43 research outputs found
A Brief Survey on Cluster based Energy Efficient Routing Protocols in IoT based Wireless Sensor Networks
The wireless sensor network (WSN) consists of a large number of randomly distributed nodes capable of detecting environmental data, converting it into a suitable format, and transmitting it to the base station. The most essential issue in WSNs is energy consumption, which is mostly dependent on the energy-efficient clustering and data transfer phases. We compared a variety of algorithms for clustering that balance the number of clusters. The cluster head selection protocol is arbitrary and incorporates energy-conscious considerations. In this survey, we compared different types of energy-efficient clustering-based protocols to determine which one is effective for lowering energy consumption, latency and extending the lifetime of wireless sensor networks (WSN) under various scenarios
On the Relevance of Using Open Wireless Sensor Networks in Environment Monitoring
This paper revisits the problem of the readiness for field deployments of wireless sensor networks by assessing the relevance of using Open Hardware and Software motes for environment monitoring. We propose a new prototype wireless sensor network that fine-tunes SquidBee motes to improve the life-time and sensing performance of an environment monitoring system that measures temperature, humidity and luminosity. Building upon two outdoor sensing scenarios, we evaluate the performance of the newly proposed energy-aware prototype solution in terms of link quality when expressed by the Received Signal Strength, Packet Loss and the battery lifetime. The experimental results reveal the relevance of using the Open Hardware and Software motes when setting up outdoor wireless sensor networks
Energy Efficient Hybrid Routing Protocol Based on the Artificial Fish Swarm Algorithm and Ant Colony Optimisation for WSNs
Wireless Sensor Networks (WSNs) are a particular type of distributed self-managed network with limited energy supply and communication ability. The most significant challenge of a routing protocol is the energy consumption and the extension of the network lifetime. Many energy-efficient routing algorithms were inspired by the development of Ant Colony Optimisation (ACO). However, due to the inborn defects, ACO-based routing algorithms have a slow convergence behaviour and are prone to premature, stagnation phenomenon, which hinders further route discovery, especially in a large-scale network. This paper proposes a hybrid routing algorithm by combining the Artificial Fish Swarm Algorithm (AFSA) and ACO to address these issues. We utilise AFSA to perform the initial route discovery in order to find feasible routes quickly. In the route discovery algorithm, we present a hybrid algorithm by combining the crowd factor in AFSA and the pseudo-random route select strategy in ACO. Furthermore, this paper presents an improved pheromone update method by considering energy levels and path length. Simulation results demonstrate that the proposed algorithm avoids the routing algorithm falling into local optimisation and stagnation, whilst speeding up the routing convergence, which is more prominent in a large-scale network. Furthermore, simulation evaluation reports that the proposed algorithm exhibits a significant improvement in terms of network lifetime
Energy efficient data collection and dissemination protocols in self-organised wireless sensor networks
Wireless sensor networks (WSNs) are used for event detection and data collection in
a plethora of environmental monitoring applications. However a critical factor limits
the extension of WSNs into new application areas: energy constraints. This thesis
develops self-organising energy efficient data collection and dissemination protocols in
order to support WSNs in event detection and data collection and thus extend the use
of sensor-based networks to many new application areas.
Firstly, a Dual Prediction and Probabilistic Scheduler (DPPS) is developed. DPPS
uses a Dual Prediction Scheme combining compression and load balancing techniques
in order to manage sensor usage more efficiently. DPPS was tested and evaluated
through computer simulations and empirical experiments. Results showed that DPPS
reduces energy consumption in WSNs by up to 35% while simultaneously maintaining
data quality and satisfying a user specified accuracy constraint.
Secondly, an Adaptive Detection-driven Ad hoc Medium Access Control (ADAMAC)
protocol is developed. ADAMAC limits the Data Forwarding Interruption problem
which causes increased end-to-end delay and energy consumption in multi-hop sensor
networks. ADAMAC uses early warning alarms to dynamically adapt the sensing
intervals and communication periods of a sensor according to the likelihood of any
new events occurring. Results demonstrated that compared to previous protocols such
as SMAC, ADAMAC dramatically reduces end-to-end delay while still limiting energy
consumption during data collection and dissemination. The protocols developed in this thesis, DPPS and ADAMAC, effectively alleviate
the energy constraints associated with WSNs and will support the extension of sensorbased
networks to many more application areas than had hitherto been readily possible
Nonuniform Coverage Control on the Line
This paper investigates control laws allowing mobile, autonomous agents to
optimally position themselves on the line for distributed sensing in a
nonuniform field. We show that a simple static control law, based only on local
measurements of the field by each agent, drives the agents close to the optimal
positions after the agents execute in parallel a number of
sensing/movement/computation rounds that is essentially quadratic in the number
of agents. Further, we exhibit a dynamic control law which, under slightly
stronger assumptions on the capabilities and knowledge of each agent, drives
the agents close to the optimal positions after the agents execute in parallel
a number of sensing/communication/computation/movement rounds that is
essentially linear in the number of agents. Crucially, both algorithms are
fully distributed and robust to unpredictable loss and addition of agents
Swarm intelligence and its applications to wireless ad hoc and sensor networks.
