In critical sectors like healthcare, banking, and surveillance, Internet of Things (IoT) applications require reliable communication protocols that ensure high performance under energy constraints. These sectors demand both reliable data delivery and high throughput to support real-time operations, making the balance between these metrics crucial, particularly when devices operate at varying distances. This article provides valuable insights for selecting reliable transport protocols in IoT deployments, especially in scenarios where trade-offs between reliability, throughput, and energy consumption must be carefully managed. We examine the performance impact of distance on the Simple Automatic ReQuest (SARQ) protocol compared to traditional UDP and TCP in IoT environments. Through experimental analysis across varying distances, we evaluated three key performance metrics: throughput, reliability (packet delivery ratio), and energy consumption. Our findings show that distance significantly affects performance, even in short ranges. UDP experiences about 20% decline in throughput and packet delivery ratio as distance increases from 10 to 50 meters, while SARQ maintains consistent performance, demonstrating its robustness to distance-related challenge, and TCP maintains high reliability (100%) but suffers from lower throughput and higher energy consumption, even for short distances. In terms of energy efficiency, SARQ’s consumption lies between UDP’s minimal requirements and TCP’s higher consumption. These characteristics make SARQ particularly suitable for IoT applications requiring balanced performance, offering a practical solution that combines reliable communication with reasonable throughput and energy efficiency.

Due to the surge in IoT devices, numerous protocols have been proposed to meet their needs. UDP is commonly used for IoT because of its simplicity, low latency, minimal overhead, and low energy consumption. In contrast, TCP is less suitable for IoT due to its higher resource demands, complexity, and greater energy consumption, which are challenging for small devices with limited resources. Therefore, often, reliability is provided by upper-layer protocols, mainly by the applications themselves. In fact, classical IoT applications such as sensing, identification and actuating generate multiple copies of data due to the hardware redundancy and periodic updates. Examples of such applications include, agricultural, environmental, traffic, and healthcare monitoring. UDP and TCP protocols may be inadequate for IoT applications requiring reliable, real-time communication. In critical situations like battlefields or disasters, sensors might only send a few messages before being destroyed. Therefore, the network itself must ensure reliability in these scenarios. Moreover, smart textiles are more and more integrating sensory devices and require reliable transmissions. In fact, with the very stringent resource constraints on one hand, and the requirements to respect the Specific Absorption Rate (SAR) of human bodies, on the other hand, re-transmissions and power must be kept at their lowest levels. In this paper, we propose a simple Automatic Request (SARQ) transport protocol that uses acknowledgments and a retransmission mechanism. Through a realistic simulation setup using Contiki motes in cooja simulator, we show that our protocol exhibits slightly higher energy consumption and resource requirements than UDP, but far less than TCP. Conversely, we show that our protocol exhibits 99% Packet Delivery Ratio (PDR), while UDP and TCP exhibit 74% and 99% PDR, respectively.

In collaborative projects context, collaborators are usually partitioned into working teams to perform collaborative tasks in a shared environment or platform. Indeed, one of the grouping criteria most used is based on the collaborators’ skills and on forming similar teams according to the skills of their members which is a costly and a complex problem. In this paper, we address the optimization problem of forming automatically working teams by minimizing the knowledge-difference cost among formed teams. The knowledge index of each group depends on the skills of their collaborators’ members in the form of a sum of collaborators’ fuzzy rating indexes. The proposed algorithm is called improved particle swarm optimization with multi-parent order crossover (IPSOMPOX). To evaluate the performance of the proposed algorithm, we apply it on a real-life DBLP dataset with different numbers of teams and collaborators. The proposed algorithm is compared with the standard PSO and has proved better performance as shown in our results.

