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Recently, the Internet of Things (IoT) has witnessed rapid development. However, the scarcity of computing resources on the ground has constrained the application scenarios of IoT. Low Earth Orbit (LEO) satellites have drawn people's attention due to their broader coverage and shorter transmission delay. They are capable of offloading more IoT computing tasks to mobile edge computing (MEC) servers with lower latency in order to address the issue of scarce computing resources on the ground. Nevertheless, it is highly challenging to share bandwidth and power resources among multiple IoT devices and LEO satellites. In this paper, we explore the efficient data offloading mechanism in the LEO satellite-based IoT (LEO-IoT), where LEO satellites forward data from the terrestrial to the MEC servers. Specifically, by optimally selecting the forwarding LEO satellite for each IoT task and allocating communication resources, we aim to minimize the data offloading latency and energy consumption. Particularly, we employ the state-of-the-art Decision Transformer (DT) to solve this optimization problem. We initially obtain a pre-trained DT through training on a specific task. Subsequently, the pre-trained DT is fine-tuned by acquiring a small quantity of data under the new task, enabling it to converge rapidly, with less training time and superior performance. Numerical simulation results demonstrate that in contrast to the classical reinforcement learning approach (Proximal Policy Optimization), the convergence speed of DT can be increased by up to three times, and the performance can be improved by up to 30%. [ABSTRACT FROM AUTHOR]

Vehicular fog computing (VFC) is a technology that enhances vehicular applications by offloading the task of the resource-restricted vehicle to the resourceful fog node (vehicle or static fog node). The mobility of vehicles leads to various challenges in task offloading, i.e., low successful offloading ratio, high response time, etc. Apart from this, the available idle computation resource at the fog node is different at the time of advertisement of computation capability and at the time of task allocation. This change results in less efficient resource utilization and unequal load balance at the fog node. Hence to overcome this, we have proposed an approach based on future mobility and imminent computation awareness (FMICA). The future mobility prediction allows us to offload the task to the fog node, which will be near during task retrieval. Additionally, imminent computation awareness of the available resources (at the imminent time τ ) allows us to efficiently utilize the idle resources through offloading higher deadline tasks at the τ time. FMICA optimizes resources according to various criteria such as offloading delay, load variance, within-range ratio, and successful offloading ratio using a Strength Pareto Evolutionary Algorithm 2 (SPEA2). An order preference by similarity to the ideal solution (TOPSIS) and multiple criteria decision-making (MCDM) technique is used to select the best solution. Through the experimental analysis, we have found that the FMICA has an improvement of 37.64%, 25.57%, 9.96%, and 12.62% in metrics average response time, successful offloading ratio, resource utilization, and average load variance against available approach, respectively. [ABSTRACT FROM AUTHOR]

To realize transparent monitoring and resilience improvement of low- voltage distribution network, both the data acquisition scope and frequency have been greatly expanded. Cloud-edge collaboration leverages the edge server's real-time response capabilities and the cloud server's robust data processing power to enhance the performance of high-frequency data acquisition processing. Nonetheless, it continues to confront challenges such as the entanglement of optimization variables, the presence of uncertain information, and a lack of awareness regarding acquisition frequencies. In this paper, we propose a machine learning-based cloud-edge collaborative data processing optimization algorithm to minimize the weighted sum of data processing delay and device energy consumption for distribution network resilience improvement. The joint optimization problem is decoupled into device-edge data offloading subproblem and edge-cloud data splitting subproblem, which are solved by the proposed upper confidence bound (UCB) based frequency-aware device-edge data offloading optimization algorithm and the exponential-weight algorithm for exploration and exploitation (EXP3) based edge-cloud data splitting optimization algorithm, respectively. Simulation results show that the proposed algorithm is superior to existing algorithms in performances of energy consumption and total processing delay. [ABSTRACT FROM AUTHOR]

Data-intensive applications coupled with limited mobile resources make opportunistic computation offloading imperative. Therefore, efficient and reliable offloading strategies are crucial for achieving optimal performance in terms of stabilizing data queues at various data rates, efficient task management and reduced waiting times for users. This paper proposes a novel algorithm, Matrix Factorization-Based Deep Reinforcement Learning Approach for Stable Online Offloading in Mobile Edge Networks (MFDROO), that combines Orthogonal Non-negative Matrix Factorization (ONMF) and a Deep Reinforcement Learning approach (DRL) to address the problem of stable online offloading in large networks. The proposed algorithm utilizes ONMF to model network resource heterogeneity by decomposing it into a set of features, capturing the underlying structure of data traffic flows by removing irrelevant information from data. This reduces computational overhead and improves performance. Additionally, MFDROO incorporates a DRL agent to learn optimal offloading decisions over time. By utilizing ONMF in conjunction with DRL, MFDROO overcomes online offloading challenges. It optimizes user computation rates and enhances system performance. Additionally, it maintains data-queue stability for large networks or higher data rates. Large-scale network simulations were extensively conducted to demonstrate MFDROO effectiveness. Maintaining stability while scaling up the users number is a challenge in network computation. Our results demonstrate that MFDROO tackles this challenge and ensures that even with a 3.3% increase in user numbers (up to 100 users), computation networks remain stable and outperform other existing algorithms in terms of utility maximization and stability. This improvement ensures optimal performance and provides scalability and efficiency for various applications.