Swarm intelligence, as inspired by natural biological swarms, has numerous powerful
properties for distributed problem solving in complex real world applications such
as optimisation and control. Swarm intelligence properties can be found in natural
systems such as ants, bees and birds, whereby the collective behaviour of unsophisticated
agents interact locally with their environment to explore collective problem solving
without centralised control. Recent advances in wireless communication and digital
electronics have instigated important changes in distributed computing. Pervasive
computing environments have emerged, such as large scale communication networks
and wireless ad hoc and sensor networks that are extremely dynamic and unreliable.
The network management and control must be based on distributed principles where
centralised approaches may not be suitable for exploiting the enormous potential of
these environments. In this thesis, we focus on applying swarm intelligence to the
wireless ad hoc and sensor networks optimisation and control problems.
Firstly, an analysis of the recently proposed particle swarm optimisation, which is
based on the swarm intelligence techniques, is presented. Previous stability analysis
of the particle swarm optimisation was restricted to the assumption that all of the
parameters are non random since the theoretical analysis with the random parameters
is difficult. We analyse the stability of the particle dynamics without these restrictive
assumptions using Lyapunov stability and passive systems concepts. The particle
swarm optimisation is then used to solve the sink node placement problem in sensor
networks.
Secondly, swarm intelligence based routing methods for mobile ad hoc networks
are investigated. Two protocols have been proposed based on the foraging behaviour
of biological ants and implemented in the NS2 network simulator. The first protocol
allows each node in the network to choose the next node for packets to be
forwarded on the basis of mobility influenced routing table. Since mobility is one of
the most important factors for route changes in mobile ad hoc networks, the mobility
of the neighbour node using HELLO packets is predicted and then translated into a
pheromone decay as found in natural biological systems. The second protocol uses
the same mechanism as the first, but instead of mobility the neighbour node remaining
energy level and its drain rate are used. The thesis clearly shows that swarm
intelligence methods have a very useful role to play in the management and control
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problems associated with wireless ad hoc and sensor networks. This thesis has given
a number of example applications and has demonstrated its usefulness in improving
performance over other existing methods
Wireless Sensor Networks
The aim of this book is to present few important issues of WSNs, from the application, design and technology points of view. The book highlights power efficient design issues related to wireless sensor networks, the existing WSN applications, and discusses the research efforts being undertaken in this field which put the reader in good pace to be able to understand more advanced research and make a contribution in this field for themselves. It is believed that this book serves as a comprehensive reference for graduate and undergraduate senior students who seek to learn latest development in wireless sensor networks
Bio-Inspired Tools for a Distributed Wireless Sensor Network Operating System
The problem which I address in this thesis is to find a way to organise and manage a network
of wireless sensor nodes using a minimal amount of communication. To find a solution I explore
the use of Bio-inspired protocols to enable WSN management while maintaining a low
communication overhead. Wireless Sensor Networks (WSNs) are loosely coupled distributed
systems comprised of low-resource, battery powered sensor nodes. The largest problem with
WSN management is that communication is the largest consumer of a sensor node’s energy.
WSN management systems need to use as little communication as possible to prolong their operational
lifetimes. This is the Wireless Sensor Network Management Problem. This problem
is compounded because current WSN management systems glue together unrelated protocols
to provide system services causing inter-protocol interference. Bio-inspired protocols provide a
good solution because they enable the nodes to self-organise, use local area communication, and
can combine their communication in an intelligent way with minimal increase in communication.
I present a combined protocol and MAC scheduler to enable multiple service protocols to
function in a WSN at the same time without causing inter-protocol interference. The scheduler
is throughput optimal as long as the communication requirements of all of the protocols remain
within the communication capacity of the network. I show that the scheduler improves a dissemination
protocol’s performance by 35%. A bio-inspired synchronisation service is presented
which enables wireless sensor nodes to self organise and provide a time service. Evaluation of
the protocol shows an 80% saving in communication over similar bio-inspired synchronisation
approaches. I then add an information dissemination protocol, without significantly increasing
communication. This is achieved through the ability of our bio-inspired algorithms to combine
their communication in an intelligent way so that they are able to offer multiple services
without requiring a great deal of inter-node communication.Open Acces
ENAMS: Energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence.
