that stand in the way of mass acceptance. We have been investigating these practical concerns within the context of an autonomous application that takes care of houseplants using a sensor network and a mobile robot. We believe that emphasizing autonomy and thereby minimizing demands on users will help us address the many practical concerns that will arise not only in PlantCare but also in many other ubiquitous applications. In this paper, we discuss the technical challenges that we have encountered while trying to make PlantCare a reality and report on our experience in addressing these challenges. <s> BIB001 </s> A Review on Sensor Network Issues and Robotics <s> Robotic Sensor Network Applications <s> Wireless sensor networks have received considerable attention for their potential as a cheap, easily deployed, distributed monitoring tool. Recently, researchers have begun to investigate the use of wireless sensor networks to drive closed-loop control systems. However, such composite systems are nontrivial to design due to the system interface dichotomy: control systems typically assume periodic, high frequency sensor updates whereas sensor networks provide aperiodic, low frequency, and laggy sensor updates. Utilizing robot navigation and pursuit-evasion games as benchmarks, our research focuses on improving control system performance by exploiting the properties of wireless sensor networks. ::: We developed and deployed a real-world, medium-scale wireless sensor network for playing pursuit-evasion games. Using our experience from this deployment, we highlight the difficulties in using sensor network data to accurately localize robots. Several techniques designed to compensate for such difficulties are developed and incorporated into an unified system architecture. To test our architecture, an application-level simulator, accounting for many of the sensor network characteristics that frustrate control design, is developed. This simulator allows us to identify components of our system architecture that can improve the performance of control systems operating in networks of sensors. Amongst the components, intelligent path planning is identified as uniquely important in improving robot localization accuracy during navigation. ::: Path planning techniques that use information maps, exploiting the knowledge of node topology and sensor models, are developed. Information is a metric for measuring the ability of a region in the environment to aid in robot localization. In particular, for each region in the environment, an information map computes the change in entropy expected by a robot in this area using Markov localization. We adapt sensor network models for use with information maps and verify the ability of such maps to improve robot localization. Additionally, automatic path planning techniques based on information maps are developed that minimize localization error. We compare the performance of these planners with other path planners, and demonstrate that this technique is effective for generating paths that increase the accuracy of localization while typically requiring fewer detections. <s> BIB002 </s> A Review on Sensor Network Issues and Robotics <s> Robotic Sensor Network Applications <s> This chapter contains sections titled: Wireless Sensor Networks: Generalities and Applications Sensor Nodes Mobility Mobile Relays Mobile Sinks Comparing Different Approaches to Mobility Comparative Evaluation Some Open Problems and Research Directions Conclusions Exercises Acknowledgments Bibliography <s> BIB003 </s> A Review on Sensor Network Issues and Robotics <s> Robotic Sensor Network Applications <s> Most wireless sensor networks consist of a large number of static, low-power, short-lived, and unreliable sensors. In this paper, we considered sensor networks con- sisting of both static and mobile nodes. Integrating both types of devices enables new applications, such as nodes replacement, hole and partition recovery, and autonomous deployment and redeployment. We designed a smart mobile robot and implemented an application of nodes replacement to demonstrate its use, via our nodes replacement algo- rithm. In this algorithm, the mobile robots can navigate towards low-energy sensor nodes and replace them automatically, with new sensor nodes, having no location information. The navigation algorithm is based on received signal strength between the mobile robot and the communicating node. The experimental results confirm that the mobile robots successfully achieved their assigned tasks. <s> BIB004 </s> A Review on Sensor Network Issues and Robotics <s> Robotic Sensor Network Applications <s> Robotics can have many applications in wireless sensor networks. Robotics can be used to help solve many problems in wireless sensor networks, such as localizing nodes, acting as data mules, repositioning nodes, detecting and reacting to sensor failure, aggregate sensor data, and even sometimes provide mobile battery chargers for the nodes. Inversely, wireless sensor networks can help solve many problems in robotics, such as localization of the robot, path planning, mapping, and sensing. <s> BIB005
Most of the problems in traditional sensor networks may be addressed by incorporating intelligent, mobile robots directly into it. Mobile robots provide the means to explore and interact with the environment in a dynamic and decentralized way. In addition to enabling mission capabilities well beyond those provided by sensor networks, these new systems of networked sensors and robots allow for the development of new solutions to classical problems such as localization and navigation BIB003 . Many problems in sensor networks can be solved when putting robotics into use, problems like node positioning and localization, acting as data mule, detecting and reacting to sensor failure, for nodes mobile battery chargers, and so forth. And also wireless sensor networks can help solve many problems in robotics such as robot path planning, localization mapping, and sensing in robots BIB005 . Mobile nodes can be implemented as autonomous perceptive mobile robots or as perceptive robots whose sensor systems address environmental task and navigational task. Robotic sensor networks are particular mobile sensor networks or we can say robotic sensor networks are distributed systems in which mobile robots carry sensors around an environment to sense phenomena and to produce in depth environmental assessments. There are many applications of wireless sensor networks in robotics like robotics advanced sensing, coordination in robots, robot path planning, and robot localization, robot