Application Development on Heterogeneous
Sensor/Actuator Networks
-
names and affiliation –
Application Development on Heterogeneous Sensor/Actuator Networks ............................................. 1
names and affiliation – ................................................................................................................... 1
1 Project Summary ............................................................................................................................ 1
2 Project Description ......................................................................................................................... 2
2.1
Innovative claims for the proposed research .......................................................................... 2
2.2
Introduction and Technical Background ................................................................................. 3
2.2.1
Sensor Networks ............................................................................................................. 4
2.2.2
Formal Models for Embedded design ............................................................................. 4
2.2.3
Background on Markov Networks for Optimization Problems ...................................... 4
2.3
Proposed Research .................................................................................................................. 4
2.3.1
SAN architecture ............................................................................................................ 4
2.3.2
Component-based embedded overlay networks (ovo bas nije najbolje!!!!!) .................. 5
2.3.3
Probabilistic Reasoning for SAN Quality-of-Service ..................................................... 6
2.4
References .............................................................................................................................. 6
1 Project Summary
During the last few years sensor networks have attracted a significant research attention. Although
several experimental networks have been implemented, no commercialized system has been reported yet,
which indicates that there is a need for more research and development efforts to bridge the gap. A
sensor/actuator network (SAN) combine remote sensing with remote actuation, and connects the physical
world to the existing high-speed network via gateway-type nodes that may be mobile themselves. The
challenge in such a setting is that the collaboration between local nodes is in the environment of a selforganizing network, which is non-deterministic by the nature, while the system’s behavior is expected to be
deterministic. Two levels of abstraction exist in the SAN including: the network level and the application
level. At the network level, the SAN architecture and SAN connection to the existing network
infrastructure are the most important issues. At the application level, the SAN can be approximated by a
distributed control network model where some of the dominate issues are: fulfilling system’s response time
constraints, deadlock free functioning of a system in the given environment, robustness and fault-tolerance,
availability of application development tools, etc. However, the network and application levels are
interdependent with respect to overall system performance. Thus, application development on the SAN is
much more complex then in the case of sensor-only networks.
The focus of the proposed research will be on a novel methodology for Sensor/Actuator Network
application development. Some unique features of this methodology include: 1) It handles the time critical
events and actions, 2) The network is intelligent and capable of assigning a level of global importance to
the individual local events and actions, 3) Suitability to various hardware/operating system platforms such
as Linux, Java, and Real-Time Operating Systems running on powerful microprocessors (ARM for
example), scaling down to tiny microcontrollers with or without OS, etc. 4) Suitability to various wireless
links, such as low-bandwidth links, broadband links, etc. 5) Facilitates a design optimizations at both
network and application level, 6) Considers correctness of the network with respect to given specification,
and, 7) Performance of the Sensor/Actuator Network is at least as good as the performance of the sensoronly network.
The proposed methodology is component-based, representing application running on a SAN as a set
of concurrent components with well-defined interfaces. It is also communication-oriented, in a sense that
communication actions in the system are extracted as components as well. Each component may provide
services to some other components in the system, as defined by its interface. For example, database
component embedded into sensor node may provide persistency service to the main application component
running on the same node; Ethernet link component may provide broadband network connectivity service;
etc. Also, at the SAN level, temperature sensor node may provide current temperature reading service to a
display node, etc. This generic and formal model will support time critical events and actions by annotating
component interface with Quality-of-Service (QoS) attributes such that client component may require
certain level of service quality from the server component. One example is medium-access control (MAC)
protocol in the SAN where some nodes have long messages to send (like sounds and videos recorded by
microphones and cameras in a conference room), but some other nodes have short but time critical
messages to send (like motion detectors outside the room reporting that somebody is coming into the room
so that some of microphones and cameras can re-focus to record introduction of the newcomer; or simply,
smoke detectors alarming possible fire). In this case, MAC protocol will provide as good as it can service
to each client, but all components in the SAN will cooperate to make an intelligent decision about the best
global strategy based on requested and provided QoS. The strategy for making intelligent global decisions
will use iterative algorithms and Markov net model applied to the space of QoS attributes defined in
component interfaces specifying nodes in the SAN. The same model will be used for components within
single node to support heterogeneous implementation platforms and design methodologies (System-onChip, microprocessor-based boards, ASIC, different OSs, etc).
