As a promising computing paradigm,Mobile Edge Computing(MEC)provides communication and computing capability at the edge of the network to address the concerns of massive computation requirements,constrained battery ca...As a promising computing paradigm,Mobile Edge Computing(MEC)provides communication and computing capability at the edge of the network to address the concerns of massive computation requirements,constrained battery capacity and limited bandwidth of the Internet of Things(IoT)systems.Most existing works on mobile edge task ignores the delay sensitivities,which may lead to the degraded utility of computation offloading and dissatisfied users.In this paper,we study the delay sensitivity-aware computation offloading by jointly considering both user's tolerance towards delay of task execution and the network status under computation and communication constraints.Specifically,we use a specific multi-user and multi-server MEC system to define the latency sensitivity of task offloading based on the analysis of delay distribution of task categories.Then,we propose a scoring mechanism to evaluate the sensitivity-dependent utility of task execution and devise a Centralized Iterative Redirection Offloading(CIRO)algorithm to collect all information in the MEC system.By starting with an initial offloading strategy,the CIRO algorithm enables IoT devices to cooperate and iteratively redirect task offloading decisions to optimize the offloading strategy until it converges.Extensive simulation results show that our method can significantly improve the utility of computation offloading in MEC systems and has lower time complexity than existing algorithms.展开更多
Mobile Edge Computing(MEC)can support various high-reliability and low-delay applications in Maritime Networks(MNs).However,security risks in computing task offloading exist.In this study,the location privacy leakage ...Mobile Edge Computing(MEC)can support various high-reliability and low-delay applications in Maritime Networks(MNs).However,security risks in computing task offloading exist.In this study,the location privacy leakage risk of Maritime Mobile Terminals(MMTs)is quantified during task offloading and relevant Location Privacy Protection(LPP)schemes of MMT are considered under two kinds of task offloading scenarios.In single-MMT and single-time offloading scenario,a dynamic cache and spatial cloaking-based LPP(DS-CLP)algorithm is proposed;and under the multi-MMTs and multi-time offloading scenario,a pseudonym and alterable silent period-based LPP(PA-SLP)strategy is proposed.Simulation results show that the DS-CLP can save the response time and communication cost compared with traditional algorithms while protecting the MMT location privacy.Meanwhile,extending the alterable silent period,increasing the number of MMTs in the maritime area or improving the pseudonym update probability can enhance the LPP effect of MMTs in PA-SLP.Furthermore,the study results can be effectively applied to MNs with poor communication environments and relatively insufficient computing resources.展开更多
基金supported by the Hong Kong Scholars Program with No.2021-101in part by the National Natural Science Foundation of China under Grant No.62002377,62072424,61772546,61625205,61632010,61751211,61772488,61520106007+2 种基金Key Research Program of Frontier Sciences,CAS,No.QYZDY-SSW-JSC002NSFC with No.NSF ECCS-1247944,and NSF CNS 1526638in part by the National key research and development plan No.2017YFB0801702,2018YFB1004704.
文摘As a promising computing paradigm,Mobile Edge Computing(MEC)provides communication and computing capability at the edge of the network to address the concerns of massive computation requirements,constrained battery capacity and limited bandwidth of the Internet of Things(IoT)systems.Most existing works on mobile edge task ignores the delay sensitivities,which may lead to the degraded utility of computation offloading and dissatisfied users.In this paper,we study the delay sensitivity-aware computation offloading by jointly considering both user's tolerance towards delay of task execution and the network status under computation and communication constraints.Specifically,we use a specific multi-user and multi-server MEC system to define the latency sensitivity of task offloading based on the analysis of delay distribution of task categories.Then,we propose a scoring mechanism to evaluate the sensitivity-dependent utility of task execution and devise a Centralized Iterative Redirection Offloading(CIRO)algorithm to collect all information in the MEC system.By starting with an initial offloading strategy,the CIRO algorithm enables IoT devices to cooperate and iteratively redirect task offloading decisions to optimize the offloading strategy until it converges.Extensive simulation results show that our method can significantly improve the utility of computation offloading in MEC systems and has lower time complexity than existing algorithms.
基金supported by the National Key Research and Development Program of China (2021YFE0105500)the National Natural Science Foundation of China (61801166).
文摘Mobile Edge Computing(MEC)can support various high-reliability and low-delay applications in Maritime Networks(MNs).However,security risks in computing task offloading exist.In this study,the location privacy leakage risk of Maritime Mobile Terminals(MMTs)is quantified during task offloading and relevant Location Privacy Protection(LPP)schemes of MMT are considered under two kinds of task offloading scenarios.In single-MMT and single-time offloading scenario,a dynamic cache and spatial cloaking-based LPP(DS-CLP)algorithm is proposed;and under the multi-MMTs and multi-time offloading scenario,a pseudonym and alterable silent period-based LPP(PA-SLP)strategy is proposed.Simulation results show that the DS-CLP can save the response time and communication cost compared with traditional algorithms while protecting the MMT location privacy.Meanwhile,extending the alterable silent period,increasing the number of MMTs in the maritime area or improving the pseudonym update probability can enhance the LPP effect of MMTs in PA-SLP.Furthermore,the study results can be effectively applied to MNs with poor communication environments and relatively insufficient computing resources.
