3,799 research outputs found
AM-DisCNT: Angular Multi-hop DIStance based Circular Network Transmission Protocol for WSNs
The nodes in wireless sensor networks (WSNs) contain limited energy
resources, which are needed to transmit data to base station (BS). Routing
protocols are designed to reduce the energy consumption. Clustering algorithms
are best in this aspect. Such clustering algorithms increase the stability and
lifetime of the network. However, every routing protocol is not suitable for
heterogeneous environments. AM-DisCNT is proposed and evaluated as a new energy
efficient protocol for wireless sensor networks. AM-DisCNT uses circular
deployment for even consumption of energy in entire wireless sensor network.
Cluster-head selection is on the basis of energy. Highest energy node becomes
CH for that round. Energy is again compared in the next round to check the
highest energy node of that round. The simulation results show that AM-DisCNT
performs better than the existing heterogeneous protocols on the basis of
network lifetime, throughput and stability of the system.Comment: IEEE 8th International Conference on Broadband and Wireless
Computing, Communication and Applications (BWCCA'13), Compiegne, Franc
Stochastic Approach for Energy-Efficient Clustering in WSN
Abstract- Wireless sensor networks are self-organizing networks in which sensor nodes with limited resource are scattered in an area of interest to gather information. WSNs need to have effective node2019;s energy management methods for stable and seamless communication. Power efficient clustering is done in WSN to prolong the life of the network. In WSN, many algorithms are developed to save energy of sensor nodes and to increase the lifetime of the network. This paper provides an energy efficient clustering algorithm inspired by prophet routing protocol to enhance the cluster based operation of the nodes. Adaptive learning is implemented for head selection for efficientcommunication. Simulation results confirm the efficiency of the mechanism
Applications of Repeated Games in Wireless Networks: A Survey
A repeated game is an effective tool to model interactions and conflicts for
players aiming to achieve their objectives in a long-term basis. Contrary to
static noncooperative games that model an interaction among players in only one
period, in repeated games, interactions of players repeat for multiple periods;
and thus the players become aware of other players' past behaviors and their
future benefits, and will adapt their behavior accordingly. In wireless
networks, conflicts among wireless nodes can lead to selfish behaviors,
resulting in poor network performances and detrimental individual payoffs. In
this paper, we survey the applications of repeated games in different wireless
networks. The main goal is to demonstrate the use of repeated games to
encourage wireless nodes to cooperate, thereby improving network performances
and avoiding network disruption due to selfish behaviors. Furthermore, various
problems in wireless networks and variations of repeated game models together
with the corresponding solutions are discussed in this survey. Finally, we
outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference
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
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Structural Changes in Data Communication in Wireless Sensor Networks
Wireless sensor networks are an important technology for making distributed
autonomous measures in hostile or inaccessible environments. Among the
challenges they pose, the way data travel among them is a relevant issue since
their structure is quite dynamic. The operational topology of such devices can
often be described by complex networks. In this work, we assess the variation
of measures commonly employed in the complex networks literature applied to
wireless sensor networks. Four data communication strategies were considered:
geometric, random, small-world, and scale-free models, along with the shortest
path length measure. The sensitivity of this measure was analyzed with respect
to the following perturbations: insertion and removal of nodes in the geometric
strategy; and insertion, removal and rewiring of links in the other models. The
assessment was performed using the normalized Kullback-Leibler divergence and
Hellinger distance quantifiers, both deriving from the Information Theory
framework. The results reveal that the shortest path length is sensitive to
perturbations.Comment: 12 pages, 4 figures, Central European Journal of Physic
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