摘要
局域按需簇维护(local and on-demand maintenance of clusters,LDMC)具有多重优势,但仍可能因对受损簇即时维护导致频繁的局部业务中断和能量浪费,提出一种改进的物联网感知层簇维护优化算法,综合权衡对受损簇进行即时维护的成本和延时维护的代价,为应对不同受损状态和业务需要优化设定启动簇维护的条件,以降低网络维护开销和节点能耗,进一步延长网络生命周期。网络仿真软件(network simulation 2,NS2)仿真结果表明,与LDMC方法相比,该改进优化算法可减少业务中断次数和时长、降低簇维护时的能量消耗、增加数据发送总量,在仿真条件下网络生命周期最多可延长16.3%;且网络规模越大,该改进算法的优化效应越明显。
The method of local and on-demand maintenance of clusters （LDMC） has many advantages, but it still will result in frequent interruptions of local service and waste of energy due to timely maintaining on the damaged clusters. An improved optimization algorithm of clusters maintenance for sensing layer of the internet of things （IoT） is proposed to solve the problem. The algorithm focuses on balancing the cost of timely mainte- nance and the loss of delayed one on the damaged clusters, it optimizes the conditions to initiate clusters mainte- nance in response to different damaged cases and demands of businesses, so as to reduce expense on maintenance of clustered-network and save energy of nodes, so that the lifetime of network can be further prolonged. The simulation results based on NS2 show that the improved method can reduce the frequency or duration of business interrupts, cut down the energy consumption used in clusters maintenance and increase the sum of transmitted data, the lifetime of network can be prolonged 16.3% at the most under certain condition of the simulation com- pared with the LDMC method. The bigger the scale of network is, the more obvious the advantages of the im- proved algorithm are.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2017年第1期198-205,共8页
Systems Engineering and Electronics
基金
国家自然科学基金(61170219)
教育部-中国移动科研基金研发项目(MCM20150202)
重庆市教委项目(KJ1602201)资助课题
关键词
物联网
簇维护
成本
代价
生命周期
优化
internet of things （IoT）
clusters maintenance
cost
loss
lifetime
optimization
