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Автор: Grafiati

Опубліковано: 12 жовтня 2024

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Статті в журналах з теми "Task allocation to sensors"

Hiraga, Motoaki, Toshiyuki Yasuda, and Kazuhiro Ohkura. "Evolutionary Acquisition of Autonomous Specialization in a Path-Formation Task of a Robotic Swarm." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 5 (September 20, 2018): 621–28. http://dx.doi.org/10.20965/jaciii.2018.p0621.

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Task allocation is an important concept not only in biological systems but also in artificial systems. This paper reports a case study of autonomous task allocation behavior in an evolutionary robotic swarm. We address a path-formation task that is a fundamental task in the field of swarm robotics. This task aims to generate the collective path that connects two different locations by using many simple robots. Each robot has a limited sensing ability with distance sensors, a ground sensor, and a coarse-grained omnidirectional camera to perceive its local environment and the limited actuators composed of two colored LEDs and two-wheeled motors. Our objective is to develop a robotic swarm with autonomous specialization behavior from scratch, by exclusively implementing a homogeneous evolving artificial neural network controller for the robots to discuss the importance of embodiment that is the source of congestion. Computer simulations demonstrate the adaptive collective behavior that emerged in a robotic swarm with various swarm sizes and confirm the feasibility of autonomous task allocation for managing congestion in larger swarm sizes.

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Yin, Xiang, Kaiquan Zhang, Bin Li, Arun Kumar Sangaiah, and Jin Wang. "A task allocation strategy for complex applications in heterogeneous cluster–based wireless sensor networks." International Journal of Distributed Sensor Networks 14, no. 8 (August 2018): 155014771879535. http://dx.doi.org/10.1177/1550147718795355.

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To a wireless sensor network, cooperation among multiple sensors is necessary when it executes applications that consist of several computationally intensive tasks. Most previous works in this field concentrated on energy savings as well as load balancing. However, these schemes merely considered the situations where only one type of resource is required which drastically constrains their practical applications. To alleviate this limitation, in this article, we investigate the issue of complex application allocation, where various distinctive types of resources are demanded. We propose a heuristic-based algorithm for distributing complex applications in clustered wireless sensor networks. The algorithm is partitioned into two phases, in the inter-cluster allocation stage, tasks of the application are allocated to various clusters with the purpose of minimizing energy consumption, and in the intra-cluster allocation stage, the task is distributed to appropriate sensor nodes with the consideration of both energy cost and workload balancing. In so doing, the energy dissipation can be reduced and balanced, and the lifetime of the system is extended. Simulations are conducted to evaluate the performance of the proposed algorithm, and the results demonstrate that the proposed algorithm is superior in terms of energy consumption, load balancing, and efficiency of task allocation.

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Zha, Zhihua, Chaoqun Li, Jing Xiao, Yao Zhang, Hu Qin, Yang Liu, Jie Zhou, and Jie Wu. "An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs." Journal of Sensors 2021 (April 5, 2021): 1–12. http://dx.doi.org/10.1155/2021/5582646.

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Research on intelligent transportation wireless sensor networks (ITWSNs) plays a very important role in an intelligent transportation system. ITWSNs deploy high-yield and low-energy-consumption traffic remote sensing sensor nodes with complex traffic parameter coordination on both sides of the road and use the self-organizing capabilities of each node to automatically establish the entire network. In the large-scale self-organization process, the importance of tasks undertaken by each node is different. It is not difficult to prove that the task allocation of traffic remote sensing sensors is an NP-hard problem, and an efficient task allocation strategy is necessary for the ITWSNs. This paper proposes an improved adaptive clone genetic algorithm (IACGA) to solve the problem of task allocation in ITWSNs. The algorithm uses a clonal expansion operator to speed up the convergence rate and uses an adaptive operator to improve the global search capability. To verify the performance of the IACGA for task allocation optimization in ITWSNs, the algorithm is compared with the elite genetic algorithm (EGA), the simulated annealing (SA), and the shuffled frog leaping algorithm (SFLA). The simulation results show that the execution performance of the IACGA is higher than EGA, SA, and SFLA. Moreover, the convergence speed of the IACGA is faster. In addition, the revenue of ITWSNs using IACGA is higher than those of EGA, SA, and SFLA. Therefore, the proposed algorithm can effectively improve the revenue of the entire ITWSN system.

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Xu, Haitao, Hongjie Gao, Chengcheng Zhou, Ruifeng Duan, and Xianwei Zhou. "Resource Allocation in Cognitive Radio Wireless Sensor Networks with Energy Harvesting." Sensors 19, no. 23 (November 22, 2019): 5115. http://dx.doi.org/10.3390/s19235115.

