The mineral industry relies on comminution processes, such as crushing and milling, to reduce ore size for further treatment. Crushers play a central role in this stage, yet their performance is strongly influenced by the lithology of the incoming ore, as different rock types exhibit distinct mechanical properties. Despite its importance, the literature on lithology characterization in crushing circuits is scarce, with most efforts focused on milling processes through the use of machine vision and few works addressing lithology characterization in crushing circuits. To bridge this gap, we propose a novel data-driven soft sensor for estimating the probability distribution of multiclass lithology in real time for crushing circuits. The method combines measurements of crusher motor current and rotational speed with signal processing and lightweight machine learning algorithms, ensuring deployment feasibility in resource-constrained environments, such as industrial Programmable Logic Controllers (PLCs). Model evaluation was conducted using Kullback–Leibler (KL) divergence and cosine similarity between true and predicted lithology distributions. The Extra Trees-based soft sensor achieved the best performance, with an average KL divergence of 0.065 and a cosine similarity of 0.98, demonstrating the effectiveness of this approach for lithology characterization in crushing circuits. • Soft sensor estimates real-time lithology distributions in crushing circuits. • Soft labels model geological uncertainty, replacing unreliable hard labels. • Teacher–student setup: RF generates soft labels; regressors learn distributions.

Abstract Industrial safety inspections, particularly in the mining sector and the industrial Internet of Things (IoT), increasingly rely on multi-robot systems to perform safety inspections and mapping in remote, confined, and hazardous areas inaccessible to humans. However, conventional multi-robot systems often depend on a central robot for data management, leading to vulnerabilities in fault tolerance and data replication during inspections. This centralized approach to inspection data management can result in single points of failure and potential trust disputes among stakeholders, including government agencies, industry representatives, and local communities. To address these challenges, this work proposes a system that combines blockchain technology with a Proof-of-Authority (PoA) consensus mechanism to improve the reliability and performance of multi-robot inspections. By leveraging blockchain’s inherent properties of fault tolerance, data replication, transparency, and immutability, our system mitigates the risks associated with centralized control in multi-robot operations in all safety inspection pipelines. We implemented and evaluated our proposed system using CoppeliaSIM, a widely used robot simulator, to validate its feasibility. The evaluation included multiple scenarios: normal operation, node failure, network latency simulations, and a comparison with a centralized system. The results show that our solution enhances fault tolerance and ensures data integrity in multi-robot systems. While the decentralized system has a higher average transaction latency (approximately 1.9 seconds) compared to the centralized system (1.42 milliseconds), it avoids the vulnerabilities linked to single points of failure. This approach not only enhances the reliability of industrial safety inspections but also provides secure data sharing among diverse stakeholders in the industrial safety ecosystem.

Central Bank Digital Currency (CBDC) is the digital form of a country’s fiat currency, based on Decentralized Ledger Technology (DLT). The increasing interest in CBDCs raises the concern of how to verify user’s identities for achieving regulatory demands, while maintaining user privacy in the CBDC Network. In this paper we explore Self-Sovereign Identities (SSI) as a way for users to have full control over credentials issued by trusted Financial Institutions in a CBDC Governance Framework. These credentials can then be used to generate privacy-preserving proofs by their holders to authenticate them in different service providers in the CBDC Network.

There has been growing attention to the realm of Self-Sovereign Identities (SSI) in the past few years, with significant effort being put into the development of standards and specifications, such as Decentralized Identifiers (DIDs), to be in conformity with essential identity prerequisites such as decentralization, privacy and interoperability. However, the portability of DIDs is still an under-researched topic, even though it is a requirement of great importance in order to guarantee user autonomy amidst the numerous implementations being developed by the industry. In this paper, we explore the concept of portability of DIDs, highlighting how the current standards and protocols don't fully address this major point. We also define key requirements for addressing this feature, and discuss some major concerns on security and privacy that may emerge with the development of DID portability schemes.

