This work-in-progress presents a strategy to implement software-defined wireless networks, particularly, to support IoT applications that require managing messages with different transmission priorities, e.g., voice, video and text data. These network implementations use IEEE 802.11e as the link protocol, and add a network controller that implements the Oblivious Forwarding Protocol and manage three transmission priorities for the messages. The performance and behavior of this network implementation was evaluated using simulations in NS-3. Preliminary results show that the network can provide communication support to IoT systems that involve up to 30 devices. The results also indicate that there is ample space to move forward this limit, e.g., by tuning the network parameters.

Every year, natural disasters cause important losses in terms of human lives and property. For example, unexpected floods and wild fires affecting urban areas are more frequent every day due to climate change. When it happens, most of the physical and service infrastructure in the affected area collapse or is eventually shut down to protect civilians. This negatively impacts on the gathering of information required by first response organizations to count on awareness situation that helps them make decisions. Trying to contribute to reduce the information and communication limitations during natural disaster response processes, this paper proposes the use of digital twin systems that help first responders obtain situation awareness and make better and on-time decisions. The proposed structure of these systems is based on both the blackboard pattern to collect, manage and share information, and a crowdsourcing approach to gather information from civilians.

production battery in the oil industry is a set of equipment and facilities used in the initial stages of crude oil processing at an oilfield. At various stages of operation, it may be necessary to restrict or increase its processing capacity, meaning that the gross volume to be received must be adjusted. In remote areas where connectivity and infrastructure may be limited, it is necessary for an operator to approach the well in person to make manual adjustments directly to the pumping system. Proper planning of the well route for adjusting regimes directly impacts the operating costs of the oilfield. In this work in progress, a mixed integer linear programming model for this problem is proposed, by considering the production, operation, and modification restrictions of the pumping regime of each well. Preliminary results are presented, which show that the model can be used in practice.

Internet of Things (IoT) is a technology that serves as the basis for smart environments. The ever-expanding set of applications that provide intelligence in different scenarios is continually growing and expanding. From precision agriculture to the development of sustainable smart cities, the need to have a communications infrastructure that allows the connectivity of sensors, actuators, and users becomes essential. In what is known as the “last mile,” wireless networks are the fastest growing group. For these to be operational, it is necessary to connect them to the Internet through gateways. These gateways handle different communication technologies and are therefore expensive nodes to install and maintain. Defining their location and their ability to “route” messages involves a combinatorial optimization problem. In this work in progress, a multiobjective integer linear programming model (minimizing number of gateways, used energy, and transmission time) is presented. To the best of our knowledge, previous literature does not include any optimization framework encompassing all these three objectives together.

The recent growth of social networking and sensing applications has enabled people to perform crowd computing. In turn, this activity has opened several opportunities to address people's needs in various application areas, such as tourism, security, entertainment and emergency response. However, the design of these applications has also brought several challenges to software designers. Since users of these systems interact among them in heterogeneous physical scenarios, their interactions should be formally considered in the design of these applications in order to determine whether or not the services embedded in the system are suitable to support those interactions. The lack of guidelines to address this modeling issue not only jeopardizes the suitability of these applications, but also implies the services provided by the system can be evaluated only after implementation, which is clearly risky and expensive. This article proposes an initial modeling language that allows software designers to address this challenge through the representation of the interaction among users of a system. The interaction model represented using the proposed language allows designers to evaluate, at the design time, the information flow and the availability of interaction supporting services in the system. The usability and usefulness of the proposal are shown using a running example.

The widespread availability of smartphones with wireless communication and geo-positioning capabilities has open new opportunities to services for casual passersby, e.g. the delivery of advertising when they visit a certain area. Typically such messages are delivered to cellular phones, consuming thus part of the energy of these devices. Since these devices are constrained in battery-energy, disseminating messages using little energy represents a challenge for designers of this type of solutions. This paper presents a study based on simulations that intends to identify good strategies to irradiate messages collaboratively, minimizing the energy consumption in the devices involved in the information exchange. The simulation results show that the best irradiation strategy will depend on the number of devices in the area to be irradiated. Moreover, such a strategy will have more influence if the devices are dispersed. These findings should help designers of mobile collaborative applications based on messages irradiation, to identify the best dissemination strategy depending on the current network context.