The primary objective of this research is to develop the aforementioned methodology and an efficient
method of embedding QoS into a component interface. We will also study languages suitable for metadescription of component interfaces and explore optimization opportunities in the SAN environment. A
second objective is to develop a set of algorithms for collaborative, intelligent decision making, suitable for
implementation under constraints that exist in the SAN environment (low-power consumption, ad-hoc
nature of the network, etc.). For this purpose, we will use Markov networks and iterative algorithms (for
example, message passing algorithm). A third objective of this work is to combine the expertise in
communications, coding theory, and computer science at the University of Arizona, and to enhance the
educational infrastructure by integrating the research results and relevant background material into existing
and new engineering courses.
2 Project Description
2.1 Innovative claims for the proposed research
The primary abstraction in traditional networking application development paradigms (including
Internet, wired LAN/WAN networking, wireless networks, mobile networks, mobile ad-hoc networks, etc.)
is layering as a powerful information hiding tool for dealing with high system complexity. Due to rigid
resource and operational constraints (extremely low-power consumption requirements, large number of
small devices in a system, long system’s life-time, specific communication and activity patterns, and nonattained self-organizing operation), development of embedded networks algorithms suitable for the
sensor/actuator network applications can’t be adequately supported by the traditional approaches. To
achieve necessary level of efficiency and optimality, concurrency at all levels in the system (starting from
the lowest transistor level, through hardware on-chip and on-board architectures, up to operating system,
networking architecture and application software levels) must not only be visible but extensively used in
the system level design process in an efficient way. Instead of layering, scalability, reactive execution,
event-based communication, component-based design, and formal methods must be harnessed to manage
complexity in design of such systems. Complexity of the application development process grows with the
latest advances in the SAN technology: Traditionally, the sensor network applications have primarily
considered information dissemination and location tracking. Introduction of actuators as well as promising
low-power high-bandwidth radio links (such as Ultra-Wide Band radios) promote SAN as the most
attractive platform for applications like streaming multimedia, security applications, intelligent spaces, etc.
All these new attractive applications require infrastructure that supports applications with certain level of
QoS, particularly real-time guaranties.
We will develop a methodology that facilitates control and streaming SAN applications by providing
intelligent and adaptable QoS-aware application development infrastructure in a form of set of inherent
algorithm building blocks. It will: 1) scale down to extremely low-bit rate communication channels
(<1Kb/s), 2) scale up to large numbers of SAN nodes, 3) efficiently operate in high-bit rate communication
environment (up to 300Mb/s), 4) obey rigid power consumption constraints, 5) transparently facilitate
modularity in every phase of the system design, and 6) make intelligent self-configuration decisions.
The cornerstone of designing optimal SAN is to facilitate different concurrency models that fit the
best to the parallelism inherent in the application. To address difficulties in programming such systems,
interfaces of constituting components must be well defined and described in a formal way. The formal
interface description, that may be transparently applied at all system levels, provides means for building a
system that is aware of it’s own function. In this way, the system is able to optimize communication and
computation according to instant state of the global function. In addition, if provided with appropriate
intelligence, the system can predict functional requirements and proactively reallocate available system
resources. A meta-description of component interface addresses the difficulties of specifying specific parts
of the system behavior that should be considered as inputs into self-adaptation decision-making process.
For example, activity within a SAN node can be approximated by a set of possible event-firing traces, and
used as an input for resource reallocation process. The challenge is to identify minimal set of system events
sufficient for good decision-making, and component meta-description helps to address it. Additionally, the
component meta-description facilitates formal methodologies such as verification, model checking, and
automatic optimization, that are very desirable in self-administrating systems like SAN.
Markov net model is used as a foundation for developing self-adaptation algorithms. The problem
specification that leverages application of Markov net model in this case is: Given a set of event traces, that
represent recent system behavior history, and a set of features specifying required system behavior in the
next future (e.g. QoS), find a minimal set of events and possible mapping from the set to a set of system
(configuration parameter, parameter value) pairs such that there is a high probability that the specified
behavior features will be satisfied on the set of traces, defined over the found minimal set of events, under
the condition that found set of (configuration parameter, parameter value) pairs is satisfied. Different
algorithms for solving the problem are possible, particularly if external constraints are introduced, like, low
algorithm complexity, low power consumption, maximal distance between communication nodes, etc.
A test-bed, based on existing of-the-shelf products, will be used to test developed algorithms.
2.2 Introduction and Technical Background
It is predicted that in the near future advances in processor, memory, wireless communication
technology and Micro ElectroMechanical Systems (MEMS) will enable small, low-cost and low-power
sensor and actuator nodes capable of wireless communication and significant computation [37]. Each of the
nodes operates in its locality: sensors can measure the temperature, light intensity, humidity, noise level,
etc. while actuators can influence mechanical features within the environment, like: position, rotation,
velocity, flow, etc. It is envisioned that application of the sensor/actuator networks (SAN) may
revolutionize the way we live and work, particularly in environments like: disaster area, factory floor,
office, vehicle in large metropolis areas, etc. [36][2], as well as in military applications [39]. Reported
results and developed prototypes from several undergoing research projects are encouraging [35][36].
