Lectures take place in the "Fresco Room" on the first floor of the castle (Via Frangipane 6, Bertinoro).

Sunday, July 26

16.00-21.00 Registration (Via Frangipane 4, Bertinoro)
19.00 Reception (canteen, Via Frangipane 2, Bertinoro)

Late arrival: If you arrive after the opening hours of the registration desk, go to the main gate of the castle in Via Frangipane 6. Close to the large display there is a bell to call the housekeeper. He will give you the key to your room.

Monday, July 27

07.30-08.45 Breakfast (canteen)
08.45-09.00 K. Roemer, F. Mattern: Welcome
09.00-10.30 A. Campbell: People-Centric Sensing and the Rise of the Global Mobile Sensor Network
10.30-11.00 Coffee break
11.00-12.30 L. Mottola: Operating Systems for Networked Embedded Devices
12.30-14.00 Lunch (canteen)
14.00-15.30 K. Whitehouse: Macroprogramming Sensor Networks
15.30-16.00 Coffee break
16.00-17.30 K. Langendoen: Energy-Efficient Medium Access Control
19.00-20.30 Dinner (restaurant)

Tuesday, July 28

07.30-09.00 Breakfast (canteen)
09.00-10.30 B. Rinner: Smart Cameras and Visual Sensor Networks
10.30-11.00 Coffee break
11.00-12.30 L. Mottola: Real-World Deployment of WSN Applications
12.30-14.00 Lunch (canteen)
14.00-15.30 K. Langendoen: Distributed Localization Algorithms
15.30-16.00 Coffee break
16.00-17.30 K. Whitehouse: Debugging Sensor Networks
19.00-20.30 Dinner (canteen)

Wednesday, July 29

07.30-09.00 Breakfast (canteen)
09.00-10.30 S. Prabh: Data Aggregation and Data Dissemination in Wireless Sensor Networks
10.30-11.00 Coffee break
11.00-12.30 Participant's Workshop I
12.30-14.00 Lunch (canteen)
14.30-23.30 Excursion and dinner in Ravenna

Thursday, July 30

07.30-09.00 Breakfast (canteen)
09.00-10.30 T. Voigt: Contiki COOJA Hands-on Crash Course
10.30-11.00 Coffee break
11.00-12.30 T. Voigt: Contiki COOJA Hands-on Crash Course
12.30-14.00 Lunch (canteen)
14.00-15.30 T. Voigt: Contiki COOJA Hands-on Crash Course
15.30-16.00 Coffee break
16.00-17.30 F. Mattern: Visions of the Future
19.00-20.30 Dinner (canteen)
20.30-22.00 Participant's Workshop II

Friday, July 31

07.30-09.00 Breakfast (canteen)
09.00-10.30 P. Lukowicz: Wearable Sensing
10.30-11.00 Coffee break
11.00-12.30 F. Mattern: Towards the Internet of Things
12.30-14.00 Lunch (canteen)
14.00-15.30 A. Salehi: Global Sensor Networks
15.30-16.00 Coffee break
16.00-17.30 A. Salehi: Global Sensor Networks (hands-on course)
19.00-20.30 Dinner (restaurant)

Saturday, August 1

07.30-09.00 Breakfast (canteen)
09.00-10.30 K. Roemer: Time Synchronization for Sensor Networks
10.30-11.00 Coffee break
11.00-12.30 S. Karnouskos: Future Enterprises based on Real-World Services
12.30-14.00 Lunch (canteen)
14.00 Departure

Note: This schedule is subject to change depending on weather conditions and other circumstances. Some sessions might be moved to the evening to gain free time during the day.

K. Roemer, F. Mattern: Welcome

Welcome message by the organizers.