The deployment of 5G has exposed capacity constraints in realizing the key vision of the Internet of Everything (IoE). Therefore, the researchers are exploring potentials of Digital Twin Network (DTN) in wireless networks. DTN is a novel technology to create virtual replicas of physical environment for testing, optimizing, and managing wireless networks. The integration of Artificial Intelligence (AI) and DTN appears to be a promising approach to address communication systems by providing an efficient environment for testing and improving AI models before deployment in real networks for effective network management, optimal resource allocation, and precise behavior prediction. Therefore, AI-enabled DTN in 6G represents a compelling avenue to address multifaceted challenges faced by wireless networks. In this comprehensive work, we offer a holistic survey that delves into the state-of-the-art approaches for AI-enabled DTNs in 6G. Firstly, we discuss the evolution of wireless networks and concept of AI-enabled DTN in 6G. Secondly, we discuss the role of AI-enabled DTN in 6G and driving advancements in fundamental components of 6G including resource allocation, caching, data offloading, and data security. Thirdly, we conduct a detailed discussion on key enabling technologies for realizing the capabilities of AI-enabled DTN in 6G. Fourthly, several applications of AI-enabled DTN in 6G are discussed for the practical relevance and significance in various industries such as smart cities, healthcare, and transportation etc. Finally, we provide lessons learned and highlight existing challenges and research directions to embark on further research efforts in the realm of AI-enabled DTN in 6G.

Wireless network traffic is exploding, mainly due to the growth of video streaming services. To cope with the increase of mobile traffic in wireless networks, techniques for installing content caches in base stations or devices are being investigated. Although the total amount of content is huge, the capacity of a cache installed in base stations or devices is limited, so efficient caching methods are needed to improve the hit ratio. To this end, we can consider content recommendation systems on devices. Since many users tend to select and watch videos from recommended content, a cache can improve its hit ratio by storing content that is more likely to be recommended on each device. This paper discusses how caching systems differ when devices recommend content versus when they do not. It also discusses how caching systems differ when recommendations are made based on cached content versus when they are not. Content with high average preferences should be cached without recommendations, while the caching system should take personal preferences into account when recommending content personally preferred by each device. This is especially true for cache-independent recommendation systems, since each device will recommend personal favorites regardless of cached content. If the cache size is very large compared to the number of recommended contents, the consideration of cached content in recommendation systems may become less important, since much personal content should be cached anyway. The simulation results show that caching schemes with recommendation systems can differ significantly from those without.

Recently, the Internet of Things (IoT) has witnessed rapid development. However, the scarcity of computing resources on the ground has constrained the application scenarios of IoT. Low Earth Orbit (LEO) satellites have drawn people’s attention due to their broader coverage and shorter transmission delay. They are capable of offloading more IoT computing tasks to mobile edge computing (MEC) servers with lower latency in order to address the issue of scarce computing resources on the ground. Nevertheless, it is highly challenging to share bandwidth and power resources among multiple IoT devices and LEO satellites. In this paper, we explore the efficient data offloading mechanism in the LEO satellite-based IoT (LEO-IoT), where LEO satellites forward data from the terrestrial to the MEC servers. Specifically, by optimally selecting the forwarding LEO satellite for each IoT task and allocating communication resources, we aim to minimize the data offloading latency and energy consumption. Particularly, we employ the state-of-the-art Decision Transformer (DT) to solve this optimization problem. We initially obtain a pre-trained DT through training on a specific task. Subsequently, the pre-trained DT is fine-tuned by acquiring a small quantity of data under the new task, enabling it to converge rapidly, with less training time and superior performance. Numerical simulation results demonstrate that in contrast to the classical reinforcement learning approach (Proximal Policy Optimization), the convergence speed of DT can be increased by up to three times, and the performance can be improved by up to 30%.

Smart cities rely on real-time sensor data to improve services and quality of life. However, the rapid growth of sensor data poses challenges in transmission, storage, and processing. This paper presents a case study on estimating sensor data generation and the role of Opportunistic Networks (OppNets) in data collection in Ahmedabad and Gandhinagar, India. We highlight the challenges of managing large amounts of sensor data, particularly in densely populated cities. We propose OppNets as a promising solution, as they can leverage the mobility of devices to relay data in a distributed manner. We present a detailed analysis of sensor requirements and data generation for different smart city applications and discuss the potential benefits of OppNets for smart city data collection. Our study shows that Ahmedabad and Gandhinagar require approximately 4.6 million and 1.3 million sensors, producing an estimated 2702 Terabytes and 704 Terabytes of sensor data daily, respectively. [ABSTRACT FROM AUTHOR]

In the era of fifth-generation (5G)-enabled vehicular edge computing (VEC), efficient data offloading strategies are essential. The complexities inherent in this environment, such as vehicle speed, direction, position coordinates, signal strength, data to be offloaded, server load, and energy consumption, magnify the need for innovative solutions. Given the intricate nature of this environment, there's a demand for a novel and robust algorithm to effectively address its complexities. Traditional models and algorithms, while functional, exhibit inefficiencies and high computational demands. This paper presents an innovative data offloading approach, capitalizing on the power of deep reinforcement learning through the implementation of a cutting-edge application known as double deep networks (DDQN). The proposed algorithm merges the capabilities of deep neural networks and techniques from deep learning, facilitating flexible choices that adjust to evolving circumstances. This algorithm incorporates an optimization process within a multi-user context and a sophisticated trade-off analysis, effectively balancing between the different objectives to optimize overall system performance. Our DDQN model, trained on a synthetic dataset, outshines extant methods in key performance indicators. Specifically, we achieved an 80% improvement in energy efficiency, a 72.6% reduction in communication overhead, and a 9.8% improvement in delay. This strategy offers a potent solution for optimizing 5G-enabled VEC, paving the way for enhanced real-time vehicular network services and addressing the challenges of data offloading within this framework. [ABSTRACT FROM AUTHOR]