During last several years research in sensor networks has been mainly focused on solving
fundamental operational problems, like building test-beds for experiments, designing small low-power
hardware devices, developing routing algorithms with extremely low power consumption requirements,
facilitating scalability up to huge number of sensors, etc. It is anticipated that these advances will continue,
and even be accelerated with new technology developments, particularly new achievements in radio
technology that offer tremendous throughput rates within very low power budget. As a consequence, sensor
networks become very powerful with respect to both computation and communication potential, moving
focus of research attention towards finding ways to utilize the potential in new powerful applications. So
far, information dissemination [36][37] and target positioning [3] were dominant applications for sensor
networks. Without any doubt, these applications are of high importance, particularly in military and disaster
area emergency services. However, for mass adoption they could hardly be a “killer-app”. In summary,
technology infrastructure for sensor networks is available and ready to support applications that would be
massively adopted. These emerging applications assume collaboration between local nodes and ability to
provide real-time services. One hypothetical example of such an application could be “music kiosk”:
network of small self-sustain boxes, conveniently located allover a town serving as a virtual music market
for people walking on the streets or driving their cars.
Fortunately, there has been a great deal of research activity in the area of ad-hoc networks focused on
advanced application development (such as, mobile collaboration, real-time multimedia, QoS intensive
applications, etc.) during the last few years [43][44][45]. The mobile ad-hoc networks are somewhat similar
to sensor/actuator networks due to existence of power consumption awareness, network self-configuration,
mobility, and wireless communication. Although the research in ad-hoc networks can be used as a
foundation for research of similar problems in sensor/actuator networks, there is a different deployment
scenario, different application context, different hardware/software platforms, and much severe power
consumption constraints in the latter case. Therefore, in order to achieve massively adopted applications for
sensor/actuator networks, new theoretical developments as well as new software tools facilitating
application development are required.
2.2.1 Sensor Networks
A sensor network assumes a large number of sensor nodes spread in a wide area, measuring a variety
of parameters (such as temperature, light intensity, humidity, flow intensity, etc.) in the node’s locality. The
network is self-organizing, thus exploiting synergism of local information to support applications of a
global nature that individual nodes would not be able to do individually. These micro-nodes are not as
reliable as their expensive macro-level counterparts, but their small size, low cost, wireless connection, low
power consumption, and an ad-hoc network organization can enable a large number of network nodes to
collaborate in applications featuring high quality and fault tolerance. Such applications relate to the broad
area that is usually referred to as Ubiquitous Computing, Smart Spaces, Ad-hoc Networks, Mobile
Collaboration, Intelligent Classroom, or Pervasive Computing. However, all these computational concepts
are oriented towards human users communicating and accessing information anywhere anytime using
wireless multimedia PDA-like devices. Applications of sensor networks are more general assuming
communication and computational infrastructure embedded into communicating physical objects that
provide services to each other as well as to human users, like it is envisioned in [54]. The Sensor/Actuator
Network (SAN) combine remote sensing with remote actuation, and connects the physical world to the
existing high-speed network via gateway type nodes that may be mobile themselves. For example, locationaware applications can utilize radio signals from multiple sensors to provide location information to mobile
users [49]. In a SAN, the collaboration between local nodes, both sensors and actuators, is necessary in
order to execute pre-specified actions.
Introducing actuator nodes in the sensor network not only emphasizes the importance of the known
issues present in the traditional sensor-only network, but also opens a new set of problems that must be
solved in order to exploit efficiently the increased system capabilities.