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The progress of science and technology and the expansion of the Internet of Things make the information transmission between communication infrastructure and wireless sensors become more and more convenient. For the power-limited wireless sensors, the life time can be extended through the energy-harvesting technique. Additionally, wireless sensors can use the unauthored spectrum resource to complete certain information transmission tasks based on cognitive radio. Harvesting enough energy from the environments, the wireless sensors, works as the second users (SUs) can lease spectrum resource from the primary user (PU) to finish their task and bring additional transmission cost to themselves. To minimize the overall cost of SUs and to maximize the spectrum profit of the PU during the information transmission period, we formulated a differential game model to solve the resource allocation problem in the cognitive radio wireless sensor networks with energy harvesting, considering the SUs as the game players. By solving the proposed resource allocation game model, we found the open loop Nash equilibrium solutions and feedback Nash equilibrium solutions for all SUs as the optimal control strategies. Ultimately, series numerical simulation experiments have been made to demonstrate the rationality and effectiveness of the game model.

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Bagherinia, Ali. "Optimized Task Allocation in Sensor Networks." International Journal of Information Technology, Modeling and Computing 1, no. 3 (August 31, 2013): 43–49. http://dx.doi.org/10.5121/ijitmc.2013.1305.

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Elmogy, Ahmed M., Alaa M. Khamis, and Fakhri O. Karray. "Market-Based Approach to Mobile Surveillance Systems." Journal of Robotics 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/841291.

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The active surveillance of public and private sites is increasingly becoming a very important and critical issue. It is, therefore, imperative to develop mobile surveillance systems to protect these sites. Modern surveillance systems encompass spatially distributed mobile and static sensors in order to provide effective monitoring of persistent and transient objects and events in a given area of interest (AOI). The realization of the potential of mobile surveillance requires the solution of different challenging problems such as task allocation, mobile sensor deployment, multisensor management, cooperative object detection and tracking, decentralized data fusion, and interoperability and accessibility of system nodes. This paper proposes a market-based approach that can be used to handle different problems of mobile surveillance systems. Task allocation and cooperative target tracking are studied using the proposed approach as two challenging problems of mobile surveillance systems. These challenges are addressed individually and collectively.

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Semnani, Samaneh Hosseini, and Otman A. Basir. "Multi-Target Engagement in Complex Mobile Surveillance Sensor Networks." Unmanned Systems 05, no. 01 (January 2017): 31–43. http://dx.doi.org/10.1142/s2301385017500030.

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Efficient use of the network’s resources to collect information about objects (events) in a given volume of interest (VOI) is a key challenge in large-scale sensor networks. Multi-sensor multi-target tracking in surveillance applications is an example where the network’s success in tracking targets, efficiently and effectively, hinges significantly on the network’s ability to allocate the right set of sensors to the right set of targets so as to achieve optimal performance which minimizes the number of uncovered targets. This task can be even more complicated when both the sensors and the targets are mobile. To ensure timely tracking of mobile targets, the surveillance sensor network needs to perform the following tasks in real-time: (i) target-to-sensor allocation; (ii) sensor mobility control and coordination. The computational complexity of these two tasks presents a challenge, particularly in large scale dynamic network applications. This paper proposes a formulation based on the Semi-flocking algorithm and the distributed constraint optimization problem (DCOP). The semi-flocking algorithm performs multi-target motion control and coordination, a DCOP modeling algorithm performs the target engagement task. As will be demonstrated experimentally in the paper, this algorithmic combination provides an effective approach to the multi-sensor/multi-target engagement problem, delivering optimal target coverage as well as maximum sensors utilization.

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Stanulovic, Jelena, Nathalie Mitton, and Ivan Mezei. "Routing with Face Traversal and Auctions Algorithms for Task Allocation in WSRN." Sensors 21, no. 18 (September 13, 2021): 6149. http://dx.doi.org/10.3390/s21186149.

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Four new algorithms (RFTA1, RFTA2, GFGF2A, and RFTA2GE) handling the event in wireless sensor and robot networks based on the greedy-face-greedy (GFG) routing extended with auctions are proposed in this paper. In this paper, we assume that all robots are mobile, and after the event is found (reported by sensors), the goal is to allocate the task to the most suitable robot to act upon the event, using either distance or the robots’ remaining energy as metrics. The proposed algorithms consist of two phases. The first phase of algorithms is based on face routing, and we introduced the parameter called search radius (SR) at the end of this first phase. Routing is considered successful if the found robot is inside SR. After that, the second phase, based on auctions, is initiated by the robot found in SR trying to find a more suitable one. In the simulations, network lifetime and communication costs are measured and used for comparison. We compare our algorithms with similar algorithms from the literature (k-SAAP and BFS) used for the task assignment. RFTA2 and RFTA2GE feature up to a seven-times-longer network lifetime with significant communication overhead reduction compared to k-SAAP and BFS. Among our algorithms, RFTA2GE features the best robot energy utilization.

