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

Abstract

Abstract-State-of-the-art trajectory compression methods usually involve high space-time complexity or yield unsatisfactory compression rates, leading to rapid exhaustion of memory, computation, storage and energy resources. Their ability is commonly limited when operating in a resource-constrained environment especially when the data volume (even when compressed) far exceeds the storage limit. Hence we propose a novel online framework for error-bounded trajectory compression and ageing called the Amnesic Bounded Quadrant System (ABQS), whose core is the Bounded Quadrant System (BQS) algorithm family that includes a normal version (BQS), Fast version (FBQS), and a Progressive version (PBQS). ABQS intelligently manages a given storage and compresses the trajectories with different error tolerances subject to their ages. In the experiments, we conduct comprehensive evaluations for the BQS algorithm family and the ABQS framework. Using empirical GPS traces from flying foxes and cars, and synthetic data from simulation, we demonstrate the effectiveness of the standalone BQS algorithms in significantly reducing the time and space complexity of trajectory compression, while greatly improving the compression rates of the state-of-the-art algorithms (up to 45%). We also show that the operational time of the target resource-constrained hardware platform can be prolonged by up to 41%. We then verify that with ABQS, given data volumes that are far greater than storage space, ABQS is able to achieve 15 to 400 times smaller errors than the baselines. We also show that the algorithm is robust to extreme trajectory shapes.

On-the-fly mobility event detection over aircraft trajectories

“…Dead-reckoning policies like [26] and mobility tracking protocols in [14] can be employed on board of vehicles to relay positional updates only upon significant deviations in their known course. A bounded quadrant system suggested in [16] enables estimation of various error bounds for trajectory compression in ageing-aware fashion suitable for tracking devices of limited storage. The one-pass, error-bounded algorithm in [15] involves local distance checking and optimizations to achieve higher compression.…”

Trajectory simplification

“…BQS [27,28] picks at most eight significant points, forming a convex hull to enclose all the points in the buffer. Then, an upper bound and a lower bound are derived such that in most cases, a point can be quickly decided for removal or preservation with cost O(1).…”

“…However, the running time of BQS still remains O(N 2 ) in the worst case. FBQS [27,28] is a fast version of BQS [27] that avoids deviation calculation and eliminates the necessity of maintaining a buffer. Consequently, the time complexity is reduced to O(N ) but more points will be preserved in FBQS than in BQS.…”

“…Firstly, most of the algorithms evaluated in [33,35] are heuristic and no longer state-of-the-art. There have been a considerable number of more advanced algorithms proposed afterwards, with more complex distance metric [8,5] and processing logic [27,28,26]. There also emerges a new brach of direction-preserving simplification algorithms [19,20,30,31], which claimed to guarantee both direction and position errors.…”

One-pass trajectory simplification using the synchronous Euclidean distance

“…Transmitting and storing raw trajectory data consumes too much network bandwidth and storage capacity [2, 5, 15-17, 20, 22-24, 27, 34]. It is known that these issues can be resolved or greatly alleviated by trajectory compression techniques via removing redundant data points of trajectories [2,4,5,7,10,12,[15][16][17][18]20,23,24,27], among which the piece-wise line simplification technique is widely used [2, 4, 5, 7, 15-17, 20, 23], due to its distinct advantages: (a) simple and easy to implement, (b) no need of extra knowledge and suitable for freely moving objects, and (c) bounded errors with good compression ratios [15,27].…”