In this study, we investigate the edge offloading of sensory data in the internet of things. Multiple edge servers cooperatively offloading all or part of the sensory data initially sent to a cloud center, which improves users' experience. In the process of cooperative offloading, the transmission of sensory data and information exchange among edge servers will consume system resources, resulting in a cost of cooperation. How to maximize the offloading ratio of sensing data while maintaining a low collaboration cost is a challenge. This study first formulates a joint optimization problem of the sensing data offloading ratio and cooperative ratio satisfying the constraints of network delay and system energy consumption. Subsequently, a distributed alternating direction method of multipliers (ADMM) via constraint projection and variable splitting is proposed to solve the problem. The numerical results show that the proposed method greatly improves network delay and energy consumption compared to the fairness cooperation algorithm. [ABSTRACT FROM AUTHOR]

Abstract Computation offloading is the solution for IoT devices of limited resources and high-cost processing requirements. However, the network related issues such as latency and bandwidth consumption need to be considered. Data transmission reduction is one of the solutions aiming to solve network related problems by reducing the amount of data transmitted. In this paper, we propose a generalized formal data transmission reduction model independent of the system and the data type. This formalization is based on two main ideas: 1) Not sending data until a significant change occurs, 2) Sending a lighter size entity permitting the cloud to deduct the data captured by the IoT device without actually receiving it. This paper includes the mathematical representation of the model, general evaluation metrics formulas as well as detailed projections on real world use cases.

Intelligent telemedicine technology has been widely applied due to the quick development of the Internet of Things (IoT). The edge-computing scheme can be regarded as a feasible solution to reduce energy consumption and enhance the computing capabilities for the Wireless Body Area Network (WBAN). For an edge-computing-assisted intelligent telemedicine system, a two-layer network architecture composed of WBAN and Edge-Computing Network (ECN) was considered in this paper. Moreover, the age of information (AoI) was adopted to describe the time cost for the TDMA transmission mechanism in WBAN. According to the theoretical analysis, the strategy for resource allocation and data offloading in edge-computing-assisted intelligent telemedicine systems can be expressed as a system utility function optimizing problem. To maximize the system utility, an incentive mechanism based on contract theory (CT) was considered to motivate edge servers (ESs) to participate in system cooperation. To minimize the system cost, a cooperative game was developed to address the slot allocation in WBAN, while a bilateral matching game was utilized to optimize the data offloading problem in ECN. Simulation results have verified the effectiveness of the strategy proposed in terms of the system utility. [ABSTRACT FROM AUTHOR]

The exponential growth of video data in networks has led to video flow occupying a significant proportion of network traffic, causing congestion and poor service quality. To address this issue, it is crucial to quickly offload data and ensure high-quality service for users, especially in the context of cloud-edge collaboration. We propose a strategy for collaborative data offloading between cloud and edge computing, analogous to water flow (WFO). When users simultaneously access the same data from the same data source, WFO can serve more users within the limited bandwidth of the cloud while maintaining the quality of service. WFO creates a water flow-like data link between nodes to enable data offloading, using multiple nodes in collaboration to offload data for a single node. Experimental results show that compared with typical methods, such as fair-queue and first-come-first-served, WFO can significantly reduce the data offloading delay, guarantee service quality, and effectively reduce network congestion. Moreover, the number of service nodes can be as numerous as possible. [ABSTRACT FROM AUTHOR]

Smart cities make use of a variety of smart technology to improve societies in better ways. Such intelligent technologies, on the other hand, pose significant concerns in terms of power usage and emission of carbons. The suggested study is focused on technological networks for big data-driven systems. With the support of software-defined technologies, a transportation-aided multicast routing system is suggested. By using public transportation as another communication platform in a smart city, network communication is enhanced. The primary objective is to use as little energy as possible while delivering as much data as possible. The Attribute Decision Making with Capacitated Vehicle (CV) Routing Problem (RP) and Half Open Multi-Depot Heterogeneous Vehicle Routing Problem is used in the proposed research. For the optimum network selection, a Multi-Attribute Decision Making (MADM) method is utilized. For the sake of reducing energy usage, the Capacitated Vehicle Routing Problem (CVRP) is employed. To reduce the transportation cost and risk, Half Open Multi-Depot Heterogeneous Vehicle Routing Problem is used. Moreover, a mixed-integer programming approach is used to deal with the problem. To produce Pareto optimal solutions, an intelligent algorithm based on the epsilon constraint approach and genetic algorithm is created. A scenario of Auckland Transport is being used to validate the concept of offloading the information onto the buses for energy-efficient and delay-tolerant data transfer. Therefore the experiments have demonstrated that the buses may be used effectively to carry out the data by customer requests while using 30% of less energy than the other systems. [ABSTRACT FROM AUTHOR]

Computation offloading is the solution for IoT devices of limited resources and high-cost processing requirements. However, the network related issues such as latency and bandwidth consumption need to be considered. Data transmission reduction is one of the solutions aiming to solve network related problems by reducing the amount of data transmitted. In this paper, we propose a generalized formal data transmission reduction model independent of the system and the data type. This formalization is based on two main ideas: 1) Not sending data until a significant change occurs, 2) Sending a lighter size entity permitting the cloud to deduct the data captured by the IoT device without actually receiving it. This paper includes the mathematical representation of the model, general evaluation metrics formulas as well as detailed projections on real world use cases. [ABSTRACT FROM AUTHOR]