A. Campbell: People-Centric Sensing and the Rise of the Global Mobile Sensor Network (slides)

Technological advances in sensing, computation, storage, and communications will turn the ubiquitous mobile phone into a global mobile sensing device carried by billions of people world-wide. Sensing will be people- centric, enabling a different way to sense, learn, visualize, and share information about ourselves, friends, communities, the way we live, and the world we live in. People-centric sensing juxtaposes the traditional view of small-scale, mote-based sensor networks with one in which people, carrying sensor-enabled mobile phones, enable opportunistic sensing coverage - ultimately, leading to the dawn of a global mobile sensor network. In the MetroSense Project's vision of people-centric sensing, users are the key architectural system component, enabling a host of new application areas such as personal, public, and social sensing. See [1-5] for more on the MetroSense project.
  1. Hong Lu, Wei Pan, Nicholas D. Lane, Tanzeem Choudhury, Andrew T. Campbell: SoundSense: Scalable Sound Sensing for People-Centric Sensing Applications on Mobile Phones, To appear in Proc. of 7th ACM Conference on Mobile Systems, Applications, and Services (MobiSys '09), June 2009.
  2. Emiliano Miluzzo, Nicholas D. Lane, Kristof Fodor, Ronald A. Peterson, Hong Lu, Mirco Musolesi, Shane. B. Eisenman, Xiao Zheng, Andrew T. Campbell: Sensing Meets Mobile Social Networks: The Design, Implementation and Evaluation of the CenceMe Application, In Proc. of 6th ACM Conference on Embedded Networked Sensor Systems (SenSys '08), November 2008.
  3. Andrew T. Campbell, Shane B. Eisenman, Nicholas D. Lane, Emiliano Miluzzo, Ronald A. Peterson, Hong Lu, Xiao Zheng, Mirco Musolesi, Kristof Fodor, and Gahng-Seop Ahn: The Rise of People-Centric Sensing, In IEEE Internet Computing: Mesh Networking, pp. 30-39, July/August, 2008.
  4. Nicholas D. Lane, Hong Lu, Shane B. Eisenman, Andrew T. Campbell: Cooperative Techniques Supporting Sensor-based People-centric Inferencing, in Proc. of Sixth Conf. on Pervasive Computing, May 2008.
  5. Andrew T. Campbell, Shane B. Eisenman, Nicholas D. Lane, Emiliano Miluzzo, Ronald Peterson: People-Centric Urban Sensing (Invited Paper), In Proc. of Second ACM/IEEE Annual International Wireless Internet Conference (WICON 2006), August 2006.

L. Mottola: Operating Systems for Networked Embedded Devices (slides)

Operating systems for networked embedded devices represent a radical departure from their counterparts in mainstream computing. Nevertheless, although the functionality provided are drastically different, they are as critical as in a traditional setting. In this lecture, we survey the current landscape of operating system support for networked embedded devices along different dimensions, ranging from concurrency model to energy abstractions and networking support. The bird-eye view on the current state of the art is also the opportunity to identify open problems and research directions in the field.
  1. N. Cooprider, W. Archer, E. Eide, D. Gay, and J. Regehr: Efficient Memory Safety for TinyOS, In Proc. of the 5th ACM Conference on Embedded Network Sensor Systems (SENSYS), 2007.
  2. A. Dunkels, B. Grönwall, and T. Voigt: Contiki - a lightweight and flexible operating system for tiny networked sensors, In Proc. of the 1st IEEE Workshop on Embedded Networked Sensors (Emnets-I), 2004.
  3. Q. Cao, T. Abdelzaher, J. Stankovic, and T. He: The LiteOS Operating System: Towards Unix-Like Abstractions for Wireless Sensor Networks, In Proc. of the 7th ACM/IEEE Conf. on Information Processing in Sensor Networks (IPSN/SPOTS), 2008.
  4. A. Eswaran, A. Rowe, and R. Rajkumar: Nano-RK: An Energy-Aware Resource-Centric Operating System for Sensor Networks, In IEEE Real-Time Systems Symposium, 2005.
  5. K. Lorincz, B. Chen, J. Waterman, G. Werner-Allen, and M. Welsh: Resource Aware Programming in the Pixie OS, In Proc. of the 6th ACM Conference on Embedded Network Sensor Systems (SENSYS), 2008.

L. Mottola: Real-World Deployment of WSN Applications (slides)

Despite the significant body of research in Wireless Sensor Networks (WSNs), field deployments still involve major challenges. This makes the leap from "theory" to "practice" a major ordeal, potentially hindering the development of the field. In this lecture, we discuss the issues arising in deploying real-world WSNs through a series of paradigmatic examples. In doing so, we distill the fundamental characteristics germane to different deployment experiences, and highlight the shortcomings of existing solutions to identify research directions in the field.
  1. A. Mainwaring, D. Culler, J. Polastre, R. Szewczyk, and J. Anderson: Wireless sensor networks for habitat monitoring, In Proc. of the 1st Int. Wkshp. on Wireless Sensor Networks and Applications, 2002.
  2. A. Arora et al: ExScal: elements of an extreme scale wireless sensor network, In Proc. of the 11th Conf. on Embedded and Real-Time Computing Systems and Applications, 2005.
  3. G. Werner-Allen, K. Lorincz, J. Johnson, J. Lees, and M. Welsh: Fidelity and yield in a volcano monitoring sensor network, In Proc. of 7th Symp. on Operating Systems Design and Implementation (OSDI), 2006.
  4. G. Barrenetxea, F. Ingelrest, G. Schaefer, and M. Vetterli: The hitchhiker's guide to successful wireless sensor network deployments, In Proc. of the 6th ACM Conference on Embedded Network Sensor Systems (SENSYS), 2008.
  5. M. Ceriotti, L. Mottola, G. P. Picco, A. L. Murphy, S. Guna, M. Corr., M. Pozzi, D. Zonta, and P. Zanon: Monitoring Heritage Buildings with Wireless Sensor Networks: The Torre Aquila Deployment, In Proc. of the 8th ACM/IEEE Conf. on Information Processing in Sensor Networks (IPSN/SPOTS), 2009.

