ACE: Exploiting Correlation for Energy-Efficient and
Continuous Context Sensing
Suman Nath
Microsoft Research
MobiSys’12, June 25–29, 2012, Low Wood Bay, Lake District, UK
ACE (Acquisitional Context Engine), a middleware that supports continuous context-aware applications while mitigating sensing costs for inferring contexts. ACE provides user’s current context to applications running on it. In addition, it dynamically learns relationships among various context attributes (e.g., whenever the user is Driving, he is not AtHome). ACE exploits these automatically learned relationships for two powerful optimizations.
CITA: Code In The Air Simplifying Sensing and Coordination
Tasks on Smartphones
Lenin Ravindranath, Arvind Thiagarajan, Hari Balakrishnan, and Samuel Madden
MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
HotMobile’12 February 28–29, 2012, San Diego, CA, USA
This paper introduced the CITA architecture for simplifying the tasking applications for users and developers. Developers, who can create tasks by writing only server side code, even for tasks that involve multiple end users and their devices, a variety of sensors, and the server. In our current implementation, these tasks are written in JavaScript. End users, who are able to specify their own tasks by “mixing and matching” available activities and tasks via a web UI (or a smartphone UI)
Snooze: Energy Management in 802.11n WLANs
Ki-Young Jang, Shuai Hao, Anmol Sheth, Ramesh Govindan
University of Southern California, Technicolor Research
ACM CoNEXT’11, December 6-9, 2011
The design and implementation of Snooze, an energy management technique for 802.11n which uses two novel and inter-dependent mechanisms: client micro-sleeps and antenna configuration management. In Snooze, the AP monitors traffic on the WLAN and directs client sleep times and durations as well as antenna configurations, without significantly affecting throughput or delay. Snooze achieves 30~85% energy-savings over CAM across workloads ranging from VoIP and video streaming to file downloads and chats.
COCA: Computation Offload to Clouds using AOP
Hsing-Yu Chen, Yue-Hsun Lin and Chen-Mou Cheng
National Taiwan University and Carnegie Mellon University
2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
COCA is a programming framework that allows smartphones application developers to offload part of the computation to servers in the cloud easily. COCA works at the source level. By harnessing the power of AOP, COCA inserts appropriate offloading code into the source code of the target application based on the result of static and dynamic profiling.
Collaborative Sensing over Smart SensorsPDF Just 2 pages? It is Workshop paper and contains 4 pages?
Vassileios Tsetsos, Nikolaos Silvestros, and Stathes Hadjiefthymiades
Pervasive Computing Research Group, Dept of Informatics and Telecommunications, University of Athens Panepistimiopolis, Ilissia,Greece.
2nd Student Workshop on Wireless Sensor Netwotks, Athens 2009
IPAC (Integrated Platform for Autonomic Computing) adopts a novel and pragmatic approach to context-aware computing.
Enhanced Collaborative Sensing Scheme
for User Activity RecognitionPDF Just Poster! Find full paper It is just Demo poster paper of two pages. No Full paper available.
Yuki Nishida, Yoshihiro Kawahara and Tohru Asami
The University of Tokyo
SenSys’11, November 1–4, 2011, Seattle, WA, USA.
Share raw sensor data among users, yield new information
add information of noisy environment.
CoSense – A Collaborative Sensing Platform for Mobile DevicesPDF Fine, but just poster. Check full paper It need payment
Samuli Hemminki,Kai Zhao , Aaron Yi Ding, Martti Rannanjärv
Department of Computer Science, University of Helsinki, Finland
SenSys '13 Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Opportunistically distributes sensing tasks between familiar devices in close proximity.
Energy-efficient collaborative context sensing on mobile phonesPDF This is proposal, not paper! But check this paper OptiMuS
Sensing as a Service: A Cloud Computing System
for Mobile Phone SensingPDF
Xiang Sheng, Xuejie Xiao, Jian Tang and Guoliang Xue
Syracuse University and Arizona State University
SENSORS'2012: IEEE Sensors Conference; October 29-31, 2012, Taipei
Identify unique challenges of designing and implementing an S2aaS cloud, review existing systems and methods, present viable solutions.