Abstract Risk-aware Business Process Management (R-BPM) has been addressed in research since more than a decade. However, the integration of the two independent research streams is still ongoing with a lack of research focusing on the conceptual modeling perspective. Such an integration results in an increased meta-model complexity and a higher entry barrier for modelers in creating conceptual models and for addressees of the models in comprehending them. Multi-view modeling can reduce this complexity by providing multiple interdependent viewpoints that, all together, represent a complex system. Each viewpoint only covers those concepts that are necessary to separate the different concerns of stakeholders. However, adopting multi-view modeling discloses a number of challenges particularly related to managing consistency which is threatened by semantic and syntactic overlaps between the viewpoints. Moreover, usability and efficiency of multi-view modeling have never been systematically evaluated. This paper reports on the conceptualization, implementation, and empirical evaluation of e-BPRIM, a multi-view modeling extension of the Business Process-Risk Management-Integrated Method (BPRIM). The findings of our research contribute to theory by showing, that multi-view modeling outperforms diagram-oriented modeling by means of usability and efficiency of modeling, and quality of models. Moreover, the developed modeling tool is openly available, allowing its adoption and use in R-BPM practice. Eventually, the detailed presentation of the conceptualization serves as a blueprint for other researchers aiming to harness multi-view modeling.

Fault detection and localization is a central element in the management of network failures. It allows inferring the exact failure of a network from all the observed symptoms. Because failures in network systems are inevitable, detection and diagnosis efficiency is important for the stability, coherence and performance of a communication system. In this paper, we propose a unified approach to diagnosing nodes faults in a computer network. In our approach, the problems of choosing probing stations and probes, classically separated, are solved jointly in order to obtain, with lower complexity, minimum sets of probes and probing stations capable of detecting and localizing any failed node. The optimality of our new method has been tested through the simulation of the network configuration.

The components of a computer network are vulnerable to a variety of faults such as a link break or node failure. In order to prevent faulty components from impeding the execution of network applications, it is very important to detect, locate and repair faulty components. Existing approaches to fault localization in communication systems use active or passive measures. Active measures involve additional traffic for network monitoring. On the other hand, the passive measures use the existing end-to-end data in the network in order to extract the necessary information and thus introduces no additional traffic in the network. In this paper, we propose an end-to-end approach that uses passive measures for fault isolation in communication networks and formulate the issue of fault isolation as an optimization problem. In fact, we used the Boolean Particle Swarm Optimization algorithm inferring the best node(s) to be tested, with the objective of minimizing the expected cost for all the faulty elements in the network. The performance of the proposed schemes is evaluated through an intensive simulation of different network scenarios.

The Internet is known for its ability to scale and adapt its routes to every change. These routes are made by every Autonomous System (AS) peering with each other as neighbors with the Border Gateway Protocol (BGP). Unfortunately, this amount of trust, between all these Autonomous Systems allowing protocols like the BGP to function properly, is not found in the Physical world between logistic service providers. The Physical Internet, however, with its standardized PI-Containers and Internet-derived protocols, has every promising aspect to face this challenge. We propose in this paper a new routing approach based on the PI-BGP (Physical Internet-Border Gateway Protocol) equivalent of the BGP in the Internet. We developed this new protocol to offer a new perspective to tackle the problem of routing the PI-containers in the Physical Internet.

To be competitive, transportation systems must be able to analyze and to evaluate, in real-time, critical differences between the short-term planned actions and the actual performed actions generating states of undesirable or unacceptable risk. We propose a method for monitoring the dynamic evolution of risk in the operational flow of a transportation system. It consists in an approach assessing risk associated to delays affecting the transportation operations. We use the FMEA (Failure Modes and Effects Analysis) around failure scenarios rather than failure modes, and we evaluate risk using probability and cost. A scenario probability is estimated dynamically in discrete points based on events occurrences during the process execution. The proposed approach useful to transportation managers is based on consistent and meaningful risk evaluation criteria to facilitate cost-based decisions during execution. The implementation of this method is performed by monitoring a container delivery process facing delays risks.

In this paper, we introduce a fleet supervision system used to monitor on-road evolutions of commercial transport vehicles. This monitoring is accomplished by the automatic dispatch, during crossing of a tracking location, of information issued from the localization system on the vehicle. This information can be sent to a server through a direct Transmission Control Protocol/Internet Protocol connection in a general packet radio service network (GPRS) or through a satellite network. The information is provided by the system to some allowed users via an Internet website. The fleet supervision system also enables analyzing messages sent by vehicles to identify differences between real data and planned data. We are more particularly interested in the diagnostics of delays, which can take place during deliveries. In fact, while crossing already fixed tracking locations, these delays are detected as early as possible, and their consequences are predicted so that corrective or preventive actions are undertaken as soon as possible.