###### Abstract  
The data sequence of spectrum sensing results injected from dedicated spectrum sensor nodes (SSNs) and the data traffic from upstream secondary users (SUs) lead to unpredictable data loads in a sensor network-aided cognitive radio ad hoc network (SN-CRN). As a result, network congestion may occur at a SU acting as fusion center when the offered data load exceeds its available capacity, which degrades network performance. In this paper, we present an effective approach to mitigate congestion of bottlenecked SUs via a proposed distributed power control framework for SSNs over a rectangular grid based SN-CRN, aiming to balance resource load and avoid excessive congestion. To achieve this goal, a distributed power control framework for SSNs from interior tier (IT) and middle tier (MT) is proposed to achieve the tradeoff between channel capacity and energy consumption. In particular, we firstly devise two pricing factors by considering stability of local spectrum sensing and spectrum sensing quality for SSNs. By the aid of pricing factors, the utility function of this power control problem is formulated by jointly taking into account the revenue of power reduction and the cost of energy consumption for IT or MT SSN. By bearing in mind the utility function maximization and linear differential equation constraint of energy consumption, we further formulate the power control problem as a differential game model under a cooperation or noncooperation scenario, and rigorously obtain the optimal solutions to this game model by employing dynamic programming. Then the congestion mitigation for bottlenecked SUs is derived by alleviating the buffer load over their internal buffers. Simulation results are presented to show the effectiveness of the proposed approach under the rectangular grid based SN-CRN scenario. c 2017 17 2132 2017 13 2017  
by seeking to underlay, overlay, or interweave their signals with those of the existing PUs without significantly impacting their communications. Spectrum sensing is one of the key enabling technologies for the establishment of CRNs, because it constantly allows for the opportunistic identification and use of the SOPs from a licensed primary network without causing harmful interference to the PUs. In order to improve the sensing performance, collaborative spectrum sensing has been proposed as an effective way to reliably detect the activity of PUs by addressing the issues imposed by the hidden PU terminal problem and the wireless channel impairments, such as the heavy shadowing and fading [6; 7; 8]. In this way, cooperation is achieved by allowing different SUs to collaborate and share their spectrum sensing results (SSR) through a fusion center (FC), which makes a global decision on the occupancy status of the licensed band. However, this centralized FC is not available in decentralized CRANETs. Clearly, each SU under this scenario must perform the distributed collaborative spectrum sensing, which is preferred to the centralized FC scheme because of its scalability, fault tolerance and flexibility [9]. In order to facilitate the spectrum sensing functionality, high sampling rates, high resolution analog to digital converters with large dynamic range, and high speed signal processors are required to be incorporated into an individual SU transceiver [10], which increases hardware cost and power consumption, especially for the double-radio sensing architecture of SU transceiver. An alternative approach is to adopt the cost-effective and dedicated spectrum sensor nodes (SSNs) that perform distributed collaborative spectrum sensing and report SSR to SUs acting as FCs in CRANETs [11]. Technically, s wireless sensor network can be naturally exploited to assist a CRANET by providing SSR about the current spectrum occupancy of PUs in a cooperation fashion.