Energy consumption is a main research issue in wireless sensor networks; and particularly in those where nodes collaborate to reach a goal. This article explores the energy consumption in mobile devices participating in a human-based wireless sensor network. Specifically, the paper proposes the use of a message predictor to help detect and reduce the number of unnecessary control packets delivered by the nodes as a way to keep updated the network topology. In order to evaluate this proposal, the Optimized Link State Routing protocol was modified to add a message predictor between the routing and the network layers. Eleven simulations were performed using a particular setting. The preliminary results indicate the use of the message predictor can help reduce considerably the nodes energy consumption without affecting the routing capability of the protocol. Although these results are still preliminary, they are highly encouraging.

VHF radio systems commonly used to support search and rescue activities after disasters limit the flow of information among response teams deployed in the field. It generates islands of information that jeopardizes the coordination and effectiveness of the response activities. This article proposes a communication model that uses opportunistic networks and real-time messages delivery to help address such limitation. Since the communication model is computable, it is possible to diagnose the flow of information expected for a particular work scenario. The diagnose results allow identifying elements that could help improve the information flow in such scenario. This proposal allows first responders to address the stated problem during the phases of preparedness, response and recovery from a disaster.

We consider the problem of power-aware quality of service (QoS) control for soft real-time embedded systems. Applications can have time-varying and scarcely known resource requirements, and can be activated and terminated at any time. However, they have the capability to switch among a discrete set of operation modes with different QoS levels and resource requirements. In addition, the platform provides resources with power-scaling capabilities and may be subject to power constraints. We present a QoS control architecture achieving optimum trade-offs between overall QoS and power consumption of the system, based on two nested control loops. The external one decides dynamically the optimum configuration for the system, in terms of application QoS modes and resource power modes, while the internal one modulates the resource allocations on a job by job basis, so as to respect timing constraints. We demonstrate the effectiveness of the approach by extensive simulations with trace data of real multimedia applications.

When dealing with soft real-time tasks with highly variable execution times in open systems, an approach that is becoming popular is to use feedback scheduling techniques to dynamically adapt the bandwidth reserved to each task. According to this model, each task is assigned an adaptive reservation, with a variable budget and a constant period. The response times of the jobs of the task are monitored and if different from expected (i.e. much larger or much shorter than the task relative deadline), a feedback control law adjusts the reservation budget accordingly. However, when the feedback law algorithm demands an increase of the reservation budget, the system must run a schedulability test to check if there is enough spare bandwidth to accommodate such increase. The schedulability test must be very efficient, as it may be performed at each budget update, i.e. potentially at each instance of a task. In this paper, we tackle the problem of performing an efficient on-line schedulability test for Resource Reservation systems implemented through the Sporadic Server on Fixed Priority scheduling. We propose five different tests with different complexity and performance. In particular, we propose a novel on-line test, called Spare Pot algorithm which shows a good cost/performance ratio.

The paper addresses the problem of on-line scheduling of mandatory/reward-based systems in which tasks have a hard real-time mandatory part and an optional part with a non-decreasing reward function associated to its execution. Four methods, generically called SH, are proposed. They are based on the detection of singularities, special instants that appear along the execution of the system. By applying some heuristic rules, the singularities and some of the following slots are used to process optional parts. To the best of the authors' knowledge, papers published up to now on the subject of reward maximization require the functions to be continuously differentiable. Moreover, they cannot be executed on-line. On the contrary, the only requirement of the SH methods is that the functions are computable at every instant. After the system is proved to be schedulable by any of the available exact test off-line techniques, SH methods can be executed on-line. Their performance is evaluated using simulations performed on a synthetic set of tasks proposed in one of the outstanding papers on the subject and on sets of tasks randomly generated. The results are analysed and explained. The main conclusionis that , in all cases, the SH methods outperform the Best Incremental Return, often used as a yardstick.

This paper presents a probabilistic model of a client/server multimedia system based on the theory of Markov processes. The server provides different qualities of service (hard real-time, soft real-time and best-effort) and is operated using a new scheduling concept based on a hybrid deterministic/probabilistic analysis. The model also provides answers to some fundamental engineering design questions related to disk price/performance tradeoffs.

When applying the Rate Monotonic discipline to schedule a set of periodic preemptible real-time tasks, the scheduler may be able to distinguish only a limited number of priority levels. This is common in control applications using low cost embedded controllers. If the number of tasks to be scheduled is larger than the number of distinguishable levels, the set of tasks must be partitioned in a set of priority classes. RM can be used only to arbitrate conflicts between tasks of different classes. In this paper a method to determine the minimum number of priority levels necessary to schedule the set of tasks is formally proved and its complexity analysed. Finally, a systematic method to obtain all the possible partitions with the minimum number of classes, resembling the Quine's method to minimize Boolean functions, is also given.