###### Abstract
The concept of sensor network embedded into CRANETs has further called sensor network-aided CRANETs (SN-CRNs), which has been considered as one of the most appealing approaches to perform cost-effective spectrum sensing in CR systems [7; 11; 12]. Similar to most other traditional wireless networks or wireline Internet, network congestion in SN-CRNs will also occur when offered data load that exceed the available capacity of a SU due to buffer overflow caused by the data sequence of the SSR injected from SSNs together with the data traffic from upstream SUs. This therefore leads to energy consumption of SSNs, aggressive retransmission, queuing delay, and blocking of new flows from upstream SUs. Indubitably, a congestion control technique in the transport layer is essential to balance resource loads and avoid excessive congestion. However, the congestion control mechanism for the traditional Transmission Control Protocol (TCP) via the acknowledgement-triggered or window-based methods was initially designed and optimized to perform in reliable wired links with constrained bit error rates and round trip times (RTTs) [13]. A recent study [14] has reported that the performance of HTTP download deteriorates as much as about 40% under the TCP window control in an IEEE P1900.4 based cognitive wireless system by using User Datagram Protocol (UDP) and TCP transport protocols. On the other hand, some other research efforts about congestion control have also been conducted from the perspective of finding methods to modify the TCP protocol, such as TCP monitoring delayed acknowledgment, segment-based selective acknowledgement, TCP adaptive delayed-acknowledgment window, etc. [15], aiming to accommodate the challenging multi-hop wireless environments. Unfortunately, it has been shown that these methods of TCP modification and extension cannot be directly applied into SN-CRNs due to sudden large-scale bandwidth fluctuation, periodic interruption caused by spectrum sensing and channel switching [16]. Recently, there have also been previous works on congestion control for multi-hop CRANETs from a cross-layer design perspective. In [17], an end-to-end congestion control framework was proposed under the constraint of the non-uniform channel availability by taking into account the interactions from the physical layer to the transport layer. In [18], a cross-layer framework for joint optimization of MAC, scheduling, routing and congestion control was presented to maximize the throughput of a set of multi-hop end-to-end packet flows. However, the end-to-end control policy in [17; 18] is ill suited for operation over wireless links characterized by higher RTTs. On the contrary, the hop-by-hop control reacts to congestion faster where the rates are adjusted at intermediate SUs by feedback information about the congestion state of congested SUs. A cross-layer framework to jointly achieve both congestion and power control through a non-convex optimization method was proposed in [19]. In [20], an optimization framework achieving tradeoff between energy efficiency and network utility maximization was devised, which can jointly balance interference, collision, and congestion among SUs by adjusting transmit power, persistence probability, together with data rate simultaneously via interaction between MAC and other layers. However, the proposed frameworks in [19; 20] are just suited to mitigate the congestion caused by the data traffic from upstream SUs in multi-hop CRANETs, ignoring the impact of the data sequence of the SSR injected from SSNs on the congestion of SUs. To the best of our knowledge, aside from some studies on congestion control for CRANETs as mentioned before, there is no related work reported in the literature related to congestion control over SN-CRNs. As a result, there is a strong motivation to explore congestion mitigation approach in SN-CRNs. Under this scenario, it is certainly not a surprise that the channel capacity between any SSN and FC is a concave function of the transmit power of this SSN and channel conditions [21].