{"id":653,"date":"2011-09-22T18:17:04","date_gmt":"2011-09-22T18:17:04","guid":{"rendered":"http:\/\/www.syslog.cl.cam.ac.uk\/?p=653"},"modified":"2011-09-22T18:17:29","modified_gmt":"2011-09-22T18:17:29","slug":"mobicom-day-2","status":"publish","type":"post","link":"https:\/\/www.syslog.cl.cam.ac.uk\/2011\/09\/22\/mobicom-day-2\/","title":{"rendered":"Mobicom. Day 2"},"content":{"rendered":"
\n

Day 2 of MobiCom 2011 started with my talk on SociableSense. Fourteen papers were presented over four sessions, including two best papers.<\/p>\n

SESSION: Applications<\/strong><\/p>\n

SociableSense: Exploring the Trade-offs of Adaptive Sampling and Computation Offloading for Social Sensing<\/strong>, Kiran K. Rachuri, Cecilia Mascolo, Mirco Musolesi, and Peter J. Rentfrow (University of Cambridge, United Kingdom)<\/p>\n

Our work. Details at:<\/p>\n

http:\/\/www.syslog.cl.cam.ac.uk\/2011\/07\/15\/efficient-social-sensing-based-on-smart-phones\/<\/a><\/p>\n

Overlapping Communities in Dynamic Networks: Their Detection and how they can help Mobile Applications<\/strong>, Nam P. Nguyen, Thang N. Dinh, Sindhura Tokala, and My T. Thai (University of Florida, USA)<\/p>\n

A better understanding of mobile networks in terms of overlapping communities, underlying structure, organisation helps in developing efficient applications such as routing in MANETs, worm containment, and sensor reprogramming in WSNs. So, the detection of network communities is important, however, they are large and dynamic, and overlapping communication. \u00c2\u00a0Can community detection be performed in a quick and efficient way.<\/p>\n

They propose a two phase limited input dependent framework to address this. Phase 1: basic communities detection (basic communities are dense parts of the networks). Phase 2: update network communities when changes are introduced, i.e., handle: adding a node\/edge, and removing a node\/edge. \u00c2\u00a0The evaluation is based on MIT reality mining data. \u00c2\u00a0They evaluate the proposed scheme with respect to two applications: routing in MANETs and worm containment.<\/p>\n

Detecting Driver Phone Use Leveraging Car Speakers<\/strong>, Jie Yang and Simon Sdhom> (Stevens Institute of Technology, USA); Gayathri Chandrasekaranand Tam Vu (Rutgers University, USA); Hongbo Liu (Stevens Institute of Technology, USA);Nicolae Cecan (Rutgers University, USA); Yingying Chen (Stevens Institute of Technology, USA);Marco Gruteser and Richard P. Martin(Rutgers University, USA)<\/p>\n

(Joint Best Paper Award)<\/strong><\/p>\n

80% of people talk on cell phone while driving. The consequences of this might be dangerous (18% accidents). They claim that hands-free devices do not help because of the effects in the cognitive load on the driver. Several mobile apps in the market trying to solve that. (zoom safer \u00c3\u00afzup, cellsafety). Recent measures:<\/p>\n

-hard blocking: jammers, blocking calls etc<\/p>\n

-soft interaction: delay calls, route to voice mail, automatic reply<\/p>\n

Current apps that actively prevent cell phone use in vehicle only detect the phone is in vehicle or not through: GPS, handover, signal strength, speedometer etc. None of them have capability to find whether phone is used by driver or passenger. They use an acoustic ranging approach to solve this problem. \u00c2\u00a0They identify the position of the cell phone based on the car speakers and mobile phone, and based on speakers emitting different sounds at different times. Cell phone mic has wider range of frequency range: so beep frequency to outside user hearing range. \u00c2\u00a0Evaluation shows that the accuracy of detection is over 90%.<\/p>\n

I Am the Antenna: Accurate Outdoor AP Location Using Smartphones<\/strong>, Zengbin Zhang, Xia Zhou, Weile Zhang, Yuanyang Zhang, Gang Wang, Ben Y. Zhao, and Haitao Zheng (University of Calfornia at Santa Barbara, USA)<\/p>\n

The density of APs in the environment is very high. How to find the location of an AP? \u00c2\u00a0Conventional AP location methods:<\/p>\n

- Directional antenna: Fast, very accurate but expensive<\/p>\n

- Signal map: Simple but time consuming<\/p>\n

- RSS gradient: Low accuracy, low measurement overhead but low accuracy<\/p>\n

Their solution is based on the effect \u00c2\u00a0of user orientation degree to an AP on RSS. The body of the user can affect the SNR (they observed around 13dBm difference). They also tested the generality of the effect with multiple phones, protocols, different users, and environments, and \u00c2\u00a0RSS profiles all followed the same trend.<\/p>\n

Evaluation is in a campus, with three scenarios. 1. Simple line of sight (no blocks) 2. complex line of sight (vehicles etc) 3. Non line of sight (line of sight is completely blocked). Metric: absolute angular error: detected direction - actual direction. results: error < 30 degree for 80% cases, in simple LOS (line of sight); error < 65 degree for 80% cases in Non LOS.<\/p>\n

