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Mobile containers are a keystone human innovation. Ethnographic data indicate that all human groups use containers such as bags, quivers and baskets, ensuring that individuals have important resources at the ready and are prepared for opportunities and threats before they materialize. Although there is speculation surrounding the invention of carrying devices, the current hard archaeological evidence only reaches back some 100,000 years. The dearth of ancient evidence may reflect not only taphonomic processes, but also a lack of attention to these devices. To begin investigating the origins of carrying devices we focus on exploring the basic cognitive processes involved in mobile container use and report an initial study on young children's understanding and deployment of such devices. We gave 3- to 7-year-old children ( N = 106) the opportunity to spontaneously identify and use a basket to increase their own carrying capacity and thereby obtain more resources in the future. Performance improved linearly with age, as did the likelihood of recognizing that adults use mobile carrying devices to increase carrying capacity. We argue that the evolutionary and developmental origins of mobile containers reflect foundational cognitive processes that enable humans to think about their own limits and compensate for them.

Recently, the integration of unmanned aerial vehicles (UAVs) with edge computing has emerged as a promising paradigm for providing computational support for Internet of Things (IoT) applications in remote, disaster-stricken, and maritime areas. In UAV-aided edge computing, the offloading decision plays a central role in optimizing the overall system performance. However, the trajectory directly affects the offloading decision. In general, IoT devices use ground offload computation-intensive tasks on UAV-aided edge servers. The UAVs plan their trajectories based on the task generation rate. Therefore, researchers are attempting to optimize the offloading decision along with the trajectory, and numerous studies are ongoing to determine the impact of the trajectory on offloading decisions. In this survey, we review existing trajectory-aware offloading decision techniques by focusing on design concepts, operational features, and outstanding characteristics. Moreover, they are compared in terms of design principles and operational characteristics. Open issues and research challenges are discussed, along with future directions.

This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server. Considering different real-time computation tasks at different WDs, every task is decided to be processed locally at its WD or to be offloaded to and processed at one of the edge servers or the cloud server. In this paper, we investigate low-complexity computation offloading policies to guarantee quality of service of the MEC network and to minimize WDs’ energy consumption. Specifically, both a linear programing relaxation-based (LR-based) algorithm and a distributed deep learning-based offloading (DDLO) algorithm are independently studied for MEC networks. We further propose a heterogeneous DDLO to achieve better convergence performance than DDLO. Extensive numerical results show that the DDLO algorithms guarantee better performance than the LR-based algorithm. Furthermore, the DDLO algorithm generates an offloading decision in less than 1 millisecond, which is several orders faster than the LR-based algorithm.

The field of information security and privacy is currently attracting a lot of research interest. Simultaneously, different computing paradigms from Cloud computing to Edge computing are already forming a unique ecosystem with different architectures, storage, and processing capabilities. The heterogeneity of this ecosystem comes with certain limitations, particularly security and privacy challenges. This systematic literature review aims to identify similarities, differences, main attacks, and countermeasures in the various paradigms mentioned. The main determining outcome points out the essential security and privacy threats. The presented results also outline important similarities and differences in Cloud, Edge, and Fog computing paradigms. Finally, the work identified that the heterogeneity of such an ecosystem does have issues and poses a great setback in the deployment of security and privacy mechanisms to counter security attacks and privacy leakages. Different deployment techniques were found in the review studies as ways to mitigate and enhance security and privacy shortcomings.

Task offloading is an important concept for edge computing and the Internet of Things (IoT) because computationintensive tasks must be offloaded to more resource-powerful remote devices. Task offloading has several advantages, including increased battery life, lower latency, and better application performance. A task offloading method determines whether sections of the full application should be run locally or offloaded for execution remotely. The offloading choice problem is influenced by several factors, including application properties, network conditions, hardware features, and mobility, influencing the offloading system’s operational environment. This study provides a thorough examination of current task offloading and resource allocation in edge computing, covering offloading strategies, algorithms, and factors that influence offloading. Full offloading and partial offloading strategies are the two types of offloading strategies. The algorithms for task offloading and resource allocation are then categorized into two parts: machine learning algorithms and non-machine learning algorithms. We examine and elaborate on algorithms like Supervised Learning, Unsupervised Learning, and Reinforcement Learning (RL) under machine learning. Under the non-machine learning algorithm, we elaborate on algorithms like non(convex) optimization, Lyapunov optimization, Game theory, Heuristic Algorithm, Dynamic Voltage Scaling, Gibbs Sampling, and Generalized Benders Decomposition (GBD). Finally, we highlight and discuss some research challenges and issues in edge computing.