Fog computing is a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and location-awareness Internet of Things (IoT) services. After the emergence of IoT-based services, the industry of internet-based devices has grown. The number of these devices has raised from millions to billions, and it is expected to increase further in the near future. Thus, additional challenges will be added to the traditional centralized cloud-based architecture as it will not be able to handle that growth and to support all connected devices in real-time without affecting the user experience. Conventional data aggregation models for Fog enabled IoT environments possess high computational complexity and communication cost. Therefore, in order to resolve the issues and improve the lifetime of the network, this study develops an effective hierarchical data aggregation with chaotic barnacles mating optimizer (HDAG-CBMO) technique. The HDAG-CBMO technique derives a fitness function from many relational matrices, like residual energy, average distance to neighbors, and centroid degree of target area. Besides, a chaotic theory based population initialization technique is derived for the optimal initial position of barnacles. Moreover, a learning based data offloading method has been developed for reducing the response time to IoT user requests. A wide range of simulation analyses demonstrated that the HDAG-CBMO technique has resulted in balanced energy utilization and prolonged lifetime of the Fog assisted IoT networks. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing (MCS) has been an effective sensing paradigm by exploiting the pervasive sensor-rich mobile devices for sensor data collection. Online task assignment is an important issue for mobile crowdsensing since tasks typically arrive dynamically and need to be handled in an online manner. In this paper, we study online task assignment for maximizing the total profit of the MCS platform while satisfying the time window requirement of each task. We first describe the crowdsensing model and then study the online task assignment in the following two different scenarios: (1) user-offline-arriving scenario, where all users are fully available throughout the whole sensing period and their movements are fully planned by the platform; (2) user-online-arriving scenario, where users arrive and depart dynamically and each user has a specific participatory time window for task executions. For the former scenario, we propose a benchmark algorithm and also an online heuristic algorithm. The benchmark algorithm tries to provide a best-case performance by assuming all future task arrival information is known in advance. The online algorithm adopts bipartite-matching-based strategy for task assignment and further performs minimal detour based data offloading for reducing the data upload cost, whenever possible. For the latter scenario, we propose an effective online algorithm, which adopts a maximum-profit-first strategy for task assignment and also minimal detour based data offloading for reduction of data upload cost whenever applicable. For all the proposed algorithms, we present their detailed design and deduce their time complexities. Extensive simulations are conducted and the results demonstrate that our proposed algorithms can largely increase the total profit of the platform as compared with existing work. [ABSTRACT FROM AUTHOR]

Recent advancements in the Internet of Things (IoT) have enhanced the quality of life globally. Billions of devices are brought under the ambit of IoT to make them smarter. IoT-based applications are generating voluminous data and managing this widespread amount of data in real-time through Cloud Technology, which offers high computational and storage facilities. However, sending all data to the cloud can bring serious concerns for applications, which are critical and require instant action without any delay. Edge computing has recently emerged as an effective technology to handle the instant processing of tasks of IoT-based applications locally. Additionally, an important concern in IoT networks is response to emergency tasks on time to increase the performance of large-scale IoT systems. As such, scheduling of tasks becomes vital, where emergency and non-emergency tasks can be prioritized to offload data to the nearby edge and cloud servers respectively and enhance Quality of Service (QoS). The execution order of tasks and allocating resources for computation to avoid delays are two of the most important factors that must be addressed during task scheduling in Edge Computing. With the aforementioned issues, we design a Priority aware Task Scheduling (PaTS) algorithm for sensor networks to schedule priority aware tasks to offload data on edge and cloud servers. The problem is formulated as a multi-objective function and the efficiency of the proposed algorithm is evaluated using the Bio-inspired NSGA-2 technique. The overall improvement for average queue delay, computation time, and energy obtained for 200 tasks is 17.2%, 7.08% and 11.4%, respectively. The results obtained show significant improvement when compared with the benchmark algorithms demonstrating the effectiveness of the proposed solution. Similarly, comparative results for tasks when increased from 200 to 1000 tasks also shows subsequent improvements.

Due to the advancements of high-speed networks, mobile edge computing (MEC) has received significant attention to bring processing and storage resources in client's proximity. The MEC is also a form of Edge Network or In-network computing where the resources are brought closer to the user end (edge) of the network while increasing QoE. On the other hand, the increase in the utilization of the internet of things (IoT) gadgets results in the generation of cybersecurity issues. In recent times, the advent of machine learning (ML) and deep learning (DL) techniques paves way in the detection of existing traffic conditions, data offloading, and cyberattacks in MEC. With this motivation, this study designs an effective deep learning based data offloading and cyberattack detection (DL-DOCAD) technique for MEC. The goal of the DL-DOCAD technique is to enhance the QoE in MEC systems. The proposed DL-DOCAD technique comprises traffic prediction, data offloading, and attack detection. The DL-DOCAD model applies a gated recurrent unit (GRU) based predictive model for traffic detection. In addition, an adaptive sampling cross entropy (ASCE) approach is employed for the maximization of throughput and decision making for offloading users. Moreover, the birds swarm algorithm based feed forward neural network (BSA-FFNN) model is used as a detector for cyberattacks in MEC. The utilization of BSA to appropriately tune the parameters of the FFNN helps to boost the classification performance to a maximum extent. A comprehensive set of simulations are performed and the resultant experimental values highlight the improved performance of the DL-DOCAD technique with the maximum detection accuracy of 0.992. [ABSTRACT FROM AUTHOR]

