1,225 research outputs found
Coverage Protocols for Wireless Sensor Networks: Review and Future Directions
The coverage problem in wireless sensor networks (WSNs) can be generally
defined as a measure of how effectively a network field is monitored by its
sensor nodes. This problem has attracted a lot of interest over the years and
as a result, many coverage protocols were proposed. In this survey, we first
propose a taxonomy for classifying coverage protocols in WSNs. Then, we
classify the coverage protocols into three categories (i.e. coverage aware
deployment protocols, sleep scheduling protocols for flat networks, and
cluster-based sleep scheduling protocols) based on the network stage where the
coverage is optimized. For each category, relevant protocols are thoroughly
reviewed and classified based on the adopted coverage techniques. Finally, we
discuss open issues (and recommend future directions to resolve them)
associated with the design of realistic coverage protocols. Issues such as
realistic sensing models, realistic energy consumption models, realistic
connectivity models and sensor localization are covered
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
Extending the Lifetime of Wireless Sensor Networks Based on an Improved Multi-objective Artificial Bees Colony Algorithm
Reducing the sensors\u27 energy expenditure to prolong the network lifespan as long as possible remains a fundamental problem in the field of wireless networks. Particularly in applications with inaccessible environments, which impose crucial constraints on sensor replacement. It is, therefore, necessary to design adaptive routing protocols, taking into account the environmental constraints and the limited energy of sensors. To have an energy-efficient routing protocol, a new cluster heads’ (CHs) selection strategy using a modified multi-objective artificial bees colony (MOABC) optimization is defined. The modified MOABC is based on the roulette wheel selection over non-dominated solutions of the repository (hyper-cubes) in which a rank is assigned to each hypercube based on its density in dominated solutions of the current iteration and then a random food source is elected by roulette from the densest hypercube. The proposed work aims to find the optimal set of CHs based on their residual energies to ensure an optimal balance between the nodes\u27 energy consumption. The achieved results proved that the proposed MOABC-based protocol considerably outperforms recent studies and well-known energy-efficient protocols, namely: LEACH, C-LEACH, SEP, TSEP, DEEC, DDEEC, and EDEEC in terms of energy efficiency, stability, and network lifespan extension
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
Lifetime centric load balancing mechanism in wireless sensor network based IoT environment
Wireless sensor network (WSN) is a vital form of the underlying technology of the internet of things (IoT); WSN comprises several energy-constrained sensor nodes to monitor various physical parameters. Moreover, due to the energy constraint, load balancing plays a vital role considering the wireless sensor network as battery power. Although several clustering algorithms have been proposed for providing energy efficiency, there are chances of uneven load balancing and this causes the reduction in network lifetime as there exists inequality within the network. These scenarios occur due to the short lifetime of the cluster head. These cluster head (CH) are prime responsible for all the activity as it is also responsible for intra-cluster and inter-cluster communications. In this research work, a mechanism named lifetime centric load balancing mechanism (LCLBM) is developed that focuses on CH-selection, network design, and optimal CH distribution. Furthermore, under LCLBM, assistant cluster head (ACH) for balancing the load is developed. LCLBM is evaluated by considering the important metrics, such as energy consumption, communication overhead, number of failed nodes, and one-way delay. Further, evaluation is carried out by comparing with ES-Leach method, through the comparative analysis it is observed that the proposed model outperforms the existing model
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