K. Whitehouse: Macroprogramming Sensor Networks (slides)

To program a sensor network, one must specify how each node will react to timer events, sensor events, and messages from other nodes. This paradigm -- sometimes called "node-level programming" -- can be a difficult and error prone way to design a system: to predict how a program will execute, the programmer must have a mental model of every node and how they will interact to produce a global outcome. In contrast, "macroprogramming systems" allow the programmer to specify the desired global objective of the entire network, and this global objective is then automatically compiled down to the appropriate local actions for each node. This process is much easier for the programmer, but much harder for the compiler. In this session, we will discuss a range of techniques for specifing global objectives and for automatically compiling them down to local node-level actions.
  1. Sam Madden, Michael J. Franklin, Joseph M. Hellerstein and Wei Hong: TinyDB: An Acqusitional Query Processing System for Sensor Networks. ACM TODS, 2005.
  2. Omprakash Gnawali, Ben Greenstein, Ki-Young Jang, August Joki, Jeongyeup Paek, Marcos Vieira, Deborah Estrin, Ramesh Govindan, Eddie Kohler: The TENET Architecture for Tiered Sensor Networks, In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (Sensys), 2006.
  3. Geoffrey Mainland, Greg Morrisett, Matt Welsh, and Ryan Newton: Sensor Network Programming with Flask, In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (Sensys), 2007.
  4. David Chiyuan Chu, Lucian Popa, Arsalan Tavakoli, Joseph M. Hellerstein, Philip Levis, Scott Shenker and Ion Stoica: The Design and Implementation of A Declarative Sensor Network System, In Proceedings of the ACM Conference on Embedded networked Sensor Systems (Sensys), 2007.
  5. Timothy Hnat, Tamim Sookoor, Pieter Hooimeijer, Westley Weimer, and Kamin Whitehouse: MacroLab: A Vector-based Macroprogramming Framework for Cyber-Physical Systems, In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (Sensys), 2008.

K. Langendoen: Energy-Efficient Medium Access Control (slides)

Since radio communication is expensive in terms of energy consumption, yet collaboration between nodes is essential for providing emerging services, managing the communication protocol stack is the key to success. The performance of the Medium Access Control (MAC) layer determines the energy consumption to a large extent, since it decides when to switch the radio on/off. In contrast to typical WLAN designs, which optimize for latency, throughput and fairness, WSN-specific MAC protocols focus on energy consumption and memory footprint. Impressive energy savings can be obtained by putting the radio into sleep mode for long periods of time, and a wide range of new MAC protocols have been proposed in the last few years. We will survey the most important protocols, and classify them according to three key issues: number of used channels, degree of organization, and notification mechanism.
  1. K. Langendoen and G. Halkes: Energy-Efficient Medium Access Control, In R. Zurawski (Ed.), Embedded Systems Handbook, pp. 34.1-34.29, CRC Press, 2005.
  2. J. Polastre, J. Hill, and D. Culler: Versatile low power media access for wireless sensor networks, pp. 95-107, In Proc. Sensys 2004, November 2004.
  3. W. Ye, J. Heidemann, and D. Estrin: An Energy-efficient MAC Protocol for Wireless Sensor Networks, In Proc. 21st Conference of the IEEE Computer and Communications Societies (INFOCOM), pp. 1567-1576. June 2002.
  4. L. van Hoesel and P. Havinga: A Lightweight Medium Access Protocol (LMAC) for Wireless Sensor Networks, In Proc. INSS 2004, June 2004.

B. Rinner: Smart Cameras and Visual Sensor Networks (slides)

Smart cameras combine video sensing, processing, and communication on a single embedded platform. Networks of smart cameras are real-time distributed embedded systems that perform computer vision using multiple cameras. This new approach has emerged thanks to a confluence of simultaneous advances in four key disciplines: computer vision, image sensors, embedded computing, and sensor networks. Recently these visual sensor networks have gained a lot of interest in research and industry; applications include surveillance, assisted living and smart environments. This tutorial focuses on the networking aspects of smart camera systems where visual data is processed in real-time using distributed sensing and computing nodes. Although this distribution of sensing and processing introduces several complications, we believe that the problems it solves are much more important than the challenges of designing and building a distributed smart camera network. As in many other applications, distributed systems scale much more effectively, require less network bandwidth and achieve shorter response times than do centralized systems. We conclude this tutorial by a description of applications and case studies of visual sensor networks. We further discuss recent trends of this exciting research field.
  1. H. Aghajan and A. Cavallaro (Eds.): Multi-Camera Networks: Principles and Applications, Elsevier 2009.
  2. I. F. Akyildiz, T. Melodia, and K. R. Chowdhury: A Survey on Wireless Multimedia Sensor Networks, Computer Networks, vol. 51, pp. 921-960, 2007.
  3. B. Rinner and W. Wolf: A Bright Future for Distributed Smart Cameras, Proceedings of the IEEE, 96(10), October 2008.

