The use of computational geometry techniques to resolve the issues of coverage and connectivity in wireless sensor networks

Wireless Sensor Networks (WSNs) enhance the ability to sense and control the physical environment in various applications. The functionality of WSNs depends on various aspects like the localization of nodes, the strategies of node deployment, and a lifetime of nodes and routing techniques, etc. Coverage is an essential part of WSNs wherein the targeted area is covered by at least one node. Computational Geometry (CG) -based techniques significantly improve the coverage and connectivity of WSNs. This paper is a step towards employing some of the popular techniques in WSNs in a productive manner. Furthermore, this paper attempts to survey the existing research conducted using Computational Geometry-based methods in WSNs. In order to address coverage and connectivity issues in WSNs, the use of the Voronoi Diagram, Delaunay Triangulation, Voronoi Tessellation, and the Convex Hull have played a prominent role. Finally, the paper concludes by discussing various research challenges and proposed solutions using Computational Geometry-based techniques.Web of Science2218art. no. 700

Bodacious-instance coverage mechanism for wireless sensor network

Copyright © 2020 Shahzad Ashraf et al. Due to unavoidable environmental factors, wireless sensor networks are facing numerous tribulations regarding network coverage. These arose due to the uncouth deployment of the sensor nodes in the wireless coverage area that ultimately degrades the performance and confines the coverage range. In order to enhance the network coverage range, an instance (node) redeployment-based Bodacious-instance Coverage Mechanism (BiCM) is proposed. The proposed mechanism creates new instance positions in the coverage area. It operates in two stages; in the first stage, it locates the intended instance position through the Dissimilitude Enhancement Scheme (DES) and moves the instance to a new position, while the second stage is called the depuration, when the moving distance between the initial and intended instance positions is sagaciously reduced. Further, the variations of various parameters of BiCM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius have been explored, and the optimized parameters are identified. The performance metric has been meticulously analyzed through simulation results and is compared with the state-of-the-art Fruit Fly Optimization Algorithm (FOA) and, one step above, the tuned BiCM algorithm in terms of mean coverage rate, computation time, and standard deviation. The coverage range curve for various numbers of iterations and sensor nodes is also presented for the tuned Bodacious-instance Coverage Mechanism (tuned BiCM), BiCM, and FOA. The performance metrics generated by the simulation have vouched for the effectiveness of tuned BiCM as it achieved more coverage range than BiCM and FOA

Distributed navigation of multi-robot systems for sensing coverage

A team of coordinating mobile robots equipped with operation specific sensors can perform different coverage tasks. If the required number of robots in the team is very large then a centralized control system becomes a complex strategy. There are also some areas where centralized communication turns into an issue. So, a team of mobile robots for coverage tasks should have the ability of decentralized or distributed decision making. This thesis investigates decentralized control of mobile robots specifically for coverage problems. A decentralized control strategy is ideally based on local information and it can offer flexibility in case there is an increment or decrement in the number of mobile robots. We perform a broad survey of the existing literature for coverage control problems. There are different approaches associated with decentralized control strategy for coverage control problems. We perform a comparative review of these approaches and use the approach based on simple local coordination rules. These locally computed nearest neighbour rules are used to develop decentralized control algorithms for coverage control problems. We investigate this extensively used nearest neighbour rule-based approach for developing coverage control algorithms. In this approach, a mobile robot gives an equal importance to every neighbour robot coming under its communication range. We develop our control approach by making some of the mobile robots playing a more influential role than other members of the team. We develop the control algorithm based on nearest neighbour rules with weighted average functions. The approach based on this control strategy becomes efficient in terms of achieving a consensus on control inputs, say heading angle, velocity, etc. The decentralized control of mobile robots can also exhibit a cyclic behaviour under some physical constraints like a quantized orientation of the mobile robot. We further investigate the cyclic behaviour appearing due to the quantized control of mobile robots under some conditions. Our nearest neighbour rule-based approach offers a biased strategy in case of cyclic behaviour appearing in the team of mobile robots. We consider a clustering technique inside the team of mobile robots. Our decentralized control strategy calculates the similarity measure among the neighbours of a mobile robot. The team of mobile robots with the similarity measure based approach becomes efficient in achieving a fast consensus like on heading angle or velocity. We perform a rigorous mathematical analysis of our developed approach. We also develop a condition based on relaxed criteria for achieving consensus on velocity or heading angle of the mobile robots. Our validation approach is based on mathematical arguments and extensive computer simulations

