223 research outputs found
Demo: Snap â Rapid Sensornet Deployment with a Sensornet Appstore
Despite ease of deployment being seen as a primary advantage
of sensor networks, deployment remains difficult.
We present Snap, a system for rapid sensornet deployment
that allows sensor networks to be deployed, positioned, and
reprogrammed through a sensornet appstore. Snap uses a
smartphone interface that uses QR codes for node identification, a map interface for node positioning, and dynamic loading of applications on the nodes. Snap nodes run the Contiki
operating system and its low-power IPv6 network stack that
provides direct access from nodes to the smartphone application.
We demonstrate rapid sensor node deployment, identification,
positioning, and node reprogramming within seconds, over
a multi-hop sensornet routing path with a WiFi-connected
smartphone
The ContikiMAC Radio Duty Cycling Protocol
Low-power wireless devices must keep their radio
transceivers off as much as possible to reach a low power
consumption, but must wake up often enough to be able to
receive communication from their neighbors. This report
describes the ContikiMAC radio duty cycling mechanism,
the default radio duty cycling mechanism in Contiki 2.5,
which uses a power efficient wake-up mechanism with
a set of timing constraints to allow device to keep their
transceivers off. With ContikiMAC, nodes can participate
in network communication yet keep their radios turned
off for roughly 99% of the time. This report describes the
ContikiMAC mechanism, measures the energy consumption
of individual ContikiMAC operations, and evaluates
the efficiency of the fast sleep and phase-lock optimizations
Demo: An Interoperability Development and Performance Diagnosis Environment
Interoperability is key to widespread adoption of sensor network technology, but interoperable systems have traditionally been difficult to develop and test. We demonstrate an interoperable system development and performance diagnosis environment in which different systems, different software, and different hardware can be simulated in a single network configuration. This allows both development, verification, and performance diagnosis of interoperable systems. Estimating the performance is important since even when systems interoperate, the performance can be sub-optimal, as shown in our companion paper that has been conditionally accepted for SenSys 2011
EfïŹcient Mobile Data Collection with Mobile Collect
WISENET (NES)PromosCONE
On Link Estimation in Dense RPL Deployments
The Internet of Things vision foresees billions of
devices to connect the physical world to the digital world. Sensing
applications such as structural health monitoring, surveillance or
smart buildings employ multi-hop wireless networks with high
density to attain sufficient area coverage. Such applications need
networking stacks and routing protocols that can scale with
network size and density while remaining energy-efficient and
lightweight. To this end, the IETF RoLL working group has
designed the IPv6 Routing Protocol for Low-Power and Lossy
Networks (RPL). This paper discusses the problems of link quality
estimation and neighbor management policies when it comes
to handling high densities. We implement and evaluate different
neighbor management policies and link probing techniques in
Contikiâs RPL implementation. We report on our experience
with a 100-node testbed with average 40-degree density. We show
the sensitivity of high density routing with respect to cache sizes
and routing metric initialization. Finally, we devise guidelines for
design and implementation of density-scalable routing protocols
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
From Real to Complex: Enhancing Radio-based Activity Recognition Using Complex-Valued CSI
Activity recognition is an important component of many pervasive computing
applications. Radio-based activity recognition has the advantage that it does
not have the privacy concern and the subjects do not have to carry a device on
them. Recently, it has been shown channel state information (CSI) can be used
for activity recognition in a device-free setting. With the proliferation of
wireless devices, it is important to understand how radio frequency
interference (RFI) can impact on pervasive computing applications. In this
paper, we investigate the impact of RFI on device-free CSI-based
location-oriented activity recognition. We present data to show that RFI can
have a significant impact on the CSI vectors. In the absence of RFI, different
activities give rise to different CSI vectors that can be differentiated
visually. However, in the presence of RFI, the CSI vectors become much noisier
and activity recognition also becomes harder. Our extensive experiments show
that the performance of state-of-the-art classification methods may degrade
significantly with RFI. We then propose a number of counter measures to
mitigate the impact of RFI and improve the location-oriented activity
recognition performance. We are also the first to use complex-valued CSI to
improve the performance in the environment with RFI
Trust and obfuscation principles for quality of information in emerging pervasive environments
Non peer reviewedPostprin
A Resource Intensive Traffic-Aware Scheme for Cluster-based Energy Conservation in Wireless Devices
Wireless traffic that is destined for a certain device in a network, can be
exploited in order to minimize the availability and delay trade-offs, and
mitigate the Energy consumption. The Energy Conservation (EC) mechanism can be
node-centric by considering the traversed nodal traffic in order to prolong the
network lifetime. This work describes a quantitative traffic-based approach
where a clustered Sleep-Proxy mechanism takes place in order to enable each
node to sleep according to the time duration of the active traffic that each
node expects and experiences. Sleep-proxies within the clusters are created
according to pairwise active-time comparison, where each node expects during
the active periods, a requested traffic. For resource availability and recovery
purposes, the caching mechanism takes place in case where the node for which
the traffic is destined is not available. The proposed scheme uses Role-based
nodes which are assigned to manipulate the traffic in a cluster, through the
time-oriented backward difference traffic evaluation scheme. Simulation study
is carried out for the proposed backward estimation scheme and the
effectiveness of the end-to-end EC mechanism taking into account a number of
metrics and measures for the effects while incrementing the sleep time duration
under the proposed framework. Comparative simulation results show that the
proposed scheme could be applied to infrastructure-less systems, providing
energy-efficient resource exchange with significant minimization in the power
consumption of each device.Comment: 6 pages, 8 figures, To appear in the proceedings of IEEE 14th
International Conference on High Performance Computing and Communications
(HPCC-2012) of the Third International Workshop on Wireless Networks and
Multimedia (WNM-2012), 25-27 June 2012, Liverpool, U
- âŠ