AoI-aware energy control and computation offloading for industrial IoT

The integration of Maritime Internet of Things (M-IoT) technology and unmanned aerial/surface vehicles (UAVs/USVs) has been emerging as a promising navigational information technique in intelligent ocean systems. In this article, we consider the UAV-assisted M-IoT network where USVs offload computation-intensive maritime tasks via non-orthogonal multiple access (NOMA) to the UAV equipped with the mobile-edge computing (MEC) server subject to the UAV mobility. To improve the energy efficiency of offloading transmission and workload computation, we focus on minimizing the total energy consumption by jointly optimizing the USVs’ offloaded workload, transmit power, computation resource allocation, as well as the UAV trajectory subject to the USVs’ latency requirements. Despite the nature of mixed discrete and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a two-layered algorithm for solving it efficiently. Specifically, the top-layered algorithm is proposed to solve the problem of optimizing the UAV trajectory based on the idea of deep reinforcement learning (DRL), and the underlying algorithm is proposed to optimize the underlying multidomain resource allocation problem based on the idea of the Lagrangian multiplier method. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of NOMA-enabled computation offloading in terms of overall energy consumption. 

Joint Multi-Domain Resource Allocation and Trajectory Optimization in UAV-Assisted Maritime IoT Networks

GSEs will be one of the key components of the exploding market of the Internet of Things – which is of extreme interest to many large and small companies in Europe. The Internet of Things essentially refers to a tagging of all uniquely identifiable objects, or things, so they can be identified and tracked in their daily life. RFID (Radio-frequency identification) is generally seen as a prerequisite for SEs.

/pdf/cooperative-radio-communications-for-green-smart-16hroj2aez.pdf

Cooperative Radio Communications for Green Smart Environments

Dynamic energy efficient task offloading and resource allocation for NOMA-enabled IoT in smart buildings and environment

As the computing resources and the battery capacity of mobile devices are usually limited, it is a feasible solution to offload the computation-intensive tasks generated by mobile devices to edge servers in mobile edge computing (MEC). In this paper, we study the multi-user multi-server task offloading problem in mobile edge computing systems, where all the users compete for the limited communication resources and computing resources. We formulate the offloading problem with the goal of minimizing the cost of the users and maximizing the profits of the edge servers. We propose a hierarchical Economic and Efficient Task Offloading and Resource Purchasing (EETORP) framework that includes a two-stage joint optimization process. Then, we prove that the problem is NP-complete. For the first stage, we formulate the offloading problem as a multi-channel access game (MCA-Game) and prove theoretically the existence of at least one Nash equilibrium strategy in the MCA-Game. Next, we propose a game-based multi-channel access (GMCA) algorithm to obtain the Nash equilibrium strategy and analyze the performance guarantee of the obtained offloading strategy in the worst case. For the second stage, we model the computing resource allocation between the users and edge servers by Stackelberg game theory, and reformulate the problem as a resource pricing and purchasing game (PAP-Game). We prove theoretically the property of incentive compatibility and the existence of Stackelberg equilibrium. A game-based pricing and purchasing (GPAP) algorithm is proposed. Finally, a series of both parameter experiments and comparison experiments are carried out, which validate the convergence and effectiveness of the GMCA and GPAP algorithms.

https://dl.acm.org/doi/pdf/10.1145/3597023

Distributed Task Offloading and Resource Purchasing in NOMA-enabled Mobile Edge Computing: Hierarchical Game Theoretical Approaches

With the development of Internet of Things (IoT), more and more computation-intensive tasks are generated by IoT devices. Due to the limitation of battery and computing capacity of IoT devices, these tasks can be offloaded to mobile edge computing (MEC) and cloud for processing. However, as the channel states and task generation process are dynamic, and the scales of task offloading problem and solution space size are increasing rapidly, the collaborative task offloading for MEC and cloud faces severe challenges. In this paper, we integrate the two conflicting offloading goals, which are maximizing the task finish ratio with tolerable delay and minimizing the power consumption of devices. We formulate the task offloading problem to balance the two conflicting goals. Then, we reformulate it as an MDP-based dynamic task offloading problem. We design a deep reinforcement learning (DRL)-based dynamic task offloading (DDTO) algorithm to solve this problem. Our DDTO algorithm can adapt to the dynamic and complex environment and adjust the task offloading strategies accordingly. Experiments are also carried out which show that our DDTO algorithm can converge quickly. The experiment results also validate the effectiveness and efficacy of our DDTO algorithm in balancing finish ratio and power. 

Dynamic task offloading for Internet of Things in mobile edge computing via deep reinforcement learning

In this paper, we study the decoding metric of turbo codes in very impulsive environments. Channel impairments, for example in ad hoc networks, contain not only the thermal noise, but also the network interference generated by other nodes, which exhibits an impulsive nature. We use stable distributions to model this interference due to their heavy tail property. In order to adapt to this type of interference, we propose the p-norm as a decoding metric to replace the conventional Euclidean distance. Simulation results show that the p-norm allows a significant error correction improvement compared with other metrics in very impulsive cases, and its performance appears to be close to the optimal decoder.

Decoding Metric Study for Turbo Codes in Very Impulsive Environment

In wireless ad hoc network communications, both the network interference and the thermal noise should be considered in receiver design, due to the strong impairments each may cause on the quality of the reception at the destination. Since the closure under convolution of stable distributions only holds for the same stability index members, in general the additive convolution of impulsive stable interference and lighter tailed Gaussian thermal noise will not result in a stable pattern. It is therefore a challenge to adequately model the distribution of such a process. In this context we consider an optimal receiver design and develop an importance sampling approach to perform estimation of the optimal receiver in the presence of convolved stable and Gaussian noises. Such an approximation approach to the optimal receiver is computationally expensive, hence we also develop as comparisons several suboptimal realizations of linear and non-linear receivers, including an approximation approach based on the Normal Inverse Gaussian (NIG) distribution. We demonstrate that the computationally efficient NIG receiver provides an alternative solution for the optimal receiver approximation. In addition we show that the p-norm receiver appears to have robust performance no matter what kind of noise is dominant.

https://hal-imt.archives-ouvertes.fr/hal-00721733/document

Receiver study for cooperative communications in convolved additive α-stable interference plus Gaussian thermal noise

Interference in several situations, like multiple access interference (MAI) in dense sensor networks, shows strong but rarely occurring pulses that greatly disturb communication, leading to serious performance degradation in classical decoders based on the Gaussian assumption. We propose to represent this impulsive noise with symmetric α-stable (SαS) distribution, well adapted due to its heavy tailed property. Therefore, the Euclidean distance derived from the Gaussian distribution is no longer valid for the SαS environment. We attempt to use the p-norm as a distance metric and evaluate it in the decoding scheme of turbo codes. Based on our simulation results, the new metric significantly reduces the bit error rate (BER) compared with the decoders using the Euclidean distance. It indicates that the p-norm could be an appropriate metric adapted to the MAI environment for turbo code decoding.

Turbo code decoding in MAI environment

Remote Localization of Network-based Automatic Guided Vehicles with a Novel Quantized Set-membership Approach

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