Real-world applications in healthcare and supply chain domains produce, exchange, and share data in a multi-stakeholder environment. Data owners want to control their data and privacy in such settings. On the other hand, data consumers demand methods to understand when, how, and who produced the data. These requirements necessitate data governance frameworks that guarantee data provenance, privacy protection, and consent management. We introduce a decentralized data governance framework based on blockchain technology and proxy re-encryption to let data owners control and track their data through privacy-enhancing and consent management mechanisms. Besides, our framework allows the data consumers to understand data lineage through a blockchain-based provenance mechanism. We have used Digital e-prescription as the use case since it has multiple stakeholders and sensitive data while enabling the medical fraternity to manage patients’ prescription data, involving patients as data owners, doctors, and pharmacists as data consumers. Our proof-of-concept implementation and evaluation results based on CosmWasm and pyUmbral PRE show that the proposed decentralized system guarantees transparency, privacy, and trust with minimal overhead.

Abstract It is essential for agents to work together with others to accomplish common objectives, without pre-programmed coordination rules or previous knowledge of the current teammates, a challenge known as ad-hoc teamwork. In these systems, an agent estimates the algorithm of others in an on-line manner in order to decide its own actions for effective teamwork. A common approach is to assume a set of possible types and parameters for teammates, reducing the problem into estimating parameters and calculating distributions over types. Meanwhile, agents often must coordinate in a decentralised fashion to complete tasks that are displaced in an environment (e.g., in foraging, de-mining, rescue or fire control), where each member autonomously chooses which task to perform. By harnessing this knowledge, better estimation techniques can be developed. Hence, we present On-line Estimators for Ad-hoc Task Execution (OEATE), a novel algorithm for teammates’ type and parameter estimation in decentralised task execution. We show theoretically that our algorithm can converge to perfect estimations, under some assumptions, as the number of tasks increases. Additionally, we run experiments for a diverse configuration set in the level-based foraging domain over full and partial observability, and in a “capture the prey” game. We obtain a lower error in parameter and type estimation than previous approaches and better performance in the number of completed tasks for some cases. In fact, we evaluate a variety of scenarios via the increasing number of agents, scenario sizes, number of items, and number of types, showing that we can overcome previous works in most cases considering the estimation process, besides robustness to an increasing number of types and even to an erroneous set of potential types.

Smart contracts allow application developers to automate business processes through a decentralized computation architecture.Contemporary blockchain platforms such as Ethereum and Hyperledger Fabric offer support for smart contracts through consensus mechanisms such as Proof-of-Work (PoW) or other types of transaction validation and ordering services.This article exploits smart contracts in the Byzantine Fault Tolerant (BFT) blockchain platforms.In particular, we explore Tendermint and Hyperledger Besu, BFT blockchain platforms, and apply them to a decentralized e-prescription case study to evaluate their effectiveness.We adopt Hyperledger Besu and Tendermint in this research, given that both are BFT-based blockchains.Also, it is noteworthy that smart contracts in BFT blockchain platforms such as Tendermint are not well established and not widely adopted yet.Our article empirically evaluates the performance of smart contracts in Tendermint and Hyperledger Besu using a decentralized medical prescription case study and compares their results with Ethereum, a PoW blockchain.Our results demonstrate that BFT blockchain platforms are efficient for multistakeholder applications such as e-prescription and supply chains.To the best of our knowledge, this is the first study investigating the implementation of smart contracts in BFT blockchain platforms, such as Tendermint and Hyperledger Besu.