K. Langendoen: Distributed Localization Algorithms (slides)

This tutorial studies the problem of determining the node locations in ad-hoc sensor networks. This is a non-trivial problem as the lack of infrastructure, combined with noisy range measurements and the need for energy efficiency make for a challenging set of requirements. In this tutorial we will first present some basic algorithms, including lateration used by GPS navigation and WLAN-style fingerprinting. Then we focus on WSN-specific solutions, and show a three-phase structure found in many of these algorithms: i) determine node-anchor distances, ii) compute node positions, and iii) optionally refine the positions through an iterative procedure. We present a detailed analysis comparing the various alternatives for each phase, as well as a head-to-head comparison of the complete algorithms.
  1. K. Langendoen and N. Reijers: Distributed Localization in Wireless Sensor Networks: A Quantitative Comparison, Computer Networks 43(4), pp. 500-518, 2003.
  2. A. Savvides, C.-C. Han, and M. Srivastava: Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors, In Proc. 7th ACM Intl. Conf. on Mobile Computing and Networking (MobiCom), pp. 166-179, July 2001.
  3. D. Niculescu and B. Nath: Ad-hoc Positioning System, In Proc. IEEE GlobeCom 2001, pp. 2926-2931, June 2001.

K. Whitehouse: Debugging Sensor Networks (slides)

Debugging a sensor network is difficult because of the confluence of two challenges. First, sensor networks are complex, non-determistic systems that are riddled with race conditions, and accordingly a large amount of information must be collected at run time to determine the cause of a bug. At the same time, however, sensor networks have very limited network, storage, and CPU resource for collecting this information, and any resource usage by a debugger can interfere with normal system operation and cause "heisenbugs" -- those bugs that change or disappear once a debugger is enabled. In this talk, we will discuss a range of techniques that have been used to improve visibility into and control over program execution, while minimizing resource contention with the application that is being debugged.
  1. N. Ramanathan, K. Chang, R. Kapur, L. Girod, E. Kohler, and D. Estrin: Sympathy for the Sensor Network Debugger, In Proceedings of the ACM Conf. on Embedded Networked Sensor Systems (Sensys), 2005.
  2. L. Luo, T. He, G. Zhou, L. Gu, T. Abdelzaher and J. A. Stankovic: Achieving Repeatability of Asynchronous Events in Wireless Sensor Networks with EnviroLog, In Proc. of the 25th IEEE Conf. on Computer Communications (InfoCom), 2006.
  3. Jing Yang, M.L. Soffa, L. Selavo, and K. Whitehouse. Clairvoyant: A Comprehensive Source-Level Debugger for Wireless Sensor Networks, In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (Sensys), 2007.
  4. M. M. Khan, H. K. Le, H. Ahmadi, T. F. Abdelzaher, and J. Han: Dustminer: troubleshooting interactive complexity bugs in sensor networks, In Proceedings of the ACM Conference on Embedded Network Sensor Systems (Sensys), 2008.
  5. Kay Romer and Junyan Ma: PDA: Passive Distributed Assertions for Sensor Networks, In Proceedings of the ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN/SPOTS), 2009.

S. Prabh: Data Aggregation and Data Dissemination in Wireless Sensor Networks (slides)

Data aggregation refers to the process of synthesizing information from multiple data sources. This lecture will discuss the use of data aggregation in wireless sensor networks (WSN). It will introduce the fundamentals of data-centric networking and its application to data aggregation methods suited for WSN. It will present an overview of some of the WSN data aggregation systems developed previously. Data dissemination is a complimentary process of data aggregation. This lecture will discuss some of the issues related to efficient data dissemination in WSN and conclude with an overview of some of the emerging data aggregation research directions.
  1. John Heidemann, Fabio Silva, Chalermek Intanagonwiwat, Ramesh Govindan, Deborah Estrin, Deepak Ganesan: Building efficient wireless sensor networks with low-level naming, In Proceedings of the eighteenth ACM symposium on Operating systems principles (SOSP), pp. 146-159, 2001.
  2. Suman Nath, Phillip B. Gibbons, Srinivasan Seshan, Zachary Anderson: Synopsis diffusion for robust aggregation in sensor networks, ACM Transactions on Sensor Networks, Volume 4, Number 2, pp. 1-40, 2008.
  3. Samuel Madden, Michael J. Franklin, Joseph M. Hellerstein, Wei Hong: TAG: a Tiny AGgregation Service for Ad-Hoc Sensor Networks, ACM SIGOPS Operating Systems Review, Volume 36, Number SI, pp. 131-146, 2002.
  4. K. Shashi Prabh, Tarek F. Abdelzaher: Energy-Conserving Data Cache Placement in Sensor Networks, ACM Transactions on Sensor Networks, Volume 1, Number 2, pp. 178-203, 2005.