• Chiming in at the advent of 6 G, communication security is increasingly taken into account. • Use efficient iterative method to optimize the variables that affect the cost. • In order to cut system costs down, we propose an interference power price control algorithm based on simulated annealing algorithm, which prioritizes ground users based on this mechanism. • We have innovatively utilized idle roadside units by collaborating with ground users to interfere with eavesdroppers eavesdropping on data, thereby improving the security of the data transmission link. • Ensure that safety performance is maximized in the case of the highest group interest. For the past few years, the security of data transmission links has been increasingly valued. However, with the increasing requirements for the security performance of data transmission links, how to cut the cost of upgrading while improving security has become a serious challenge. In response to the issue of ensuring low-cost and secure data transmission in wireless communication environments with eavesdroppers, this article innovatively starts with idle roadside units and studies how to incentivize roadside units to transmit interference power to interfere with eavesdroppers, thereby efficiently and economically improving the security performance of mobile user data transmission. Firstly, the impact of roadside unit transmission interference power on the security performance of data transmission links was analyzed by establishing a theoretical model; Then, this paper proposes a three-layer model consisting of ground user groups, eavesdropping users, roadside units, and UAVs equipped with mobile edge computing servers. In this model, by encouraging idle roadside units to send interference power, ground users are assisted to safely unload their own data to mobile edge computing servers. Meanwhile, considering the selfishness of idle roadside units, an improved algorithm based on simulated annealing algorithm is proposed to achieve the optimal mapping between ground users and idle roadside units. Finally, through the analysis of simulation results, this scheme can achieve the goal of reducing system costs while improving the security of data transmission. [ABSTRACT FROM AUTHOR]

The data offloading mechanism is one of the critical strategies needed on edge networks to help cloud computing network performance in serving user data requests. This strategy should be optimized to prevent network congestion. The main problem of this strategy is how to assess the priority of cached data so that the cache memory buffer capacity can be optimized. In this paper, we modeled the cached data offloading strategy using the Knapsack Problem 0/1 (KP01) approach. Several researchers proposed a meta-heuristic algorithm to solve cached data offloading using the KP01 approach. Meta-heuristic algorithms require a reliable solution selection method to find the global optimal solution. However, some studies still use the roulette wheel selection method to provide a set of solutions. The RWS method has a weakness of imbalance the particle fitness with its cumulative probability. Therefore, it is difficult to find the global optimal solution. This study proposed a nested-Roulette Wheel Selection (nRWS) method on hybrid Ant Colony Optimization (ACO) and Genetic Algorithm (GA) to address the cached data offloading optimization using the KP01 approach. The simulation results show that the proposed nRWS method is able to find the global optimal solution in terms of the value of the objective function and hit ratio which is superior to previous studies.

This paper proposes a predicted k-hop-limited multi-RSU-considered (PKMR) vehicle to vehicle to roadside unit (RSU) (VVR) data offloading method based on the architecture of the Software Defined Network (SDN) controller inside the multi-access edge computing (MEC) server. In the proposed method, a source vehicle that wants to offload data traffic can use a VVR path that connects the source vehicle and the ahead/rear RSU to perform RSU data offloading when the source vehicle approaches the ahead RSU or leaves the rear RSU. Since some RSUs’ signal ranges may overlap, multi-RSU deployment and RSU handoff between signal-overlapping RSUs must be managed to utilize VVR-based RSU data offloading as much as possible. Based on a vehicle’s periodically reported contexts received by the MEC server, the SDN controller inside the MEC server can execute the proposed PKMR method, which adopts (i) the time-extended prediction mechanism to find the potential VVR paths that exist in a coming time period [tc, tc+T] and (ii) a quality function that takes vehicles’ and RSUs’ network conditions into consideration to select the most suitable VVR data offloading path. The performance evaluation results indicate that the proposed PKMR method produces better data offloading performance than the traditional self-offloading method.

User Equipment (UE) is equipped with limited cache resources that can be utilized to offload data traffic through device-to-device (D2D) communications. Data caching at a UE level has the potential to significantly alleviate data traffic burden from the backhaul link. Moreover, in wireless networks, users exhibit mobility that poses serious challenges to successful data transmission via D2D communications due to intermittent connectivity among users. Users' mobility can be exploited to efficiently cache contents by observing connectivity patterns among users. Therefore, it is crucial to develop an efficient data caching mechanism for UE while taking into account users' mobility patterns. In this work, we propose a mobility-aware data caching approach to enhance data offloading via D2D communication. First, we model users' connectivity patterns. Then, contents are cached in UE' cache resources based on users' data preferences. In addition, we also take into account signal-to-interference and noise ratio (SINR) requirements of the users. Hence, our proposed caching mechanism exploits connectivity patterns of users to perform data placement based on users' own demands and neighboring users to enhance data offloading via cache resources. We performed extensive simulations to investigate the performance of our proposed mobility-aware data caching mechanism. The performance of our proposed caching mechanism is compared to most deployed data caching mechanisms, while taking into account the dynamic nature of the wireless channel and the interference experienced by the users. From the obtained results, it is evident that our proposed approach achieves 14%, 16%, and 11% higher data offloading gain than the least frequently used, the Zipf-based probabilistic, and the random caching schemes in case of an increasing number of users, cache capacity, and number of contents, respectively. Moreover, we also analyzed cache hit rates, and our proposed scheme achieves 8% and 5% higher cache hit rate than the least frequently used, the Zipf-based probabilistic, and the random caching schemes in case of an increasing number of contents and cache capacity, respectively. Hence, our proposed caching mechanism brings significant improvement in data sharing via D2D communications. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITS) is a smart-transportation system for road-side assistance and data exchange support by integrating cloud and wireless networks. ITS facilitates vehicle-to-vehicle and vehicle-to-anything (V2X) data exchanges for satisfying user demands. The rate of big data granting to the vehicular users is interrupted by the fundamental attributes such as mobility and link instability of the vehicles. To address the issues in vehicular data exchange big data, this article introduces displacement-aware service endowment scheme with the benefits of data offloading. Displacement-aware big data endowment ensures responsive availability of vehicle request information despite unfavorable location and density factors. The time congruency in V2V and V2X data exchanges are adopted for minimizing data exchange dropouts. In the data offloading phase, extraneous information and big data responses are detained based on data exchange relevance to improve congestion free big data endowment. The distinct methods work in a co-operative manner to improve big data quality of fast configuring smart vehicles to provide reliable big data in smart city environments. [ABSTRACT FROM AUTHOR]