Floods are a source of anxiety for the people living in the city of São Paulo, Brazil. Every year, the city suffers a financial loss of more than US$ 35 million caused by damage to property, and countless lives are lost as a result of the flooding. Strategies such as Disaster Management can reduce and prevent flash floods and also assist their victims. Moreover, social networks such as Twitter can play a crucial role in offering assistance at the Disaster Management response stage because they produce a massive number of localized geo messages, which can help identify the flood victims. We argue that the mining of social network opinion raises a severe challenge since the Machine Learning (ML) algorithms cannot reflect the context of the messages in-depth, and thus this needs to be improved by combining textual data with contextual data. In this study, we combine multiple sources of weather data with the social network posts to obtain a Situational Awareness (SAW) of flash floods and hence be able to support the Disaster Management Response stage in São Paulo. We show that by combining meteorological with social network data, we can identify the flood victims with a greater degree of precision. The model that was designed for identifying the victims of flooding in São Paulo achieved a 87.69% rate of precision. Furthermore, contextual data inclusion led to a 22.8% increase in SAW of urban floods from tweets and contextual data, which shows that multimodal approaches are more promising than unimodal strategies. Finally, this work adopts a novel approach first by demonstrating that simply applying social network posts to ML strategies is not an efficient method of obtaining a SAW of flash floods. Second, we proved through this study that empirical strategies for establishing potential flood areas are more effective than the adoption of geostatistical approaches (Semivariogram) because the Semivariogram technique is more suitable for understanding scenarios that have not had any prior human interference or damage (e.g., when locating mineral reserves). Thus, particularly in the case of São Paulo, when the trash is disposed of close to drainage systems, this causes clogging of gutters and hence leads to floods.

Real-world applications in healthcare and supply chain domains produce, exchange, and share data in a multi-stakeholder environment. Data owners want to control their data and privacy in such settings. On the other hand, data consumers demand methods to understand when, how, and who produced the data. These requirements necessitate data governance frameworks that guarantee data provenance, privacy protection, consent management, and selective disclosure. We introduce a decentralized data governance framework based on blockchain technology, proxy re-encryption, and Boneh, Boyen, and Shacham (BBS) signatures to let data owners control, selectively share and track their data through privacy-enhancing, consent management, and selective disclosure mechanisms. Besides, our framework allows the data consumers to understand data lineage through a blockchain-based provenance mechanism. We use Digital medical e-prescription as the use case since it handles sensitive data in a multi-stakeholder environment while showing how the medical community can manage patients’ sensitive prescription data, involving patients as data owners, and doctors, and pharmacists as data consumers. Our proof-of-concept implementation and evaluation results based on CosmWasm, Hyperledger Besu, Ethereum, pyUmbral PRE, and BBS signatures show that the proposed decentralized system is platform-agnostic, scalable and guarantees a higher degree of transparency, privacy, and trust with minimal overhead.

Electronic prescription (e-Prescription) is a digital way to manage medical prescriptions and reduce inconsistencies in the communication between doctors, patients, and pharmacies. Smart contracts allow the automation of tasks and business rules in a decentralized architecture (i.e., without the need for an intermediary or central authority). Platforms such as Ethereum and Hyperledger Fabric support smart contracts development through a consensus mechanism such as Proof-of-Work or another criterion among the network's participating nodes. This paper explores Tendermint, a Byzantine Fault Tolerant (BFT) based consensus mechanism that has not yet been widely adopted for smart contracts platforms. We apply our devised model to the healthcare application domain, more precisely in the field of e-prescription, and our results demonstrate that smart contracts can be implemented on a BFT-based platform. To the best of our knowledge, this is the first study investigating the implementation of smart contracts in a BFT-based platform such as Tendermint. We exploit this domain as there can exist some conflicting interests of profit-taking. For example, pharmacists can increase the medication dosage above the one prescribed by doctors for profit-taking. Such a scenario can occur particularly in countries where healthcare is free of charge and offered as a public service (e.g., Brazil). We show that smart contracts in BFT-based blockchain can help in solving problems in these application scenarios. Finally, our two key contributions in this paper are two-fold: (i) exploit smart-contracts in BFT-based platforms where they (smart contracts) are not very established yet in the blockchain domain; (ii) provide a smart-contract-based e-prescription solution to reduce the costs (no need for a central authority) and scams particularly in countries where the medical service is free and public.

