12,485 research outputs found
An objective based classification of aggregation techniques for wireless sensor networks
Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented
Implicit Cooperative Positioning in Vehicular Networks
Absolute positioning of vehicles is based on Global Navigation Satellite
Systems (GNSS) combined with on-board sensors and high-resolution maps. In
Cooperative Intelligent Transportation Systems (C-ITS), the positioning
performance can be augmented by means of vehicular networks that enable
vehicles to share location-related information. This paper presents an Implicit
Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle
(V2V) connectivity in an innovative manner, avoiding the use of explicit V2V
measurements such as ranging. In the ICP approach, vehicles jointly localize
non-cooperative physical features (such as people, traffic lights or inactive
cars) in the surrounding areas, and use them as common noisy reference points
to refine their location estimates. Information on sensed features are fused
through V2V links by a consensus procedure, nested within a message passing
algorithm, to enhance the vehicle localization accuracy. As positioning does
not rely on explicit ranging information between vehicles, the proposed ICP
method is amenable to implementation with off-the-shelf vehicular communication
hardware. The localization algorithm is validated in different traffic
scenarios, including a crossroad area with heterogeneous conditions in terms of
feature density and V2V connectivity, as well as a real urban area by using
Simulation of Urban MObility (SUMO) for traffic data generation. Performance
results show that the proposed ICP method can significantly improve the vehicle
location accuracy compared to the stand-alone GNSS, especially in harsh
environments, such as in urban canyons, where the GNSS signal is highly
degraded or denied.Comment: 15 pages, 10 figures, in review, 201
Stochastic Sensor Scheduling via Distributed Convex Optimization
In this paper, we propose a stochastic scheduling strategy for estimating the
states of N discrete-time linear time invariant (DTLTI) dynamic systems, where
only one system can be observed by the sensor at each time instant due to
practical resource constraints. The idea of our stochastic strategy is that a
system is randomly selected for observation at each time instant according to a
pre-assigned probability distribution. We aim to find the optimal pre-assigned
probability in order to minimize the maximal estimate error covariance among
dynamic systems. We first show that under mild conditions, the stochastic
scheduling problem gives an upper bound on the performance of the optimal
sensor selection problem, notoriously difficult to solve. We next relax the
stochastic scheduling problem into a tractable suboptimal quasi-convex form. We
then show that the new problem can be decomposed into coupled small convex
optimization problems, and it can be solved in a distributed fashion. Finally,
for scheduling implementation, we propose centralized and distributed
deterministic scheduling strategies based on the optimal stochastic solution
and provide simulation examples.Comment: Proof errors and typos are fixed. One section is removed from last
versio
Joint Estimation and Localization in Sensor Networks
This paper addresses the problem of collaborative tracking of dynamic targets
in wireless sensor networks. A novel distributed linear estimator, which is a
version of a distributed Kalman filter, is derived. We prove that the filter is
mean square consistent in the case of static target estimation. When large
sensor networks are deployed, it is common that the sensors do not have good
knowledge of their locations, which affects the target estimation procedure.
Unlike most existing approaches for target tracking, we investigate the
performance of our filter when the sensor poses need to be estimated by an
auxiliary localization procedure. The sensors are localized via a distributed
Jacobi algorithm from noisy relative measurements. We prove strong convergence
guarantees for the localization method and in turn for the joint localization
and target estimation approach. The performance of our algorithms is
demonstrated in simulation on environmental monitoring and target tracking
tasks.Comment: 9 pages (two-column); 5 figures; Manuscript submitted to the 2014
IEEE Conference on Decision and Control (CDC
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Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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