SESSION: Cellular Networks<\/strong><\/p>\n

Traffic-Driven Power Saving in Operational 3G Networks<\/strong>, \u00c2\u00a0Chunyi Peng, Suk-Bok Lee, Songwu Lu, and Haiyun Luo (University of California at Los Angeles, USA)<\/p>\n

Transmission power of Base Stations increases linearly with the traffic load. The cooling power keeps constant and its comparable to the transmission power. As a result, high energy is consumed energy even at zero traffic. Existing solutions do not address practical issues and they follow a theoretical analysis. In this work, they propose a traffic-driven approach that exploits traffic dynamics to turn off under-utilised BSs for system-wide energy efficiency. They claim that traffic is quite predictable in the base station. There\u00e2\u20ac\u2122s a lot of potential to save energy in quite hours but also in peak hours. Their solution also tries to be compatible with current 3G standard\/deployment. Issues addressed: Issue 1: how to satisfy location dependent coverage and capacity constraints. Issue 2: how to estimate traffic load ?<\/p>\n

Solution: based on profiling: estimate traffic envelope via profiling and leverage near-term stability. The set of BS active in idle hours should be a subset of the ones in peak hours. Their condition is that they should not switch BSs more than once per day. Provide location-dependent capacity. Their estimation is a moving average with 24 daily intervals. However, frequent on\/off switching is undesirable: takes several minutes. It should be based on traffic characteristics.<\/p>\n

MOTA: Engineering an Operator Agnostic Mobile Service<\/strong>, Supratim Deb, Kanthi Nagaraj, and Vikram Srinivasan (Bell Labs Research, India)<\/p>\n

Cellular coverage varies with respect to locations. Users may not be happy with a single service provider, and there is a case for users choosing services from multiple providers. Dual sim phones are already popular in asia. Users are using services based on the cost from the providers. Goal of this work: Ability for users to join the network of choice at will based on location, pricing, and applications.<\/p>\n

Solution: to propose changing operator from the user-side. They consider several solutions: Option 1: Centralised approach making decisions but operators unlikely to share network planning information. Option 2: Users use signal strength from different base stations. This is insufficient and can result in poor user experience.<\/p>\n

They propose MOTA in which a service aggregator is introduced: new intermediary between users and operator and is responsible for maintaining customer relationships and handles all control plane operations that cannot be handled by a single operator. The also use a Utility function that incorporates fairness. Evaluation is based on the data from one of the largest cellular operators in India.<\/p>\n

Anonymization of Location Data Does Not Work: A Large-Scale Measurement Study<\/strong>, Hui Zang and Jean Bolot (Sprint Applied Research, USA)<\/p>\n

Call Detail Records (CDR) keep a lot of information about the phone calls of the users and they can be linked to a location. They can be used for marketing, security, LBS, Mobility Modelling, however, privacy might be breached if such data is released. Traditional approaches to protect privacy of users is through anonymisation, however, this works shows that does not work. CDR contains: mobile id, time of call, call durations, start cell id, start sector id, end sector id, call direction, caller id. If mobile id and caller id are anonymised, can we detect the user. Its shown that with gender, zipcode, and birthdate, 87% of USA population can be identified.<\/p>\n

Their dataset consists of more than 30 billion call records made by 25 million cell phone users across the USA. Their approach is to infer top N locations for each user and correlate this with publicly available information such as census data. They show that the top 1 location does not yield small anonymity sets, but top 2 and 3 locations do at the sector or cell-level granularity. They also provide possible solutions based on spatial and time domain approaches for publishing location data without compromising on privacy.<\/p>\n

SESSION: Infrastructureless Networking.<\/strong><\/p>\n

Enhance & Explore: An Adaptive Algorithm to Maximize the Utility of Wireless Networks<\/strong>, Adel Aziz and Julien Herzen (\u00c3\u2030cole Polytechnique F\u00c3\u00a9d\u00c3\u00a9rale de Lausanne, Switzerland); Ruben Merz (Deutsche Telekom Laboratories, Germany); Seva Shneer (Heriot-Watt University, UK); andPatrick Thiran (\u00c3\u2030cole Polytechnique F\u00c3\u00a9d\u00c3\u00a9rale de Lausanne, Switzerland)<\/p>\n

This work addresses the problem of providing efficiency and fairness in wireless networks. Their approach is based on maximising a utility function. They propose an algorithm called Enhance and Explore that maximises the utility function. The challenges in designing this scheme are: work on existing mac, non-network wide message passing, and wireless capacity is unknown a priory.<\/p>\n

They consider two scenarios: WLAN setting: inter-flow problem and optimally allocate resources. Multi-hop setting: intra-flow problem and avoid congestion. They show analytically that the proposed algorithm converges to a point of optimal utility. Evaluation is through experiments in a testbed and simulations in ns-3.<\/p>\n

Scoop: Decentralized and Opportunistic Multicasting of Information Streams<\/strong>, Dinan Gunawardena, Thomas Karagiannis, and Alexandre Proutiere (Microsoft Research Europe, UK); Elizeu Santos-Neto (University of British Columbia, Canada); and Milan Vojnovic (Microsoft Research Europe, UK)<\/p>\n

This work aims at leveraging mobility for content delivery in networks of devices experiencing intermittent connectivity. Main challenge: routing \/ relaying strategies. Existing solutions include epidemic routing. Drawback of existing works are: simplifying assumptions on mobility, and interact contact times are exponentially distributed. This work proposes SCOOP that<\/p>\n