Participant's Workshop (Session I)

Each of the following oral presentations by selected participants has a 15 min timeslot (10 min presentation + 5 min questions).
Maurizio Bocca: Wireless Sensor Networks Applications (slides)
The presentation will present the main research challenges and results of various applications of wireless sensor networks in the area of i) indoor situation modeling and security, ii) structural health monitoring, and iii) networked control systems.
Denise Dudek: FleGSens - secure area monitoring using wireless sensor networks
Within the scope of the FleGSens project, a wireless sensor network (WSN) for the surveillance of critical areas and properties is currently being developed. A vital aim of the project is to provide information security in terms of integrity and authenticity of generated alarms even in the presence of both non-malicious and malicious interference. Thus, throughout the development process, a strong attacker who may even compromise a percentage of the network's nodes is considered. The intended prototype comprises 200 nodes monitoring a land strip of about 500 metres in length and 40 metres in width. The talk gives an overview of the system and its basic protocols, focusing on protocols for secure trespass detection and node failure detection.
Dominique Guinard: Architecting a Web of Things
The Web of Things is an evolution of the Internet of Things that looks at seamlessly integrating things (e.g. embedded devices, sensor and actuator networks, RFID tagged objects) to the Web so that each thing becomes a node of the World Wide Web just as any other Web server. In this talk we will present the foundations and technologies of the concept as well as the challenges we identified so far.
Stefan Guna: DiCE - A Distributed Approach to Monitoring Global Properties in Wireless Sensor Networks
In this talk, we introduce DiCE, a system for autonomous, decentralized monitoring of application-level global properties in wireless sensor networks. We target sensing and control applications that require detection of deviations from a specified behavior and for which, unlike in conventional WSNs, relying on an external sink that collects and evaluates the global state is undesirable. In DiCE, the relevant properties are specified by the programmer with a simple declarative language, and monitored by the underlying run-time in a fully decentralized fashion. Through large-scale simulations and a real-life testbed, we show that DiCE detects property violations in a timely and energy-efficient manner.
Felix Jonathan Oppermann: Towards an End-User-Requirements-Driven Design of Wireless Sensor Networks (slides)
A wireless sensor network (WSN) is a network composed of a large number of small and relatively cheap sensor nodes. A WSN allows the reliable monitoring of a large distributed phenomenon. It is envisioned to apply WSN in a wide range of different scenarios, including application areas like habitat monitoring, catastrophe management, and home automation, but currently, the adoption of WSNs outside of the scientific community is still limited.
To allow a widespread use of WSNs, it is necessary to enable technically less skilled personnel to successfully deploy a WSN on their own. While these end-users can be assumed to be experts in their application domain, for example in biology or in geology, they cannot be expected to be especially trained in computer science. For this reason, one must ensure that the design and deployment process requires as little technical knowledge as possible and can be automated as far as possible.
An important aspect of the design process of a WSN is the selection of adequate building blocks, in hardware as well as in software. In order to make the design process of WSNs easier and more reliable for unacquainted end-users, a methodology is required, to synthesize a possible structure for an applicable WSN based solely on the requirements and constraints of the intended task and the available components. The ultimate goal is to offer the end-user a tool to automatically generate a mission specific selection out of the available hardware and software components, based on this task definition at "the push of a button."
Mert Ozcan: Ubiquitous Cities
Being a PhD candidate of architectural design with a background in interaction design, software and embedded systems, I've always been in the pursuit of using pervasive computing technologies in the context of 'ubiquitous cities'. Ubiquitous cities are smart, connected cities that use sensor networks and other hardware and software technologies that improve their infrastuctures(physical and service-based) to enhance the habitants' everyday life experiences within the citylife domain. I'd like to briefly talk about the projects I've been personally involved with, in this context.

