151,293 research outputs found
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Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art
Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP
Adam Smith goes mobile : managing services beyond 3G with the digital marketplace
The next generation of mobile communications systems is expected to offer new business opportunities to existing and new market players. A market-based middleware framework has been recently proposed whereby service providers, independent of network operators, are able to tender online service contracts to network operators in a dynamic and competitive manner. This facilitates a seamless service provision over disparate networks in a consumer-centric manner. Service providers select network bearers according to the network operators' ability to meet the QoS target, which in turn is influenced, among other things, by user's price and quality requirements. The benefits of this proposal are the complementarity of numerous network resources, the decoupling of services and networks in a self-organising distributed environment, and increased competition to consumersâ advantag
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Centralised Versus Market-Based Control Under Environment Uncertainty: Case of the Mobile Task Allocation Problem (MTAP)
This paper aims at comparing the centralised versus the market-based approach. This is done in the context of the mobile task allocation problem (MTAP) from the perspective of environmental uncertainty. MTAP is defined as an optimization problem for planning the assignment of service tasks to mobile workers. Environmental uncertainty is introduced through the injection of stochastic tasks and dynamic travel delays. A multi-agent simulator is employed to experiment the behaviour of each approach in reaction to different uncertainty levels. Preliminary results suggest a tentative conceptual model to evaluate the
suitability of each approach to address MTAP in function of uncertainty. It is suggested that uncertaintyâs effect on achieved performance is moderated by the timeliness of decision making, workersâ degree of local knowledge, and problemâs complexity and size
Incentive-compatible route coordination of crowdsourced resources
Technical ReportWith the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresen-ce-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in
which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agentâs flexibility is exploited to maximize the coverage of a
mobility field, with an objective to maximize the revenue collected from satisfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1-approximation algorithm to solve the 2 problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agentâs truthfulness about its flexibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments
Whatâs in it for me? Incentive-compatible route coordination of crowdsourced resources
With the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresence-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agentâs flexibility is exploited to maximize the coverage of a mobility field, with an objective to maximize the revenue collected from satisfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1/2-approximation algorithm to solve the problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agentâs truthfulness about its flexibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments.Supported in part by NSF Grants, #1430145, #1414119, #1347522, #1239021, and #1012798
Tragedy of the Regulatory Commons: LightSquared and the Missing Spectrum Rights
The endemic underuse of radio spectrum constitutes a tragedy of the regulatory commons. Like other common interest tragedies, the outcome results from a legal or market structure that prevents economic actors from executing socially efficient bargains. In wireless markets, innovative applications often provoke claims by incumbent radio users that the new traffic will interfere with existing services. Sometimes these concerns are mitigated via market transactions, a la âCoasian bargaining.â Other times, however, solutions cannot be found even when social gains dominate the cost of spillovers. In the recent âLightSquared debacle,â such spectrum allocation failure played out. GPS interests that access frequencies adjacent to the band hosting LightSquaredâs new nationwide mobile network complained that the wireless entrant would harm the operation of locational devices. Based on these complaints, regulators then killed LightSquaredâs planned 4G network. Conservative estimates placed the prospective 4G consumer gains at least an order of magnitude above GPS losses. âWin winâ bargains were theoretically available, fixing GPS vulnerabilities while welcoming the highly valuable wireless innovation. Yet transaction costsâlargely caused by policy choices to issue limited and highly fragmented spectrum usage rights (here in the GPS band)âproved prohibitive. This episode provides a template for understanding market and non-market failure in radio spectrum allocation
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent âdevicesâ, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew âcognitive devicesâ are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
A trustworthy mobile agent infrastructure for network management
Despite several advantages inherent in mobile-agent-based approaches to network management as compared to traditional SNMP-based approaches, industry is reluctant to adopt the mobile agent paradigm as a replacement for the existing manager-agent model; the management community requires an evolutionary, rather than a revolutionary, use of mobile agents. Furthermore, security for distributed management is a major concern; agent-based management systems inherit the security risks of mobile agents. We have developed a Java-based mobile agent infrastructure for network management that enables the safe integration of mobile agents with the SNMP protocol. The security of the system has been evaluated under agent to agent-platform and agent to agent attacks and has proved trustworthy in the performance of network management tasks
Controlled Matching Game for Resource Allocation and User Association in WLANs
In multi-rate IEEE 802.11 WLANs, the traditional user association based on
the strongest received signal and the well known anomaly of the MAC protocol
can lead to overloaded Access Points (APs), and poor or heterogeneous
performance. Our goal is to propose an alternative game-theoretic approach for
association. We model the joint resource allocation and user association as a
matching game with complementarities and peer effects consisting of selfish
players solely interested in their individual throughputs. Using recent
game-theoretic results we first show that various resource sharing protocols
actually fall in the scope of the set of stability-inducing resource allocation
schemes. The game makes an extensive use of the Nash bargaining and some of its
related properties that allow to control the incentives of the players. We show
that the proposed mechanism can greatly improve the efficiency of 802.11 with
heterogeneous nodes and reduce the negative impact of peer effects such as its
MAC anomaly. The mechanism can be implemented as a virtual connectivity
management layer to achieve efficient APs-user associations without
modification of the MAC layer
Incentive compatible route coordination of crowdsourced resources and its application to GeoPresence-as-a-Service
With the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresen- ce-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent's exibility is exploited to maximize the coverage of a mo- bility field, with an objective to maximize the revenue collected from sat- isfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1 2 -approximation algorithm to solve the problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent's truthfulness about its exibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments.Supported in part by NSF Grants, #1430145, #1414119, #1347522, #1239021, and #1012798
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