One of the main challenges in urban development faced by large cities is related to traffic jam. Despite increasing efforts to maximize the vehicle flow in large cities, to provide greater accuracy to estimate the traffic jam and to maximize the flow of vehicles in the transport infrastructure, without increasing the overhead of information on the control-related network, still consist in issues to be investigated. Therefore, using artificial intelligence method, we propose a solution of inter-vehicle communication for estimating the congestion level to maximize the vehicle traffic flow in the transport system, called TRAFFIC. For this, we modeled an ensemble of classifiers to estimate the congestion level using TRAFFIC. Hence, the ensemble classification is used as an input to the proposed dissemination mechanism, through which information is propagated between the vehicles. By comparing TRAFFIC with other studies in the literature, our solution has advanced the state of the art with new contributions as follows: (i) increase in the success rate for estimating the traffic congestion level; (ii) reduction in travel time, fuel consumption and CO2 emission of the vehicle; and (iii) high coverage rate with higher propagation of the message, maintaining a low packet transmission rate.

The development of new smart objects for the sensing and actuation of a given place or environment led both the academia and industry to research and propose new protocols and intelligent systems to support such objects. One of the systems that has been gaining prominence is the smart residential environments. In this context, homes are equipped with smart objects to manage the living resources. However, managing such objects in residential environments requires data contextualization, i.e. collecting data from heterogeneous devices and actuate on the environment through context information generated from such data. To solve this problem, we propose an intelligent decision system based on the fog computing paradigm, which provides an efficient management of residential applications. The proposed solution is evaluated both in simulated and real environments. When compared with other studies from the literature in a simulated environment, the proposed solution shows a higher success rate with a lower delay in the decision-making process, higher efficiency in information dissemination with a lower overhead in the communication infrastructure, and increased robustness in processing with a lower power consumption. These results are also observed when considering a real environment evaluation.

This paper proposes a fault-tolerant indoor localization system that employs Recurrent Neural Networks (RNNs) for the localization task. A decision module is designed to detect failures and this is responsible for the allocation of RNNs that are suitable for each situation. As well as the fault-tolerant system, several architectures and models for RNNs are exploited in the system: Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM) and Simple RNN. The system uses as inputs a collection of Wi-Fi Received Signal Strength Indication (RSSI) signals, and the RNN classifies the position of an agent on the basis of this collection. A fault-tolerant mechanism has been designed to handle two types of failures: (i) momentary failure, and (ii) permanent failure. The results show that the RNNs are suitable for tackling the problem and that the whole system is reliable when employed for a series of failures.

Vehicular ad hoc networks (<?tex type="bfontsize" fvalue="7" svalue="7"?>VANETs<?tex type="efontsize"?>) have unique features and rely on vehicle-to-vehicle (V<?tex type="bfontsize" fvalue="7" svalue="7"?>2<?tex type="efontsize"?>V) communication to mitigate adversities in traffic dynamics management and to support drivers providing safety alerts. Congestions, originating from an incident, frequently endanger traffic and, consequently, cause all kinds of losses. In this scenario, the paper herein proposes <?tex type="bfontsize" fvalue="7" svalue="7"?>eFIRST<?tex type="efontsize"?>, a robust solution to autonomous detection of the current congestion condition in order to disseminate safety alerts and to reduce problems with the interruption of traffic in a section of the highway. The approach is supported only by V<?tex type="bfontsize" fvalue="7" svalue="7"?>2<?tex type="efontsize"?>V communication and the local neighborhood identification records, which are brought together in a fuzzy strategy and in the adaptive adjustment of the transmission signal power. The estimate of local traffic conditions establishes the dynamic reach of transmission for the vehicle, supporting the connective maintenance. In these circumstances, the drivers receive an alert emitted soon enough for the proper response action. The results during simulations show how the elaborated solution leads to minimized delays, with low communication overload, besides relevantly mapping the congest levels and efficiently providing the event coverage to satisfactory propagation distances inside the area of interest for the dissemination. Promptly, the alert finds vehicles far from the traffic accident located at nearly 1/6 from the evaluated extension. In accordance with the intelligent protocols, this evaluation contributes providing grants for the ratification of fuzzy approximation as an adaptive strategy to fluctuations in vehicular density in different traffic basis.

