Design support for Wireless Sensor Networks based on the IEEE 802.15.4 standard

2008 ◽  
Author(s):  
Sebastiaan Wielens ◽  
Michael Galetzka ◽  
Peter Schneider
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ieee 802.15.4 ◽  
Wireless Sensor ◽  
Design Support ◽  
Ieee 802.15.4 Standard

Evaluating the Performance of the IEEE 802.15.4 Standard in Supporting Time-Critical Wireless Sensor Networks

2011 ◽  
pp. 142-158
Author(s):  
Carlos Lino ◽  
Carlos Tavares Calafate ◽  
Pietro Manzoni ◽  
Juan-Carlos Cano ◽  
Arnoldo Díaz
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ieee 802.15.4 ◽  
Wireless Sensor ◽  
Critical Events ◽  
Routing Overhead ◽  
Monitoring Time ◽  
Important Research Topic ◽  
Ieee 802.15.4 Standard ◽  
Time Critical

The performance of wireless sensor networks (WSNs) at monitoring time-critical events is an important research topic, mainly due to the need to ensure that the actions to be taken upon these events are timely. To determine the effectiveness of the IEEE 802.15.4 standard at monitoring time-critical events in WSNs, we introduce a routing scheme based on drain announcements that seeks minimum routing overhead. We carried out a novel performance evaluation of the IEEE 802.15.4 technology under different conditions, to determine whether or not near-real-time event monitoring is feasible. By analyzing different simulation metrics such as packet loss rate, average end-to-end delay, and routing overhead, we determine the degree of effectiveness of the IEEE 802.15.4 standard at supporting time-critical tasks in multi-hop WSNs, evidencing its limitations upon the size and the amount of traffic flowing through the network.

scholarly journals An Efficient Superframe Structure with Optimal Bandwidth Utilization and Reduced Delay for Internet of Things Based Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1971 ◽  
Author(s):  
Sangrez Khan ◽  
Ahmad Naseem Alvi ◽  
Muhammad Awais Javed ◽  
Byeong-hee Roh ◽  
Jehad Ali
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Large Scale ◽  
Ieee 802.15.4 ◽  
Fine Tuning ◽  
Wireless Sensor ◽  
Bandwidth Utilization ◽  
Iot Applications ◽  
Ieee 802.15.4 Standard

Internet of Things (IoT) is a promising technology that uses wireless sensor networks to enable data collection, monitoring, and transmission from the physical devices to the Internet. Due to its potential large scale usage, efficient routing and Medium Access Control (MAC) techniques are vital to meet various application requirements. Most of the IoT applications need low data rate and low powered wireless transmissions and IEEE 802.15.4 standard is mostly used in this regard which offers superframe structure at the MAC layer. However, for IoT applications where nodes have adaptive data traffic, the standard has some limitations such as bandwidth wastage and latency. In this paper, a new superframe structure is proposed that is backward compatible with the existing parameters of the standard. The proposed superframe overcomes limitations of the standard by fine-tuning its superframe structure and squeezing the size of its contention-free slots. Thus, the proposed superframe adjusts its duty cycle according to the traffic requirements and accommodates more nodes in a superframe structure. The analytical results show that our proposed superframe structure has almost 50% less delay, accommodate more nodes and has better link utilization in a superframe as compared to the IEEE 802.15.4 standard.

scholarly journals An Energy-Efficient MAC Scheduler based on a Switched-Beam Antenna for Wireless Sensor Networks

2013 ◽  
Vol 9 (2) ◽  
pp. 117 ◽  
Author(s):  
Luca Catarinucci ◽  
Sergio Guglielmi ◽  
Luca Mainetti ◽  
Vincenzo Mighali ◽  
Luigi Patrono ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ieee 802.15.4 ◽  
Mac Protocol ◽  
Beam Steering ◽  
Communication Protocols ◽  
Wireless Sensor ◽  
Standard Solution ◽  
Ieee 802.15.4 Standard ◽  
And Storage