2.2.2
Formal Models for Embedded design
2.2.3
Background on Markov Networks for Optimization Problems
2.3 Proposed Research
We envision sensor/actuator networks applied to a variety of physical environments, like factory
floors, large buildings, conference rooms, data-center rooms, kinder gardens, offices, etc. The SANs will
provide different services to multiple applications, like location service, collaboration, identification,
information acquisition, communication service, etc. Hence, the SAN will consist of a large number of
heterogeneous devices ranging from ultra-tiny ultra-low-cost massively deployed passive ID tags [55],
through small low-rate short-range RF sensors [56][57],
2.3.1 SAN architecture
A functional constellation of the distributed sensor/actuator network (SAN) consists of a number of
functional nodes (FN) and functional communication channels (FCC). The functional nodes are:
heterogeneous sensing nodes (SN), function specific actuator nodes (AN), network gateway nodes (GN),
and mobile user nodes (UN). Each node in the network is assumed to be autonomous. A physical
deployment of the functional nodes allows any combination of the functional nodes to be deployed within a
single physical device node (PDN). Also, there are two types of functional communication channels:
internal communication channel (ICC) and external communication channel (ECC). The ICC connects two
or more peers within the same SAN while the ECC connects a single SAN node to some parent network
(for example Internet). The abstract communication channels may be deployed on heterogeneous physical
communication channels (PCC), including custom radio link, WirelessLAN (IEEE 802.11), Bluetooth,
wired Ethernet, satellite link, different links for cell phone communications, to name a few. Note that FCC
is considered for deployment as an overlay network and PCC may be defined on different communication
protocol layers of the underlying link. The Physical Sensor/Actuator Network (PSAN) is a deployment of a
SAN if for each FN and FCC from SAN there is corresponding PDN and PCC from PSAN representing it’s
deployment.
Each PDN in SAN has at least one private communication channel and there is at least one PDN in
SAN with external communication channel. For example, a PDN hosting SN and/or AN has at least one
PCC, either ICC or ECC, but may have multiple connections of different type. The PDN hosting GN must
have at least two PCCs that may be of different type.
We are focused on an application development framework that is an optimal trade-off between several
conflicting requirements. So, we define design space for our framework by the following premises:
1. The framework must efficiently handle diversity of candidate hardware platforms, operating
systems, programming languages, and physical communication channels. Market conditions and
technology development dictate simultaneous deployment of multiple solutions in a single SAN,
so that open system approach is much more realistic then custom designed system regardless of
the eventually better performance in specific narrow application domains.
2. The last several years have witnessed growing research efforts invested in sensor networks. It is
reasonable to expect competing technologies at all levels of the system architecture to emerge
soon. All these alternatives must be available to an application and supported by the application
development framework. This makes the development of the framework an extremely complex
task. Traditionally, the design complexity issue is approached by the principle of hiding, such as
layering in a design of communication protocol stacks. However, while layering is efficient in
reducing design complexity, it generates sub-optimal designs that may be acceptable at traditional
networks but may not in resource constrained SANs.
3. SAN is expected to consist of different physical communication links available to the nodes, but a
significant number of links will be wireless. Also, large-scale sensor networks are expected to
have a huge number of nodes . As a consequence, low power consumption is a must. Having in
mind that a radio communication link consumes a significant portion of available energy, the
minimization of amount of data transferred over radio links becomes a very important
optimization criterion.
4. SAN has a very specific application space. Most of the nodes in SAN are silent most of the time
except for some, probably short, periods of time when SAN is expected to respond to the changes
in its environment. We consider this wake-up and response time as the very important feature of
the SAN. While the SAN is silent, the most important concern is low power consumption since
periods of silence may be long. However, while SAN is in active mode, it must obey real-time
constraints dictated by a running application. Trade-off between constraints imposed by the
application and overall system life-time is crucial.
5. Deployment environment of the SAN can only be loosely approximated with a test-bed, because
of unpredictable behavior in a target environment such as disaster areas, inaccessible terrains, etc.
As a consequence, the verification gets much more advantages over the testing in SANs than in
traditional designs.
2.3.2 Component-based embedded overlay networks (ovo bas nije najbolje!!!!!)
For optimization of the amount of transferred data (as the main driver for power consumption), we
will adopt a generic principle based on meta-description and apply it on different layers. The idea to apply
meta description on communication protocol layer is simple but effective: In a network where nodes
infrequently change running application it is more efficient to provide “knowledge” about currently actual
protocol to the involved nodes then to transfer information about current protocol state within each
message. For example, let us consider a system with smoke detector, temperature sensor and valve actuator.
Smoke detector and temperature sensor communicate measured values to actuator as a single byte. If both
sensors and actuator has been “pre-educated” with this knowledge, then single-byte message is sufficient.
Let us now introduce light sensor into the network, and let us assume that this sensor communicate
measured value to the actuator in two-byte format. The light sensor and the actuator are now supplied with
corresponding “knowledge”, and they are able to communicate. But, due to different data formats, actuator
needs some information about data source to be embedded into message. Consequently, “knowledge” about
one-byte protocol must be updated in the sense to provide this information. Hence, some collaboration at
the meta level is done first, protocols in involved nodes are correspondingly updated, and after that system
continues to communicate with optimal amount of data transferred. Otherwise, if there is no meta
“knowledge” in the system (what, to the best of our knowledge, is the case in all existing systems), some
form of “generic” protocol, that is able to support all possible variants of communication, must be adopted.