Crowdsourcing to Smartphones: Incentive Mechanism Design for Mobile Phone SensingPDF
Dejun Yang, Guoliang Xue, Xi Fang
Arizona State University
Mobicom '12 Proceedings of the 18th annual international conference on Mobile computing and networking
Designed incentive mechanisms for mobile phone sensing.
Mobile Sensing for Social CollaborationsPDF
Chi Harold Liu, Pan Hui
IBM Research - China and Deutsche Telekom Laboratories
ACM CSCW Workshop of Mobile Collaboration in the Developing World, Hangzhou China, March 2011
The main objective of this paper is to receive early feedback from researchers and practitioners in the areas of mobile computing and social collaboration, and to brainstorm and synergy new collaboration opportunities.
Energy-Efficient Sensor Node Control Based on Sensed Data and Energy Monitoring PDF
Ho-Guen Song, Dae-Cheol Jeon, Hee-Dong Park
Korea Nazarene University, Cheonan
Springer-Verlag Berlin Heidelberg 2011
Proposes an energy-efficient sensor node control mechanism to prolong sensor networks’ lifespan by minimizing and equalizing energy consumption of sensor nodes.
Energy-efficient Tasking in Participatory Sensing SystemsPDF
Kevin Wiesner, Sebastian Feld
Institute for Computer Science Ludwig-Maximilians-Universität München
Location-based applications and services (LBAS) 2013
Proposed to energy-efficient task distribution and monitoring concept for participatory sensing system and evaluate it by Means of simulation.
Wireless Sensor Network:
A Promising Approach for Distributed Sensing Tasks PDF
Prof. Madhav Bokare, Mrs. Anagha Ralegaonkar
SSBES`s Institute Of Technology and Management ,Nanded, India
Journal of Engineering Technology and Management Science Vol. I No.1 December-January 2012
Most sensor networks actively monitor their surroundings, and it is often easy to deduce information other than the data monitored.
Sensing task assignment via sensor selection for maximum target coverage in WSNsPDF
Marjan Naderan,Mehdi Dehghan , Hossein Pedram
Amirkabir University of Technology,Tehran,Iran
Assigning the sensing task to cover maximum number of targets while minimizing the energy consumption of the sensing operation.
Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDENPDF Good reference for platform design
Prem Prakash Jayaraman, Charith Perera, Dimitrios Georgakopoulos and Arkady Zaslavsky
CSIRO Computational Informatics Canberra, Australia
9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing October 20–23, 2013 Austin, Texas, United States.
To develop a platform that is autonomous, scalable, interoperable and supports efficient sensor data collection, processing, storage and sharing.
Opportunistic Collaboration in Participatory Sensing EnvironmentsPDF Interesting paper
Niwat Thepvilojanapong, Shinichi Konomi , Yoshito Tobe, Yoshikatsu Ohta
Tokyo Denki University
MobiArch’10, September 24, 2010, Chicago, Illinois, USA.
To achieve energy efficiency and reduce data redundancy, we propose Aquiba protocol that exploits opportunistic collaboration of pedestrians.
Dynamic Mobile Cloud Computing: Ad Hoc and
Opportunistic Job Sharing pdf
Niroshinie Fernando, Seng W. Loke and Wenny Rahayu
La Trobe University, Australia
2011 Fourth IEEE International Conference on Utility and Cloud Computing, 281-286
Explore the feasibility of a mobile cloud computing framework to use local resources to solve these problems. The framework aims to determine a priori the usefulness of sharing workload at runtime. The results of experiments conducted in Bluetooth transmission and an initial prototype are also presented.
To offload or not to offload: an efficient code
partition algorithm for mobile cloud computing pdf
Yuan Zhang, Hao Liu, Lei Jiao, Xiaoming Fu
University of Göttingen, Germany, Tsinghua University, China
2012 IEEE International Conference on Cloud Networking, 80-86
Proposed an efficient code partition algorithm for mobile code offloading. Our algorithm is based on the observation that when a method is offloaded, the subsequent invocations will be offloaded with a high chance. Unlike the current approach which makes an individual decision for each component, our algorithm finds the offloading and integrating points on a sequence of calls by depth-first search and a linear time searching scheme.
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