The growth in many countries of the population in need of healthcare and with reduced mobility in many countries shows the demand for the development of assistive technologies to cater for this public, especially when they require home treatment after being discharged from the hospital. To this end, interactive applications on mobile devices are often integrated into intelligent environments. Such environments usually have limited resources, which are not capable of processing great volumes of data and can expend much energy due to devices being in communication to a cloud. Some approaches have tried to minimize these problems by using fog microdatacenter networks to provide high computational capabilities. However, full outsourcing of the data analysis to a microfog can generate a reduced level of accuracy and adaptability. In this work, we propose a healthcare system that uses data offloading to increase performance in an IoT-based microfog, providing resources and improving health monitoring. The main challenge of the proposed system is to provide high data processing with low latency in an environment with limited resources. Therefore, the main contribution of this work is to design an offloading algorithm to ensure resource provision in a microfog and synchronize the complexity of data processing through a healthcare environment architecture. We validated and evaluated the system using two interactive applications of individualized monitoring: (1) recognition of people using images and (2) fall detection using the combination of sensors (accelerometer and gyroscope) on a smartwatch and smartphone. Our system improves by 54% and 15% on the processing time of the user recognition and Fall Decision applications, respectively. In addition, it showed promising results, notably (a) high accuracy in identifying individuals, as well as detecting their mobility; and (b) efficiency when implemented in devices with scarce resources.

In order to control the population of vectors responsible for the transmission of diseases, such as chikungunya, dengue and huánglónging (HLB), with greater effectiveness and less impact to the environment, we had the dissemination of lines of research focused on the applied biological control, which consists of the release of natural predators from these vectors. However, when this task is performed manually by humans, they are susceptible to the risks presented by the environment, whether it is difficult to access the affected area or the presence of venomous animals, such as snakes and hornets. Given this scenario, and following the development of Unmanned Aerial Vehicles (UAVs), there is a proposal to use them to automate this task. However, external factors, such as wind, can influence the movement of these vehicles and consequently the effectiveness in performing certain tasks. Thus, this work had as purpose the study about the influence of the wind under free falling objects, which, in the scenario of this work, are the glasses containing the natural enemies of a certain pest, and the proposition of a strategy to be taken by the aircraft, in order to ensure the effectiveness of the task at hand. During the tests performed in a simulated environment, it was observed that the mathematical modeling of the wind and the strategy taken by the aircraft at run time were able to achieve good results, allowing the cup to be deposited very close to the pre-defined location.

Typically, the electric power companies employ a group of power meter readers to collect data on the customers energy consumption. This task is usually carried out manually, which can lead to high cost and errors, causing financial losses. Some approaches have tried to minimize these problems, using strategies such as discovering the minimal route or relying on vehicles to perform the readings. However, errors in the manual readings can occur and vehicles suffer from congestion and high fuel and maintenance costs. In this work, we go further and propose an architecture to the Automatic Meter Reading (AMR) system using Unmanned Aerial Vehicles (UAV). The main challenge of the solution is to design a robust and lightweight protocol that is capable of dealing with wireless communication collisions. Therefore, the main contribution of this work is the design of a new protocol to ensure wireless communication from UAV to the power meters. We validated and evaluated the architecture in an urban scenario, with results showing a decrease of time and distance when compared to other approaches. We also evaluated the system proposed with Linear Flight Plan, the Ant Colony Optimization and Guided Local Search metaheuristic. Our mechanism attains an improvement of 98% in reducing the message collisions and reducing the energy consumption of the power meters.

