## Appendix
### Problem Solving Steps
#### Step 8: Admission Control Heuristic Algorithm  
Through the link-state routing protocol, the Dijkstra Algorithm can be applied to each node to calculate the routing table and can also compute the shortest path between any two nodes in the network. The algorithm denotes two states for the nodes, tentative and permanent. It is briefly described the steps of Dijkstra algorithm in Table A3.  
To keep our routing as flexible as possible, the Dijkstra Algorithm only computes shortest path for each node pair. Nevertheless, in this paper, it is also important to construct the second shortest path, the third shortest path, and so on; that is so called \"K shortest paths.\" A significant amount of research has centered on the K shortest paths problem as mentioned in [14, 30], and [PERSON] _et al_. in [31] proposes the adoption of the K shortest paths (KSP) algorithm. The algorithm is suitable for an undirected graph with nonnegative arc weight and can compute K simple paths (_i.e._, without cycles between the paths) in \(O(Kc(n\),\(m))\) time. Where \(c(n\),\(m)\) is the time to compute shortest paths from one node to all the other nodes?  
The experiment environment is constructed in four grid topologies of 3 \(\times\) 3, 5 \(\times\) 5, 7 \(\times\) 7 and 9 \(\times\) 9 squares, with one node placed in each intersection point as shown in Figure 5. Each sensor node has a radio transmission range of 250 m and a radio interference range of 550 m. The only sink is located on the center of the first row in each square with sessions randomly generated by the other nodes in the topology. The ns-2 simulator evaluates the performance of the NODQC routing algorithm with different types of routing algorithms.  
This means that, if the Dijkstra Algorithm is the shortest path algorithm, the complexity of the K shortest paths algorithm will be \(O(Kn^{2})\). Furthermore, metrics used for calculating the candidate shortest paths not only include arc weight, but also the predictive link mean delay. In other words,there are two types of the \"K shortest paths\". If not all K shortest paths calculated by the original arc weight can satisfy the end-to-end delay requirement, the predictive link mean delay will be the metric for each link and calculate the corresponding K shortest paths, _i.e._, the K fastest paths, to satisfy the maximum allowable QoS requirements as possibly as we can.  
By determining both the K shortest paths and the K fastest paths, our admission control heuristic algorithm is therefore constructed to give due consideration to both the \"system perspective\" and \"user perspective\". Unfortunately, the K shortest paths may still not satisfy the basic assumption, the QoS provisioning. Thus, other K fastest paths are provided to meet the QoS requirement, and the original arc weight corresponding to each of which cannot exceed a threshold \(\beta\) which confines the effect to the whole system. The steps are shown in detail in Table 14.  
The power regression function can predict the link mean delay of each link. Through admission control heuristic algorithm, any required traffic unable to satisfy the QoS requirement will be rejected with the predictive end-to-end delay. This mechanism therefore reduces system-wide impact can be reduced and other flows in advance and ultimately achieve superior performance.