A Platform for Pervasive Building Monitoring Services Using Wireless Sensor Networks

2012 ◽  
pp. 361-389
Author(s):  
Abolghasem (Hamid) Asgari

At the core of pervasive computing model, small, low cost, robust, distributed, and networked processing devices are placed, which are thoroughly integrated into everyday objects and activities. Wireless Sensor Networks (WSNs) have emerged as pervasive computing technology enablers in several field, including environmental monitoring and control. Using this technology as a pervasive computing approach, researchers have been trying to persuade people to be more aware of their environment and energy usage in the course of their every day life. WSNs have brought significant benefits as far as monitoring is concerned, since they are more efficient and flexible compared to wired sensor solutions. In this chapter, the authors propose a Service Oriented Architecture for developing an enterprise networking environment used for integrating enterprise level applications and building management systems with other operational enterprise services and functions for the information sharing and monitoring, controlling, and managing the enterprise environment. The WSN is viewed as an information service provider not only to building management systems but also to wider applications in the enterprise infrastructure. The authors also provide specification, implementation, and deployments of the proposed architecture and discuss the related tests, experimentations, and evaluations of the architecture.

Author(s):  
Asgari (Hamid) Abolghasem

At the core of pervasive computing model, small, low cost, robust, distributed, and networked processing devices are placed, which are thoroughly integrated into everyday objects and activities. Wireless Sensor Networks (WSNs) have emerged as pervasive computing technology enablers in several field, including environmental monitoring and control. Using this technology as a pervasive computing approach, researchers have been trying to persuade people to be more aware of their environment and energy usage in the course of their every day life. WSNs have brought significant benefits as far as monitoring is concerned, since they are more efficient and flexible compared to wired sensor solutions. In this chapter, the authors propose a Service Oriented Architecture for developing an enterprise networking environment used for integrating enterprise level applications and building management systems with other operational enterprise services and functions for the information sharing and monitoring, controlling, and managing the enterprise environment. The WSN is viewed as an information service provider not only to building management systems but also to wider applications in the enterprise infrastructure. The authors also provide specification, implementation, and deployments of the proposed architecture and discuss the related tests, experimentations, and evaluations of the architecture.


Author(s):  
Audrey NANGUE ◽  
◽  
Elie FUTE TAGNE ◽  
Emmanuel TONYE

The success of the mission assigned to a Wireless Sensor Network (WSN) depends heavily on the cooperation between the nodes of this network. Indeed, given the vulnerability of wireless sensor networks to attack, some entities may engage in malicious behavior aimed at undermining the proper functioning of the network. As a result, the selection of reliable nodes for task execution becomes a necessity for the network. To improve the cooperation and security of wireless sensor networks, the use of Trust Management Systems (TMS) is increasingly recommended due to their low resource consumption. The various existing trust management systems differ in their methods of estimating trust value. The existing ones are very rigid and not very accurate. In this paper, we propose a robust and accurate method (RATES) to compute direct and indirect trust between the network nodes. In RATES model, to compute the direct trust, we improve the Bayesian formula by applying the chaining of trust values, a local reward, a local penalty and a flexible global penalty based on the variation of successful interactions, failures and misbehaviors frequency. RATES thus manages to obtain a direct trust value that is accurate and representative of the node behavior in the network. In addition, we introduce the establishment of a simple confidence interval to filter out biased recommendations sent by malicious nodes to disrupt the estimation of a node's indirect trust. Mathematical theoretical analysis and evaluation of the simulation results show the best performance of our approach for detecting on-off attacks, bad-mouthing attacks and persistent attacks compared to the other existing approaches.


2011 ◽  
Vol 1 (2) ◽  
pp. 55-65 ◽  
Author(s):  
K.H. Kwong ◽  
H.G. Goh ◽  
B. Stephen ◽  
T.T. Wu ◽  
K. Sasloglou ◽  
...  

2010 ◽  
Vol 33 (9) ◽  
pp. 1086-1093 ◽  
Author(s):  
Javier Lopez ◽  
Rodrigo Roman ◽  
Isaac Agudo ◽  
Carmen Fernandez-Gago

Author(s):  
Alan McGibney ◽  
Suzanne Lesecq ◽  
Claire Guyon-Gardeux ◽  
Safietou R. Thior ◽  
Davide Pusceddu ◽  
...  

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 3024 ◽  
Author(s):  
Carolina Del-Valle-Soto ◽  
Ramiro Velázquez ◽  
Leonardo J. Valdivia ◽  
Nicola Ivan Giannoccaro ◽  
Paolo Visconti

The continuous evolution of the Internet of Things (IoT) makes it possible to connect everyday objects to networks in order to monitor physical and environmental conditions, which is made possible due to wireless sensor networks (WSN) that enable the transfer of data. However, it has also brought about many challenges that need to be addressed, such as excess energy consumption. Accordingly, this paper presents and analyzes wireless network energy models using five different communication protocols: Ad Hoc On-Demand Distance Vector (AODV), Multi-Parent Hierarchical (MPH), Dynamic Source Routing (DSR), Low Energy Adaptive Clustering Hierarchy (LEACH) and Zigbee Tree Routing (ZTR). First, a series of metrics are defined to establish a comparison and determine which protocol exhibits the best energy consumption performance. Then, simulations are performed and the results are compared with real scenarios. The energy analysis is conducted with three proposed sleeping algorithms: Modified Sleeping Crown (MSC), Timer Sleeping Algorithm (TSA), and Local Energy Information (LEI). Thereafter, the proposed algorithms are compared by virtue of two widely used wireless technologies, namely Zigbee and WiFi. Indeed, the results suggest that Zigbee has a better energy performance than WiFi, but less redundancy in the topology links, and this study favors the analysis with the simulation of protocols with different nature. The tested scenario is implemented into a university campus to show a real network running.


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