An optimized “generic” protocol is described in [19].
Each component consists of component interface and component body, where the interface defines
interaction between the component and it’s environment while the body implements component’s behavior.
The meta-description based approach to communication design follows modern system design
principles stressing communication-based design as a major success factor for component-based system
design [20]. It is a powerful implementation and design tool transparent with respect to model of
computation and adopted concurrency models. As a consequence, the same design approach can be applied
on different levels of abstraction in the system under design.
The high level of abstraction supported by the approach leads towards implementation in a form of an
overlay network [6][10].
2.3.3 Probabilistic Reasoning for SAN Quality-of-Service
Our basic idea is about how to optimally and instantly adopt SAN to current environment state. In
order to achieve adaptation, we introduce Quality-of-Service (QoS) into SAN. To the best of our
knowledge, this is the first time that QoS is considered in the context of SAN (ovo bi trebalo da se jos vise
proveri!!!!!!!!!!). We assign QoS labels to (sub)set of objects of type Event existing in SAN (ovde treba jos
malo da razmislim, ali umesto Event, treba da stoji “that is an instance of meta-type”, sa ciljem da QoS
moze da se asocira ne samo Eventu nego I svim ostalim tipovima koji postoje u sistemu, kao sto su
Method, Attribute, itd. Zadrzacu se na Event da bi ideja mogla lakes da se objasni). Resources available in
the SAN are parameterized and formally described. Furthermore, the parameterization and formal
description are applied on each individual transaction in real-time. For example, all SAN nodes in a radius
around selected node, may be considered to have a single shared bus for communication. The parameters of
such an bus may be: bandwidth, current network topology, etc. Transaction parameters may be
maximal/minimal length of the message, level of applied encryption, strength of applied Error Correction
Code, priority that the transaction has on the bus, etc. So, we extend MAC layer in SAN such that it is
capable of carrying QoS information and allow each node to make local decisions. Similar approach has
been investigated in [26], but in the more restricted case of hardware design for System-on-Chips. Now, we
have the following problem:
2.4 References
2.4.1 Sensor Networks References
[1] S.S.Pradhan and K.Ramachendran, “Distributed source coding using syndromes (DISCUS): Design
and construction”, Proc. IEEE Data Compression Conference (DCC), Snowbird, UT, USA, 29-31
March 1999.
[2] Lizhi Charlie Zhong, Rahul Shah, Chunlong Guo, Jan Rabaey, "An Ultra-Low Power and Distributed
Access Protocol for Broadband Wireless Sensor Networks," IEEE Broadband Wireless Summit, Las
Vegas, N.V., May 2001.
[3] Chris Savarese, Jan M. Rabaey, Jan Beutel, "Locationing in Distributed Ad-Hoc Wireless Sensor
Networks," ICASSP 2001.
[4] Hui Zhang, Vandana Prabhu, Varghese George, Marlene Wan, Martin Benes, Arthur Abnous, "A 1V
Heterogeneous Reconfigurable Processor IC for Baseband Wirelss Applications", Proceedings of
ISSCC2000 [PDF].
[5] Suet-Fei Li, Marlene Wan, Jan Rabaey, "Configuration Code Generation and Optimization for LowEnergy Reconfigurable DSPs", Proceedings of SIPS99 [PDF].
[6] Alex C. Snoeren, Kenneth Conley, and David K. Gifford , "Mesh-Based Content Routing using
XML", 18th ACM Symposium on Operating Systems Principles (SOSP’01), Banff, Canada, October
2001
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
Luca Benini and Giovanni De Micheli, “Networks on Chips: A New SoC Paradigm”, IEEE
Computer, January 2002, pp.70-78.
Pierre Guerrier and Alain Greiner, “A generic architecture for on-chip packet-switched
interconnections”, DATE 2000, pp.250, conference link
William J. Dally, and Brian Towles, "Route Packets, Not Wires: On-Chip Interconnection
Networks," in Proceedings of the 38th Design Automation Conference, Las Vegas, NV, June 2001.
(Invited paper)
David G. Andersen, Hari Balakrishnan, M. Frans Kaashoek, Robert Morris, "Resilient Overlay
Networks", 18th Symposium on Operating Systems Principles(SOSP’01), Banff, Canada, October
2001.