In a network environment composed of different types of computing centers that can be divided into different layers (clod, edge layer, and others), the interconnection between them offers the possibility of peer-to-peer task offloading. For many resource-constrained devices, the computation of many types of tasks is not feasible because they cannot support such computations as they do not have enough available memory and processing capacity. In this scenario, it is worth considering transferring these tasks to resource-rich platforms, such as Edge Data Centers or remote cloud servers. For different reasons, it is more exciting and appropriate to download various tasks to specific download destinations depending on the properties and state of the environment and the nature of the functions. At the same time, establishing an optimal offloading policy, which ensures that all tasks are executed within the required latency and avoids excessive workload on specific computing centers is not easy. This study presents two alternatives to solve the offloading decision paradigm by introducing two well-known algorithms, Graph Neural Networks (GNN) and Deep Q-Network (DQN). It applies the alternatives on a well-known Edge Computing simulator called PureEdgeSim and compares them with the two default methods, Trade-Off and Round Robin. Experiments showed that variants offer a slight improvement in task success rate and workload distribution. In terms of energy efficiency, they provided similar results. Finally, the success rates of different computing centers are tested, and the lack of capacity of remote cloud servers to respond to applications in real-time is demonstrated. These novel ways of finding a download strategy in a local networking environment are unique as they emulate the state and structure of the environment innovatively, considering the quality of its connections and constant updates. The download score defined in this research is a crucial feature for determining the quality of a download path in the GNN training process and has not previously been proposed. Simultaneously, the suitability of Reinforcement Learning (RL) techniques is demonstrated due to the dynamism of the network environment, considering all the key factors that affect the decision to offload a given task, including the actual state of all devices.

The explosive growth of the Internet of Things (IoT) applications has imposed a dramatic increase of network data and placed a high computation complexity across various connected devices. The IoT devices capture valuable information, which allows the industries or individual users to make critical live dependent decisions. Most of these IoT devices have resource constraints such as low CPU, limited memory, and low energy storage. Hence, these devices are vulnerable to cyber-attacks due to the lack of capacity to run existing general-purpose security software. It creates an inherent risk in IoT networks. The multi-access edge computing (MEC) platform has emerged to mitigate these constraints by relocating complex computing tasks from the IoT devices to the edge. Most of the existing related works are focusing on finding the optimized security solutions to protect the IoT devices. We believe distributed solutions leveraging MEC should draw more attention. This paper presents a comprehensive review of state-of-the-art network intrusion detection systems (NIDS) and security practices for IoT networks. We have analyzed the approaches based on MEC platforms and utilizing machine learning (ML) techniques. The paper also performs a comparative analysis on the public available datasets, evaluation metrics, and deployment strategies employed in the NIDS design. Finally, we propose an NIDS framework for IoT networks leveraging MEC.

The Internet of Vehicles (IoV) is employed to gather real-time traffic information for drivers, and base stations in 5G systems are used to assist in traffic data transmission. For rapid implementation, the applications in vehicles are available to be offloaded to edge nodes (ENs) which are enhanced from micro base stations. Despite the benefits of IoV and ENs, the explosive growth of offloaded vehicle applications exceeds the capacity of ENs, causing the overload of fractional ENs. Therefore, it is necessary to offload the computing applications in overloaded ENs to other idle ENs, while it is a challenge to select appropriate offloading destination ENs. In this paper, we first consider edge computing framework for computation offloading in IoV under the architecture of 5G networks. We then formulate a multi-objective optimization problem to select suitable destination ENs, which aims to minimize the vehicle application offloading delay and offloading cost as well as realizing the load balance of ENs. Moreover, a computation offloading method for IoV, named COV, is designed to solve the multi-objective optimization problem. Finally, various simulation analyses demonstrate the effectiveness and efficiency of COV.

Fog computing is one of the major components of future 6G networks. It can provide fast computing of different application-related tasks and improve system reliability due to better decision-making. Parallel offloading, in which a task is split into several sub-tasks and transmitted to different fog nodes for parallel computation, is a promising concept in task offloading. Parallel offloading suffers from challenges such as sub-task splitting and mapping of sub-tasks to the fog nodes. In this paper, we propose a novel many-to-one matching-based algorithm for the allocation of sub-tasks to fog nodes. We develop preference profiles for IoT nodes and fog nodes to reduce the task computation delay. We also propose a technique to address the externalities problem in the matching algorithm that is caused by the dynamic preference profiles. Furthermore, a detailed evaluation of the proposed technique is presented to show the benefits of each feature of the algorithm. Simulation results show that the proposed matching-based offloading technique outperforms other available techniques from the literature and improves task latency by 52% at high task loads.

In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc network (VANET) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks to the proper MEC server on the UAV due to the limited computation ability. To counter the problems above, we first model and analyze the transmission model and the security assurance model from the vehicle to the MEC server on UAV, and the task computation model of the local vehicle and the edge UAV. Then, the vehicle offloading problem is formulated as a multi-objective optimization problem by jointly considering the task offloading, the resource allocation, and the security assurance. For tackling this hard problem, we decouple the multi-objective optimization problem as two subproblems and propose an efficient iterative algorithm to jointly make the MEC selection decision based on the criteria of load balancing and optimize the offloading ratio and the computation resource according to the Lagrangian dual decomposition. Finally, the simulation results demonstrate that our proposed scheme achieves significant performance superiority compared with other schemes in terms of the successful task processing ratio and the task processing delay.