Algorithms for task assignment in wireless networks of microcontroller sensor nodes and autonomous robots

U bežičnoj mreži senzora i robota, senzorski moduli vrše nadzor fizičkih veličina od značaja, a roboti imaju ulogu izvršilaca zadataka koji im se dodeljuju primenom odgovarajućeg algoritma. Nakon detekcije događaja od strane statičkih senzorskih čvorova i prosleđivanja informacija o događajima robotima, potrebno je dodeliti zadatke robotima na efikasan način. Dodela zadataka vrši se u skladu sa prirodom različitih scenarija koji se mogu javiti u praksi. U okviru disertacije razmatran je slučaj kada se konkurentno javlja više događaja kojima je potrebno dodeliti izvršioce. U pogledu energetske efikasnosti, u ovakvim sistemima kao ključni problemi javljaju se minimizacija ukupne dužine kretanja robota i optimizacija komunikacije u mreži. Od komunikacinih protokola za otkrivanje izvršilaca, u ovoj disertaciji predstavljena su poboljšanja postojećeg iMesh protokola i uveden je novi vCell protokol zasnovan na lokalizovanom formiranju ćelija Voronoi dijagrama. Takođe, upoređene su performanse novog protokola sa postojećim (pravougaoni kvorum i iMesh) u gustim mrežama, retkim mrežama i mrežama sa rupama u topologiji. Uz to, uvedeni su algoritmi za ažuriranje lokacije kojima mreža reaguje na kretanje robota. Rezultati simulacija pokazuju da vCell postiže efikasnost blizu 100% u nalaženju najbližeg robota u gustim mrežama. U retkim mrežama, efikasnost mu je do 40% bolja u odnosu na ostala rešenja. Kao glavni rezultat u disertaciji prikazani su novi algoritmi za dodelu robota kao izvršilaca zadataka događajima, čime su prevaziđni nedostaci više do sada poznatih rešenja ovog problema. Za zadati skup događaja i skup robota, svakom događaju dodeljen je po jedan robot koji je zadužen za obilazak lokacije događaja. Tokom pojedinačnih rundi, robotima je dozvoljen obilazak jednog događaja kada se vrši uparivanje, ili više događaja, kada se vrši sekvencijalna dodela. U distribuiranom slučaju, statički senzorski uređaji detektuju događaje i prijavljuju ih obližnjim robotima. Algoritam PDM koji se odnosi na unapređeno uparivanje sa mogućnošću razmene partnera, eliminiše dugačke ivice koje se mogu javiti prilikom uparivanja. Algoritam SQD za sekvencijalnu dodelu događaja robotima iterativno pronalazi par robot-događaj sa najmanjim međusobnim rastojanjem, uvrštava izabrani događaj u listu za oblazak izabranog robota i ažurira poziciju robota. Takođe su predložene generalizacije koje omogućavaju da događaji budu posećeni od strane više robota i koje uzimaju u obzir vremenska ograničenja. Distribuirani algoritam MAD, koji je zasnovan na iMesh informacionoj strukturi i lokalnim aukcijama u robotskoj mreži, vrši dodelu robota događajima na lokalizovan i energetski efikasan način. Rezultati simulacija potvrđuju prednosti predloženih algoritama u odnosu na postojeća rešenja, kako u pogledu skraćivanja dužina putanja robota, tako i u produženju životnog vremena sistema.In a typical wireless sensor and robot network, sensor nodes monitor physical values of interest, while robots perform some automated tasks. The tasks are assigned to robots by means of an appropriate algorithm. Upon the occurrence of events which are detected by sensor nodes, the information about the events needs to be delivered to robots. Afterwards, it is necessary to assign tasks to robots in an efficient way. Task assignment is performed according to the nature of different scenarios which might occur in practice. This thesis is focused on the case when multiple events, all of which require to be visited by robots, happen simultaneously. Regarding energy efficiency, the key issues which arise in such systems are minimization of robot travel paths, and optimization of the network traffic. In this thesis, the following service discovery protocols are presented: improvements of the existing iMesh protocol, and the novel vCell protocol, which is based on localized formation of an information structure which resembles Voronoi diagram. Furthermore, the performaces of new vCell protocol is compared with the existing protocols (Quorum and iMesh) in dense networks, sparse networks, and networks with holes in topology. Also, location update algorithms are introduced, which deal with robot mobility. The simulations show that vCell achieves nearly 100% success rate in finding the nearest robot in dense networks. In sparse networks, it outperforms the other existing solutions by up to 40%. As a key contributtion, the novel dispatch lgorithms have been introduced. Given a set of events and a set of robots, the dispatch problem is to allocate one robot for each event to visit it. In a single round, each robot may be allowed to visit only one event (matching dispatch), or several events in a sequence (sequence dispatch). In a distributed setting, each event is discovered by a sensor and reported to a robot. In this thesis, novel algorithms are presented, whichh are aimed at overcoming the shortcomings of several existing solutions. Pairwise distance based matching algorithm (PDM) eliminates long edges by pairwise exchanges between matching pairs. Sequence dispatch algorithm (SQD) iteratively finds the closest event-robot pair, includes the event in dispatch schedule of the selected robot and updates its position accordingly. When event-robot distances are multiplied by robot resistance (inverse of the remaining energy), the corresponding energybalanced variants are obtained. Also, generalizations are introduced which handle multiple visits and timing constraints. Distributed algorithm MAD is based on information mesh infrastructure and local auctions within the robot network for obtaining the optimal dispatch schedule for each robot. The simulations conducted confirm the advantages of our algorithms over other existing solutions in terms of average robot-event distance and lifetime

