11,680 research outputs found
Unified clustering and communication protocol for wireless sensor networks
In this paper we present an energy-efficient cross layer protocol for providing application specific reservations in wireless senor networks called the “Unified Clustering and Communication Protocol ” (UCCP). Our modular cross layered framework satisfies three wireless sensor network requirements, namely, the QoS requirement of heterogeneous applications, energy aware clustering and data forwarding by relay sensor nodes. Our unified design approach is motivated by providing an integrated and viable solution for self organization and end-to-end communication is wireless sensor networks. Dynamic QoS based reservation guarantees are provided using a reservation-based TDMA approach. Our novel energy-efficient clustering approach employs a multi-objective optimization technique based on OR (operations research) practices. We adopt a simple hierarchy in which relay nodes forward data messages from cluster head to the sink, thus eliminating the overheads needed to maintain a routing protocol. Simulation results demonstrate that UCCP provides an energy-efficient and scalable solution to meet the application specific QoS demands in resource constrained sensor nodes. Index Terms — wireless sensor networks, unified communication, optimization, clustering and quality of service
An Energy Driven Architecture for Wireless Sensor Networks
Most wireless sensor networks operate with very limited energy sources-their
batteries, and hence their usefulness in real life applications is severely
constrained. The challenging issues are how to optimize the use of their energy
or to harvest their own energy in order to lengthen their lives for wider
classes of application. Tackling these important issues requires a robust
architecture that takes into account the energy consumption level of functional
constituents and their interdependency. Without such architecture, it would be
difficult to formulate and optimize the overall energy consumption of a
wireless sensor network. Unlike most current researches that focus on a single
energy constituent of WSNs independent from and regardless of other
constituents, this paper presents an Energy Driven Architecture (EDA) as a new
architecture and indicates a novel approach for minimising the total energy
consumption of a WS
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
Energy Consumption Of Visual Sensor Networks: Impact Of Spatio-Temporal Coverage
Wireless visual sensor networks (VSNs) are expected to play a major role in
future IEEE 802.15.4 personal area networks (PAN) under recently-established
collision-free medium access control (MAC) protocols, such as the IEEE
802.15.4e-2012 MAC. In such environments, the VSN energy consumption is
affected by the number of camera sensors deployed (spatial coverage), as well
as the number of captured video frames out of which each node processes and
transmits data (temporal coverage). In this paper, we explore this aspect for
uniformly-formed VSNs, i.e., networks comprising identical wireless visual
sensor nodes connected to a collection node via a balanced cluster-tree
topology, with each node producing independent identically-distributed
bitstream sizes after processing the video frames captured within each network
activation interval. We derive analytic results for the energy-optimal
spatio-temporal coverage parameters of such VSNs under a-priori known bounds
for the number of frames to process per sensor and the number of nodes to
deploy within each tier of the VSN. Our results are parametric to the
probability density function characterizing the bitstream size produced by each
node and the energy consumption rates of the system of interest. Experimental
results reveal that our analytic results are always within 7% of the energy
consumption measurements for a wide range of settings. In addition, results
obtained via a multimedia subsystem show that the optimal spatio-temporal
settings derived by the proposed framework allow for substantial reduction of
energy consumption in comparison to ad-hoc settings. As such, our analytic
modeling is useful for early-stage studies of possible VSN deployments under
collision-free MAC protocols prior to costly and time-consuming experiments in
the field.Comment: to appear in IEEE Transactions on Circuits and Systems for Video
Technology, 201
From carbon nanotubes and silicate layers to graphene platelets for polymer nanocomposites
In spite of extensive studies conducted on carbon nanotubes and silicate layers for their polymer-based nanocomposites, the rise of graphene now provides a more promising candidate due to its exceptionally high mechanical performance and electrical and thermal conductivities. The present study developed a facile approach to fabricate epoxy–graphene nanocomposites by thermally expanding a commercial product followed by ultrasonication and solution-compounding with epoxy, and investigated their morphologies, mechanical properties, electrical conductivity and thermal mechanical behaviour. Graphene platelets (GnPs) of 3.5
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
- …