Wireless Sensor Networks (WSN) are often composed of a wide range of sensor nodes, which may vary greatly in their type of hardware platform, as well as their sensing and mobility capabilities. The ability of a sensor to move is a particularly important feature in dynamic scenarios, since mobile sensors can fill the gap caused by the failures of those that are stationary, and thus extend the lifetime and span of a WSN. However, there remains the problems of intersensory communication in the field when integrating mobile sensors into the Sensor Web in dynamic scenarios since it does not have the necessary interoperability for automatically managing the different types of sensor data and activities involved in such scenarios. This paper tackles this problem by adopting an approach consisting of an enhanced messaging protocol and a dynamic sensor management component. In validating the proposal, two different realistic scenarios were simulated to evaluate the achieved results in terms of interoperability and performance. The results provided evidence that the proposal complies with Sensor Web standards as well as being suitable for near real-time data publication, and is thus able to support applications in dynamic scenarios.

Currently, there is an increasing number of patients that are treated in-home, mainly in countries such as Japan, USA and Europe. As well as this, the number of elderly people has increased significantly in the last 15 years and these people are often treated in-home and at times enter into a critical situation that may require help (e.g. when facing an accident, or becoming depressed). Advances in ubiquitous computing and the Internet of Things (IoT) have provided efficient and cheap equipments that include wireless communication and cameras, such as smartphones or embedded devices like Raspberry Pi. Embedded computing enables the deployment of Health Smart Homes (HSH) that can enhance in-home medical treatment. The use of camera and image processing on IoT is still an application that has not been fully explored in the literature, especially in the context of HSH. Although use of images has been widely exploited to address issues such as safety and surveillance in the house, they have been little employed to assist patients and/or elderly people as part of the home-care systems. In our view, these images can help nurses or caregivers to assist patients in need of timely help, and the implementation of this application can be extremely easy and cheap when aided by IoT technologies. This article discusses the use of patient images and emotional detection to assist patients and elderly people within an in-home healthcare context. We also discuss the existing literature and show that most of the studies in this area do not make use of images for the purpose of monitoring patients. In addition, there are few studies that take into account the patient's emotional state, which is crucial for them to be able to recover from a disease. Finally, we outline our prototype which runs on multiple computing platforms and show results that demonstrate the feasibility of our approach.

Cities with a large number of people are currently facing urban mobility problems, especially the problem of traffic congestions. This not only has an adverse effect on the economy of the city, but also impairs the quality of life of its citizens. One measure that can be adopted to mitigate these problems is the use of systems that help identify, reduce, and/or avoid these traffic jams, such as intelligent transport systems. In this context, we propose an intelligent traffic information system called UCONDES, which is based on inter-vehicle communications and can be applied to detect and reduce congestion in urban centers. Simulation results shows that, when compared to original vehicular mobility trace, our solution reduces the average trip time, and the overall CO2 emission and fuel consumption. More specifically, the average travel time for drivers was reduced by approximately 26%, resulting in a reduction of fuel consumption by 23% and the CO2 emission by 25%.

In this work, we study the design of a Wireless Sensor Network based on the Small world models. By modeling a sensor network with small world features, it is possible to decrease the average path length to interconnect the sink and sensor nodes. The goal of this work is to analysis the Newman & Watts and Kleinberg small world models in wireless sensor networks. The simulation results showed that both models are able to create a sensor network with small world features, however, the Newman & Watts model has better results regarding the path length, clustering coefficient and data communication latency. On the other hand, the Kleinberg model reduces more the energy consumption during data communication.

This work proposes an intelligent decision system for a residential infrastructure based on wireless sensors and actuator networks, called ResiDI. ResiDI is equipped with battery-powered nodes to ensure that they are deployable anywhere in the house without the need for wiring, drilling or any pre-existing infrastructure. The key intelligence of ResiDI is distributed in the decider nodes, which are able to make decisions locally without the need to send traffic from the sensor nodes to the sink. The network intelligence core is based on a neural network that seeks to improve the accuracy of the decision-making, together with a temporal correlation mechanism that is targeted at reducing the energy consumption. When compared with an approach adopted in the literature, the results show that ResiDI is efficient in different scenarios in all evaluations performed.

