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In this paper, we consider an underwater linear network, where an autonomous underwater vehicle (AUV) gathers data from a set of underwater devices. The AUV monitors a set of physical processes, where the status of each process can be sensed by one or more devices and each device is capable of sensing one or more processes. The AUV needs to maintain freshness of its information status about the monitored processes. To quantify the freshness of the information at the AUV, we consider the concept of the age of information (AoI), which represents the amount of time elapsed since the most recently delivered update information was generated. A framework is proposed to optimize the AUV's linear movement trajectory and scheduling of process status updates with the objective of minimizing the normalized weighted sum of the average AoI of the monitored physical processes. The formulated optimization problem is a non-convex mixed integer problem, which cannot be solved by the standard optimization techniques. We develop a solution approach based on the technique of deep reinforcement learning (DRL). Specifically, we leverage an actor-critic DRL approach to find the optimum locations and stopping time of the data gathering points. Simulation results illustrate that the proposed framework maintains robustness under different scenarios and provides better performance when compared with baseline and $K$ -means clustering approaches. [ABSTRACT FROM AUTHOR]

In this article, we present a method to detect and identify mobile network signals. The method relies on features extracted from the power spectral density of the signals. The performance of the proposed method is evaluated using over-the-air measurements acquired in various cellular bands, in different geographical areas, and in rural and urban settings. Results verify the high detection and identification accuracy of the method. [ABSTRACT FROM AUTHOR]

In this paper, we consider parallel and sequential task offloading to multiple mobile edge computing servers. The task consists of a set of inter-dependent sub-tasks, which are scheduled to servers to minimize both offloading latency and failure probability. Two algorithms are proposed to solve the scheduling problem, which are based on genetic algorithm and conflict graph models, respectively. Simulation results show that these algorithms provide performance close to the optimal solution, which is obtained through exhaustive search. Furthermore, although parallel offloading uses orthogonal channels, results demonstrate that the sequential offloading yields a reduced offloading failure probability when compared to the parallel offloading. On the other hand, parallel offloading provides less latency. However, as the dependency among sub-tasks increases, the latency gap between parallel and sequential schemes decreases. [ABSTRACT FROM AUTHOR]

In this letter, a collision-free sequential task offloading scheme to multiple mobile-edge computing servers is proposed. The problem is formulated as a multi-objective optimization of latency and offloading failure probability. An exact solution technique is developed to obtain a benchmark optimal solution for the problem. A more computationally efficient heuristic algorithm is additionally developed, whose sub-optimal solution yields a performance close to optimal. Simulation results illustrate that the proposed offloading scheme can effectively reduce both latency and offloading failure probability. [ABSTRACT FROM AUTHOR]

We study the problem of reducing the download time of multiple files requested by multiple clients from multiple cloud/fog storage servers. Given possible previous file downloads by the clients, network coding can be efficiently exploited to expedite the download process. Since each client can tune to only one server at a time, the sets of clients served by the different servers must be disjoint in order to guarantee a maximum reduction in download time. To accomplish disjoint download mechanisms, a dual conflict network coding graph is proposed. Given the intractability of the long-term optimal solution, we propose an online algorithm using the designed dual conflict graph. For the case of one file request per client, both asymptotic lower and upper bounds of the performance of the proposed conflict-free algorithm are derived. Simulation results show that this proposed algorithm exhibits near optimum performance compared to the optimum solution, and a significant reduction in download time as compared to the per-server network coding scheme. Furthermore, imperfect feedback environment scenarios are investigated. A maximum likelihood approach is employed at the server to estimate the network state, which is then incorporated in our proposed algorithm to reduce the download time in such scenarios. [ABSTRACT FROM AUTHOR]