软件学报
軟件學報
연건학보
JOURNAL OF SOFTWARE
2013年
3期
507-525
,共19页
吴磊%武德安%刘明%王晓敏%龚海刚
吳磊%武德安%劉明%王曉敏%龔海剛
오뢰%무덕안%류명%왕효민%공해강
机会网络%循环周期%动态规划%延迟概率%数据传输
機會網絡%循環週期%動態規劃%延遲概率%數據傳輸
궤회망락%순배주기%동태규화%연지개솔%수거전수
opportunistic network%cycle period%dynamic programming%delay probability%data delivery
提出一种在机会网络中基于周期性间歇连通的数据传输策略 PICD(periodic intermittently connected-based data delivery in opportunistic networks).通过有效利用节点间的周期间歇连通性改善数据传输性能.节点传输概率的计算则充分考虑了其与汇聚点间存在的间歇多跳路径,并将其与消息容忍的传输延迟相结合.首先,采用随机动态规划的方法建立与延迟相关的传输概率模型;然后,通过基于多跳的函数空间迭代法求出一个周期内的与延迟相关的传输概率分布矩阵;节点面向不同消息延迟的传输概率则基于分布矩阵计算获得,以此作为选择下一跳的依据.与延迟相关的概率转发机制提高了消息在容忍的延迟内被成功递交的可能.仿真实验结果表明,与现有的几种数据传输算法相比,在节点具有循环运动特征的环境下,PICD具有较高的数据传输成功率和较低的递交延迟.
提齣一種在機會網絡中基于週期性間歇連通的數據傳輸策略 PICD(periodic intermittently connected-based data delivery in opportunistic networks).通過有效利用節點間的週期間歇連通性改善數據傳輸性能.節點傳輸概率的計算則充分攷慮瞭其與彙聚點間存在的間歇多跳路徑,併將其與消息容忍的傳輸延遲相結閤.首先,採用隨機動態規劃的方法建立與延遲相關的傳輸概率模型;然後,通過基于多跳的函數空間迭代法求齣一箇週期內的與延遲相關的傳輸概率分佈矩陣;節點麵嚮不同消息延遲的傳輸概率則基于分佈矩陣計算穫得,以此作為選擇下一跳的依據.與延遲相關的概率轉髮機製提高瞭消息在容忍的延遲內被成功遞交的可能.倣真實驗結果錶明,與現有的幾種數據傳輸算法相比,在節點具有循環運動特徵的環境下,PICD具有較高的數據傳輸成功率和較低的遞交延遲.
제출일충재궤회망락중기우주기성간헐련통적수거전수책략 PICD(periodic intermittently connected-based data delivery in opportunistic networks).통과유효이용절점간적주기간헐련통성개선수거전수성능.절점전수개솔적계산칙충분고필료기여회취점간존재적간헐다도로경,병장기여소식용인적전수연지상결합.수선,채용수궤동태규화적방법건립여연지상관적전수개솔모형;연후,통과기우다도적함수공간질대법구출일개주기내적여연지상관적전수개솔분포구진;절점면향불동소식연지적전수개솔칙기우분포구진계산획득,이차작위선택하일도적의거.여연지상관적개솔전발궤제제고료소식재용인적연지내피성공체교적가능.방진실험결과표명,여현유적궤충수거전수산법상비,재절점구유순배운동특정적배경하,PICD구유교고적수거전수성공솔화교저적체교연지.
@@@@This paper propos a periodic intermittently connected-based data delivery in opportunistic networks (PICD). By effectively utilizing periodic intermittent connectivity between any two nodes, PICD can improve data delivery:First, for a certain node, based on its periodic intermittently-connected paths to the sink and its data delay tolerance, PICD establishes the delay probability model by means of random dynamic programming. Next, PICD will calculate the node’s periodic delay probability distribution matrix by the function space iteration. Next, according to the node’s distribution matrix, the current data delivery probability can be calculated by specifying period and tolerable delay, and this will become the key-player of the next-hop choosing. In short, the probability forwarding mechanism is delay-relevant and can increase data delivery probability within a tolerable amount of delay. Simulation shows that compared with existing algorithms, which only take advantage of single-hop delivery probability, PICD is better at data delivery and can also lower delay.