Participant's Workshop (Session II)

Each of the following oral presentations by selected participants has a 15 min timeslot (10 min presentation + 5 min questions).
Jose Pinto: Data Dissemination for Networks of Heterogeneous Vehicles and Sensors
This talk describes the efforts being made at LSTS-FEUP (Laboratorio de Sistemas e Tecnologia Subaquatica, Faculdade de Engenharia da Universidade do Porto) in order to integrate data received simultaneously from operational consoles, unmanned vehicles, wireless sensor networks and drifting sensors, allowing real-time access of data using ubiquitous technologies.
Ramona Rednic: ClassAct: Accelerometer-based Real-Time Activity Classifier
The talk will present the work and achievements towards the development of a body sensor network which allows identification of posture on-line, in real-time, solely using accelerometers. The motivating application is the monitoring of operatives during bomb disposal missions. Postural information, together with a number of other physiological parameters, allows the prediction of the onset of Uncompensable Heat Stress, condition often encountered by operatives in such missions.
Gumstix Verdex embedded computers are used as the basis for the hardware platform, along with an in-house developed expansion board. Body segments acceleration data is gathered from three-axis accelerometers and processed into postural information via the use of decision trees.
Classification of eight mission-like activities (standing, kneeling, crawling, sitting, walking, and lying on front, back and one side) is performed within the network, in real time. Differentiating between static (such as standing) and dynamic (such as walking) is aided by the extraction of features from data. A success of 97.27% correct classification over eight postures was achieved using windowed variance and nine accelerometers. In an effort to minimize the number of devices used, trials have also been conducted using only two accelerometers (placed on the upper and lower part of the leg or hip and ankle) resulting in an accuracy of 99.15% and 96.45% correctly classified samples for static and dynamic activities respectively. The approach could be extended to posture classification in other task oriented applications.
Klaas Thoelen: Middleware Support for Group Communication in WSNs (slides)
The presented research focuses on middleware support for context- and application aware group communication in WSNs. Although many group communication mechanisms for WSNs have been presented in literature, they typically target specific network conditions or topologies and exploit group characteristics (size, geographical spread, stability). Our research identifies key features of state-of-the-art group communication mechanisms, investigates how to relate them to application requirements (reliability, priority of requests), resource capacity (memory available, battery level) and network status (link quality, network load), and exploits this information to select an effective and efficient mechanism to reach a particular group of nodes. Our research is driven by an industry case in transport and logistics. WSNs are considered as edge networks of a globally dispersed multi-tiered system, rather than as standalone networks; in addition, sensors are used and managed by multiple applications and migrate from one network to another.
Juan Ye: Exploiting Semantics with Situation Lattices in Pervasive Computing (slides)
Pervasive computing is the key enabler towards bringing information technology seamlessly into the real world. It facilitates the construction of wireless networks of devices with sensing, computing, and communication capabilities which both observe and respond intelligently to their environment. These sensors produce a large volume of data exhibiting varying degrees of granularity, heterogeneity, uncertainty, dynamism, and dependency. Pervasive computing systems must often act largely autonomously, and so a critical challenge is to distil high-level, domain-meaningful information (situations) from low-level sensor data. This talk will describe a sound data structure, the situation lattice that is effective in defining, detecting and analysing human activities from pervasive sensors.
Mo Haghighi: SCEMS: Smart central energy management system
iIn the recent years, due to the increasing cost of energy, climate change and natural disasters, monitoring environmental conditions has become the centre point of interest in the efforts made by governments and world organizations to reduce energy consumption and lowering demands for natural resources.
On the smaller scale, considering effective energy management in domestic level by every household and business not only contributes to tackling global energy crisis but also have huge advantages for households and businesses leading to significant reduction in their energy costs.
SCEMS is a centralised monitoring system that enables users to remotely keep close watch on energy consumption within their place of interest. This system is consisted of a number of wireless sensors and switches installed around an environment to sense the level of certain parameters such as light density, temperature and humidity. A number of wireless switches are also attached to various appliances and devices in order to adjust their energy consumptions.
This system provides users with collected sensors data on their internet-enabled devices such as PCs and mobile phones. Users can also switch on/off any appliances at anytime via the same system.
SCEMS also has an intelligent engine for recording the energy consumption patterns over different periods of time. The recorded data will then be matched to the environmental parameters in order to generate norms of operation for autonomous functioning of the system at different times.
John Kestner: Proverbial Wallet: physical financial sense (slides)
The Proverbial Wallet is used to illustrate an on-body network of tactile and ambient information accessories, connecting people physically with information accessed via the internet. It attempts to guide the user toward responsible decisions by delivering the appropriate message at the appropriate time. This is done by routing financial information through an Internet-connected cell phone to a Bluetooth-enabled wallet. Haptic feedback is used to connect intangible virtual transactions and account balances to the user.s physical world, and at the same time provide a subtle, private channel of communication for sensitive data.

