Service Request-Oriented Resource Allocation Algorithm For Internet Of Things Users

With the rapid development of Internet of Things(Io T),connecting everything together becomes realizable.However,enormous terminals in Io T are restricted by limited costs and sizes,which is challenging for Io T.Hence,providing resources for resource-constrained Io T users to gurantee their performances is critical.To solve this problem,this thesis focuses the research on resource allcation algorithm for resource-constrained users.Besides,this thesis considers both the scenario with one kind of service requests and the scenario with multiple service requests,which are shown as follows:Firstly,the thesis focuses on resource allocation for the Io T users with communication service requests.To connect things in Io T together,one essestial part is communication signal transmission.Unfortunately,the traditional communication service points cannot satisfy the explosive requests from tremendous Io T users.In order to provide better communication services for Io T users,the edge communication points are introduced as access points in the thesis,and the user association problem between the access points and Io T users is then addressed.Aiming at maximizing the sum of users' achievable data rates,the thesis proposes the difference based matching algorithm to solve user association problem.In the proposed algorithm,the user's achievable data rate is not the only selection parameter.When the number of users that send requests to the same access point exceeds its service quota,the access point will select users according to the differences between the current rates and next suboptimal rates.To further improve the users' sum rate while ensureing the user fairness,the thesis then proposes the Lagrangian multiplier based user association algorithm.In this method,the condition for optimal solution is firstly derived by leveraging Lagrange dual function and Karush-Kuhn-Tucker(KKT)condition.Then the Lagrangian multiplier based user association algorithm is proposed by using this deduced result.Simulation results show that the proposed algorithm can achieve high user rate and ensure better user fairness.Secondly,the thesis focuses on resource allocation for the Io T users with computation service requests.To provide more computation resources for computation-constrained Io T users,a three-tier computation offloading framework is developed under network virtualization scenario.Accordingly,a two-step sequential process is designed to stimulate the proposed framework.In the first step,a contract theory based incentive mechanism is proposed in which more temporary edge computing nodes can be motivated and then join the multi-access edge computing network.Without perfect edge computing node information,the optimal contract items(the edge computing node's computation contribution and reward)between the multi-access edge computing operator and edge computing nodes can be achieved by taking account of individual rationality and incentive compatible constraints.After acquiring the edge computing nodes' computation contributions,the computing resource allocation problem between the edge computing nodes and Io Ts is then considered in the second step.Since the Io Ts have private information,a Bayesian matching game with externality is leveraged to model the problem.Whereas,the conventional matching algorithm cannot ensure stability.Hence,an iterative matching algorithm that can always converge to stable results is developed.Simulation results demonstrate that the proposed two-step sequential decision process can significantly improve social welfare considering the practical scenarios,with reasonable computational complexity.Thirdly,the thesis focuses on resource allocation for the Io T users with energy service requests.To ensure the energy-constrained Io T users work well,unmanned aerial vehicles are served as carriers of wireless power chargers to charge the energy-constrained devices periodically.Aiming at maximizing the total amount of charging energy under the constraints of unmanned aerial vehicles and wireless power chargers,a bipartite matching with one-sided preferences is introduced to model the charging relationship between the energy-constrained devices and unmanned aerial vehicles.Nevertheless,the traditional one-shot static matching is not suitable for this dynamic scenario,and thus the problem is further solved by the novel multiple-stage dynamic matching.Besides,the wireless charging process is history dependent since the current matching result will influence the future initial charging status,and consequently,the Markov decision process and Bellman equation are leveraged.Then,by combining the Markov decision process and random serial dictatorship matching algorithm together,a four-step algorithm is proposed.In the proposed algorithm,the local Markov decision processes for the energy-constrained devices are set up first according to the initial states.Next,using the random serial dictatorship algorithm,all possible actions can be presented according to the current state.Then,the joint Markov decision process is built based on the first two steps.At last,the Bellman equation is utilized to select the optimal branch.Simulation results demonstrate the effectiveness of the proposed algorithm.Finally,the thesis focuses on resource trading for the Io T users with multiple service requests.The first three parts mainly focus on the scenarios with single service requests,while Io T user' service request is not limited to one and it may vary with time in practical worlds.Thus,in the last part,the resource allocation problem taking account of multiple resource requests is dealt with.In order to provide high quality services for Io T users with multiple requirements,a novel multi-access edge computing based network paradigm is proposed in this work.In the proposed framework,the multi-access edge computing nodes are labeled as temporal service points.The service providers rent resources from resource providers,and then deploy them into multi-access edge computing nodes,which is flexible and cost-effective.Aiming to address the resource trading and price negotiation problem between the service providers and resource providers,a matching with contracts model is leveraged in the proposed paradigm.In the matching with contracts model,the service providers can be regarded as buyers while the resources providers are deemed as seller.The buyers and sellers can trade different resources such as communication resource,computation resource,energy resource and storage resource.Next,the thesis further proposes a distributed price negotiation algorithm to solve the problem.The simulations are presented to demonstrate that the proposed algorithm can ensure stable trading result and competitive equilibrium.