Evangelos Kranakis

Under a dramatic increase in mobile data traffic, a promising solution for edge computing systems to maintain their local service is the task migration that may be implemented by means of Autonomous mobile agents (AMA). In designing an optimal scheme for task offloading to AMA, we define a system cost as a minimization objective function that comprises two parts. First, an offloading cost which can be interpreted as the cost of using computational resources from the AMA. Second, a penalty cost due to potential task expiration. To minimize the expected (timeaverage) cost over a given time horizon, we formulate a Dynamic programming (DP). However, the DP Equation suffers from the well-known curse of dimensionality, which makes computations intractable, especially for infinite system state space. To reduce the computational burden, we identify three important properties of the optimal policy and show that it suffices to evaluate the DP Equation on a finite subset of the state space only. We then prove that the optimal task offloading decision at a state can be inferred from that at its adjacent states, further reducing the computational load. We present simulations to verify the theoretical results and to provide insights into the considered system.

Under a dramatic increase in mobile data traffic, a promising solution for edge computing systems to maintain their local service is the task migration that may be implemented by means of Autonomous mobile agents (AMA). In designing an optimal scheme for task offloading to AMA, we define a system cost as a minimization objective function that comprises two parts. First, an offloading cost which can be interpreted as the cost of using computational resources from the AMA. Second, a penalty cost due to potential task expiration. To minimize the expected (time-average) cost over a given time horizon, we formulate a Dynamic programming (DP). However, the DP Equation suffers from the well-known curse of dimensionality, which makes computations intractable, especially for infinite system state space. To reduce the computational burden, we identify three important properties of the optimal policy and show that it suffices to evaluate the DP Equation on a finite subset of the state space only. We then prove that the optimal task offloading decision at a state can be inferred from that at its adjacent states, further reducing the computational load. We present simulations to verify the theoretical results and to provide insights into the considered system.

Truck platooning is an innovative logistics approach to lower operational costs, particularly fuel consumption, while addressing contemporary transportation challenges. While recent studies on truck platooning have emphasized platoons’ energy savings, stability, and safety, there has been limited exploration of platoon formation and control. This paper uses optimal control theory to address the dispatching control of trucks with arriving platoons. In particular, trucks arrive at a highway station while platoons arrive alongside it. The station controls the truck holding and dispatching, where trucks are sent out with or without a platoon. Dispatching trucks with an arriving platoon reduces fuel consumption while waiting for a platoon to arrive increases the dwell time (i.e., transportation delay). We assume that an arriving platoon determines the number of trucks (i.e., the batch size) it can accept. Only a single truck can be dispatched if a platoon is absent. Hence, we formulate the dispatching control problem and derive the optimal policy for the discounted costs and the average cost governing the dispatch of trucks alongside platoons. We proved the optimality of threshold policies. Numerical results for the average cost case are presented. They are consistent with the optimal ones.

The underwater acoustic communication medium introduces significant challenges that often limit communication to flooding protocols. This work presents the Network Allocation Technique in Flooding (NATFLOOD) protocol, intended for use with Underwater Acoustic Sensor Networks (UASNs). NATFLOOD applies a novel Adaptive Network Allocation Vector (ANAV) to reduce duplicates and collisions. The performance of NATFLOOD is tested in a comparative simulation against DFLOOD, a leading UASN flooding protocol. In simulation, NATFLOOD demonstrates an average reduction in duplicates and collisions of 24.29 ± 0.01% when compared to DFLOOD. This reduction is achieved by the ability of NATFLOOD to solve the broadcast problem with 19.96 ± 0.03% fewer packets than DFLOOD. This reduction is achieved with an increase to protocol completion time of 2.21 ± 0.02%.