Onur Karatalay’s research while affiliated with McGill University and other places

In this paper, we address the problem of energy-efficient resource allocation in a multi-device D2D-assisted fog computing scenario, where the goal is to minimize the total energy consumption subject to constraints on the transmit powers, computation resources and task processing times. The considered problem is non-convex and finding its global optimum is generally intractable; hence we propose two sub-optimal approaches to solve it. First, by investigating the relationship between the task processing time and the total energy consumption, we show how the original problem can be relaxed into a sequence of convex subproblems whose solutions can be efficiently obtained via standard algorithms. Second, to further reduce computational complexity, we propose a low-complexity heuristic resource allocation strategy which does not require calculating gradients and the Hessian matrices in the solution process. We also develop a lower bound on the total energy consumption for the considered task offloading scenario as a benchmark for comparison purpose. Computer simulations under a wide range of conditions and parameter settings show that both methods achieve a near-optimal solution in comparison to the lower bound.

Device-to-device (D2D) communication is an enabling technology for fog computing by allowing the sharing of computation resources between mobile devices. However, temperature variations in the device CPUs affect the computation resources available for task offloading, which unpredictably alters the processing time and energy consumption. In this paper, we address the problem of resource allocation with respect to task partitioning, computation resources and transmit power in a D2D-aided fog computing scenario, aiming to minimize the expected total energy consumption under probabilistic constraints on the processing time. Since the formulated problem is non-convex, we propose two sub-optimal solution methods. The first method is based on difference of convex (DC) programming, which we combine with chance-constraint programming to handle the probabilistic time limitations. Considering that DC programming is dependent on a good initial point, we propose a second method that relies on only convex programming, which eliminates the dependence on user-defined initialization. Simulation results demonstrate that the latter method outperforms the former in terms of energy efficiency and run-time.

In distributed device-to-device (D2D) communications, no common reference time is available and the devices must employ distributed synchronization techniques. In this context, pulse-based synchronization, which can be implemented by distributed phase-locked loops is preferred due to its scalability. Several factors degrade the performance of pulse-based synchronization, such as duplexing scheme, clock skew and propagation delays. Furthermore, in distributed networks, devices should be aware of the synchronization status of others in order to initiate data communications. To address these prevailing issues, we first introduce a half-duplex timing-advance synchronization algorithm wherein each device alternates between being a transmitter and receiver in their exchange of synchronization pulses at each clock period. Based on this algorithm, we propose a novel fully-distributed pulse-based synchronization protocol for half-duplex D2D communications in 5G wireless networks. The protocol allows participating devices to become aware of the global synchronization status, so that they can complete the synchronization process ideally at the same time and proceed to data communication. In simulation experiments over multi-path frequency selective channels, the proposed synchronization protocol is shown to outperform a benchmark approach from the recent literature over a wide range of conditions, e.g., clock skew, number of devices, and network topology.