2,447 research outputs found
PluralisMAC: a generic multi-MAC framework for heterogeneous, multiservice wireless networks, applied to smart containers
Developing energy-efficient MAC protocols for lightweight wireless systems has been a challenging task for decades because of the specific requirements of various applications and the varying environments in which wireless systems are deployed. Many MAC protocols for wireless networks have been proposed, often custom-made for a specific application. It is clear that one MAC does not fit all the requirements. So, how should a MAC layer deal with an application that has several modes (each with different requirements) or with the deployment of another application during the lifetime of the system? Especially in a mobile wireless system, like Smart Monitoring of Containers, we cannot know in advance the application state (empty container versus stuffed container). Dynamic switching between different energy-efficient MAC strategies is needed. Our architecture, called PluralisMAC, contains a generic multi-MAC framework and a generic neighbour monitoring and filtering framework. To validate the real-world feasibility of our architecture, we have implemented it in TinyOS and have done experiments on the TMote Sky nodes in the w-iLab.t testbed. Experimental results show that dynamic switching between MAC strategies is possible with minimal receive chain overhead, while meeting the various application requirements (reliability and low-energy consumption)
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
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
QUOIN: Incentive Mechanisms for Crowd Sensing Networks
Crowd sensing networks play a critical role in big data generation where a large number of mobile devices collect various kinds of data with large-volume features. Although which information should be collected is essential for the success of crowd-sensing applications, few research efforts have been made so far. On the other hand, an efficient incentive mechanism is required to encourage all crowd-sensing participants, including data collectors, service providers, and service consumers, to join the networks. In this article, we propose a new incentive mechanism called QUOIN, which simultaneously ensures Quality and Usability Of INformation for crowd-sensing application requirements. We apply a Stackelberg game model to the proposed mechanism to guarantee each participant achieves a satisfactory level of profits. Performance of QUOIN is evaluated with a case study, and experimental results demonstrate that it is efficient and effective in collecting valuable information for crowd-sensing applications
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
Relay Node Placement and Trajectory Computation of Mobile Data Collectors in Wireless Sensor Networks
Recent research has shown that introducing mobile data collectors (MDC) can significantly improve the performance of wireless sensor networks. There are important design problems in this area, such as determining the number and positions of relay nodes, determining their buffer capacities to ensure there is no data loss, and calculating a suitable trajectory for MDC(s). In this thesis, we first propose an integrated integer linear program (ILP) formulation that calculates the optimal number and positions of the relay nodes with the requisite buffer capacities. We then present two algorithms for calculating the trajectory of the MDC, based on the locations and the load of each individual relay node, in a way that minimizes the energy dissipation of the relay nodes. Our simulation results demonstrate that our approach is feasible for networks with hundreds of sensor nodes and leads to significant improvements compared to conventional data communication strategies
On-demand fuzzy clustering and ant-colony optimisation based mobile data collection in wireless sensor network
In a wireless sensor network (WSN), sensor nodes collect data from the environment and transfer this data to an end user through multi-hop communication. This results in high energy dissipation of the devices. Thus, balancing of energy consumption is a major concern in such kind of network. Appropriate cluster head (CH) selection may provide to be an efficient way to reduce the energy dissipation and prolonging the network lifetime in WSN. This paper has adopted the concept of fuzzy if-then rules to choose the cluster head based on certain fuzzy descriptors. To optimise the fuzzy membership functions, Particle Swarm Optimisation (PSO) has been used to improve their ranges. Moreover, recent study has confirmed that the introduction of a mobile collector in a network which collects data through short-range communications also aids in high energy conservation. In this work, the network is divided into clusters and a mobile collector starts from the static sink or base station and moves through each of these clusters and collect data from the chosen cluster heads in a single-hop fashion. Mobility based on Ant-Colony Optimisation (ACO) has already proven to be an efficient method which is utilised in this work. Additionally, instead of performing clustering in every round, CH is selected on demand. The performance of the proposed algorithm has been compared with some existing clustering algorithms. Simulation results show that the proposed protocol is more energy-efficient and provides better packet delivery ratio as compared to the existing protocols for data collection obtained through Matlab Simulations
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