8,009 research outputs found
Survey on Data-Centric based Routing Protocols for Wireless Sensor Networks
The great concern for energy that grew with the technological advances in the
field of networks and especially in sensor network has triggered various
approaches and protocols that relate to sensor networks. In this context, the
routing protocols were of great interest. The aim of the present paper is to
discuss routing protocols for sensor networks. This paper will focus mainly on
the discussion of the data-centric approach (COUGAR, rumor, SPIN, flooding and
Gossiping), while shedding light on the other approaches occasionally. The
functions of the nodes will be discussed as well. The methodology selected for
this paper is based on a close description and discussion of the protocol. As a
conclusion, open research questions and limitations are proposed to the reader
at the end of this paper
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
MODLEACH: A Variant of LEACH for WSNs
Wireless sensor networks are appearing as an emerging need for mankind.
Though, Such networks are still in research phase however, they have high
potential to be applied in almost every field of life. Lots of research is done
and a lot more is awaiting to be standardized. In this work, cluster based
routing in wireless sensor networks is studied precisely. Further, we modify
one of the most prominent wireless sensor network's routing protocol "LEACH" as
modified LEACH (MODLEACH) by introducing \emph{efficient cluster head
replacement scheme} and \emph{dual transmitting power levels}. Our modified
LEACH, in comparison with LEACH out performs it using metrics of cluster head
formation, through put and network life. Afterwards, hard and soft thresholds
are implemented on modified LEACH (MODLEACH) that boast the performance even
more. Finally a brief performance analysis of LEACH, Modified LEACH (MODLEACH),
MODLEACH with hard threshold (MODLEACHHT) and MODLEACH with soft threshold
(MODLEACHST) is undertaken considering metrics of throughput, network life and
cluster head replacements.Comment: IEEE 8th International Conference on Broadband and Wireless
Computing, Communication and Applications (BWCCA'13), Compiegne, Franc
A network-aware framework for energy-efficient data acquisition in wireless sensor networks
Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN
EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design
The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application
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