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He, Jianhua, Siqi Tao, Yang Deng, Libin Chen, and Zhiying Mou. "Research on Multi-Sensor Resource Dynamic Allocation Auction Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 2 (April 2019): 330–36. http://dx.doi.org/10.1051/jnwpu/20193720330.

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This paper designs a multi-sensor resource dynamic allocation method based on auction algorithm. Tasks are prioritized according to the needs of the engineering field. Task priority is used as the basis for multi-sensor resource allocation order, taking into account the target's threat value and information needs. The sensor and task pairing function is established and used to measure the sensor resource dynamic allocation, we also use Analytic Hierarchy Process to determine the weight of each performance parameter in the pairing function (such as detection probability, intercept probability, positioning accuracy, tracking accuracy, recognition probability, etc.). The auction algorithm is improved by adding resource dynamic allocation constraints, which not only ensures the continuous execution of the target task, but also improves the dynamic allocation efficiency of multi-sensor resources. The simulation results show that the allocation method in this paper is scientific and reasonable.

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Zhu, Xiaojuan, Kuan-Ching Li, Jinwei Zhang, and Shunxiang Zhang. "Distributed Reliable and Efficient Transmission Task Assignment for WSNs." Sensors 19, no. 22 (November 18, 2019): 5028. http://dx.doi.org/10.3390/s19225028.

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Task assignment is a crucial problem in wireless sensor networks (WSNs) that may affect the completion quality of sensing tasks. From the perspective of global optimization, a transmission-oriented reliable and energy-efficient task allocation (TRETA) is proposed, which is based on a comprehensive multi-level view of the network and an evaluation model for transmission in WSNs. To deliver better fault tolerance, TRETA dynamically adjusts in event-driven mode. Aiming to solve the reliable and efficient distributed task allocation problem in WSNs, two distributed task assignments for WSNs based on TRETA are proposed. In the former, the sink assigns reliability to all cluster heads according to the reliability requirements, so the cluster head performs local task allocation according to the assigned phase target reliability constraints. Simulation results show the reduction of the communication cost and latency of task allocation compared to centralized task assignments. Like the latter, the global view is obtained by fetching local views from multiple sink nodes, as well as multiple sinks having a consistent comprehensive view for global optimization. The way to respond to local task allocation requirements without the need to communicate with remote nodes overcomes the disadvantages of centralized task allocation in large-scale sensor networks with significant communication overheads and considerable delay, and has better scalability.

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Дисертації з теми "Task allocation to sensors"

Pizzocaro, Diego. "Instantaneous multi-sensor task allocation in static and dynamic environments." Thesis, Cardiff University, 2011. http://orca.cf.ac.uk/31333/.

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A sensor network often consists of a large number of sensing devices of different types. Upon deployment in the field, these sensing devices form an ad hoc network using wireless links or cables to communicate with each other. Sensor networks are increasingly used to support emergency responders in the field usually requiring many sensing tasks to be supported at the same time. By a sensing task we mean any job that requires some amount of sensing resources to be accomplished such as localizing persons in need of help or detecting an event. Tasks might share the usage of a sensor, but more often compete to exclusively control it because of the limited number of sensors and overlapping needs with other tasks. Sensors are in fact scarce and in high demand. In such cases, it might not be possible to satisfy the requirements of all tasks using available sensors. Therefore, the fundamental question to answer is: “Which sensor should be allocated to which task?", which summarizes the Multi-Sensor Task Allocation (MSTA) problem. We focus on a particular MSTA instance where the environment does not provide enough information to plan for future allocations constraining us to perform instantaneous allocation. We look at this problem in both static setting, where all task requests from emergency responders arrive at once, and dynamic setting, where tasks arrive and depart over time. We provide novel solutions based on centralized and distributed approaches. We evaluate their performance using mainly simulations on randomly generated problem instances; moreover, for the dynamic setting, we consider also feasibility of deploying part of the distributed allocation system on user mobile devices. Our solutions scale well with different number of task requests and manage to improve the utility of the network, prioritizing the most important tasks.

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Viguria, Jimenez Luis Antidio. "Distributed Task Allocation Methodologies for Solving the Initial Formation Problem." Thesis, Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24731.

