Integrated Dead Reckoning (DR) System and Global Navigation Satellite System (GNSS) has been widely used as the backbone of any navigation system for the Internet of Things (IoT) and vehicle navigation applications. The dollar level microelectrotechnical system (MEMS) Inertial Measurement Units (IMUs) aided by vehicle wheel odometer has been recently used as a low-cost DR system to bridge GNSS gaps in harsh environments, such as urban canyons, tunnels, and under bridges. However, the DR drift errors increase with time rapidly and cannot satisfy most of IoT and land vehicle navigation requirements. The GNSS may not provide accurate position or even go to complete outage for more than 15 mins in downtown cores or long tunnels; therefore, the Tethered positioning error can reach several hundred meters. While the land vehicles are supposed to travel on the roads, the feedback from a digital map can be used to constrain its position. This paper proposes a new method for tight integration of digital map and DR system (IMU/wheel odometer) to provide reliable navigation solutions in challenging GNSS environments for extended periods. A fuzzy logic map matching algorithm is utilized to identify the correct road link on which the vehicle moves. A feedback filter is designed to send a correct map matched position as well as the road link as measurement updates to the Kalman Filter (KF) of the Tethered positioning system. The proposed tight integration of digital map and DR system is evaluated using the datasets collected by Profound Positioning Inc. (PPI) in Calgary, AB, Canada. The results show the proposed method has an average of 0.15% of relative horizontal position error for Calgary datasets, which shows considerable improvement over the Tethered solution only with 3.3% of relative horizontal position error. The average azimuth error of the proposed system is 1.3 degrees, while the Tethered positioning system, which does not enjoy from road link azimuth feedback, shows an average azimuth error of 9.7 degrees.