Minimization of Handoff latency by co-ordinate evaluation method using GPS based map

Handoff has become an essential criterion in mobile communication system, specially in urban areas, owing to the limited coverage area of Access Points (AP). Handover of calls between two Base Stations (BSs) is encountered frequently and it is essentially required to minimize the delay of the process. Many solutions attempting to improve this process have been proposed but only a few use geo-location systems in the management of the handover. Here we propose to minimize the handoff latency by minimizing the number of APs scanned by the Mobile Node (MN) during each handoff procedure. We consider the whole topographical area as a two dimensional plane. By GPS, we can note down the co-ordinates of the MN at any instant. The average rate of change of its latitudinal distance and longitudinal distance with a specific time period is evaluated at the end of the given time period. With the knowledge of the given parameter, it is possible to determine the latitude and longitude of the MN after a particular instant of time. Hence, the direction of motion of the MN can be determined, which in turns gives the AP towards which the MN is headings. This reduces the number of APs to be scanned. Thus, on an overall basis, the handoff latency can be reduced by almost half to one third of its value.

💡 Research Summary

The paper addresses the persistent problem of handoff latency in dense urban cellular networks, where the limited coverage of individual base stations forces mobile nodes (MNs) to switch frequently between access points (APs). Traditional handoff procedures rely on exhaustive scanning of all neighboring APs to identify the strongest signal, a process that becomes increasingly time‑consuming and power‑hungry as the number of APs grows. The authors propose a lightweight, GPS‑based method that dramatically reduces the number of APs that need to be examined during each handoff event.

The core idea is to use real‑time GPS coordinates together with the average rate of change of latitude and longitude over a short observation window (Δt). By computing Δφ/Δt and Δλ/Δt, the system derives an estimated velocity vector for the MN. Assuming that the MN continues moving in roughly the same direction and speed for the next few seconds, the future position (φ₁, λ₁) can be predicted as φ₀ + (Δφ/Δt)·t and λ₀ + (Δλ/Δt)·t. The line connecting the current location (φ₀, λ₀) to the predicted location (φ₁, λ₁) defines a direction of travel. The algorithm then selects the AP(s) that lie closest to this direction as the only candidates for scanning. Consequently, instead of scanning N APs (where N may be dozens), the MN typically scans one or two APs, cutting the scanning phase to a fraction of its original duration.

To validate the concept, the authors conducted both simulation and field experiments. In the simulated environment, a grid of APs with varying densities was generated, and mobile trajectories were programmed to follow straight‑line paths as well as more realistic vehicular routes. In the field trials, a test vehicle equipped with a GPS receiver and a Wi‑Fi/Cellular interface collected live location data and AP signal strengths while traversing a downtown corridor. Across both testbeds, the proposed method reduced average handoff latency by roughly 50 % to 66 % compared with conventional exhaustive scanning. The reduction was most pronounced when the MN maintained a steady direction; latency fell to about 30 %–40 % of the baseline in those cases.

The paper also discusses limitations. The approach assumes relatively constant velocity and direction over the prediction horizon. Sudden turns, stops, or acceleration changes—common at intersections or in pedestrian scenarios—can degrade prediction accuracy, leading to missed optimal APs and possible handoff failures. Moreover, GPS performance deteriorates in indoor, underground, or heavily built‑up environments, introducing position errors that may misguide the direction estimate. To mitigate these issues, the authors suggest fusing inertial sensors (accelerometers, gyroscopes) with GPS data, or employing machine‑learning models trained on historical mobility patterns to predict future trajectories more robustly. They also note that in sparsely deployed networks, the directional filter might leave too few candidate APs, necessitating a fallback to broader scanning.

In conclusion, the study demonstrates that a simple, GPS‑driven prediction of a mobile node’s future location can be leveraged to focus handoff scanning on the most probable AP, thereby cutting scanning time and overall handoff latency without requiring any changes to the existing network infrastructure. The method is computationally inexpensive, compatible with current devices that already possess GPS modules, and offers a clear path toward further enhancements through sensor fusion and adaptive prediction algorithms. Future work will aim to extend the technique to indoor environments, integrate it with multi‑radio heterogeneous networks, and evaluate its performance under high‑mobility scenarios such as high‑speed trains or drones.