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Mobile sensor networks have been shown to be a powerful tool for enabling a number of activities that require recording of environmental parameters at various spatial and temporal distributions. These mobile sensor networks could be implemented using a team of robots, usually called robotic sensor networks. This type of sensor network involves the coordinated control of multiple robots to achieve specific measurements separated by varied distances. In most formation measurement applications, initialization involves identifying a number of interesting sites to which mobility platforms, instrumented with a variety of sensors, are tasked. This process of determining which instrumented robot should be tasked to which location can be viewed as solving the task allocation problem. Unfortunately, a centralized approach does not fit in this type of application due to the fault tolerance requirements. Moreover, as the size of the network grows, limitations in bandwidth severely limits the possibility of conveying and using global information. As such, the utilization of decentralized techniques for forming new sensor topologies and configurations is a highly desired quality of robotic sensor networks. In this thesis, several distributed task allocation algorithms will be explained and compared in different scenarios. They are based on a market approach since our interest is not only to obtain a feasible solution, but also an efficient one. Also, an analysis of the efficiency of those algorithms using probabilistic techniques will be explained. Finally, the task allocation algorithms will be implemented on a real system consisted of a team of six robots and integrated in a complete robotic system that considers obstacle avoidance and path planning. The results will be validated in both simulations and real experiments.

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PILLONI, VIRGINIA. "Dynamic deployment of applications in wireless sensor networks." Doctoral thesis, Università degli Studi di Cagliari, 2013. http://hdl.handle.net/11584/266095.

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Over the past decades, the progress inWirelss Sensor Network (WSN) technology, both in terms of processing capability and energy consumption reduction, has evolved WSNs into complex systems that can gather information about the monitored environment and make prompt and intelligent decisions. In the beginning, military applications drove the research and development of WSNs, with large-scale acoustic systems for underwater surveillance, radar systems for the collection of data on air targets, and Unattended Ground Sensor (UGS) systems for ground target detection. Typical civil WSNs are basically not complex monitoring systems, whose applications encompass environment and habitat monitoring, infrastructure security and terror threat alerts, industrial sensing for machine health monitoring, and traffic control. In these WSNs, sensors gather the required information, mostly according to a fixed temporal schedule, and send it to the sink, which interfaces with a server or a computer. Only at this point data from sensors can be processed, before being stored. Recent advances in Micro-Eletro-Mechanical Systems (MEMS), low power transceivers and microprocessor dimensions have led to cost effective tiny sensor devices that combine sensing with computation, storage and communication. These developments have contributed to the efforts on interfacing WSNs with other technologies, enabling them to be one of the pillars of the Internet of Things (IoT) paradigm. In this context, WSNs take a key role in application areas such as domotics, assisted living, e-health, enhanced learning automation and industrial manufacturing logistics, business/process management, and intelligent transportation of people and goods. In doing so, a horizontal ambient intelligent infrastructure is made possible, wherein the sensing, computing and communicating tasks can be completed using programmable middleware that enables quick deployment of different applications and services. One of the major issues with WSNs is the energy scarcity, due to the fact that sensors are mainly battery powered. In several cases, nodes are deployed in hostile or unpractical environments, such as underground or underwater, where replacing battery could be an unfeasible operation. Therefore, extending the network lifetime is a crucial concern. Lifetime improvement has been approached by many recent studies, from different points of view, including node deployment, routing schemes, and data aggregation Recently, with the consistent increase in WSN application complexity, the way distributed applications are deployed in WSNs is another important component that affects the network lifetime. For instance, incorrect execution of data processing in some nodes or the transmission of big amounts of data with low entropy in some nodes could heavily deplete battery energy without any benefit. Indeed, application tasks are usually assigned statically to WSN nodes, which is an approach in contrast with the dynamic nature of future WSNs, where nodes frequently join and leave the network and applications change over the time. This brings to issue talked in this thesis, which is defined as follows. Dynamic deployment of distributed applications in WSNs: given the requirements of WSN applications, mostly in terms of execution time and data processing, the optimal allocation of tasks among the nodes should be identified so as to reach the application target and to satisfy the requirements while optimizing the network performance in terms of network lifetime. This issue should be continuously addressed to dynamically adapt the system to changes in terms of application requirements and network topology.

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Quentel, Paul. "Architecture multi-agent distribuée et collaborative pour l’allocation de tâches à des senseurs : application aux systèmes navals." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2024. http://www.theses.fr/2024IMTA0406.