Although traditionally Wireless Sensor Network (WSNs) have been regarded as static sensor arrays used mainly for environmental monitoring, recently, its applications have undergone a paradigm shift from static to more dynamic environments, where nodes are attached to moving objects, people or animals. Applications that use WSNs in motion are broad, ranging from transport and logistics to animal monitoring, health care and military.
These application domains have a number of characteristics that challenge the algorithmic design of WSNs. Firstly, mobility has a negative effect on the quality of the wireless communication and the performance of networking protocols.
Nevertheless, it has been shown that mobility can enhance the functionality of the network by exploiting the movement patterns of mobile objects. Secondly, the heterogeneity of devices in a WSN has to be taken into account for increasing the network performance and lifetime. Thirdly, the WSN services should ideally assist the user in an unobtrusive and transparent way. Fourthly, energy-efficiency and scalability are of primary importance to prevent the network performance degradation.
This thesis contributes toward the design of a new hybrid optimization algorithm; ENAMS (Energy optimizatioN Algorithm for Mobile Sensor networks) which is based on the Evolutionary Computation and Swarm Intelligence to increase the life time of mobile wireless sensor networks. The presented algorithm is suitable for large scale mobile sensor networks and provides a robust and energy- efficient communication mechanism by dividing the sensor-nodes into clusters, where the number of clusters is not predefined and the sensors within each cluster are not necessary to be distributed in the same density. The presented algorithm enables the sensor nodes to move as swarms within the search space while keeping optimum distances between the sensors.
To verify the objectives of the proposed algorithm, the LEGO-NXT MIND-STORMS robots are used to act as particles in a moving swarm keeping the optimum distances while tracking each other within the permitted distance range in the search space
Recommended from our members
Performance Analysis of Cluster Based Communication Protocols for Energy Efficient Wireless Sensor Networks. Design, Analysis and Performance Evaluation of Communication Protocols under Various Topologies to Enhance the Lifetime of Wireless Sensor Networks.
Sensor nodes are deployed over sensing fields for the purpose of monitoring certain
phenomena of interest. The sensor nodes perform specific measurements, process the
sensed data, and send the data to a base station over a wireless channel. The base station
collects data from the sensor nodes, analyses this data, and reports it to the users.
Wireless sensor networks are different from traditional networks, because of the
following constraints. Typically, a large number of sensor nodes need to be randomly
deployed and, in most cases, they are deployed in unreachable environments; however,
the sensor nodes may fail, and they are subject to power constraints.
Energy is one of the most important design constraints of wireless sensor networks.
Energy consumption, in a sensor node, occurs due to many factors, such as: sensing the
environment, transmitting and receiving data, processing data, and communication
overheads. Since the sensor nodes behave as router nodes for data propagation, of the
other sensor nodes to the base station, network connectivity decreases gradually. This
may result in disconnected sub networks of sensor nodes. In order to prolong the
network¿s lifetime, energy efficient protocols should be designed for the characteristics
of the wireless sensor network. Sensor nodes in different regions of the sensing field can
collaborate to aggregate the data that they gathered.
Data aggregation is defined as the process of aggregating the data from sensor nodes to
reduce redundant transmissions. It reduces a large amount of the data traffic on the
network, it requires less energy, and it avoids information overheads by not sending all
of the unprocessed data throughout the sensor network. Grouping sensor nodes into
clusters is useful because it reduces the energy consumption. The clustering technique
can be used to perform data aggregation. The clustering procedure involves the selection
of cluster heads in each of the cluster, in order to coordinate the member nodes. The
cluster head is responsible for: gathering the sensed data from its cluster¿s nodes,
aggregating the data, and then sending the aggregated data to the base station.
An adaptive clustering protocol was introduced to select the heads in the wireless sensor
network. The proposed clustering protocol will dynamically change the cluster heads to
obtain the best possible performance, based on the remaining energy level of sensor
nodes and the average energy of clusters. The OMNET simulator will be used to present
the design and implementation of the adaptive clustering protocol and then to evaluate
it.
This research has conducted extensive simulation experiments, in order to fully study
and analyse the proposed energy efficient clustering protocol. It is necessary for all of
the sensor nodes to remain alive for as long as possible, since network quality decreases
as soon as a set of sensor nodes die. The goal of the energy efficient clustering protocol
is to increase the lifetime and stability period of the sensor network.
This research also introduces a new bidirectional data gathering protocol. This protocol
aims to form a bidirectional ring structure among the sensor nodes, within the cluster, in
order to reduce the overall energy consumption and enhance the network¿s lifetime. A bidirectional data gathering protocol uses a source node to transmit data to the base
station, via one or more multiple intermediate cluster heads. It sends data through
energy efficient paths to ensure the total energy, needed to route the data, is kept to a
minimum. Performance results reveal that the proposed protocol is better in terms of: its
network lifetime, energy dissipation, and communication overheads