Abstract
This paper proposes a new real-time communication scheme for 802.11e wireless networks. This scheme is called Group Sequential Communication (GSC). The GSC improves the efficiency of the Hybrid Coordination Function Controlled Channel Access (HCCA) mechanism by reducing the protocol overheads of the 802.11e amendment. The GSC approach eliminates the polling scheme used in traditional scheduling algorithm, by means of a virtual token passing procedure among members of the real-time group to whom is granted a high-priority and sequential access to communication medium. In order to improve the reliability of the proposed scheme, it is also proposed an error recovery mechanism based on block acknowledgment. The GSC was implemented in network simulator software and the performance results were compared to HCCA scheme, showing the efficient of the proposed approach when dealing with traditional industrial communication scenarios.

Abstract
Wireless Sensor Networks (WSNs) have been widely considered as a promising solution to support different types of applications on industrial environments. Many of these applications impose strict dependability requirements, since a system failure may result in economic losses, or damage for human life or to the environment. The absence of an effective approach enabling the dependability evaluation of WSNs prevents system designers to forecast these type of scenarios or to optimize decisions regarding the criticality of the devices, network topology, levels of redundancy and network robustness that minimize the occurrence of faults. To bridge the gap between research achievements and industrial development, we present in this paper a framework to support the dependability evaluation of WSNs based on the automated generation of analytical dependability models from high level AADL (Architecture Analysis and Description Language) architecture models. The main objective of this framework is to relieve the end user from a deep knowledge of dependability modeling techniques and evaluation methods, focusing on their knowledge of the behavior and structure of the system.

Abstract

Abstract
Wireless sensor networks (WSNs) can be used to support monitoring activities in a wide range of applications and communication environments. Its usage in extreme conditions, in what concerns pressure, temperature, and humidity, must be carefully assessed before the network deployment. In particular, the temperature variations have a direct impact upon the behavior of WSNs through the batteries of sensor nodes. These electrochemical devices are highly susceptible to temperature variations, which modifies the offered effective charge capacity. In this context, it is difficult to estimate the behavior of batteries over time, impairing the extraction of relevant information for energy-aware approaches. Such information, particularly battery state of charge, voltage, and lifetime, is often used by WSN simulators to predict the communication behavior in different scenarios. Nevertheless, WSN simulators generally use simplistic battery models, causing significant deviations in simulation results when compared with actualWSN deployments. This paper describes the implementation of the Temperature-Dependent Kinetic Battery Model (T-KiBaM) in the Castalia simulator, which enables a considerable improvement of the accuracy of simulations in communication environments with different temperature conditions. An experimental assessment has been performed with temperature variations over time to validate the usage of the T-KiBaM battery model. The experimental results indicate that the T-KiBaM model is quite accurate when estimating battery behavior under both different temperature set points and different temperature variations.

Abstract
Wireless sensor networks have been considered as an effective solution to a wide range of applications due to their prominent characteristics concerning information retrieving and distributed processing. When visual information can be also retrieved by sensor nodes, applications acquire a more comprehensive perception of monitored environments, fostering the creation of wireless visual sensor networks. As such networks are being more often considered for critical monitoring and control applications, usually related to catastrophic situation prevention, security enhancement and crises management, fault tolerance becomes a major expected service for visual sensor networks. A way to address this issue is to evaluate the system dependability through quantitative attributes (e.g., reliability and availability), which require a proper modeling strategy to describe the system behavior. That way, in this paper, we propose a methodology to model and evaluate the dependability of wireless visual sensor networks using Fault Tree Analysis and Markov Chains. The proposed modeling strategy considers hardware, battery, link and coverage failures, besides considering routing protocols on the network communication behavior. The methodology is automated by a framework developed and integrated with the SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) tool. The achieved results show that this methodology is useful to compare different network implementations and the corresponding dependability, enabling the uncovering of potentially weak points in the network behavior.

Abstract
Modern cities are subject to a lot of periodic or unexpected critical events, which may have different monitoring and control requirements according to the expected impacts on people safety and urban mobility. When multiple monitoring and automation systems are deployed, adaptive wireless sensor networks may adjust sensing and transmission configurations according to the detected events, optimizing the network overall operation. In this context, mobile sinks come as an effective way to enhance monitoring performance in smart city environments. However, practical issues related to the available roads and traffic load should be considered, allowing the computation of the best final positions and movement paths for each sink. Therefore, this paper proposes algorithms to compute dynamic sinks movement in reactive wireless sensor networks, supporting efficient adaptation to event-based monitoring in smart cities.