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L’évolution du contexte de défense aéronaval nécessite une modification majeure de l’architecture des systèmes de senseurs actuels afin de maitriser les futures menaces et d’intégrer les nouveaux dispositifs et senseurs à venir. Ces senseurs, hétérogènes, complémentaires et embarqués sur des plateformes navales ou aériennes, sont essentiels pour l’acquisition de données de l’environnement et l’établissement de la situation tactique. Dans ce contexte, les plateformes peuvent collaborer et partager leurs ressources senseurs pour accomplir de nouvelles fonctionnalités et établir un panorama global de la situation. Dans cette thèse, nous avons conçu et développé un système multi-agent pour l’allocation de tâches à des ressources distribuées sur des plateformes distinctes dans le but d’accomplir des capacités collaboratives. Nous présentons des scénarios illustrant les besoins opérationnels auxquels l’architecture doit répondre, établissant ainsi un cahier des charges. Ensuite, nous détaillons les étapes de la conception et de l’implémentation de cette nouvelle architecture, en décrivant chaque type d’agent et les interactions possibles entre eux. Nous proposons un algorithme d’enchère nécessitant des échanges entre les agents, soumis aux contraintes de bande passante et de latence. Enfin, nous présentons un banc d’essai intégrant des outils de capture et de visualisation de métriques du système, permettant l’évaluation des concepts d’agents et de leurs mécanismes de communication. L’objectif est de démontrer que notre architecture répond aux besoins opérationnels spécifiés, notamment le passage à l’échelle des algorithmes et des interfaces de communications des agents, la résistance aux pannes et la performance du système
The changing context of naval and aerial defense requires a major modification of current sensor system architectures to overcome future threats and to integrate next generation devices and sensors. These sensors, heterogeneous, complementary, and embedded on naval or aerial platforms, are essential for acquiring data from the environment in order to establish the tactical situation. In this context, platforms can collaborate and share their sensor resources to achieve new functionalities and set up a global overview of the situation. In this thesis, we have designed and developed a multi-agent system for allocating tasks to distributed resources on distinct platforms in order to accomplish collaborative capabilities. We present scenarios illustrating the operational needs that the architecture must meet, thus establishing a set of specifications. Then, we detail the steps involved in designing and implementing this new architecture, describing each type of agent and the possible interactions between them. We propose an auction algorithm requiring exchanges between agents, subject to bandwidth and latency constraints. Finally, we present a test bed integrating tools for capturing and display system metrics, allowing the evaluation of agent concepts and their communication mechanisms. The objective is to demonstrate that our architecture meets the specified operational requirements, in particular the scalability of the agents’ algorithms and communication interfaces, fault tolerance, and system performance

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Yu, Wanli [Verfasser], Alberto [Akademischer Betreuer] Garcia-Ortiz, Alberto [Gutachter] Garcia-Ortiz, and Karl-Ludwig [Gutachter] Krieger. "Energy aware task allocation algorithms for wireless sensor networks / Wanli Yu ; Gutachter: Alberto Garcia-Ortiz, Karl-Ludwig Krieger ; Betreuer: Alberto Garcia-Ortiz." Bremen : Staats- und Universitätsbibliothek Bremen, 2018. http://d-nb.info/1161844562/34.

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Havens, Michael E. "Dynamic allocation of fires and sensors." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FHavens.pdf.

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Norman, Victoria Catherine. "Caste and task allocation in ants." Thesis, University of Sussex, 2016. http://sro.sussex.ac.uk/id/eprint/63780/.

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Group living is a widely adopted strategy by many organisms and given the advantages offered by a social lifestyle, such as increased protection from predators or increased ability for resource exploitation, a wide variety of animals have adopted a social lifestyle. Arguably none have done this more successfully than the social insects. Indeed their efficient division of labour is often cited as a key attribute for the remarkable ecological and evolutionary success of these societies. Within the social insects the most obvious division of labour is reproductive, in which one or a few individuals monopolise reproduction while the majority of essentially sterile workers carry out the remaining tasks essential for colony survival. In almost all social insects, in particular ants, the age of a worker will predispose it to certain tasks, and in some social insects the workers vary in size such that task is associated with worker morphology. In this thesis I explore the proximate and ultimate causes of worker and reproductive division of labour in ant societies, which span a range of social complexities. I predominantly focus on both the highly derived leaf-cutting ants – a so-called ‘pinnacle' of evolution within the social insects, with a complex division of labour and a strong worker caste system – and in the more basal primitive societies of the queenless ponerine dinosaur ants, which can offer an insight in to the evolution of division of labour at the earliest stages of social lifestyles. This work demonstrates the environmental and genetic determinants of division of labour in group-living societies outside of the classical honey bee model system. This is important as it helps us to better understand the broader processes shaping behaviour and phenotype in the animal kingdom.