The use of pesticides in agriculture is essential to maintain the quality of large-scale production. The spraying of these products by using aircraft speeds up the process and prevents compacting of the soil. However, adverse weather conditions (e.g. The speed and direction of the wind) can impair the effectiveness of the spraying of pesticides in a target crop field. Thus, there is a risk that the pesticide can drift to neighboring crop fields. It is believed that a large amount of all the pesticide used in the world drifts outside of the target crop field and only a small amount is effective in controlling pests. However, with increased precision in the spraying, it is possible to reduce the amount of pesticide used and improve the quality of agricultural products as well as mitigate the risk of environmental damage. With this objective, this paper proposes a methodology based on Particle Swarm Optimization (PSO) for the fine-tuning of control rules during the spraying of pesticides in crop fields. This methodology can be employed with speed and efficiency and achieve good results by taking account of the weather conditions reported by a Wireless Sensor Network (WSN). In this scenario, the UAV becomes a mobile node of the WSN that is able to make personalized decisions for each crop field. The experiments that were carried out show that the optimization methodology proposed is able to reduce the drift of pesticides by fine-tuning of control rules.

In this research, we propose a protocol to create different logical topologies that consider the same physical network topology for the data routing problem in Heterogeneous Sensor Networks (HSNs). The HSN in question has two types of sensor nodes, called L-Sensors (sensor with Low hardware capabilities) and H-Sensors (sensors with High hardware capabilities). The proposed protocol creates different topologies, each designed to meet different application requirements, and use a fraction of the links between H-sensors. In addition, each topology has a tradeoff between latency and energy consumption during data communication. The simulation results show that our routing algorithm based on different topologies reduces energy consumption compared to a literature protocol.

Vehicular Ad hoc Network (VANET) is a promising communication technology suitable for vehicular mobile networks. Represent networks of singular features, wherein the data dissemination is fundamental. The literature is plentiful in protocols, usually specific to address individual issues in well-defined scenarios. This work efforts are concentrated, mainly, to examine operating settings in protocols like AID, DBRS, and ADDHV for disseminating messages. A benchmarking explores strategies that address challenges such as network partitioning and the broadcast storm problem, which undertake the dissemination. The results of a set of metrics obtained in different vehicular traffic schemes complete the discussion held. Considerations for answers in coverage, delay, rate of delivery, broadcast, and packet loss support this initiative and motivate the development of an adaptive solution to fluctuations in carrier density.

Vehicular Ad hoc Networks (VANETs) have attracted the attention of the research community recently as they have opened up a myriad of on the road applications and increased their potential by providing accident-free and intelligent transport systems. The envisaged applications, as well as some inherent VANET characteristics make data dissemination an essential service and a challenging task in these networks. The existing solutions for data dissemination do not effectively address broadcast storm and network partition problems when considered together. To tackle these problems, we propose a novel GEographical Data Dissemination of Alert Information and Aware of Network Partition (GEDDAI-NP), which eliminates the broadcast storm and maximizes data dissemination capabilities across network partitions with short delays and low overhead. The simulation results show that the data dissemination performed by GEDDAI-NP provides better efficiency than other algorithms, outperforming them in different scenarios in all the evaluations carried out.

A wireless sensor network (WSN) is designed to perform event detection, data collection, and reporting such data to a monitoring station. In many cases, it is necessary to know the location of sensor nodes to relate the detection of the event at a specific location. However, the geographical location of the sensor nodes in most applications can only be set after their deposition in the area of interest. Therefore, for the sensor nodes to know their location, it is necessary to use specific algorithms to solve the problem of discovering the geographical position of sensor nodes. This work addresses the problem of 3D localization in WSNs using an Unmanned Aerial Vehicle (UAV). The UAV is equipped with GPS and it flies over the monitoring area broadcasting its geographical position. Thus, the sensor nodes are able to estimate their geographical position without being equipped with GPS receiver. Simulation results show that using an UAV leads to a smaller error in the calculation of geographic location when compared to solutions presented in the literature.

