Abstract
Real-time monitoring applications may generate delay sensitive traffic that is expected to be delivered within a firm delay boundary in order to be useful. In this context, a previous work proposed an End-to-End Delay (EED) estimation mechanism for Wireless Sensor Networks (WSNs) to preview potential useless packets, and to early discard them in order to save processing and energy resources. Such estimation mechanism accounts delays using timers that make use of an Exponentially Weighted Moving Average (EWMA) function where the smoothing factor is a constant defined prior to the WSN deployment. Later experiments showed that, in order to enhance the estimation results, such smoothing factor should be defined as a function of the network load. The current work proposes an optimization of the previous estimation mechanism that works by evaluating the network load and by adapting the smoothing factor of the EWMA function accordingly. Results show that this optimization leads to a more accurate EED estimation for different network loads.

Abstract
Wireless Sensor Networks (WSNs) can be deployed using available hardware and software. The Contiki is an operative system compatible with a wide range of WSN hardware. A Contiki development environment named InstantContiki is also available and includes the Cooja simulator, useful to test WSN simulation scenarios prior to their deployment. Cooja can provide realistic results since it uses the full Contiki's source code and some motes can be emulated at the hardware level. However this implies extending the simulation runtime, which is heightened since the Cooja is single threaded, i.e, it makes use of a single core per instant of time, not taking advantage of the current multi-core processors. This chapter presents a framework to automate the configuration and execution of Cooja simulations. When a multi-core processor is available, this framework runs multiple simultaneous Cooja instances to reduce simulations runtime in exchange of higher CPU load and RAM usage.

Abstract
WSN can be deployed using widely available hardware and software solutions. The Contiki is an open source operative system compatible with a wide range of WSN hardware. A Contiki development environment named InstantContiki is also available and includes the Cooja simulation tool, useful for the simulation of WSN scenarios, prior to their deployment. This simulation tool can provide realistic results since it uses the full Contiki's source code and some motes can be emulated at the hardware level. However, the Cooja simulator uses one process per simulation, not taking advantage of multiple core processors. In this paper we propose a framework to automate the execution of simulations of multiple scenarios and configurations in Cooja. When a multiple cores processor is available, this framework can run multiple simultaneous Cooja instances, taking advantage of processing resources and contributing to reduce the total simulation runtime.

Abstract
Critical monitoring applications can use wireless sensor networks to transport delay sensitive data. This data may demand bounded delays in order to be considered useful by the receiver. In these cases, an accurate and real-time estimation of the end-to-end delay could be used to anticipate the data usefulness prior to sending it. A novel real-time and end-to-end delay estimation mechanism is proposed in this paper, which considers processing times and two new RPL metrics. Results show that our proposal is more accurate than the ETT-based solution for delay estimation, and it does not significantly degrade the network performance.

Abstract
Real-time monitoring applications deployed in Low-power and Lossy Networks may generate flows sensitive to delay, where the information is useful for the destination only if it is received within a strict delay boundary. Data packets that will likely miss the application deadline could be discarded during their routing through the network or even be not transmitted at all, thus contributing for a better usage of the network resources. This paper presents RA-EEDEM, a set of modifications made to RPL that improve the End-to-End Delay (EED) estimation accuracy. The RA-EEDEM modifications include changes to the RPL metrics and to its Objective Function (OF). The results show that RA-EEDEM improves the accuracy of EED estimation while minimizing its impact on the average EED and Packet Reception Ratio (PRR).

Abstract
Real-time monitoring applications may be used in a wireless sensor network (WSN) and may generate packet flows with strict quality of service requirements in terms of delay, jitter, or packet loss. When strict delays are imposed from source to destination, the packets must be delivered at the destination within an end-to-end delay (EED) hard limit in order to be considered useful. Since the WSN nodes are scarce both in processing and energy resources, it is desirable that they only transport useful data, as this contributes to enhance the overall network performance and to improve energy efficiency. In this paper, we propose a novel cross-layer admission control (CLAC) mechanism to enhance the network performance and increase energy efficiency of a WSN, by avoiding the transmission of potentially useless packets. The CLAC mechanism uses an estimation technique to preview packets EED, and decides to forward a packet only if it is expected to meet the EED deadline defined by the application, dropping it otherwise. The results obtained show that CLAC enhances the network performance by increasing the useful packet delivery ratio in high network loads and improves the energy efficiency in every network load.