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MICROCOMPUTER APPLICATIONS
2015年
4期
53-55,58
,共4页
水下传感器网络%节点双向选择%自适应能量优化分配%竞争公式
水下傳感器網絡%節點雙嚮選擇%自適應能量優化分配%競爭公式
수하전감기망락%절점쌍향선택%자괄응능량우화분배%경쟁공식
Underwater Sensor Networks%the Node Two-way Choice%Adaptive Optimization of Energy Distribution%Competition Formula
针对水下传感器网络节点能量负载不均衡以及水下节点路径传输损耗过大等问题,为了平衡网络的节点能量分配,提高网络能量的利用效率,提出了一种基于节点双向选择的水声网络自适应能量优化分配算法。基于节点的剩余能量情况提出下一跳候选节点的双向选择方法,防止剩余能量过低的节点在传送数据时由于传输损耗过大而中止,充分考虑节点剩余能量和传输损耗之间的权衡,提出了最优转发节点的竞争公式,再通过比较候选节点的竞争值来选举最佳的下一跳节点。实验仿真表明,自适应能量优化分配算法能够有效地提高水下传感器网络的能量利用效率,并通过与对比算法组的比较证明了算法在能量优化上的有效性。
針對水下傳感器網絡節點能量負載不均衡以及水下節點路徑傳輸損耗過大等問題,為瞭平衡網絡的節點能量分配,提高網絡能量的利用效率,提齣瞭一種基于節點雙嚮選擇的水聲網絡自適應能量優化分配算法。基于節點的剩餘能量情況提齣下一跳候選節點的雙嚮選擇方法,防止剩餘能量過低的節點在傳送數據時由于傳輸損耗過大而中止,充分攷慮節點剩餘能量和傳輸損耗之間的權衡,提齣瞭最優轉髮節點的競爭公式,再通過比較候選節點的競爭值來選舉最佳的下一跳節點。實驗倣真錶明,自適應能量優化分配算法能夠有效地提高水下傳感器網絡的能量利用效率,併通過與對比算法組的比較證明瞭算法在能量優化上的有效性。
침대수하전감기망락절점능량부재불균형이급수하절점로경전수손모과대등문제,위료평형망락적절점능량분배,제고망락능량적이용효솔,제출료일충기우절점쌍향선택적수성망락자괄응능량우화분배산법。기우절점적잉여능량정황제출하일도후선절점적쌍향선택방법,방지잉여능량과저적절점재전송수거시유우전수손모과대이중지,충분고필절점잉여능량화전수손모지간적권형,제출료최우전발절점적경쟁공식,재통과비교후선절점적경쟁치래선거최가적하일도절점。실험방진표명,자괄응능량우화분배산법능구유효지제고수하전감기망락적능량이용효솔,병통과여대비산법조적비교증명료산법재능량우화상적유효성。
Aiming at the problems that network node energy load is imbalance and the path transmission loss of underwater node is too heavy in underwater sensor network, a hydroacoustic network adaptive optimization of energy distribution based on the node’s two-way choice is proposed in order to balance the energy distribution node network and improve the energy efficiency of the net-work. The method of next hop node’s two-way choice is proposed based on the residual energy of node. It can prevent the node with less energy left from being disrupted caused by transformation loss when the data are transmitted. It takes a full consideration of the trade-off between nodes remaining energy and transmission loss so as to propose the competition formula of optimal transforming node, and then selects the best next hop node by comparing the competing value of candidate nodes. The simulation results show that adaptive energy optimization allocation algorithm can effectively improve the energy efficiency of underwater sensor networks and prove the effectiveness of the algorithm on energy optimization by comparing with contrast algorithm group.