T. Voigt: Contiki COOJA Hands-on Crash Course (slides, notes)

In this crash course, I will present the Contiki operating system, Europe's leading operating system for tiny embedded sensor systems. The course starts with some background on Contiki. After that, the participants will through practical exercises learn and experiment with some of the cool Contiki features. I will also introduce the simulators MSPSim and COOJA.
  1. Joakin Eriksson, Frederik Österlind, Niclas Finne, Adam Dunkels, Thiemo Voigt, and Nicolas Tsiftes: Accurate, network-scale power profiling for sensor network simulators, In Proc. 6th European Conference on Wireless Sensor Networks (EWSN), February 2009.
  2. Adam Dunkels, Frederik Österlind, and Zhitao He: An Adaptive Communication Architecture for Wireless Sensor Networks, In Proc. ACM SenSys 2007, November 2007.
  3. Contiki.

F. Mattern: Visions of the Future (slides)

As Niels Bohr once remarked, "prediction is very difficult, especially if it's about the future". But a hundred years ago, someone predicted something absolutely wonderful for the year 2010: the mobile phone. This would allow not only monarchs and chancellors to run their businesses from a distance, but also the happy time for love would begin - because the couples would always know what the partner would be doing. The past technology forecasts promised many other fantastic things - teaching machines replace teachers, color fax machines and screen phones to be found in every home, and household robots doing the dishes and serving coffee. Only the Web, E-commerce, search engines, SMS text messages, game consoles, blogs, Ebay, camera phones, YouTube, Facebook - that is, all the blessings of the information age which did not exist 20 years ago, whose name had not even been invented, but without which we could barely live today, have not been predicted by anyone!

What can we learn from the fact that what professionals from think tanks, distinguished engineers, University professors and other acknowledged experts predicted some 30 or 40 years ago about the future of computer and communication technology and its economic and social consequences turned out to be mostly wrong? And how realistic are today's promises of a wonderful future where physical objects are networked, where ordinary things become smart (or even "intelligent"), communicate with each other and provide us helpful services and background assistance for any purpose and in any situation?

  1. Friedemann Mattern: Hundert Jahre Zukunft - Visionen zum Computer- und Informationszeitalter. In: Friedemann Mattern (Ed.): Die Informatisierung des Alltags - Leben in smarten Umgebungen. Springer, pp. 351-419, 2007.

F. Mattern: Towards the Internet of Things (slides)

The term "Internet of Things" has come to describe a number of technologies that enable the Internet to reach out into the real world of physical objects. In fact, the trend of information and communication technology seems to be clear: The ongoing miniaturization of electronic devices enables processors and sensors being embedded into more and more everyday things - not only electrical devices, but also very mundane things such as key chains or even clothes. Many of these devices will then be interwoven and connected together by wireless networks.

A world full of smart things that may communicate with each other and that interact with global services sounds fascinating, giving rise to many new applications and business opportunities. But how realistic are the promises? To approach this question, we will first elaborate the vision of the "Internet of Things" and summarize its enabling technologies. We take a broad view and identify long-term trends which, by extrapolation, give us some hints on what to expect in the future. We then discuss the main challenges and analyze what progress is necessary to overcome current technological hurdles.

In our talk we will also present several applications and prototypes of cooperating real-world objects that have been realized at ETH Zurich, and we shall briefly discuss the social and economic consequences and challenges of a future world pervaded by invisible computers.

  1. Neil Gershenfeld: When Things Start to Think, Henry Holt, New York, 1999
  2. T. Kindberg, J. Barton, J. Morgan, G. Becker, D. Caswell, P. Debaty, G. Gopal, M. Frid, V. Krishnan, H. Morris, J. Schettino, B. Serra, M. Spasojevic: People, Places, Things: Web Presence for the Real World. Mobile Networks and Applications 7(5): 365-376, 2002
  3. N. Gershenfeld, R. Krikorian, D. Cohen: The Internet of Things. Scientific American, October, 76-81, 2004
  4. International Telecommunication Union: ITU Internet Report 2005: The Internet of Things. ITU, 2005
  5. Christian Floerkemeier, Marc Langheinrich, Elgar Fleisch, Friedemann Mattern, Sanjay E. Sarma (Eds.) The Internet of Things. First International Conference, IOT 2008, Zurich. LNCS 4952, Springer, 2008

