In low-duty-cycle wireless sensor networks,designers have to cope with unreliable links and limited communication capacity.In this work,we propose COST,a coding scheme that leverages spatial-temporal diversity to achi...In low-duty-cycle wireless sensor networks,designers have to cope with unreliable links and limited communication capacity.In this work,we propose COST,a coding scheme that leverages spatial-temporal diversity to achieve higher energy efficiency and lower delay of packet transmissions.We particularly address long sleeping intervals in low-duty-cycle networks by exploiting multi-path diversity.Specifically,we propose to employ an erasure-coding scheme to improve reliability.With respect to energy efficiency and delivery timeliness,we formulate the problem in optimal allocation of coded blocks over multiple paths,which is then proved to be NP-hard.We further propose a near-optimal algorithm to solve the allocation problem.Through extensive simulations,we evaluate the impact of network parameters and demonstrate the effectiveness of our proposal.展开更多
基金This work was supported in part by the National Basic Research Program of China(Grant No.2011CB302705)the National Natural Science Foundation of China(GrantNos.61003277,60903206)+1 种基金the State key DevelopmentProgram for Basic Research of China(No.2009CB3020402)the National Natural Science Foun-dation of Jiangsu Province(Grant No.BK2010102)
文摘In low-duty-cycle wireless sensor networks,designers have to cope with unreliable links and limited communication capacity.In this work,we propose COST,a coding scheme that leverages spatial-temporal diversity to achieve higher energy efficiency and lower delay of packet transmissions.We particularly address long sleeping intervals in low-duty-cycle networks by exploiting multi-path diversity.Specifically,we propose to employ an erasure-coding scheme to improve reliability.With respect to energy efficiency and delivery timeliness,we formulate the problem in optimal allocation of coded blocks over multiple paths,which is then proved to be NP-hard.We further propose a near-optimal algorithm to solve the allocation problem.Through extensive simulations,we evaluate the impact of network parameters and demonstrate the effectiveness of our proposal.
