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 percent). We also show that the operational time of the target resource-constrained hardware platform can be prolonged by up to 41 percent. 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.

Optimal control of Heating, Ventilation, and Air Conditioning (HVAC) is an important step towards reducing the carbon footprint of buildings and requires balancing energy reductions and occupant comfort. Conventional thermostats for temperature set points provide a centralised single point of user input, often leading to significant thermal discomfort for occupants. We propose to instead include users in the HVAC control loop through distributed smart-phone based votes about their thermal comfort for aggregated control of HVAC. Unlike existing approaches that require in-situ sensors or build complex comfort models of individual users, we propose a model- and sensor-free HVAC control algorithm that uses simple user input (hot/cold) and adapts to changing office occupancy or ambient temperature in real time. We develop an iterative data fusion algorithm that finds optimal temperature in offices with multiple users and propose techniques that can aggressively save energy by drifting indoor temperatures towards the outdoor temperature. Our evaluation is based on empirical data collected in 12 offices over a 3-week period and shows that adaptive HVAC control can save up to 60% of energy at a relatively small increase of 0.3°C in average occupant discomfort.

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 exponentμ_opt, from ballistic motion (μ_opt→ 1) to Lévy flight (1 <μ_opt< 3) to Brownian motion (μ_opt≥ 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.