P. Lukowicz: Wearable Sensing (slides)

The aim of the presentation is to give an overview of different sensing modalities used on wearable systems and ways to use them in different applications. It will start with a discussion of different application domains and the associated sensing needs. It will then describe a range of different sensing modalities starting with well known devices such as accelerometers and gyros and then moving to more experimental approaches including capacitive imaging of body functions, resonant magnetic motion tracking and the use fo FSR sensors for muscle activity monitoring. For each sensing modality the general principle, key applications and adequate signal processing methods will be discussed. The presentation with conclude with two concrete examples of using a multimodal wearable sensor system for the recognition of complex human activities.
  1. K. Van Laerhoven, A. Schmidt, and H.W. Gellersen: Multi-sensor context aware clothing. In Proc. Sixth Intl. Symposium on Wearable Computers (ISWC), pp. 49-56, 2002.
  2. J.A. Ward, P. Lukowicz, G. Troster, and T.E. Starner: Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers. IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1553-1567, 2006.
  3. D.J. Patterson, D. Fox, H. Kautz, and M. Philipose: Fine-grained activity recognition by aggregating abstract object usage. In Proc. Ninth Intl. Symposium on Wearable Computers, pp. 44-51, 2005.
  4. T. Stiefmeier, G. Ogris, H. Junker, P. Lukowicz, and G. Troster: Combining Motion Sensors and Ultrasonic Hands Tracking for Continuous Activity Recognition in a Maintenance Scenario. In 10th IEEE International Symposium on Wearable Computers, pp. 97-104, 2006.
  5. D. Bannach, P. Lukowicz, O. Amft: Rapid Prototyping of Activity Recognition Applications, IEEE Pervasive Computing, Volume: 7, Issue: 2, pp. 22-31, April-June 2008.

A. Salehi: Global Sensor Networks (lecture and hands-on course) (slides)

A key problem in current sensor network technology is the heterogeneity of the available software and hardware platforms which makes deployment and application development a tedious and time consuming task. To minimize the unnecessary and repetitive implementation of identical functionalities for different platforms, we present our Global Sensor Networks (GSN) middleware which supports ensor data, enables fast deployment and addition of new platforms, provides distributed querying, filtering, and combination of sensor data, and supports the dynamic adaption of the system configuration during operation. GSN's central concept is the virtual sensor abstraction which enables the user to declaratively specify XML-based deployment descriptors in combination with the possibility to integrate sensor network data through plain SQL queries over local and remote sensor data sources. This lecture instroduces GSN and includes a hands-on course.
  1. Karl Aberer, Manfred Hauswirth, and Ali Salehi: Infrastructure for data processing in large-scale interconnected sensor networks, Mobile Data Management (MDM), Germany, 2007.

K. Roemer: Time Synchronization for Sensor Networks (slides)

Time synchronization is a fundamental service in sensor networks, for example, to merge sensor data from different sensor nodes, or to coordinate sensor nodes for access to the communication channel. In this lecture we discuss fundamental challenges and approaches for time synchronization in sensor networks. We also present and discuss concrete synchronization algorithms used in sensor networks.
  1. K. Roemer, P. Blum, L. Meier: Time Synchronization and Calibration in Wireless Sensor Networks, In: I. Stojmenovic (Ed.), Handbook of Sensor Networks: Algorithms and Architectures, Wiley and Sons, September 2005, ISBN 0-471-68472-4.
  2. J. Elson, L. Girod, and D. Estrin: Fine-Grained Network Time Synchronization Using Reference Broadcasts, In Proc. OSDI 2002.
  3. S. Ganeriwal, R. Kumar, M. B. Srivastava: Timing-sync protocol for sensor networks, In Proc. SenSys 2003, pp. 138-149, November 2003.
  4. M. Maroti, B. Kusy, G. Simon, A. Ledeczi: The flooding time synchronization protocol, In Proc. SenSys 2004, pp. 39-49, November 2004.
  5. M. Ringwald, K. Roemer: Practical time synchronization for Bluetooth Scatternets, In Proc. BROADNETS 2007, pp. 337-345, 2007.

S. Karnouskos: Future Enterprises based on Real-World Services (slides)

Modern enterprises operate on a global scale and depend on complex business processes. Business continuity needs to be guaranteed, and therefore efficient information acquisition, evaluation and interaction with the real world is of key importance. The infrastructure envisioned is a heterogeneous one, where millions devices are interconnected, ready to receive instructions and create event notifications, and where the most advanced ones depict self-* behavior (e.g. self-management, self-healing, self-optimization etc) and collaborate. This can lead to a paradigm change as business logic can now be intelligently distributed to several layers such as the network or even the device layer creating new opportunities but also challenges that need to be assessed. Future Enterprise Services will be in position to better integrate state and events of the physical world in a timely manner, and hence to lead to more diverse, highly dynamic and efficient business applications.
  1. H. Plattner: Trends and Concepts in the Software Industry, Hasso Plattner Institute 2007,
  2. P. Spiess, S. Karnouskos, D. Guinard, D. Savio, O. Baecker, L. M. S. de Souza, and V. Trifa: SOA-based integration of the internet of things in enterprise services, in Proceedings of ICWS 2009 (IEEE International Conference on Web Services), July 2009.