John Heidemann, Fabio Silva, Chalermek Intanagonwiwat, Ramesh Govindan, Deborah Estrin,
Deepak Ganesan, "Building Efficient Wireless Sensor Networks with Low-Level Naming", 18th
Symposium on Operating Systems Principles (SOSP’01), Banff, Canada, October 2001.
M. Bhardwaj, T. Garnett and A. Chandrakasan,"Upper Bounds on the Lifetime of Sensor Networks",
IEEE International Conference on Communications (ICC'2001), Helsinki, Finland, June 2001.
William Adjie-Winoto, Elliot Schwartz, Hari Balakrishnan, Jeremy Lilley, The design and
implementation of an intentional naming system, Proc. 17th ACM SOSP, Kiawah Island, SC, Dec.
1999.
Wendi Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan, Energy-Efficient
Communication Protocols for Wireless Microsensor Networks, Proc. Hawaaian Int'l Conf. on
Systems Science, January 2000.
Philippe Bonnet, J. E. Gehrke, and Praveen Seshadri. Towards Sensor Database Systems. In
Proceedings of the Second International Conference on Mobile Data Management. Hong Kong,
January 2001.
Philippe Bonnet, J. E. Gehrke, and Praveen Seshadri. Querying the Physical World. IEEE Personal
Communications, Vol. 7, No. 5, October 2000, pages 10-15. Special Issue on Smart Spaces and
Environments.
M. Ahmed, G. Pottie, Information theory of wireless sensor networks: the n-helper gaussian case.
2000 IEEE International Symposium on Information Theory, Sorrento, Italy, 25-30 June 2000; p.
436.
K. Sohrabi, J. Gao, V. Ailawadhi, G.J. Pottie, Protocols for self-organization of a wireless sensor
network. IEEE Personal Communications, vol. 7, no. 5, pp. 16-27, Oct. 2000.
Ignacio Solis, Katia Obraczka and Julio Marcos, FLIP: A Flexible Protocol for Efficient
Communication Between Heterogenous Devices, Tech. Report 00-737, USC Computer Science
Department
K. Keutzer, S. Malik, A. R. Newton, J. Rabaey and A. Sangiovanni-Vincentelli.System Level
Design: Orthogonolization of Concerns and Platform-Based Design. IEEE Transactions on
Computer-Aided Design of IntegratedCircuits and Systems, 19(12), December 2000.
Alberto Sangiovanni-Vincentelli, Marco Sgroi, Luciano Lavagno. Formal Models for
Communication-based Design. Proceedings of CONCUR '00, August, 2000
E. A. de Kock, G. Essink, W. J. M. Smits, P. van der Wolf, J.-Y. Brunel, W. M. Kruijtzer, P.
Lieverse, K. A. Vissers, YAPI: Application Modeling for Signal Processing Systems, Proceedings of
the Design Automation Conference, Los Angeles, CA June 2000.
J-Y. Brunel, W. M. Kruijtzer, H. J. H. N. Kenter, F. Pétrot, L. Pasquier, E. A. de Kock, W. J. M.
Smits, COSY Communication IP's, Proceedings of the Design Automation Conference, Los Angeles,
CA June 2000.
Pai H. Chou, Gaetano Borriello, Synthesis and Optimization of Coordination Controllers for
Distributed Embedded Systems , Proceedings of the Design Automation Conference, Los Angeles,
CA June 2000.
J. da Silva Jr., M. Sgroi, F. De Bernardinis, S.F Li, A. Sangiovanni-Vincentelli and J.
Rabaey.Wireless Protocols Design: Challenges and Opportunities. Proceedings of the 8th IEEE
International Workshop on Hardware/Software Codesign, S.Diego, CA, USA, May 2000., May,
2000.
Kanishka Lahiri, Anand Raghunathan, Ganesh Lakshminarayana, Sujit Dey, Communication
Architecture Tuners: A Methodology for the Design of High-Performance Communication
Architectures for System-on-Chips , DAC 2000, Los Angeles, California, p. 513.
[27] Alec Woo and David Culler, "A Transmission Control Scheme for Media Access in Sensor
Networks", Proceedings of the ACM/IEEE International Conference on Mobile Computing and
Networking, Mobicom 2001, Rome, Italy. http://tinyos.millennium.berkeley.edu/papers/mobicom.pdf
[28] Wei Ye, John Heidemann and Deborah Estrin, "An energy-efficient MAC protocol for wireless
sensor
networks",
To
appear
in
IEEE
Infocom
2002.
http://www.isi.edu/~weiye/pub/smac_report.pdf
[29] Katayoun Sohrabi and Gregory J. Pottie, "Performance of a novel self-organization protocol for
wireless ad hoc sensor networks", Proceedings of the IEEE 50th Vehicular Technology Conference,
1999, pp.1222-1226
[30] Luca de Alfaro and Thomas Henzinger, Interface Automata, Proceedings of the Ninth Annual
Symposium on Foundations of Software Engineering (FSE), ACM Press, 2001, pp. 109-120.