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

Dynamic Coverage Control and Estimation in Collaborative Networks of Human-Aerial/Space Co-Robots

In this dissertation, the author presents a set of control, estimation, and decision making strategies to enable small unmanned aircraft systems and free-flying space robots to act as intelligent mobile wireless sensor networks. These agents are primarily tasked with gathering information from their environments in order to increase the situational awareness of both the network as well as human collaborators. This information is gathered through an abstract sensing model, a forward facing anisotropic spherical sector, which can be generalized to various sensing models through adjustment of its tuning parameters. First, a hybrid control strategy is derived whereby a team of unmanned aerial vehicles can dynamically cover (i.e., sweep their sensing footprints through all points of a domain over time) a designated airspace. These vehicles are assumed to have finite power resources; therefore, an agent deployment and scheduling protocol is proposed that allows for agents to return periodically to a charging station while covering the environment. Rules are also prescribed with respect to energy-aware domain partitioning and agent waypoint selection so as to distribute the coverage load across the network with increased priority on those agents whose remaining power supply is larger. This work is extended to consider the coverage of 2D manifolds embedded in 3D space that are subject to collision by stochastic intruders. Formal guarantees are provided with respect to collision avoidance, timely convergence upon charging stations, and timely interception of intruders by friendly agents. This chapter concludes with a case study in which a human acts as a dynamic coverage supervisor, i.e., they use hand gestures so as to direct the selection of regions which ought to be surveyed by the robot. Second, the concept of situational awareness is extended to networks consisting of humans working in close proximity with aerial or space robots. In this work, the robot acts as an assistant to a human attempting to complete a set of interdependent and spatially separated multitasking objectives. The human wears an augmented reality display and the robot must learn the human's task locations online and broadcast camera views of these tasks to the human. The locations of tasks are learned using a parallel implementation of expectation maximization of Gaussian mixture models. The selection of tasks from this learned set is executed by a Markov Decision Process which is trained using Q-learning by the human. This method for robot task selection is compared against a supervised method in IRB approved (HUM00145810) experimental trials with 24 human subjects. This dissertation concludes by discussing an additional case study, by the author, in Bayesian inferred path planning. In addition, open problems in dynamic coverage and human-robot interaction are discussed so as to present an avenue forward for future work.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155147/1/wbentz_1.pd

