4,222 research outputs found
Multi-Target Tracking in Distributed Sensor Networks using Particle PHD Filters
Multi-target tracking is an important problem in civilian and military
applications. This paper investigates multi-target tracking in distributed
sensor networks. Data association, which arises particularly in multi-object
scenarios, can be tackled by various solutions. We consider sequential Monte
Carlo implementations of the Probability Hypothesis Density (PHD) filter based
on random finite sets. This approach circumvents the data association issue by
jointly estimating all targets in the region of interest. To this end, we
develop the Diffusion Particle PHD Filter (D-PPHDF) as well as a centralized
version, called the Multi-Sensor Particle PHD Filter (MS-PPHDF). Their
performance is evaluated in terms of the Optimal Subpattern Assignment (OSPA)
metric, benchmarked against a distributed extension of the Posterior
Cram\'er-Rao Lower Bound (PCRLB), and compared to the performance of an
existing distributed PHD Particle Filter. Furthermore, the robustness of the
proposed tracking algorithms against outliers and their performance with
respect to different amounts of clutter is investigated.Comment: 27 pages, 6 figure
Energy Optimal Transmission Scheduling in Wireless Sensor Networks
One of the main issues in the design of sensor networks is energy efficient
communication of time-critical data. Energy wastage can be caused by failed
packet transmission attempts at each node due to channel dynamics and
interference. Therefore transmission control techniques that are unaware of the
channel dynamics can lead to suboptimal channel use patterns. In this paper we
propose a transmission controller that utilizes different "grades" of channel
side information to schedule packet transmissions in an optimal way, while
meeting a deadline constraint for all packets waiting in the transmission
queue. The wireless channel is modeled as a finite-state Markov channel. We are
specifically interested in the case where the transmitter has low-grade channel
side information that can be obtained based solely on the ACK/NAK sequence for
the previous transmissions. Our scheduler is readily implementable and it is
based on the dynamic programming solution to the finite-horizon transmission
control problem. We also calculate the information theoretic capacity of the
finite state Markov channel with feedback containing different grades of
channel side information including that, obtained through the ACK/NAK sequence.
We illustrate that our scheduler achieves a given throughput at a power level
that is fairly close to the fundamental limit achievable over the channel.Comment: Accepted for publication in the IEEE Transactions on Wireless
Communication
Design of a WSN Platform for Long-Term Environmental Monitoring for IoT Applications
The Internet of Things (IoT) provides a virtual view, via the Internet Protocol, to a huge variety of real life objects, ranging from a car, to a teacup, to a building, to trees in a forest. Its appeal is the ubiquitous generalized access to the status and location of any "thing" we may be interested in. Wireless sensor networks (WSN) are well suited for long-term environmental data acquisition for IoT representation. This paper presents the functional design and implementation of a complete WSN platform that can be used for a range of long-term environmental monitoring IoT applications. The application requirements for low cost, high number of sensors, fast deployment, long lifetime, low maintenance, and high quality of service are considered in the specification and design of the platform and of all its components. Low-effort platform reuse is also considered starting from the specifications and at all design levels for a wide array of related monitoring application
Context-aware adaptation in DySCAS
DySCAS is a dynamically self-configuring middleware for automotive control systems. The addition of autonomic, context-aware dynamic configuration to automotive control systems brings a potential for a wide range of benefits in terms of robustness, flexibility, upgrading etc. However, the automotive systems represent a particularly challenging domain for the deployment of autonomics concepts, having a combination of real-time performance constraints, severe resource limitations, safety-critical aspects and cost pressures. For these reasons current systems are statically configured. This paper describes the dynamic run-time configuration aspects of DySCAS and focuses on the extent to which context-aware adaptation has been achieved in DySCAS, and the ways in which the various design and implementation challenges are met
- …