To implement the proposed NWLBP model, the nodes need to record additional position information about its neighboring nodes in their lists of neighbors.
The automatic ARIMA modeling algorithm works as follows: In order to build ARIMA prediction model, sensor node needs to collect recently sensed data series {x1, x2, …, x n }.
For shorter waves, more interpolation nodes need be used.
Under the cluster model, after submitting the task, every node needs to establish connection with the cluster during the initialization process, at the same time, the HDFS must establish communication with Spark, and MapReduce and the cluster's internal nodes need to establish communication with each other, which altogether takes about 10 s.
Because of the significant error reduction observed, the number of nodes needed in the model is significantly reduced.
Next, nodes needed to be lazily created themselves and the decision was made to create nodes one at a time.
Each scenario will obviously lead to a different sensor model We assume that the fusion node needs measurements, fromall nodes in order to estimate the source location.
In the above network models, in order to maximize their profits, each cooperative jamming node needs not only to compete with other jamming nodes, but also to compete with the source node.
In this model, every time when a new edge is to be set up, a node needs to be chosen randomly.
In our congestion model, mobile node k needs to grasp the degree of congestion of each road segment, i.e., the number of evacuees.
