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State-of-the-art trajectory compression methods usually involve highspace-time complexity or yield unsatisfactory compression rates, leading torapid exhaustion of memory, computation, storage and energy resources. Theirability is commonly limited when operating in a resource-constrainedenvironment especially when the data volume (even when compressed) far exceedsthe storage limit. Hence we propose a novel online framework for error-boundedtrajectory compression and ageing called the Amnesic Bounded Quadrant System(ABQS), whose core is the Bounded Quadrant System (BQS) algorithm family thatincludes a normal version (BQS), Fast version (FBQS), and a Progressive version(PBQS). ABQS intelligently manages a given storage and compresses thetrajectories with different error tolerances subject to their ages. In theexperiments, we conduct comprehensive evaluations for the BQS algorithm familyand the ABQS framework. Using empirical GPS traces from flying foxes and cars,and synthetic data from simulation, we demonstrate the effectiveness of thestandalone BQS algorithms in significantly reducing the time and spacecomplexity of trajectory compression, while greatly improving the compressionrates of the state-of-the-art algorithms (up to 45%). We also show that theoperational time of the target resource-constrained hardware platform can beprolonged by up to 41%. We then verify that with ABQS, given data volumes thatare far greater than storage space, ABQS is able to achieve 15 to 400 timessmaller errors than the baselines. We also show that the algorithm is robust toextreme trajectory shapes.

A Novel Framework for Online Amnesic Trajectory Compression in Resource-constrained Environments