Optimizing Deployment and Maintenance of Indoor Localization Systems

Pervasive computing envisions the achievement of seamless and distraction-free support for tasks by means of context-aware applications. Context can be defined as the information which can be used to characterize the situation of an entity such as persons or objects which are relevant for the behaviour of an application. A context-aware application is one which can adapt its functionality based on changes in the context of the user or entity. Location is an important piece of context because a lot of information can be inferred about the situation of an entity just by knowing where it is. This makes location very useful for many context-aware applications. In outdoor scenarios, the Global Positioning System (GPS) is used for acquiring location information. However, GPS signals are relatively weak and do not penetrate buildings well, rendering them less than suitable for location estimation in indoor environments. However, people spend most of their time in indoor locations and therefore it is necessary to have location systems which would work in these scenarios. In the last two decades, there has been a lot of research into and development of indoor localization systems. A wide range of technologies have been applied in the development of these systems ranging from vision-based systems, sound-based systems as well as Radio Frequency (RF) signal based systems. In a typical indoor localization system deployment, an indoor environment is setup with different signal sources and then the distribution of the signals in the environment is recorded in a process known as calibration. The distribution of signals, also known as a radio map, is then later employed to estimate location of users by matching their signal observations to the radio map. However, not all the different signal technologies and approaches provide the right balance of accuracy, precision and cost to be suitable for most real world deployment scenarios. Of the different RF signal technologies, WLAN and Bluetooth based indoor localization systems are the most common due to the ubiquity of the signal deployments for communication purposes, and the accessibility of compatible mobile computing devices to the users of the system. Many of the indoor localization systems have been developed under laboratory conditions or only with small-scale controlled indoor areas taken into account. This poses a challenge when transposing these systems to real-world indoor environments which can be rather large and dynamic, thereby significantly raising the cost, effort and practicality of the deployment. Furthermore, due to the fact that indoor environments are rarely static, changes in the environment such as moving of furniture or changes in the building layout could adversely impact the performance of the localization system deployment. The system would then need to be recalibrated to the new environmental conditions in order to achieve and maintain optimal localization performance in the indoor environment. If this happens regularly, it can significantly increase the cost and effort for maintenance of the indoor localization system over time. In order to address these issues, this dissertation develops methods for more efficient deployment and maintenance of the indoor localization systems. A localization system deployment consists of three main phases; setup and calibration, localization and maintenance. The main contributions of this dissertation are proposed optimizations to the different stages of the localization system deployment lifecycle. First, the focus is on optimizing setup and calibration of fingerprinting-based indoor localization systems. A new method for dense and efficient calibration of the indoor environmental areas is proposed, with minimal effort and consequently reduced cost. During calibration, the signal distribution in the indoor environment is distorted by the presence of the person doing the calibration. This leads to a radio map which is not a very accurate representation of the environment. Therefore a model for WLAN signal attenuation by the human body is proposed in this dissertation. The model captures the pattern of change to the signal due the presence of the human body in the signal path. By applying the model, we can compensate for the attenuation caused by the person and thereby generate a more accurate map of the signal distribution in the environment. A more precise signal distribution leads to better precision during location estimation. Secondly, some optimizations to the localization phase are presented. The dense fingerprints of the environment created during the setup phase are used for generating location estimates by matching the captured signal distribution with the pre-recorded distribution in the environment. However, the location estimates can be further refined given additional context information. This approach makes use of sensor fusion and ambient intelligence in order to improve the accuracy of the location estimates. The ambient intelligence can be gotten from smart environments such as smart homes or offices, which trigger events that can be applied to location estimation. These optimizations are especially useful for indoor tracking applications where continuous location estimation and accurate high frequency location updates are critical. Lastly, two methods for autonomous recalibration of localization systems are presented as optimizations to the maintenance phase of the deployment. One approach is based on using the localization system infrastructure to monitor the signal characteristic distribution in the environment. The results from the monitoring are used by the system to recalibrate the signal distribution map as needed. The second approach evaluates the Received Signal Strength Indicator (RSSI) of the signals as measured by the devices using the localization system. An algorithm for detecting signal displacements and changes in the distribution is proposed, as well as an approach for subsequently applying the measurements to update the radio map. By constantly self-evaluating and recalibrating the system, it is possible to maintain the system over time by limiting the degradation of the localization performance. It is demonstrated that the proposed approach achieves results comparable to those obtained by manual calibration of the system. The above optimizations to the different stages of the localization deployment lifecycle serve to reduce the effort and cost of running the system while increasing the accuracy and reliability. These optimizations can be applied individually or together depending on the scenario and the localization system considered

5G wireless network support using umanned aerial vehicles for rural and low-Income areas

>Magister Scientiae - MScThe fifth-generation mobile network (5G) is a new global wireless standard that enables state-of-the-art mobile networks with enhanced cellular broadband services that support a diversity of devices. Even with the current worldwide advanced state of broadband connectivity, most rural and low-income settings lack minimum Internet connectivity because there are no economic incentives from telecommunication providers to deploy wireless communication systems in these areas. Using a team of Unmanned Aerial Vehicles (UAVs) to extend or solely supply the 5G coverage is a great opportunity for these zones to benefit from the advantages promised by this new communication technology. However, the deployment and applications of innovative technology in rural locations need extensive research