Multi-Objective Path Optimization in Fog Architectures Using the Particle Swarm Optimization Approach.

IoT systems can successfully employ wireless sensor networks (WSNs) for data gathering and fog/edge computing for processing collected data and providing services. The proximity of edge devices to sensors improves latency, whereas cloud assets provide higher computational power when needed. Fog networks include various heterogeneous fog nodes and end-devices, some of which are mobile, such as vehicles, smartwatches, and cell phones, while others are static, such as traffic cameras. Therefore, some nodes in the fog network can be randomly organized, forming a self-organizing ad hoc structure. Moreover, fog nodes can have different resource constraints, such as energy, security, computational power, and latency. Therefore, two major problems arise in fog networks: ensuring optimal service (application) placement and determining the optimal path between the user end-device and the fog node that provides the services. Both problems require a simple and lightweight method that can rapidly identify a good solution using the constrained resources available in the fog nodes. In this paper, a novel two-stage multi-objective path optimization method is proposed that optimizes the data routing path between the end-device and fog node(s). A particle swarm optimization (PSO) method is used to determine the Pareto Frontier of alternative data paths, and then the analytical hierarchy process (AHP) is used to choose the best path alternative according to the application-specific preference matrix. The results show that the proposed method works with a wide range of objective functions that can be easily expanded. Moreover, the proposed method provides a whole set of alternative solutions and evaluates each of them, allowing us to choose the second- or third-best alternative if the first one is not suitable for some reason.

Distributed Agent-Based Orchestrator Model for Fog Computing.

Fog computing is an extension of cloud computing that provides computing services closer to user end-devices at the network edge. One of the challenging topics in fog networks is the placement of tasks on fog nodes to obtain the best performance and resource usage. The process of mapping tasks for resource-constrained devices is known as the service or fog application placement problem (SPP, FAPP). The highly dynamic fog infrastructures with mobile user end-devices and constantly changing fog nodes resources (e.g., battery life, security level) require distributed/decentralized service placement (orchestration) algorithms to ensure better resilience, scalability, and optimal real-time performance. However, recently proposed service placement algorithms rarely support user end-device mobility, constantly changing the resource availability of fog nodes and the ability to recover from fog node failures at the same time. In this article, we propose a distributed agent-based orchestrator model capable of flexible service provisioning in a dynamic fog computing environment by considering the constraints on the central processing unit (CPU), memory, battery level, and security level of fog nodes. Distributing the decision-making to multiple orchestrator fog nodes instead of relying on the mapping of a single central entity helps to spread the load and increase scalability and, most importantly, resilience. The prototype system based on the proposed orchestrator model was implemented and tested with real hardware. The results show that the proposed model is efficient in terms of response latency and computational overhead, which are minimal compared to the placement algorithm itself. The research confirms that the proposed orchestrator approach is suitable for various fog network applications when scalability, mobility, and fault tolerance must be guaranteed.

Older Adults-Potential Users of Technologies.

Background and objective: The successful adoption of technology is becoming increasingly important to functional independence and successful ageing in place. A better understanding of technology usage amongst older people may help to direct future interventions aimed at improving their healthcare. We aimed to obtain the first data regarding technology use, including gerontechnologies, represented by fall detectors, from older adults in Lithuania. Material and methods: The research was carried out in the framework of the project Smart Gerontechnology for Healthy Ageing, which involved assessing the use of technologies and the readiness to use gerontechnologies, as represented by fall detectors. A total of 375 individuals that were more than 60 years of age were enrolled in the study. The self-reporting questionnaires were completed by geriatric in-patients, hospitalized in the geriatric department, and also by community-dwelling older adults. Results: Geriatric in-patients' use of computers and the internet was associated with age (every year of age decreased the probability of computer and internet use by 0.9-times) and a positive attitude towards new technologies-this predictor increased the use of a computer by six-times in comparison with people who did not have such an attitude. Sex and education had no influence on computer use for geriatric in-patients. For community-dwelling older adults, the use of computers and internet was associated with age, education (a university education increased the use of computers and the internet by four times), and a positive attitude towards technologies. Conclusions: Lithuanian older women in the study used computers, the internet, and cell phones equally with men. Increasing age was a strong negative predictor of technology use. A positive attitude to new technologies was a strong positive predictor of technology use. Most geriatric patients and community-dwelling older adults were ready to use technologies that permit ageing in place.