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Johnson, Luke B. "Decentralized task allocation for dynamic environments." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/71458.

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Анотація:

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 93-98).
This thesis presents an overview of the design process for creating greedy decentralized task allocation algorithms and outlines the main decisions that progressed the algorithm through three different forms. The first form was called the Sequential Greedy Algorithm (SGA). This algorithm, although fast, relied on a large number of iterations to converge, which slowed convergence in decentralized environments. The second form was called the Consensus Based Bundle Algorithm (CBBA). CBBA required significantly fewer iterations than SGA but it is noted that both still rely on global synchronization mechanisms. These synchronization mechanisms end up being difficult to enforce in decentralized environments. The main result of this thesis is the creation of the Asynchronous Consensus Based Bundle Algorithm (ACBBA). ACBBA broke the global synchronous assumptions of CBBA and SGA to allow each agent more autonomy and thus provided more robustness to the task allocation solutions in these decentralized environments.
by Luke B. Johnson.
S.M.

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Sarker, Md Omar Faruque. "Self-regulated multi-robot task allocation." Thesis, University of South Wales, 2010. https://pure.southwales.ac.uk/en/studentthesis/selfregulated-multirobot-task-allocation(4b92f28f-c712-4e75-955f-97b4e5bf12dd).html.

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To deploy a large group of autonomous robots in dynamic multi-tasking environments, a suitable multi-robot task-allocation (MRTA) solution is required. This must be scalable to variable number of robots and tasks. Recent studies show that biology-inspired self-organized approaches can effectively handle task-allocation in large multi-robot systems. However most existing MRTA approaches have overlooked the role of different communication and sensing strategies found in selfregulated biological societies. This dissertation proposes to solve the MRTA problem using a set of previously published generic rules for division of labour derived from the observation of ant,human and robotic social systems. The concrete form of these rules, the attractive field model (AFM), provides sufficient abstraction to local communication and sensing which is uncommon in existing MRTA solutions. This dissertation validates the effectiveness of AFM to address MRTA using two bio-inspired communication and sensing strategies: "global sensing - no communication" and "local sensing - local communication". The former is realized using a centralized communication system and the latter is emulated under a peer-topeer local communication scheme. They are applied in a manufacturing shop-floor scenario using 16 e-puck robots. A robotic interpretation of AFM is presented that maps the generic parameters of AFM to the properties of a manufacturing shopfloor. A flexible multi-robot control architecture, hybrid event-driven architecture on D-Bus, has been outlined which uses the state-of-the-art D-Bus interprocess communication to integrate heterogeneous software components. Based-on the organization of task-allocation, communication and interaction among robots, a novel taxonomy of MRTA solutions has been proposed to remove the ambiguities found in existing MRTA solutions. Besides, a set of domainindependent metrics, e.g., plasticity, task-specialization and energy usage, has been formalized to compare the performances of the above two strategies. The presented comparisons extend our general understanding of the role of information exchange strategies to achieve the distributed task-allocations among various social groups.

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Hawley, John. "Hierarchical task allocation in robotic exploration /." Online version of thesis, 2009. http://hdl.handle.net/1850/10650.

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Книги з теми "Task allocation to sensors"

Tkach, Itshak, and Yael Edan. Distributed Heterogeneous Multi Sensor Task Allocation Systems. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34735-2.

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David, Guy. Integration and task allocation: Evidence from patient care. Cambridge, MA: National Bureau of Economic Research, 2011.

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J, Prinzell Lawrence, and Langley Research Center, eds. Empirical analysis of EEG and ERPs for psychophysiological adaptive task allocation. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 2001.

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Sillince, John A. A. Extending electronic coordination mechanisms using argumentation: The case of task allocation. Sheffield: Sheffield University, School of Management, 1994.

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Brown, Roswyn Ann. The social organisation of work in two paediatric wards: In relation to patient and task allocation. [s.l.]: typescript, 1986.

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Dailey, Daniel J. Improved error detection for inductive loop sensors: Final technical report, Research Project T9233, Task 14, final report, Research Project GC8719, Task 9, "Improved Inductor Loop". [Olympia, WA?]: Washington State Dept. of Transportation, Washington State Transportation Commission, Transit, Research, and Intermodal Planning (TRIP) Division in cooperation with the U.S. Dept. of Transportation, Federal Highway Administration, 1993.

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Prinzel, Lawrence J. Application of physiological self-regulation and adaptive task allocation techniques for controlling operator hazardous states of awareness. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 2001.

