P2P网络中的节点很可能从另外的节点那里收到质量很差的服务和信息,名誉评价是解决该问题的常见方法.基于评分反馈的P2P名誉计算机制存在下述缺点:无法区分恶意评价和诚实节点给出错误评价间的差别;需要对评分可信度进行二次评价,使名誉计算速度减慢;用数字来表示节点名誉的方式不够自然.实际上,名誉评价的用途是确定节点可信度的相对顺序.因此,提出了一种基于排名反馈的P2P名誉评价机制RbRf(reputation based ranking feedback).针对RbRf和其上的恶意攻击进行了数学建模和理论分析,结果表明,RbRf中非恶意错误的影响随排名反馈的数量指数而衰减;一般恶意攻击对RbRf的影响随排名反馈数量的多项式而减小;对于有意设计的共谋攻击,由于必须给RbRf引入正确信息而导致了恶意攻击被有效中和.因此,RbRf不仅由于不再反馈打分信息而不存在评分反馈引起的名誉评价问题(如不需要对反馈信息的可信度进行二次评价),而且具有更好的抵抗恶意攻击的能力.仿真实验验证了理论分析的结果.
Actors'relocation is utilized during the network initialization to enhance real-time performance of wireless sensor and actor networks(WSANs)which is an important issue of WSANs.The actor deployment problem in WSANs is proved NP-Hard whether the amount of actors is redundant or not,but to the best of our knowledge,no effective distributed algorithms in previous research can solve the problem.Thus two actor deployment strategies which need not the boundary control compared with present deployment strategies are proposed to solve this problem approximately based on the Voronoi diagram.Through simulation experiment,the results show that our distributed strategies are more effective than the present deployment strategies in terms of real-time performance,convergence time and energy consumption.