The application of pesticides and fertilizers in agricultural areas is of prime importance for crop yields. The use of aircrafts is becoming increasingly common in carrying out this task mainly because of its speed and effectiveness in the spraying operation. However, some factors may reduce the yield, or even cause damage (e.g. crop areas not covered in the spraying process, overlapping spraying of crop areas, applying pesticides on the outer edge of the crop). Climatic conditions, such as the intensity and direction of the wind while spraying add further complexity to the control problem. In this paper, we describe an architecture based on unmanned aerial vehicles (UAVs) which can be employed to implement a control loop for agricultural applications where UAVs are responsible for spraying chemicals on crops. The process of applying the chemicals is controlled by means of the feedback obtained from the wireless sensor network (WSN) deployed on the crop field. The aim of this solution is to support short delays in the control loop so that the spraying UAV can process the information from the sensors. We evaluate an algorithm to adjust the UAV route under changes in wind intensity and direction. Moreover, we evaluate the impact of the number of communication messages between the UAV and the WSN. Results show that the adjustment of the route based on the feedback information from the sensors could minimize the waste of pesticides.

Component-based software engineering has recently emerged as a promising solution to the development of system-level software. Unfortunately, current approaches are limited to specific platforms and domains. This lack of generality is particularly problematic as it prevents knowledge sharing and generally drives development costs up. In the past, we have developed a generic approach to component-based software engineering for system-level software called OpenCom. In this paper, we present OpenComL an instantiation of OpenCom to Linux environments and show how it can be profiled to meet a range of system-level software in Linux environments. For this, we demonstrate its application to constructing a programmable router platform and a middleware for parallel environments.

Although a growing number of devices have the support for parallel processing, parallelism is not widely exploited, as it should be. This can be illustrated by the fact that all Apple desktops and laptops are currently supplied with one or more dual- core processors. Despite this, parallel programming in popular languages such as Java is not widely encouraged and often only recommended as a last resort. In addition, it is likely that the next generation parallel applications will have to operate within a diverse range of heterogeneous devices ranging from supercomputers to sensors. This paper proposes a flexible component-based middleware that aims at facilitating the construction of deadlock-free parallel and concurrent applications for heterogeneous environments. The middleware is particularly targeted to tailor applications to the target need and environment. For our prototyping, we implemented plu- gins that are capable of deploying JCSP (CSP library for Java programmers) and OCCam-pi processes. Both JCSP and OCCam-pi make use of the CSP disciplines. The CSP (Communicating Sequential Processes) paradigm helps us to avoid concurrency problems such as deadlocks. It should be stressed that there is no bias towards these languages as the proposed middleware is highly extensible.

Component-based software structuring principles are now commonplace at the application level; but componentization is far less established when it comes to building low-level systems software. Although there have been pioneering efforts in applying componentization to systems-building, these efforts have tended to target specific application domains (e.g., embedded systems, operating systems, communications systems, programmable networking environments, or middleware platforms). They also tend to be targeted at specific deployment environments (e.g., standard personal computer (PC) environments, network processors, or microcontrollers). The disadvantage of this narrow targeting is that it fails to maximize the genericity and abstraction potential of the component approach. In this article, we argue for the benefits and feasibility of a generic yet tailorable approach to component-based systems-building that offers a uniform programming model that is applicable in a wide range of systems-oriented target domains and deployment environments. The component model, called OpenCom , is supported by a reflective runtime architecture that is itself built from components. After describing OpenCom and evaluating its performance and overhead characteristics, we present and evaluate two case studies of systems we have built using OpenCom technology, thus illustrating its benefits and its general applicability.

Electronic commerce applications are lacking a bilateral negotiation model which provides the bargaining between two participants (supplier and consumer) in order to buy and sell goods. This paper proposes a negotiation protocol between two participants, as well as the similarity measures which were implemented to find a similar product when a specific one could not be found. The proposed protocol follows the bilateral model approved by the OMG. The negotiation model is composed of selling and buying Grasshopper mobile agents, which negotiate between themselves in order to get the best deal. The negotiation of the price is based on the Kasbah model. The catalogs in the model are implemented in XML to provide the interoperability among different systems. A simple prototype has been implemented to verify the viability of this concept.