In this paper, we propose a mobile blockchain (MB) based mining-as-a-service (MaaS) scheme, MB-MaaS for resource-constrained industrial internet-of-things (IIoT) environments. The scheme addresses the research gaps of fixed static allocation for miners to perform computationally intensive mining tasks through a multi-hop computational offloading (CO) scheme and addresses an auction mechanism for a fair bidding process among the miner nodes. The scheme operates in three phases. In the first phase, a multi-hop CO scheme with a fair incentive policy is formulated for miners. The CO schemes offer guaranteed offloading services to mobile devices from far-edge systems through a chain of neighbor nodes. Then, in the second phase, MaaS is proposed to leverage expensive mining tasks through 5G-enabled pico/femtocells. Integration of 5G allows massive end-to-end device and service connectivity. As IIoT ecosystems have limited memory and compute requirements, MaaS assures that the proposed consensus has a responsive validation and mining time. To make the data exchange in the consensus process lightweight, and allow a large number of sensors to share the data in a lightweight manner, an effective consensus mechanism Lightweight Proof-of-Proximity (LPoP), is proposed that forms group validations instead of single block validation. The data is exchanged through javascript object notation (JSON) format, maintaining a steady transaction rate. MB-MaaS is compared against the existing scheme for parameters bid thresholds and request servicing times, and mining and consensus formation. For example, the request serving time at 12 requests is improved by 56.78%, and a significant improvement of 26.47% is observed for processed blocks; parsing time on average is improved by 7.89%. The comparative analysis suggests that the scheme is more efficient than other competing approaches. • A fair reward and incentive policy is proposed for miner nodes through a multi-hop CO scheme in 5G-MEC. • A 5G-femtocell design is proposed to address latency constraints and expedite resource requirements. • A lightweight consensus scheme Lightweight Proof-of-Proximity (LPoP) is presented for IIoT ecosystems. • The evaluation is carried out in terms of BC node characteristics, collusive bidding, and femtocell design. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) is utilized for collecting enormous real time information driven traffics and alert drivers depending on situations. In recent times, all smart vehicles are developed with IoT devices. These devices communicate with a radio access unit (RAU) at road side. Moreover, a 5G system is equipped with a base station and connection interfaces that use optic fiber for their effective communication. For a fast mode of communication, the IoV must offload its data to the nearest edge nodes. The main problem with the IoV is that it generates enormous data which is offloaded randomly during the journey. This data exceeds the memory of the edge or road side unit (RSU) devices. This feature also causes substantial energy usage and high storage cost. To overcome the above issues, hybrid optimization techniques are suggested to offload the data with an energy efficient destination. In this research, the Clustered Block Chain Based Grasshopper Optimizer (BC-GAO) Based Task Offloading is implemented for a green IoV. The block chain provides a secure environment for sharing extensive data. The IoV data are clustered according to the vehicle location, and the grasshopper optimization is employed in selecting the RSU to offload the data. The result for the proposed technique is evaluated using OPNET Modulator and MATLAB. The simulated results are compared with the other existing techniques such as Random Offloading, Full Offloading, Mobility Aware Task Offloading, and the Lyapunov-based Dynamic Offloading Decision algorithm. The proposed algorithm computed 100 tasks in 33.27 s by consuming 19.23 joule energy, a value which is lower than all other existing techniques. [ABSTRACT FROM AUTHOR]

Edge computing-based framework is capable of improving users’ quality of experience in cognitive Internet of Things (IoT) networks. To explore the advantages of this edge computing-based framework, possible offloading and processing delay resulting from computation bottlenecks, and the offloading latency caused due to inter-cell interference must be properly considered. This paper thus considered a multi-class channel access mechanism for cognitive edge computing-based IoT networks where IoT users were categorized based on their quality of experience requirements. Essential IoT devices are permitted to offload to the edge server at any time following the hybrid channel access model, while delay-tolerant IoT devices are only permitted to offload to the server when the channel is idle following the overlay channel access model. Analyses were obtained for transmission rate and offloading delay to demonstrate the performance of the proposed mechanism, while important metrics such as total offloading latency and total offloading cost were investigated. The total offloading costs were formulated through the mixed strategy Nash equilibrium method. The proposed mechanism achieves lower offloading latencies and costs for both type 1 and type 2 CUs when compared with existing methods. The obtained results showed that multi-class channel access mechanisms can reduce packet offloading delay in cognitive edge computing-based IoT networks. [ABSTRACT FROM AUTHOR]

