Internet-of-Things (IoT) shortest path algorithms and communication case studies for maintaining connectivity in harsh environements

Research on the shortest path in networks to maintain connectivity in the Internet of Things (IoT) remains an important issue for determining minimal routes, especially in terms of time and distance, between two devices at distinct points (i.e., nodes) of the network. Many constraints exist for IoT smart devices for transmitting a large amount of information and data, such as limited resources, energy, and time consumption, as well as the potential for overwhelmed communication traffic. Several algorithms were designed and implemented to address these problems that can be simulated and considered as information message passing. The search space is often modeled by a graph, where each node corresponds to a location of a smart device, and the edges represent the paths or links that carry messages, while the absence of a path between two nodes designates a communication breakdown or obstacle. Existing pathfinding algorithms are incorporated in applications, such as Google Maps, rescue people, video games, online packet routing, and rescue applications used in harsh environments. For these latter scenarios, the infrastructure for various technologies of communication becomes vulnerable and dysfunctional, so maintaining connectivity and finding the shortest path becomes a priority. Our goal is to remedy this problem by taking advantage of modernized peer-to-peer wireless technologies, such as Wi-Fi Direct, which can be improved through autonomous wireless technology kits like Lopy 4 of Pycom, and through two alternatives of moving devices (nodes) or service drones. This paper investigates several shortest path algorithms and identifies three case studies to maintain connectivity in harsh environments

Metaheuristics as a solving approach for the infrared heating in the thermoforming process

Thermoforming process is a technique widely used in the plastic industry. This process involves three stages: i) sheet heating, ii) forming, and iii) cooling and solidification. A crucial problem occurring during this process is the non-uniform distribution of the energy flux intercepted by the plastic sheet during infrared heating. To circumvent the occurring of this phenomenon, the approach that we followed is first model the heat process as an optimization problem. Then, two meta-heuristic algorithms, simulated annealing and migrating bird optimization algorithms, are applied and compared so as to meet as much as possible the corresponding objective function. An experimental study is then conducted to evaluate the quality of the solutions produced by both algorithms. Results are presented and analysed

Uniform distribution with MBO method of the infrared radiative energy in the thermoforming process of an ABS sheet

This paper addresses the problem of distributing uniformly infrared radiative energy intercepted by a thermoplastic sheet surface during the infrared radiation transmitted by an oven with convection and conduction considerations. After discretizing this problem, we proposed an objective function that captures the uniform distribution of the radiative energy. With this approximation scheme, the corresponding problem appears to be nothing else than a variant of a quadratic assignment problem. Accordingly, we designed and applied a migrating bird optimization based algorithm (MBO for short), in order to minimize the corresponding objective function. To evaluate this approach we conducted a numerical experimental study

Numerically optimizing the distribution of the infrared radiative energy on a surface of a thermoplastic sheet surface

This paper addresses the problem of distributing uniformly the energy flux intercepted by a thermoplastic sheet surface during the infrared radiation. To do so, we discretized this problem and then formulated it as an integer linear programming problem, for which we applied two meta-heuristic algorithms namely the simulated annealing algorithm (SA) and harmony search algorithm (HSA), in order to minimize the corresponding objective function. The results produced by the numerical study we conducted on the performance of both algorithms are presented and discussed