581 research outputs found
Efficient Mobile Data Collection with Mobile Collect
WISENET (NES)PromosCONE
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
A mobile assisted coverage hole patching scheme based on particle swarm optimization for WSNs
Wireless sensor networks (WSNs) have drawn much research attention in recent years due to the superior performance in multiple applications, such as military and industrial monitoring, smart home, disaster restoration etc. In such applications, massive sensor nodes are randomly deployed and they remain static after the deployment, to fully cover the target sensing area. This will usually cause coverage redundancy or coverage hole problem. In order to effectively deploy sensors to cover whole area, we present a novel node deployment algorithm based on mobile sensors. First, sensor nodes are randomly deployed in target area, and they remain static or switch to the sleep mode after deployment. Second, we partition the network into grids and calculate the coverage rate of each grid. We select grids with lower coverage rate as candidate grids. Finally, we awake mobile sensors from sleep mode to fix coverage hole, particle swarm optimization (PSO) algorithm is used to calculate moving position of mobile sensors. Simulation results show that our algorithm can effectively improve the coverage rate of WSNs
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
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
A Reliable Network Routing Protocol Design with an Intelligent Mobile Sink and Energy Efficiency over Wireless Sensor Network
This is crucial for the successful operation of a Wireless Sensor Network, frequently called as WSN as each of the sensor nodes in the network's structure are in charge of transmitting the information from its original source to its final destination. Several researchers have developed a Mobile sink in an effort to enhance transmission quality. Although it has been shown to be beneficial, the network's overall reliability is compromised by the much more energy-intensive operation of the nodes. In this research, we offer a routing strategy that makes use of cluster and source portability to drastically reduce power usage. We have given this protocol its name: the Intelligent Mobile Sink Assisted Routing Protocol (IMSARP). To get started, we divide an entire sensor environment into regions, and within each of them, members cast proportional votes to choose who will serve as the Cluster-Head (CH). To determine how to choose the most efficient choice, nodes in the network evaluate the power consumption of all feasible routes. The proposed IMSARP method uses a cluster-based paradigm to construct a mobile-sink routing protocol. The standard quantity of energy within all the clusters serves as what drives the sink's motion. The outcomes section of this research proves the legitimacy and maintains the credibility of the proposed system by presenting the results, which include throughput, delay reduction, energy efficiency, and data transfer rate
Quality-of-service in wireless sensor networks: state-of-the-art and future directions
Wireless sensor networks (WSNs) are one of today’s most prominent instantiations
of the ubiquituous computing paradigm. In order to achieve high
levels of integration, WSNs need to be conceived considering requirements
beyond the mere system’s functionality. While Quality-of-Service (QoS) is
traditionally associated with bit/data rate, network throughput, message delay
and bit/packet error rate, we believe that this concept is too strict, in
the sense that these properties alone do not reflect the overall quality-ofservice
provided to the user/application. Other non-functional properties
such as scalability, security or energy sustainability must also be considered
in the system design. This paper identifies the most important non-functional
properties that affect the overall quality of the service provided to the users,
outlining their relevance, state-of-the-art and future research directions
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