Accelerated Benders Decomposition for Variable-Height Transport Packaging Optimisation

This paper tackles the problem of finding optimal variable-height transport packaging. The goal is to reduce the empty space left in a box when shipping goods to customers, thereby saving on filler and reducing waste. We cast this problem as a large-scale mixed integer problem (with over seven billion variables) and demonstrate various acceleration techniques to solve it efficiently in about three hours on a laptop. We present a KD-Tree algorithm to avoid exhaustive grid evaluation of the 3D-bin-packing, provide analytical transformations to accelerate the Benders decomposition, and an efficient implementation of the Benders sub problem for significant memory savings and a three order of magnitude runtime speedup

Household occupancy monitoring using electricity meters

Occupancy monitoring (i.e. sensing whether a building or room is currently occupied) is required by many building au-tomation systems. An automatic heating system may, for ex-ample, use occupancy data to regulate the indoor temperature. Occupancy data is often obtained through dedicated hardware such as passive infrared sensors and magnetic reed switches. In this paper, we derive occupancy information from elec-tric load curves measured by off-the-shelf smart electricity meters. Using the publicly available ECO dataset, we show that supervised machine learning algorithms can extract occu-pancy information with an accuracy between 83 % and 94%. To this end we use a comprehensive feature set containing 35 features. Thereby we found that the inclusion of features that capture changes in the activation state of appliances provides the best occupancy detection accuracy

How Long Are You Staying? Predicting Residence Time from Human Mobility Traces

Welcome to ACM MobiCom 2013, the 19th Annual International Conference on Mobile Computing and Networking. Over the years, MobiCom has established itself as a premier forum for publishing and presenting cutting-edge research in mobile systems and wireless networks, and this year's final program continues this wonderful tradition. The high quality and success of MobiCom can be credited to two groups. First and foremost is the authors of all of the submitted papers who submitted their very best research ideas and results. The 28 accepted papers represent leading-edge, and sometimes bleeding-edge, advances in a large variety of important mobile computing topics -- from traditional yet still very important topics, such as improving the efficiency of cellular and Wi-Fi networks, to topics focusing on the use of new wireless technologies in new mobile environments, such as enabling gesture recognition, managing indoor white space networks, and deploying performance improving femto cells. However, the hidden strength of MobiCom comes from the hard work of a very dedicated program committee, which consisted of 41 members from academia, government and industry spread across 7 different countries with expertise in many areas relevant to wireless networking and mobile computing. As Program Committee (PC) chairs, it was our task to keep MobiCom fresh and grow it to keep up with all the new challenges of the wireless and mobile community. This year, we introduced several new initiatives to further enhance the reach and quality of the conference. To increase MobiCom's visibility in industry, we included five PC members from product teams of Cisco, Google, Qualcomm and Broadcom. To ensure the strength and breadth, but most importantly the vision, of the PC, one-sixth of the PC were new members who had previously not served on the MobiCom PC. This year's MobiCom also has an invited industry session, in which speakers from Broadcom, Alcatel-Lucent, Google, and Microsoft will present the latest results and research challenges from industry in an effort to bridge the gap between academic research and how it is, or maybe isn't, relevant to industry. This year's call for papers attracted 207 submissions from five continents: Asia, Europe, Africa, North America and South America. We used HotCRP for handling the paper submission and reviewing, which was done in three phases. In the first phase, each paper was reviewed by at least three PC members, and the top 98 papers were selected for the next round. In the second phase, each paper was reviewed by at least two more PC members. In some cases when the paper was at the intersection of new topics, such as RADAR or robotics, additional expert opinions were solicited. The final phase was the PC meeting held on May 30th and 31st in Redmond, WA. A total of 34 members attended the PC meeting in-person, while 5 members attended the meeting on Skype. Over one and a half days, the PC extensively discussed the merits and flaws of the 60 toprated papers and ultimately accepted 28 papers for final publication in the conferences' proceedings. Across the three phases, each PC member reviewed about 25 papers, such that most round two papers had an average of 6 reviews (a high number for any top-tier conference). To ensure fairness and preserve the anonymity of all authors and reviewers, papers authored by PC chairs were mixed with a random selection of other papers and handled out-of-band by Alex Snoeren, who was the PC co-chair for MobiCom 2012

Automatically estimating the savings potential of occupancy-based heating strategies

A large fraction of energy consumed in households is due to space heating. Especially during daytime, the heating is often running constantly, controlled only by a thermostat – even if the inhabitants are not present. Taking advantage of the absence of the inhabitants to save heating energy by lowering the temperature thus poses a great opportunity. Since the concrete savings of an occupancy-based heating strategy strongly depend on the individual occupancy pattern, a fast and inexpensive method to quantify these potential savings would be beneficial. In this paper we present such a practical method which builds upon an approach to estimate a household’s occupancy from its historical electricity consumption data, as gathered by smart meters. Based on the derived occupancy data, we automatically calculate the potential savings. Besides occupancy data, the underlying model also takes into account publicly available weather data and relevant building characteristics. Using this approach, households with high potential for energy savings can be quickly identified and their members could be more easily convinced to adopt an occupancy-based heating strategy (either by manually adjusting the thermostat or by investing in automation) since their monetary benefits can be calculated and the risk of misinvestment is thus reduced. To prove the usefulness of our system, we apply it to a large dataset containing relevant building and household data such as the size and age of several thousand households and show that, on average, a household can save over 9% heating energy when following an occupancy-based heating regime, while certain groups, such as single-person households, can even save 14% on average

Simulating the energy savings potential in domestic heating scenarios in Switzerland

This report presents a simulation framework based on the ISO 13790 5R1C lumped capacitance building model for the evaluation of the energy savings potential of occupancy prediction algorithms. We show the derivation of the model parameters and introduce a new methodology to prepare weather data for simulating the energy consumption of a heating system when predictively controlling the thermostat

Balancing Load in Stream Processing with the Cloud

Abstract — Stream processing systems must handle stream data coming from real-time, high-throughput applications, for example in financial trading. Timely processing of streams is important and requires sufficient available resources to achieve high throughput and deliver accurate results. However, static allocation of stream processing resources in terms of machines is inefficient when input streams have significant rate variations— machines remain underutilised for long periods of average load. We present a combined stream processing system that, as the input stream rate varies, adaptively balances workload between a dedicated local stream processor and a cloud stream processor. This approach only utilises cloud machines when the local stream processor becomes overloaded. We evaluate a prototype system with financial trading data. Our results show that it can adapt effectively to workload variations, while only discarding a small percentage of input data. I