Nowadays, high-quality service becomes pivotal to ensure higher customer satisfaction for banking sectors. Through developmental methods, it helps them align their employees and resources to meet their strategic objectives. The challenge is that there is no automatic, strong and intelligent way, which helped the bank sectors to assess employee’s performance after receiving training while keeping scores. In this paper, we design and implement an effective tool based on IoT for assessing employees’ performance by applying the right evaluation metrics. Besides, we aim at determining the necessary information so that the manager has clear strategy that improves performance expectations and keeps it high. We develop also an evaluation model that can take incoming performance data of deployed sensors with work environments, performing data analysis of various employers and determine their performance while customizing the multi-criteria decision-making. The experimental results show that IoT-based tool generates remarkably higher performance than existing tools in the literature for nearly all training programs and all decision-making managers.

The use of the Internet of Things (IoT) in healthcare is increasing significantly, bringing high-quality health services, but it still generates massive data with massive energy consumption. Due to the limited resources of fog servers and their impact on limiting the time needed for health data analysis tasks, the need to handle this problem in a fast way has become a necessity. To address this issue, many optimization and IoT-based approaches have been proposed. In this paper, a dynamic and adaptive healthcare service deployment controller using hybrid bio-inspired multi-agents is proposed. This method offers optimal energy costs and maintains the highest possible performance for fog cloud computing. At first, IGWO (Improved Grey Wolf Optimization) is used to initialize the deployment process using the nearest available fog servers. Then, an efficient energy-saving task deployment was achieved through Particle Swarm Optimization (PSO) to reduce energy consumption, increase rewards across multiple fog servers, and improve task deployment. Finally, to ensure continuous control of underloaded and overloaded servers, the neighborhood multi-agent coordination model is developed to manage healthcare services between the fog servers. The developed approach is implemented in the iFogSim simulator and various evaluation metrics are used to evaluate the effectiveness of the suggested approach. The simulation outcome proved that the suggested technique provides has better performance than other existing approaches.

Broadcasting has a main importance in Wireless Sensor Networks (WSNs). Effectively, the sink node has to collect periodically, data from the environment supervised by sensors. To perform this operation, it sends requests to all nodes. Furthermore, WSNs have a dynamic behaviour due to their evolution. At any time, a node can be retrieved from the network due to an exhausting energy or a node problem. In fact, WSNs are prone to failure such as software or hardware malfunctioning, exhaustion of energy, wireless interference and environmental hazards. Thus, an appropriate broadcasting method should take into consideration this aspect and uses the less possible amount of energy to accomplish the task. In this paper, a robust tree-based scheme is proposed which is called Robust Tree Broadcasting (RTB). The new scheme has a load-balanced behaviour which induces an efficient use of energy. In addition, RTB has a high-quality fault tolerant performance.

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