Multi-access Edge Computing (MEC), as an extension of cloud computing, provides storage resources at the network edge to enable low-latency data retrieval for users. Due to limited physical sizes and constrained storage resources, individual edge servers cannot store a large amount of data when operating independently. They often need to offload data to other edge servers to serve users collaboratively. Operated by different edge infrastructure providers, edge servers usually work in a distrusted environment. Incentive and trust are the two main challenges in facilitating collaborative edge storage. This article proposes CSEdge, a novel decentralized system that tackles these challenges to enable collaborative edge storage based on blockchain. On CSEdge, edge servers can submit data offloading requests for others to contend for. Winners are selected based on their reputations. They will store the offloaded data and receive rewards for successfully finishing data offloading tasks. Via a distributed consensus, their performance will be recorded on blockchain for future reputation evaluation. A prototype of CSEdge is built on Hyperledger Sawtooth and experimentally evaluated against a baseline system and two start-of-the-art systems in a simulated MEC environment. The results demonstrate that CSEdge can effectively and efficiently facilitate collaborative edge storage among edge servers. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves amethod in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling cross entropy (ASCE) technique is utilized for data offloading in MEC systems. Moreover, the modified salp swarm algorithm (MSSA) with extreme gradient boosting (XGBoost) technique was implemented to identification and classification of cyberattack that exist in the MEC systems. For examining the enhanced outcomes of the AIMDO-SMEC technique, a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDOSMEC technique with the minimal completion time of tasks (CTT) of 0.680. [ABSTRACT FROM AUTHOR]

Machine type communication devices proposed as one of the substantial data collections in the 5G of wireless networks. However, the existing mobile communication network is not designed to handle massive access from the MTC devices instead of human type communication. In this context, we propose the device-to-device communication assisted a mobile terminal (smartphone) on data computing, focusing on data generated from a correlated source of machine type communication devices. We consider the scenario that the MTC devices after collecting the data will transmit to a smartphone for computing. With the limitation of computing resources at the smartphone, some data are offloaded to the nearby mobile edge-computing server. By adopting the sensing capability on MTC devices, we use a power exponential function to compute a correlation coefficient existing between the devices. Then we propose two grouping techniques K-Means and hierarchical clustering to combine only the MTC devices, which are spatially correlated. Based on this framework, we compare the energy consumption when all data processed locally at a smartphone or remotely at mobile edge computing server with optimal solution obtained by exhaustive search method. The results illustrated that; the proposed grouping technique reduce the energy consumption at a smartphone while satisfying a required completion time. [ABSTRACT FROM AUTHOR]

WiFi Access Points (APs) can be used to offload data or computation tasks while users are commuting. However, due to APs’ limited coverage, offloading performance is heavily impacted by the users’ mobility. This work proposes to leverage human mobility to inform offloading tasks, taking a data based approach leveraging granular mobility datasets from two cities: Porto and Beijing. We define Offloading Regions (ORs) as areas where a commuter’s mobility would enable offloading, and propose an unsupervised learning methodology to extract ORs from mobility traces. Then, we characterise and analyse ORs according to offloading opportunity metrics such as type, availability, total time to offload, and offloading delay. Results show that in 50% of the trips, users spend more than 48% of the travel time inside ORs extracted according to the proposed methodology. The ability to predict the next ORs would benefit offloading orchestration. Offloading mobility predictability, although crucial, proves to be challenging, expressed by the poor predictive performance of well-known models ($\approx $ 37% acc. for the best predictor). We show that mobility regularity properties improve predictive performance up to $\approx $ 35%. Finally, we look into the impact of further OR extraction and prediction parameters. We show that the exploration phase length does not impact the discovery of low relevance ORs, and that both filtering low relevance OR and predicting multiple ORs increase predictability. By characterising the trade-off between mobility predictability and offloading opportunities in transit, we highlighting the need for offloading systems to adopt hybrid strategies, i.e., mixing opportunistic and predictive strategies. The conclusions and findings on offloading mobility properties are likely to generalise for varied urban scenarios given the high degree of similarity between the results obtained for the two different and independently collected mobility datasets. [ABSTRACT FROM AUTHOR]

Intelligent telemedicine technology has been widely applied due to the quick development of the Internet of Things (IoT). The edge-computing scheme can be regarded as a feasible solution to reduce energy consumption and enhance the computing capabilities for the Wireless Body Area Network (WBAN). For an edge-computing-assisted intelligent telemedicine system, a two-layer network architecture composed of WBAN and Edge-Computing Network (ECN) was considered in this paper. Moreover, the age of information (AoI) was adopted to describe the time cost for the TDMA transmission mechanism in WBAN. According to the theoretical analysis, the strategy for resource allocation and data offloading in edge-computing-assisted intelligent telemedicine systems can be expressed as a system utility function optimizing problem. To maximize the system utility, an incentive mechanism based on contract theory (CT) was considered to motivate edge servers (ESs) to participate in system cooperation. To minimize the system cost, a cooperative game was developed to address the slot allocation in WBAN, while a bilateral matching game was utilized to optimize the data offloading problem in ECN. Simulation results have verified the effectiveness of the strategy proposed in terms of the system utility.

The exponential growth of video data in networks has led to video flow occupying a significant proportion of network traffic, causing congestion and poor service quality. To address this issue, it is crucial to quickly offload data and ensure high-quality service for users, especially in the context of cloud-edge collaboration. We propose a strategy for collaborative data offloading between cloud and edge computing, analogous to water flow (WFO). When users simultaneously access the same data from the same data source, WFO can serve more users within the limited bandwidth of the cloud while maintaining the quality of service. WFO creates a water flow-like data link between nodes to enable data offloading, using multiple nodes in collaboration to offload data for a single node. Experimental results show that compared with typical methods, such as fair-queue and first-come-first-served, WFO can significantly reduce the data offloading delay, guarantee service quality, and effectively reduce network congestion. Moreover, the number of service nodes can be as numerous as possible.