[31] Edward A. Lee and Yuhong Xiong, System-Level Types for Component-Based Design, First
Workshop on Embedded Software, EMSOFT2001, Lake Tahoe, CA, USA, Oct. 8-10, 2001.
[32] John Davis II, Christopher Hylands, Bart Kienhuis, Edward A. Lee, Jie Liu, Xiaojun Liu, Lukito
Muliadi, Steve Neuendorffer, Jeff Tsay, Brian Vogel, and Yuhong Xiong, Heterogeneous Concurrent
Modeling and Design in Java, Memorandum UCB/ERL M01/12, EECS, University of California,
Berkeley, CA USA 94720 March 15, 2001
[33] Jeremy Elson and Deborah Estrin, Random, Ephemeral Transaction Identifiers in Dynamic Sensor
Networks,To appear in the Proceedings of the 21st International Conference on Distributed
Computing Systems (ICDCS-21) April 16-19, 2001, Phoenix, Arizona, USA.
[34] Jeremy Elson and Deborah Estrin, Time Synchronization for Wireless Sensor Networks, To appear
in the Proceedings of the 2001 International Parallel and Distributed Processing Symposium
(IPDPS), Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile
Computing, April 2001, San Francisco, CA, USA.
[35] J. M. Kahn, R. H. Katz and K. S. J. Pister, "Next Century Challenges: Mobile Networking for "Smart
Dust", Proceedings of the fifth annual ACM/IEEE international conference on Mobile computing and
networking, August 15 - 19, 1999, Seattle, WA, USA
[36] Deborah Estrin, Ramesh Govindan, John Heidemann, and Satish Kumar, Next Century Challenges:
Scalable Coordination in Sensor Networks., In Proceedings of the ACM/IEEE International
Conference on Mobile Computing and Networking, pp. 263-270. Seattle, Washington, USA, ACM.
August, 1999.
[37] Chalermek Intanagonwiwat, Ramesh Govindan and Deborah Estrin, Directed diffusion: A scalable
and robust communication paradigm for sensor networks, In Proceedings of the Sixth Annual
International Conference on Mobile Computing and Networking (MobiCOM '00), August 2000,
Boston, Massachussetts.
[38] J. Rabaey, J. Ammer, T. Karalar, S. Li, B. Otis, M. Sheets, T. Tuan, "PicoRadios for Wireless Sensor
Networks: The Next Challenge in Ultra-Low-Power Design", Proceedings of the International SolidState Circuits Conference, San Francisco, CA, February 3-7, 2002.
[39] L. Zhong, J. Rabaey, C. Guo and R. Shah, "Data Link Layer Design for Wireless Sensor Networks",
Proceedings of IEEE MILCOM 2001, Washington D.C., October 28-31, 2001.
[40] J.L. da Silva Jr., J. Shamberger, M.J. Ammer, C. Guo, S. Li, R. Shah, T. Tuan, M. Sheets, J.M.
Rabaey, B. Nikolic, A. Sangiovanni-Vincentelli, P. Wright, "Design Methodology for PicoRadio
Networks," DATE 2001
[41] Suet-Fei Li, Roy Sutton, Jan Rabaey, "Low Power Operating System for Heterogeneous Wireless
Communication Systems," PACT 01 Conference, Barcelona, Spain September 8-12, 2001.
[42] M. Burnside, D. Clarke, A. Maywah, T. Mills, S. Devadas, and R. Rivest, "Proxy-Based Security
Protocols in Networked Mobile Devices", to appear in the Symposium on Applied Computing
(SAC'02), March 2002.
[43] Bianchi, G. and A.T. Campbell, "A Programmable MAC Framework for Utility-based Adaptive
Quality of Service Support", IEEE Journal of Selected Areas in Communications (JSAC), Special
Issue on Intelligent Techniques in High Speed Networks, Vol. 18, No. 2, pp. 244-256, February
2000.
[44] Gomez, J. and A.T. Campbell, "Havana: Supporting Application and Channel Dependent QOS in
Wireless Networks", ACM Journal on Wireless Networks (WINET), to be published, 2002.
[45] C.R. Lin and M. Gerla, Real-time support in multihop wireless networks, ACM/Baltzer Wireless
Networks, vol. 5, no. 2, 1999.