navigation, network coverage, proper data communication, data collection, and so forth. Using WSN helps emergency response robots to be conscious of the conditions such as electromagnetic field monitoring and forest fire detection. These networks improve the sensing capability and can also help robot in finding the way to the area of interest. WSNs can be helpful for coordinating multiple robots and swarm robotics because the network can assist the swarm to share sensor data, tracking its members, and so forth. To perform the coordinated tasks it sends robots to the different locations and also a swarm takes decisions based on the localization of events, allowing path planning and coordination for multiple robots to happen efficiently and optimally and direct the swarm members to the area of interest. In the localization part there are many techniques for localizing robots within a sensor network. Cameras have been put into use to identify the sensors equipped with infrared light to triangulate themselves based on distances derived from pixel size. A modified SLAM algorithm has been utilized by some methods which uses robots to localize itself within the environment and then compensates for SLAM sensor error by fusing the estimated location with the estimated location in WSN based on RSSI triangulation BIB005 . An intruder detection system was presented in  which uses both wireless sensor networks and robots. In order to learn and detect intruders in previously unknown environment, sensor network uses an unsupervised fuzzy adaptive resonance theory (ART) neural network. A mobile robot travels upon the detection of an intruder to the position where the intruder is detected. The wireless sensor network uses a hierarchical communication/learning structure where mobile robot is root node of the tree. In wireless sensor networks robotics can also play a crucial role. They can be used for replacing broken nodes, repositioning nodes, recharging batteries, and so forth. To increase the feasibility of WSNs, BIB001 used robots because they have actuation but limited coverage in sensing while sensor networks lack actuation but they can acquire data. In servicing WSNs, robot task allocation and robot task fulfillment was examined  . Problems are examined in robot task allocation such as using multitask or single-task robots in a network and how to organize their behavior to optimally service the network. The route which a robot takes to service nodes is examined in robot task fulfillment. To improve the robot localization, BIB002 adapts sensor network models with information maps and then checks the capability of such maps to improve the localization. The node replacement application was developed by BIB004 in which a robot would navigate a sensor network based on RSSI (received signal strength indication) from nearby nodes and it would then send a help signal if a mote will begin to run low on power. And, then through the network, the help signal would be passed to direct the robot to replace the node. Robots can also be used to recharge batteries. The problem of localization can also be solved using robots; they can be used to localize the nodes in the network. They can also be used in data aggregation. In a network, they can also serve as data mules; data mules are robots that move around the sensor network to collect data from the nodes and then transport that data back to sink node or perform the aggregation operations operation on data BIB005 . The use of multirobot systems for carrying sensors around the environment represents a solution that has received a considerable attention and can provide some remarkable advantages as well. A number of applications have been addressed so far by robotic sensor networks, including environmental monitoring and search and rescue. When put into use robotics sensor networks can be used for effective search and rescue, monitoring electromagnetic fields, and so forth. Search and rescue systems should quickly and accurately locate victims and map search space and locations of victims and with human responders it should maintain communication. For a search and rescue system to fulfill its mission, the system should be proficient to rapidly and reliably trace its victims within the search space and should also be well capable to handle a dynamic and potentially hostile environment. For utilizing ad hoc networks,  presented an algorithmic framework consisting of a large number of robots and small, cheap, simple wireless sensors to perform proficient and robust target tracking. Without dependence on magnetic compass or GPS localization service, they described a robotic sensor network for target tracking, focusing on algorithms which are simple for information propagation and distributed decision making. They presented a robotic sensor network system that autonomously conducts target tracking without component possessing localization capabilities. The approach provides a way out with minimal hardware assumptions (in terms of sensing, localization, broadcast, and memory/processing capabilities) while subject to a dynamic changing environment. Moreover the framework adjusts dynamically to both target movement and addition/deletion of network components. The network gradient algorithm provides an advantageous trade-off between power consumption and performance and requires relatively bandwidth  . The monitoring of EMF phenomena is extremely important in practice, especially to guarantee the protection of the people living and working where these phenomena are significant. A specific robotic sensor network oriented to monitor electromagnetic fields (EMFs) was presented  . The activities of the system are being supervised by a coordinator computer, while a number of explorers (mobile robots equipped with EMF sensors) navigate in the environment and perform EMF measurement tasks. The system is a robotic sensor network that can autonomously deploy its explorers in an environment to cope with events like a moving EMF source. The system architecture is hierarchical. The activities of the system are being supervised by the computer and to perform the EMF tasks a number of explorers (mobile robots equipped with EMF sensors navigate through the environment). The grid map of the environment is maintained by the system, in which each cell can be either free or occupied by an obstacle or by a robot. The map is supposed to be known by the coordinator and the explorers. The environment is assumed to be static and the map is used by the explorers to navigate in the environment and by the coordinator to localize the EMF source  .