Mobile Edge Computing (MEC) is a promising architecture to reduce the energy consumption of mobile devices and provide satisfactory quality-of-service to time-sensitive services. How to jointly optimize task offloading and resource allocation to minimize the energy consumption subject to the latency requirement remains an open problem, which motivates this paper. When the latency constraint is taken into account, the optimization variables, including offloading ratio, transmission power, and subcarrier and computing resource allocation, are strongly coupled. To address this issue, we first decompose the original problem into three subproblems named as offloading ratio selection, transmission power optimization, and subcarrier and computing resource allocation. Then, we propose an iterative algorithm to deal with them in a sequence. To be specific, we derive the closed-form solution of offloading ratios, employ the equivalent parametric convex programming to obtain the optimal power allocation policy, and deal with subcarrier and computing resource allocation by the primal-dual method. Simulation results demonstrate that the proposed algorithm can save 20%–40% energy compared with the reference schemes, and can converge to local optimal solutions.

As an infrastructure-free positioning and navigation method, pedestrian dead reckoning (PDR) is still a research hotspot in the field of indoor localization. Step detection (SD) and stride length estimation (SLE) are two key components of PDR, and it is a challenging problem to apply SD and SLE to different walking patterns. Focusing on this problem, this paper proposes a robust SD and SLE method based on recognizing three walking patterns (i.e., Normal Walk, March in Place, and Quick Walk) using a smartphone. First, we propose a dynamic time warping–based peak prediction with zero-crossing detection to improve the SD accuracy. In particular, the proposed SD can accurately identify the starting and ending points of each step in the three walking patterns. Second, according to the extracted features of each step, a random forest algorithm with classification proofreading is used to recognize the three walking patterns. Finally, an improved SLE model is proposed for the different walking patterns to achieve a higher SLE accuracy. The experimental results show that, on average, the SD accuracy is about 97.9%, the recognition accuracy is about 98.4%, and the relative error of the estimated walking distance is about 3.0%, which outperforms those of the existing commonly used SD and SLE methods.

To minimize the average latency of users to complete tasks, this letter jointly optimizes offloading decision, computation and bandwidth resource allocation for Mobile Edge Computing (MEC) networks via partial offloading. Since the considered problem is strongly non-convex with coupled variables, we decompose the original problem into two subproblems: (1) offloading decision; (2) communication bandwidth and computation resources allocation, and further employ Block Coordinate Descent (BCD) method to tackle them sequentially with linear computation complexity. Simulation results demonstrate that the proposed algorithm can achieve better performance on the average latency, and converge speedily.

Mobile edge computing (MEC) distributes computing and storage resources to the edge of the network closer to the user and significantly reduces user task completion latency and system energy consumption. This paper investigates the problem of computation offloading in a three-tier mobile edge computing network composed of multiple users, multiple edge servers, and a cloud server. In this network, each user’s task can be divided into multiple subtasks with serial and parallel priority relationships existing among these subtasks. An optimization model is established with the objective of minimizing the total user delay and processor cost under constraints such as the available resources of users and servers and the interrelationships among the subtasks. An improved gravitational search algorithm (IGSA) is proposed to solve this optimization model. In contrast with the other gravitational search algorithm, the convergence factor is introduced in the calculation of the resultant force and the crossover operation in a genetic algorithm is performed when generating the new particles during each iteration. The simulation results show that the proposed IGSA greatly improves the system performance compared with the existing algorithms.

How to jointly optimize task offloading and resource allocation to minimize the task failure rate and task payments remains an unresolved challenge in fog networks. Focusing on this problem, this research formulates a novel task offloading and resource allocation model with two offloading modes and on-demand virtual resource units (VRUs). This model is decomposed into two sub-problems to solve: a joint task offloading and resource allocation optimization problem and a matching problem with externalities and dynamics. First, for a given terminal node (TN) and fog node (FN), this research theoretically derives the optimal offloading ratio and resource allocation strategy to minimize the payment of TNs for two offloading modes, i.e., immediate and queued offloading. Second, in the multi-TNs and multi-FNs scenario, the problem of making the task offloading decision is transformed into a many-to-one matching game by considering externalities and dynamics. Finally, a Deferred acceptance-based Loss ratio and Payment Minimized task Offloading and resource Allocation optimization (DLPMOA) algorithm is proposed to derive a stable and Pareto-optimal match. The simulation results show that the proposed DLPMOA has better performance in terms of task failure rate, task average payment, fog computing resource utilization, and fairness than the state-of-the-art methods.

In the fog network composed of dense terminal devices and fog servers, how to reduce the system energy consumption during task offloading is a challenging problem. To solve this problem, this article first formulates the energy consumption minimization problem of partial task offloading under delay constraints with dynamic voltage scaling (DVS) and transmission power control (TPC) techniques. Second, this problem was decomposed into two subproblems to solve: 1) the partial task offloading problem with optimal energy consumption under known matching between the terminal device and fog server and 2) the optimal matching problem between terminal devices and fog servers. For the first subproblem, the optimal solution is obtained through theoretical derivation, and the EOPCO-S algorithm is proposed to solve it. For the second subproblem, we transform the original problem into a weighted bipartite graph matching problem and propose the Kuhn–Munkres-based EOPCO-M algorithm to solve it. Finally, numerical simulations are carried out to verify the theoretical derivation and the effectiveness of the proposed algorithms. Experimental results show that the proposed algorithm can significantly reduce the energy consumption of fog networks compared with several baseline algorithms.