In this paper we argue that achieving symmetric errors is the key to an improved understanding of clock synchronization. We present a clock synchronization algorithm with drift compensation that implements this symmetric error paradigm. The performance of the algorithm is evaluated by measurements in an indoor testbed using the TinyNode hardware platform. We show that the remaining error is symmetric and in the range of the clock granularity.

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Abstract-Clock synchronization is an enabling service for a wide range of applications and protocols in both wired and wireless networks. We study the implications of clock drift and communication latency on the accuracy of clock synchronization when scaling the network diameter. Starting with a theoretical analysis of synchronization protocols, we prove tight bounds on the synchronization error in a model that assumes independently and randomly distributed communication delays and slowly changing drifts. While this model is more optimistic than traditional worst-case analysis, it much better captures the nature of real-world systems such as wireless networks.The bound on the synchronization accuracy, which is roughly the square-root of the network diameter, is achieved by the novel PulseSync protocol. Extensive experiments demonstrate that PulseSync is able to meet the predictions from theory and tightly synchronizes large networks. This contrasts against an exponential growth of the skew incurred by the state-of-the-art protocol for wireless sensor networks. Moreover, PulseSync adapts much faster to network dynamics and changing clock drifts than this protocol.

Long-term location tracking, where trajectory compression is commonly used, has gained high interest for many applications in transport, ecology, and wearable computing. However, state-of-the-art compression methods involve high spacetime complexity or achieve unsatisfactory compression rate, leading to rapid exhaustion of memory, computation, storage and energy resources. We propose a novel online algorithm for errorbounded trajectory compression called the Bounded Quadrant System (BQS), which compresses trajectories with extremely small costs in space and time using convex-hulls. In this algorithm, we build a virtual coordinate system centered at a start point, and establish a rectangular bounding box as well as two bounding lines in each of its quadrants. In each quadrant, the points to be assessed are bounded by the convex-hull formed by the box and lines. Various compression error-bounds are therefore derived to quickly draw compression decisions without expensive error computations. In addition, we also propose a light version of the BQS version that achieves O(1) complexity in both time and space for processing each point to suit the most constrained computation environments. Furthermore, we briefly demonstrate how this algorithm can be naturally extended to the 3-D case.Using empirical GPS traces from flying foxes, cars and simulation, we demonstrate the effectiveness of our algorithm 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 47%). We then show that with this algorithm, the operational time of the target resourceconstrained hardware platform can be prolonged by up to 41%.

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We present a simple model to study Lévy-flight foraging with a power-law step-size distribution in a finite landscape with countable targets. We find that different optimal foraging strategies characterized by a wide range of power-law exponent μopt, from ballistic motion (μopt → 1) to Lévy flight (1 < μopt < 3) to Brownian motion (μopt ≥ 3), may arise in adaptation to the interplay between the termination of foraging, which is regulated by the number of foraging steps, and the environmental context of the landscape, namely the landscape size and number of targets. We further demonstrate that stochastic returning can be another significant factor that affects the foraging efficiency and optimality of foraging strategy. Our study provides a new perspective on Lévy-flight foraging, opens new avenues for investigating the interaction between foraging dynamics and the environment and offers a realistic framework for analysing animal movement patterns from empirical data.

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.

Vehicular ad-hoc networks with inter-vehicular communications are a prospective technology which contributes to safer and more efficient roads and offers information and entertainment services to mobile users. Since large real-world testbeds are not feasible, research on vehicular ad-hoc networks depends mainly on simulations. Therefore, it is crucial that realistic mobility models are employed. We propose a generic and modular mobility simulation framework (GMSF). GMSF simplifies the design of new mobility models and their evaluation. Besides, new functionalities can be easily added. GMSF also propose new vehicular mobility models, GIS-based mobility models. These models are based on highly detailed road maps from a geographic information system (GIS) and realistic microscopic behaviors (car-following and traffic lights management). We perform an extensive comparison of our new GIS-based mobility models with popular mobility models (Random Waypoint, Manhattan) and realistic vehicular traces from a proprietary traffic simulator. Our findings leverages important issues the networking community still has to address.