2,208 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
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
An Efficient Energy Harvesting Assited Clustering Scheme for Wireless Sensor Networks
One of the prominent challenges in Wireless sensor networks (WSNs) is the energy conservation of sensor nodes irrespective of the nature of the sensor applications due to the tiny, limited batteries of the nodes.One promising solution to preserve energy is the clustering phenomenon and this mechanism also requires adequate stress on overall overhead of the network. Various clustering solutions have been addressed to extricate the power constraints of the sensor networks and they fluctuate in their boundaries owing to the multifaceted nature of this problem. In a typical clustering process in a WSN, energy is consumed in three phases: data sensing, data forwarding and data aggregation. A potential green, untrammeled replacement towards the conventional clustered sensor networks is the harvesting and utilization of energy from an ambient power resource. Unlike many other solutions, this approach overcomes the customary trade-offs but hosts economic and application-specific constraints. Our proposed Efficient Energy Harvestingassisted Clustering (EEHC) scheme contributes the idea of forming effective clusters that are free of residual nodes and overlapping. In this environment each sensor node is equipped with the energy harvesting device.The cluster head effectively balances the load in a cluster based on energy budgeting and nearly reduces the need for reclustering. Our approach is compared against the conventional and modern clustering algorithms for WSNs and yields significant improvement in the scope of lifetime from the pecuniary perspective
Energy Efficient Approach for Collision Avoidance in Wireless Sensor Networks
One of the main challenges in the wireless sensor network is to improve the performance of the network by extending the lifetime of the sensor nodes. Excessive packet collisions lead to packet losses and retransmissions, resulting in significant overhead costs and latency which in turn makes a need to design a distributed and scalable time slot allocation. A new proposal is proposed which avoids collisions between packets and also provides increased energy efficiency and further prolong network lifetime, in wireless sensor network
DESIGN OF MOBILE DATA COLLECTOR BASED CLUSTERING ROUTING PROTOCOL FOR WIRELESS SENSOR NETWORKS
Wireless Sensor Networks (WSNs) consisting of hundreds or even thousands of
nodes, canbe used for a multitude of applications such as warfare intelligence or to
monitor the environment. A typical WSN node has a limited and usually an
irreplaceable power source and the efficient use of the available power is of utmost
importance to ensure maximum lifetime of eachWSNapplication. Each of the nodes
needs to transmit and communicate sensed data to an aggregation point for use by
higher layer systems. Data and message transmission among nodes collectively
consume the largest amount of energy available in WSNs. The network routing
protocols ensure that every message reaches thedestination and has a direct impact on
the amount of transmissions to deliver messages successfully. To this end, the
transmission protocol within the WSNs should be scalable, adaptable and optimized
to consume the least possible amount of energy to suite different network
architectures and application domains. The inclusion of mobile nodes in the WSNs
deployment proves to be detrimental to protocol performance in terms of nodes
energy efficiency and reliable message delivery. This thesis which proposes a novel
Mobile Data Collector based clustering routing protocol for WSNs is designed that
combines cluster based hierarchical architecture and utilizes three-tier multi-hop
routing strategy between cluster heads to base station by the help of Mobile Data
Collector (MDC) for inter-cluster communication. In addition, a Mobile Data
Collector based routing protocol is compared with Low Energy Adaptive Clustering
Hierarchy and A Novel Application Specific Network Protocol for Wireless Sensor
Networks routing protocol. The protocol is designed with the following in mind:
minimize the energy consumption of sensor nodes, resolve communication holes
issues, maintain data reliability, finally reach tradeoff between energy efficiency and
latency in terms of End-to-End, and channel access delays. Simulation results have
shown that the Mobile Data Collector based clustering routing protocol for WSNs
could be easily implemented in environmental applications where energy efficiency of
sensor nodes, network lifetime and data reliability are major concerns
Novel reliable and dynamic energy-aware routing protocol for large scale wireless sensor networks
Wireless sensor networks (WSN) are made up of an important number of sensors, called nodes, distributed in random way in a concerned monitoring area. All sensor nodes in the network are mounted with limited energy sources, which makes energy harvesting on top of the list of issues in WSN. A poor communication architecture can result in excessive consumption, reducing the network lifetime and throughput. Centralizing data collection and the introduction of gateways (GTs), to help cluster heads (CHs), improved WSN life time significantly. However, in vast regions, misplacement and poor distribution of GTs wastes a huge amount of energy and decreases network’s performances. In this work, we describe a reliable and dynamic with energy-awareness routing (RDEAR) protocol that provides a new GT’s election approach taking into consideration CHs density, transmission distance and energy. Applied on 20 different networks, RDEAR reduced the overall energy consumption, increased stability zone and network life time as well as other compared metrics. Our proposed approach increased network’s throughput up to 75.92% , 67.7% and 9.78% compared to the low energy adaptive clustering hierarchy (LEACH), distributed energy efficient clustering (DEEC) and static multihop routing (SMR), protocols, respectively
Biologically inspired, self organizing communication networks.
PhDThe problem of energy-efficient, reliable, accurate and self-organized target tracking in
Wireless Sensor Networks (WSNs) is considered for sensor nodes with limited physical
resources and abrupt manoeuvring mobile targets. A biologically inspired, adaptive
multi-sensor scheme is proposed for collaborative Single Target Tracking (STT) and
Multi-Target Tracking (MTT). Behavioural data obtained while tracking the targets
including the targets’ previous locations is recorded as metadata to compute the target
sampling interval, target importance and local monitoring interval so that tracking
continuity and energy-efficiency are improved. The subsequent sensor groups that track
the targets are selected proactively according to the information associated with the
predicted target location probability such that the overall tracking performance is
optimized or nearly-optimized. One sensor node from each of the selected groups is
elected as a main node for management operations so that energy efficiency and load
balancing are improved. A decision algorithm is proposed to allow the “conflict” nodes
that are located in the sensing areas of more than one target at the same time to decide
their preferred target according to the target importance and the distance to the target. A
tracking recovery mechanism is developed to provide the tracking reliability in the
event of target loss.
The problem of task mapping and scheduling in WSNs is also considered. A
Biological Independent Task Allocation (BITA) algorithm and a Biological Task
Mapping and Scheduling (BTMS) algorithm are developed to execute an application
using a group of sensor nodes. BITA, BTMS and the functional specialization of the
sensor groups in target tracking are all inspired from biological behaviours of
differentiation in zygote formation.
Simulation results show that compared with other well-known schemes, the
proposed tracking, task mapping and scheduling schemes can provide a significant
improvement in energy-efficiency and computational time, whilst maintaining
acceptable accuracy and seamless tracking, even with abrupt manoeuvring targets.Queen Mary university of London full Scholarshi
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