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Kreeb, Robert M. Commercial vehicle tire condition sensors: Task order 5 of the Commercial Motor Vehicle Technology Diagnostics and Performance Enancement Program. Washington, D.C: U.S. Department of Transportation, Federal Motor Carrier Safety Administration, Office of Bus and Truck Standards and Operations (MC-PSV), 2004.

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Dailey, Daniel J. Improved estimates of travel time from real time inductance loop sensors: Final technical report, Research Project T9233, Task 5, "Improved Travel Time Estimates". [Olympia, WA?]: Washington State Dept. of Transportation, Washington State Transportation Commission, Transit, Research, and Intermodal Planning (TRIP) Division in cooperation with the U.S. Dept. of Transportation, Federal Highway Administration, 1993.

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Office, General Accounting. International trade: Iraq's participation in U.S. agricultural export programs : report to the chairman, Task Force on Urgent Fiscal Issues, Committee on the Budget, House of Representatives. Washington, D.C: U.S. General Accounting Office, 1990.

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Частини книг з теми "Task allocation to sensors"

Tkach, Itshak, and Yael Edan. "Multi-agent Task Allocation." In Distributed Heterogeneous Multi Sensor Task Allocation Systems, 9–14. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34735-2_2.

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Tkach, Itshak, and Yael Edan. "Analytical Analysis of a Simplified Scenario of Two Sensors and Two Tasks." In Distributed Heterogeneous Multi Sensor Task Allocation Systems, 117–23. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34735-2_10.

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Tkach, Itshak, and Yael Edan. "Multi-sensor Task Allocation Systems." In Distributed Heterogeneous Multi Sensor Task Allocation Systems, 15–18. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34735-2_3.

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Yu, Wanli, Yanqiu Huang, and Alberto Garcia-Ortiz. "Energy-Aware Task Allocation in WSNs." In Mission-Oriented Sensor Networks and Systems: Art and Science, 193–226. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91146-5_6.

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dos Santos, Igor L., Flávia C. Delicato, Luci Pirmez, Paulo F. Pires, and Albert Y. Zomaya. "Resource Allocation and Task Scheduling in the Cloud of Sensors." In Mission-Oriented Sensor Networks and Systems: Art and Science, 265–305. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91146-5_8.

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Tkach, Itshak, and Yael Edan. "An Outlook of Multi-sensor Task Allocation." In Distributed Heterogeneous Multi Sensor Task Allocation Systems, 125–31. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34735-2_11.

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Tkach, Itshak, and Yael Edan. "Single-Layer Multi-sensor Task Allocation System." In Distributed Heterogeneous Multi Sensor Task Allocation Systems, 23–47. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34735-2_5.

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Tkach, Itshak, and Yael Edan. "Dual-Layer Multi-sensor Task Allocation System." In Distributed Heterogeneous Multi Sensor Task Allocation Systems, 81–91. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34735-2_7.

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Tkach, Itshak, and Yael Edan. "Introduction." In Distributed Heterogeneous Multi Sensor Task Allocation Systems, 1–8. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34735-2_1.

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Tkach, Itshak, and Yael Edan. "Evaluation Methodology." In Distributed Heterogeneous Multi Sensor Task Allocation Systems, 19–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34735-2_4.

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Тези доповідей конференцій з теми "Task allocation to sensors"

Zhang, Ziqiao, Wencen Wu, and Fumin Zhang. "Opinion-Based Task Allocation Strategy for Mobile Sensor Networks." In 2024 American Control Conference (ACC), 123–28. IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644632.

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Jia, Dingyi, Yuanjiao Zhu, Mengfei Wang, Jiahao Li, Tao Luo, Jingyi Wei, Baitao Zhang, and Jie Zhou. "A Novel Chaotic Quantum Grey Wolf Algorithm for Optimizing the Task Allocation of Water Quality Monitoring Sensor Networks." In 2024 9th International Conference on Automation, Control and Robotics Engineering (CACRE), 64–68. IEEE, 2024. http://dx.doi.org/10.1109/cacre62362.2024.10635064.

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Zhou, Chongyu, Chen-Khong Tham, and Mehul Motani. "QOATA: QoI-aware task allocation scheme for mobile crowdsensing under limited budget." In 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2015. http://dx.doi.org/10.1109/issnip.2015.7106953.

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Ross, Matt, Pierre Payeur, and Sylvain Chartier. "Task Allocation for Heterogeneous Robots Using a Self-Organizing Contextual Map." In 2019 IEEE International Symposium on Robotic and Sensors Environments (ROSE). IEEE, 2019. http://dx.doi.org/10.1109/rose.2019.8790434.

