The proliferation of heterogeneous 5G/6G services and the emergence of multi-tenant deployments have made isolation-preserving RAN slicing a central requirement for shared infrastructures. This paper studies multi-tenant RAN slicing where multiple Mobile Virtual Network Operators (MVNOs) compete for a common pool of radio and edge-compute resources and must maximize long-term economic performance while preserving slice isolation. We propose a two-timescale framework that couples (i) prediction-driven, per-cell Long Time Slot (LTS) reservation with an isolation-aware congestion/pressure cost that coordinates competing MVNO demands, and (ii) Short Time Slot (STS) per-user allocation modeled as a Markov decision process and solved via an energy-aware Soft Actor–Critic (SAC) policy. This design separates strategic capacity planning from fast, stochastic user-level control, while retaining tractability under dynamic traffic, mobility, and channel uncertainty. Extensive simulations across small- and large-scale deployments show that the proposed approach improves MVNO profit per cell per LTS by up to 26% over representative baselines, while maintaining robust isolation under asymmetric demand and rival-tenant growth.

The deployment of fog computing resources in industrial internet of things (IIoT) is essential to support time-sensitive applications. To utilize resources efficiently, a brand-new request dispatcher is required to sit between the IIoT devices and the pool of fog resources. The need for such a dispatcher stems from the challenges specific to these systems. Firstly, fog-enabled IIoT systems are highly dynamic and distributed. Second, fog nodes are typically power and resource limited. Finally, many IIoT applications feature critical time-sensitivity, referred to as timeliness, and cannot tolerate response delay beyond a specific threshold. This paper proposes an efficient dispatching algorithm to minimize energy consumption and deadline misses while keeping the system stability at a satisfactory level. We leverage Lyapunov Optimization technique to tackle the problem and handle the system dynamics. We perform extensive simulations to verify the effectiveness of the proposed method and provide sensitivity, scalability and model parameter analysis. The simulation results prove the superiority of the proposed method over the state-of-the-art method up to 22% and 10% in terms of average deadline misses and energy consumption, respectively. Further, we perform practical experiments to prove the validity of the proposed method in a real testbed.

Fog computing introduces a distributed processing capability close to end users. The proximity of computing to end users leads to lower service time and bandwidth requirements. Energy consumption is a matter of concern in such a system with a large number of computing nodes. Renewable energy sources can be utilized to lessen the burden on the main power grid and reduce the carbon footprint, but due to fluctuations, the effective utilization of renewable energy sources needs proper resource management. In this article, we deal with properly managing the resources in a fog environment where the fog nodes are equipped with onsite renewable energy. This article aims to design an efficient mechanism to dynamically dispatch requests among computing nodes and scale frequency and modulation level, based on the current workload and the availability of renewable energy sources, to minimize the service time while keeping the renewable energy utilization and stability at a satisfactory level. We state the problem as the design of a controller for a system with time-varying nonlinear state equations. Accordingly, we borrow the Lyapunov optimization technique from the control theory to design the request dispatching mechanism and prove its asymptotic optimality. We perform extensive simulations to evaluate the effectiveness of the proposed method. The simulation results demonstrate that our proposed method outperforms the naive time-aware baseline scheme up to 26% and 39%, respectively, in terms of service time and renewable energy utilization.

One of the most significant issues in wireless sensor networks is the limited source of energy that sensors are supplied with. Since network lifetime has a direct relationship with the energy of the nodes, it would be important to efficiently manage the energy consumption of the nodes. To this end, a number of energy aware routing algorithms have been proposed in the literature to increase network lifetime. These routing algorithms usually try to find the path with minimum energy usage. However, using these paths for a long time may result in rapid energy depletion of some nodes. To overcome this problem, we propose a new routing algorithm with three different versions which uses load balancing technique by selecting the paths with maximum normalized energy in each transmission round.

A new end-point admission control algorithm is proposed. The proposed algorithm helps to manage the network traffic within a domain more efficiently in the next generation network where multiple classes of service are required by different applications. In the existing solutions, when a request for a new traffic is received by the network, according to the admission control mechanism the availability of resources for the requested service class is examined and the call is accepted if the resources are available. In the proposed algorithm, users' are allowed to temporarily use lower service classes in the routers along the path, whenever the requested service class is not available, given that the end-to-end service quality level is preserved unless a lower service quality is acceptable by the application. The probe-based end-point admission control mechanism proposed here, dynamically manages the available bandwidth. In this way more calls can be admitted and the utilisation of the network is increased.

This letter proposes a new and comprehensive set of convergence conditions for a distributed optimal traffic engineering method for connectionless networks. The method was originally proposed in, but we show in this letter that the convergence conditions accompanying the method in are not comprehensive and work only in some conditions. More precisely, it is shown that the adaptation laws in will misbehave in some situations. Consequently, they can not achieve optimal resource allocation. This letter presents the correct form of the convergence conditions under which the adaptation laws can effectively lead to optimal network utilization.

Wireless body area sensor networks will revolutionize health care services by remote, continuous and non-invasive monitoring. Body area sensor networks (BASN) should monitor various physiological parameters of a person for a long period of time. Thus, efficient energy usage in sensor nodes is essential in order to provide a long life time for the network. This paper investigates the effect of adding a relay network to the network of body sensors to reduce energy consumption of sensor nodes when transmitting data to the sink.