Energy consumption is a major issue in Wireless Sensor Networks (WSNs), as nodes are powered by chemical batteries with an upper bounded lifetime. Estimating the lifetime of batteries is a difficult task, as it depends on several factors, such as operating temperatures and discharge rates. Analytical battery models can be used for estimating both the battery lifetime and the voltage behavior over time. Still, available models usually do not consider the impact of operating temperatures on the battery behavior. The target of this work is to extend the widely-used Kinetic Battery Model (KiBaM) to include the effect of temperature on the battery behavior. The proposed Temperature-Dependent KiBaM (T-KiBaM) is able to handle operating temperatures, providing better estimates for the battery lifetime and voltage behavior. The performed experimental validation shows that T-KiBaM achieves an average accuracy error smaller than 0.33%, when estimating the lifetime of Ni-MH batteries for different temperature conditions. In addition, T-KiBaM significantly improves the original KiBaM voltage model. The proposed model can be easily adapted to handle other battery technologies, enabling the consideration of different WSN deployments.

Abstract:The operation of Wireless Sensor Networks (WSNs) is subject to multiple constraints, among which one of the most critical is available energy. Sensor nodes are typically powered by electrochemical batteries. The stored energy in battery devices is easily influenced by the operating temperature and the discharge current values. Therefore, it becomes difficult to estimate their voltage/charge behavior over time, which are relevant variables for the implementation of energy-aware policies. Nowadays, there are hardware and/or software approaches that can provide information about the battery operating conditions. However, this type of hardware-based approach increases the battery production cost, which may impair its use for sensor node implementations. The objective of this work is to propose a software-based approach to estimate both the state of charge and the voltage of batteries in WSN nodes based on the use of a temperature-dependent analytical battery model. The achieved results demonstrate the feasibility of using embedded analytical battery models to estimate the lifetime of batteries, without affecting the tasks performed by the WSN nodes.

Summary Mobile agents are examples of distributed systems which may dispute for the same resources on their hosts. Treating such concurrency adequately is essential, particularly in real‐time applications. Due to intrinsic time restrictions, mobile agents in real‐time environments are only considered successful if they fulfill their mission by respecting their deadlines. Scheduling algorithms with different policies can be applied in these scenarios. However, the efficiency of these algorithms may deviate according to the missions and deadlines of the mobile agents. Also, these algorithms can be preemptive, or calculate the order of executions without interrupting an ongoing task. In this paper, we propose a middleware extension to the JADE platform that brings real‐time scheduling support with preemption to mobile agents. The proposed solution uses best effort scheduling policy in the context of soft real‐time applications. We evaluate the performance of the scheduling algorithms, with and without preemption, and the impact of the selected algorithms on mission fulfillment. The results of the proposed middleware showed a great improvement on mission accomplishment when compared to the FIFO algorithm provided by the JADE platform.

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Energy limitation is one of the major bottlenecks during the operation of many emerging applications, such as electric vehicles, water and gas meters and a number of sensors used in the context of the Internet of Things and cyberphysical systems. Energy harvesting techniques have arisen as a promising solution to minimize the energy issues found in these types of application domains. In energy harvesting systems, a critical challenge is the need to use battery models capable of accurately estimating both the input and output power of batteries. This article proposes a temperature-dependent analytical battery model capable of estimating some output quantitiesfor example, state of charge, voltage and lifetimeof batteries that use energy harvesting technologies. This model was validated by comparing its analytical results with a dataset called the Randomized Battery Usage Data Set, which is available at the data repository of the National Aeronautics and Space Administration (NASA) website. It is also presented a proof-of-concept application, demonstrating that the use of these technologies can serve as an effective means to extend the operating time of batteries, resulting in significant benefits for a number of applications. K E Y W O R D Swireless sensor network, battery modelling, energy harvesting, thermal effect

This work studies the modeling Socio-Spatial Segregation. This phenomenon occurs, most often, unintentionally in some cities, especially in large population centers. Multiagent simulation is adopted using the NetLogo software. From an existing template available in the models library, we developed some examples with variations for the basic case of social classes' segregation.