Considering the energy-efficient emergency response, subject to a given set of constraints on emergency communication networks (ECN), this article proposes a hybrid device-to-device (D2D) and device-to-vehicle (D2V) network for collecting and transmitting emergency information. First, we establish the D2D network from the perspective of complex networks by jointly determining the optimal network partition (ONP) and the temporary data caching centers (TDCC), and thus emergency data can be forwarded and cached in TDCCs. Second, based on the distribution of TDCCs, the D2V network is established by unmanned aerial vehicles (UAV)-based waypoint and motion planning, which saves the time for wireless transmission and aerial moving. Finally, the amount of time for emergency response and the total energy consumption are simultaneously minimized by a multiobjective evolutionary algorithm based on decomposition (MOEA/D), subject to a given set of minimum signal-to-interference- plus-noise ratio (SINR), number of UAVs, transmit power, and energy constraints. Simulation results show that the proposed method significantly improves response efficiency and reasonably controls the energy, thus overcoming limitations of existing ECNs. Therefore, this network effectively solves the key problem in the rescue system and makes great contributions to post-disaster decision-making.
翻译:考虑到节能应急反应,在对紧急通信网络(ECN)施加一定限制的情况下,本条提议建立一个混合装置到装置(D2D)和装置到车辆(D2V)网络,用于收集和传送紧急信息;首先,我们从复杂网络的角度建立D2D网络,共同确定最佳网络分区(ONP)和临时数据缓存中心(TDCC),从而可以在TDCCs中转发和储存应急数据;第二,根据TDCCs的分布,D2V网络由无人驾驶航空飞行器(UAV)为基础的路点和运动规划建立,以节省无线传输和空中移动的时间;最后,应急反应和总能源消耗的时间由基于分解(MOEA/D)的多目标进化算法同时最小化,但须符合一套给定的最低信号到干预加噪音比率(SINR)、UAVs数目、传输电力和能源限制;模拟结果显示,拟议的方法大大改进了应急反应效率,合理控制了ECN网络的后期决定,从而克服了ECN系统的现有主要挑战。