[46] X. Hong, M. Gerla, T. Kwon, P. Estabrook, G. Pei, and R. Bagrodia, The Mars Sensor Network:
Efficient, Energy Aware Communications, Proceedings of IEEE MILCOM 2001, McLean, VA, Oct.
2001.
[47] Andreas Savvides, Chih-Chien Han, Mani Srivastava, "Dynamic Fine-Grained Localization in AdHoc Networks of Sensors", Proceedings of the ACM SIGMOBILE 7th Annual International
Conference on Mobile Computing and Networking, Rome, Italy, July 2001.
[48] Mani Srivastava, Richard Muntz, Miodrag Potkonjak, "Smart Kindergarten: Sensor-based Wireless
Networks for Smart Developmental Problem-solving Environments", Proceedings of the ACM
SIGMOBILE 7th Annual International Conference on Mobile Computing and Networking, Rome,
Italy, July 2001.
[49] Paul Castro, Patrick Chiu, Richard Muntz, "A Probabilistic Location Service for Wireless Network
Environments", Proceedings of Ubicomp 2001.
[50] Paul Castro, Richard Muntz, "An Adaptive Approach to Indexing Pervasive Data", Proceedings of
the International Workshop on Data Engineering for Wireless and Mobile Access, May 2001.
[51] Paul Castro and Richard Muntz, "Managing Context for Smart Spaces", IEEE Personal
Communications, October 2000.
[52] Samir Goel and Tomasz Imielinski, Prediction-based Monitoring in Sensor Networks: Taking
Lessons from MPEG. ACM Computer Communication Review, Vol. 31, No. 5, October, 2001. To
appear
[53] Tomasz Imielinski and Samir Goel, DataSpace - querying and monitoring deeply networked
collections in physical space, IEEE Personal Communications Magazine, Special Issue on
"Networking the Physical World", October 2000
[54] Mark Weiser, "The Computer for the Twenty-First Century," Scientific American, pp. 94-10,
September 1991
[55] Auto-ID Center, http://www.autoidcenter.org
[56] Jason Hill, Robert Szewczyk, Alec Woo, Seth Hollar, David Culler, Kristofer Pister.System
architecture directions for network sensors. ASPLOS 2000.
[57] TinyOS project, http://tinyos.millennium.berkeley.edu
2.4.2 Next Generation Networks References
[58] Building
High-speed
Exchange
Points,
Cisco
White
Paper,
http://www.cisco.com/warp/public/750/aii/research.html
[59] Agere Systems, The Challenge for Next Generation Network Processors, White Paper,
http://www.agere.com/enterprise_metro_access/network_processors.html
[60] Intel
IXP2400
Network
Processor,
White
Paper,
http://developer.intel.com/design/network/white_paper.htm
[61] Sanjeev Kumar, Kai Li. Dynamic Memory Management for Programmable Devices. To appear in
International Symposium of Memory Management, 2002
[62] Sanjeev Kumar.ESP: A Language for Programmable Devices. Ph.D. Thesis, Department of
Computer Science, Princeton University, Jan 2002. Available as Princeton University Technical
Report TR-646-02.
[63] Sanjeev Kumar, Kai Li. Performance Impact of Using ESP to Implement VMMC Firmware. To
Appear in Workshop on Novel Uses of System Area Networks (SAN-1), 2002
[64] Sanjeev Kumar, Yitzhak Mandelbaum, Xiang Yu, Kai Li. ESP: A Language for Programmable
Devices. Programming Language Design and Implementation, 2001.
[65] Greg Eisenhauer, Fabián E. Bustamante and Karsten Schwan, "Native Data Representation: An
Efficient Wire Format for High Performance Computing", Tech. Report GIT-CC-01-18, College of
Computing, Georgia Institute of Technology, 2001.
[66] K.L. Calvert, "Architectural framework for active networks, version 1.0", University of Kentucky,
July 1999.
[67] AN Node OS Working Group, "NodeOS interface specification", November 30, 2001.
[68] AN Composable Services Working Group, Ellen Zegura, editor, "Composable Services for Active
Networks", May 1998.
[69] AN Management Working Group, Management of Active Networks,.
[70] S. Murphy, Ed., "Security Architecture Draft", Technical report, AN Security Working Group, 2000.
[71] IpInfusion, Integrating Control-Plane and Data-Plane Software Components, Technical paper.
[72] NP Forum, CSIX-L1 (Network Processor/Traffic Manager connectivity to Switch Fabrics).
[73]