Wireless Sensor Networks (WSNs) are receiving an ever increasing attention because they are one of the most important technologies enabling the Internet of Things vision. Since nodes of these networks are battery-powered, energy efficiency represents one of the main design objectives. This goal can be primarily achieved through an optimization of the communication phase, which is the most power consuming operation for a WSN node. However, the limited computational and storage resources of physical devices make the design of complex communication protocols particularly hard, suggesting, on the contrary, to integrate more simple communication protocols with hardware solutions aimed at energy saving. In this work, a new MAC protocol, compatible with the IEEE 802.15.4 standard, and a reconfigurable beam-steering antenna are presented and validated. They significantly reduce the nodes’ power consumption by exploiting scheduling techniques and directional communications. Specifically, both during transmission and receiving phases, the node activates exclusively the antenna sector needed to communicate with the intended neighbour. The designed antenna and the proposed protocol have been thoroughly evaluated by means of simulations and test-beds, which have highlighted their good performance. In particular, the MAC protocol has been implemented on the Contiki Operating System and it was compared with the IEEE 802.15.4 standard solution.

Performance Analysis of IEEE 802.15.4 Based Wireless Sensor Networks using LAR protocol for CBR and ZIGBEE Traffic Applications

2013 ◽  
Vol 10 (9) ◽  
pp. 1963-1968
Author(s):  
Samundiswary P. ◽  
Surender R
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Performance Analysis ◽  
Ieee 802.15.4 ◽  
Performance Metrics ◽  
Wireless Sensor ◽  
Personal Area Networks ◽  
Wireless Personal Area Networks ◽  
Delay Jitter ◽  
Ieee 802.15.4 Standard

IEEE 802.15.4 standard based wireless sensor networks (WSNs) emerges as the next generation wireless standard for low-rate wireless personal area networks. IEEE 802.15.4 standard offers low power, low data rate and short range networking for wireless battery powered devices. It has also started to demand much attention towards research. The performance of the network can be analysed by using different types of routing protocols. In this paper, the performance analysis of IEEE 802.15.4 based Wireless Sensor Networks is done by using  Location Aided Routing (LAR) protocols for the traffic applications such as Constant Bit Rate (CBR) and Zigbee traffic application. The LAR protocol enables the routing of data between the source and destination by using directional flooding technique. The performance metrics such as throughput, delay, jitter and packets dropped of LAR for  CBR and Zigbee traffic application is evaluated and analysed. The simulation is modelled by using QualNet.

scholarly journals Autoconfiguration with Global Addresses Using IEEE 802.15.4 Standard in Multi-hop Networks

Enfoque UTE ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 44-58
Author(s):  
Carlos Egas Acosta ◽  
David Cali ◽  
Cristian Espinosa
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Ieee 802.15.4 ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Correct Operation ◽  
Allocation Process ◽  
Ieee 802.15.4 Standard ◽  
The Internet Of Things

Wireless sensor networks continue to attract a lot of attention from academia and industry promoting large-scale deployments in applications related to the Internet of Things (IoT). Unfortunately, a network containing a large number of sensor nodes also leads to difficulty in the configuring process and assignment of identifiers to nodes. Various approaches have been proposed to solve problems of auto-configuration in Wireless sensor networks, however, still, there are some issues remaining related to automatic assign identifiers A cluster-based hierarchical global address allocation scheme is proposed for a wireless sensor network. The proposal uses the IEEE 802.15.4 protocol and aims to reduce the latency of the identifier assignments and reduce the network level processes to be performed at the node. The address allocation process assigns each node a unique global address, which allows the node to have end-to-end connectivity without network-level involvement. The scenario of adding new nodes to the network or nodes that leave it is contemplated. Finally, the proposed scheme is evaluated experimentally, verifying the correct operation of the algorithm proposed in the implemented prototype.

scholarly journals Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1012
Author(s):  
Himanshu Sharma ◽  
Ahteshamul Haque ◽  
Frede Blaabjerg
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Low Power ◽  
Smart City ◽  
Ieee 802.15.4 ◽  
Smart Cities ◽  
Traffic Monitoring ◽  
Wireless Sensor ◽  
Ieee 802.15.4 Standard

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications.

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