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Mo, Tianshuo, and Biwei Tang. "The application of particle swarm optimization algorithm in multi-robot task allocation problem." In Second International Conference on Sensors and Information Technology (ICSI 2022), edited by Lijia Pan. SPIE, 2022. http://dx.doi.org/10.1117/12.2637509.

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Fu, Tingting, and Peng Liu. "Contribution aware task allocation in sensor networks." In 2015 20th IEEE Symposium on Computers and Communication (ISCC). IEEE, 2015. http://dx.doi.org/10.1109/iscc.2015.7405572.

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Li, Doudou, Jinghua Zhu, and Yanchang Cui. "Prediction-Based Task Allocation in Mobile Crowdsensing." In 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN). IEEE, 2019. http://dx.doi.org/10.1109/msn48538.2019.00029.

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Weikert, Dominik, Christoph Steup, and Sanaz Mostaghim. "Multi-Objective Task Allocation for Wireless Sensor Networks." In 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2020. http://dx.doi.org/10.1109/ssci47803.2020.9308345.

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Hedrick, J. Karl, Brandon Basso, Joshua Love, Anouck R. Girard, and Andrew T. Klesh. "Control of Mobile Sensor Networks: A State-of-the-Art Review." In ASME 2008 Dynamic Systems and Control Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/dscc2008-2405.

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Анотація:

This paper presents a state-of-the-art survey in the broad area of Mobile Sensor Networks (MSNs). There is currently a great deal of interest in having autonomous vehicles carrying sensors and communication devices that can conduct ISR (intelligence, surveillance and reconnaissance) operations. Although this paper will discuss issues common to mobile sensor networks, the applications will generally be associated with autonomous vehicles. Areas that are addressed are: 1. Mission definition languages that allow the human to compose a mission defined in terms of tasks; 2. Communication issues including hardware, software, and network connectivity; 3. Task allocation among the assets generally by a market-based approach; 4. Path planning for individual agents; and 5. Platform motion control using autopilots with and without GPS signals and including collision avoidance.

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Pizzocaro, Diego, Alun Preece, Fangfei Chen, Tom La Porta, and Amotz Bar-Noy. "A distributed architecture for heterogeneous multi sensor-task allocation." In 2011 International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE, 2011. http://dx.doi.org/10.1109/dcoss.2011.5982152.

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Звіти організацій з теми "Task allocation to sensors"

Baccara, Mariagiovanna, SangMok Lee, and Leeat Yariv. Task Allocation and On-the-job Training. Cambridge, MA: National Bureau of Economic Research, September 2021. http://dx.doi.org/10.3386/w29312.

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David, Guy, Evan Rawley, and Daniel Polsky. Integration and Task Allocation: Evidence from Patient Care. Cambridge, MA: National Bureau of Economic Research, September 2011. http://dx.doi.org/10.3386/w17419.

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Sherry, Richard R., and Frank E. Ritter. Dynamic Task Allocation: Issues for Implementing Adaptive Intelligent Automation. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada436213.

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Jones, E. G., M. B. Dias, and Anthony Stentz. Learning-enhanced Market-based Task Allocation for Disaster Response. Fort Belvoir, VA: Defense Technical Information Center, October 2006. http://dx.doi.org/10.21236/ada528494.

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Lerman, Kristina, Chris Jones, Aram Galstyan, and Maja J. Mataric. Analysis of Dynamic Task Allocation in Multi-Robot Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada459067.

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Chinnis, Jr, Cohen James O., Bresnick Marvin S., and Terry A. Human and Computer Task Allocation in Air Defense Systems. Fort Belvoir, VA: Defense Technical Information Center, September 1985. http://dx.doi.org/10.21236/ada170954.

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Morris, Nancy M., and William B. Rouse. Adaptive Aiding for Human-Computer Control: Experimental Studies of Dynamic Task Allocation. Fort Belvoir, VA: Defense Technical Information Center, January 1986. http://dx.doi.org/10.21236/ada166704.

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Ulam, Patrick, Yochiro Endo, Alan Wagner, and Ronald Arkin. Integrated Mission Specification and Task Allocation for Robot Teams - Part 2: Testing and Evaluation. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada457295.

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Ulam, Patrick, Yochiro Endo, Alan Wagner, and Ronald Arkin. Integrated Mission Specification and Task Allocation for Robot Teams - Part 1: Design and Implementation. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada457296.

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Ted Quinn and Jerry Mauck. Digial Technology Qualification Task 2 - Suitability of Digital Alternatives to Analog Sensors and Actuators. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1057681.

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