scheduling

In recent years, we are constantly facing the increase of Internet of Things devices in the fields of health, agriculture, industry and other applications. The using of these networks will increase by improving the quality of service. Usually, data are generated heterogeneously by IoT’s sensors at different times with   different sizes and priorities, and none of previous works in this field do not consider all these modes together; on the other hand, these studies either have worked on the queue model and priority package selection, or on the channel access methods and its prioritization. Therefore, in our proposed model in this paper, with the aim of improving quality of service, thight scheduling of delay-sensitive packets, and also avoiding starvation for lower priority packets, we consider an architecture including two levels of prioritization. In the first level, the packet is selected for sending based on a type of dynamic prioritization and relative to its delay deadline field. Since in this paper the application of Internet of Things in health care is considered, the sensors generate data both of modes, periodically (fixed) and on demand (critical), and therefore, two queue models D /G/1 and M/G/1 are used. In addition, the second level includes channel prioritization. At this level, by using a control channel and setting the waiting time of the sending node for listening to the channel, we have sending the packet through the control channel. Furthermore, a consolidated channel hopping model has been used for nodes to use the available bandwidth. Also, a buffer to keep the information of the last connection of the node helps to send and receive faster with less number of switching. After simulating and comparing this model with the 802.11 standard and similar media access protocols, significant improvements in increasing the packet delivery rate, operational throughput, and also reducing the end-to-end delay are showed.

Nowadays, smart systems are being widely used in combats because there exist a lot of applications for these systems which usually depend on sensors. Matching sensors to weapons and threats or in short sensor-weapon/threat assignment is a major challenge in modern wars and is classified as a complex resource assignment optimization problem. Consequently, the result of modern wars highly depends on effective usage of sensors and weapons. In this paper, the sensor-weapon/threat assignments and their scheduling problem have been formulated. The object of this model is maximizing the destruction of threats in a given time horizon subject to operational constraints. This model has been solved using CPLEX. Furthermore, a metaheuristic algorithm based on GRASP is developed. Finally, using a set of randomly generated test instances, the results of all developed solution approaches are reported and compared.

In the combat management systems, mathematical and optimization models have significant impact to find good solutions for fire allocation and scheduling problems. In this paper, a linear integer programming model has been developed for a fire allocation and scheduling problems the aim of which was to maximize the expected value of the target distruction and strategic realms protection and efficient use of weapons by considering the operational constraints for weapon allocation. Since the available operation research solvers can not find the optimal solution of this problem in the large scale sizes, two metaheuristics based on genetic algorithm and chaotic particle swarm optimization was developed. Finally, based on randomnly generated test instances and extensive computation results, the performance of the developed algorithms was evaluated. The computational experiments reveal that the developed chaotic particle swarm optimization algorithm is more efficient especially in the limited and short CPU run time