Internet of Things (IoT) is characterized by the large volumes of data collection. Since IoT devices are themselves resource-constrained, this data is transferred to cloud-based systems for further processing. This data collected over a period of time possesses high utility as it is useful for multiple analytical, predictive and prescriptive tasks. Therefore, it is crucial that IoT devices transfer the collected data to network gateways before exhausting their storage to prevent loss of data; this issue is referred to as the "data offloading problem". This paper proposes a technique for fault tolerant offloading of data by IoT devices such that the data collected by them is transferred to the cloud with a minimal loss. The proposed technique employs opportunistic contacts between IoT and mobile fog nodes to provide a fault tolerant enhancement to the IoT architecture. The effectiveness of the proposed method is verified through simulation experiments to assess the reduction in data loss by use of proposed data offloading scheme. It is demonstrated that the method outperforms a state-of-art method. [ABSTRACT FROM AUTHOR]

The amount of traffic in wireless networks is increasing exponentially, and this problem can be mitigated using device-to-device (D2D) caching technology, which installs a cache on a mobile end device. Devices can reduce the cell load through self-offloading via content in their own cache and D2D offloading using content in others' caches. However, especially in the early stage of D2D caching systems, a limited number of devices with a small storage might be used, and it is required to develop a caching scheme with excellent performance despite the small cache size. Regarding content popularity, which is common to most users, the preference probability values are not concentrated on some pieces of content, making it difficult to achieve satisfactory performance using a small cache. On the other hand, when considering individual users, content preferences may contain large values for specific content based on individual characteristics. In addition, the performance can be improved by considering short-term content preferences that reflect changes in content preferences over time or newly created content during peak hours. In this article, the hit ratio is divided into six parts considering self- and D2D offloading, common and individual user preferences, and little and large temporal changes in content preferences during peak hours. We also conceptually divide the cache of a helper into six areas in relation to the six parts of the hit ratio, and discuss cache partitioning and proactive caching strategies according to the environment.

Mobile edge computing (MEC) becomes popular as it offers cloud services and functionalities to the edge devices, to enhance the quality of service (QoS) of end-users by offloading their computationally intensive tasks. At the same time, the rise in the number of internet of things (IoT) objectives poses considerable cybersecurity issues owing to the latest rise in the existence of attacks. Presently, the development of deep learning and hardware technologies offers a way to detect the present traffic condition, data offloading, and cyber-attacks in edge networks. The utilization of DL models finds helpful in several domains in which the MEC provides the decisive beneficiary of the approach for traffic prediction and attack detection since a large quantity of data generated by IoT devices enables deep models to learn better than shallow approaches. In this view, this paper presents a new DL based traffic prediction with a data offloading mechanism with cyber-attack detection (DLTPDO-CD) technique. The proposed model involves three major processes traffic prediction, data offloading, and attack detection. Initially, bidirectional long short term memory (BiLSTM) based traffic prediction to enable the proficient data offloading process. Then, the adaptive sampling cross entropy (ASCE) technique is executed to maximize the network throughput by making decisions related to offloading users to the WiFi system. Finally, a deep belief network (DBN) optimized by a barnacles mating optimizer (BMO) algorithm called BMO-DBN is applied as a detection tool for cyberattacks in MEC. Extensive simulation is carried out to ensure the proficient performance of the DLTPDO-CD model. The experimental outcome stated the superiority of the presented model over the compared methods under different dimensions.

The fifth-Generation mobile network introduces a new technology called Device-to-device (D2D) Communication which allows User Equipment (UE) to communicate directly with each other, without the need for Base Station or Evolved Node–B (eNB). Thus the mobile data traffic over the eNB can be reduced and throughput can be increased. The objective of this paper is to provide relaying service in cellular network, which has the capability of expanding the network coverage and reducing the traffic over cellular network by using D2D communication. It is achieved by using a technology called Proximity Service (ProSe), in which ProSe Direct Communication and ProSe Direct Discovery enable to identify the UE which are out of network coverage. Then the relay node is selected for serving the UE which are out of network coverage on the basis of Channel Quality Indicator (CQI) and the signal strength between the UE that are in coverage and the UE that are out of network coverage. The UE whose CQI value is high are selected based on certain criteria and are allowed to act as relays. The experiments show that the proposed technique with 5 UERelay maintains the D2D communication for longer period than the traditional techniques. Thus, the UE that are out of network coverage are served for more duration by extending the network coverage of the eNB and by relaying data using D2D offloading. [ABSTRACT FROM AUTHOR]

Data aggregation and dissemination in cloud-based internet of things (IoT) are main issues because of interoperability problems in communication. In an IoT environment, data handling and offloading are constant processes that avoid communication failure and increase service utilization levels. This paper introduces a machine learning (ML)-assisted data aggregation and offloading (ML-DAO) system to improve the reliability of cloud–IoT communication. The method introduced helps reduce the response time and routing cost errors in data aggregation and improve the data service rate. The data handling rate is also enhanced using the IoT assisted by fog elements that maximize edge-level communication. Cloud–IoT communication quality is measured on the basis of time and service attributes; ML techniques are designed to enhance the level's precision while aggregating the data. To achieve optimum communication quality, the proposed ML-DAO operates on certain measurable functional metrics. The performance of the system is assessed using the following metrics: route cost error, processing time, aggregation delay, service utilization rate, failure probability, and response time. Experimental results prove the consistency of the proposed scheme, as the metrics are optimized with lesser unallocated data chunks. [ABSTRACT FROM AUTHOR]

Abstract Recent years have seen a significant growth in Internet of Things (IoT) technology consisting of a large number of devices embedded with sensors and deployed to perform monitoring and actuation tasks. The IoT devices collect large volumes of data that is usually uploaded to cloud to perform analytics and predictions. One of the main challenges in IoT is the transportation of large-scale data collected over a period of time at a remote site. Cellular networks are already facing explosive growth of mobile data traffic due to the proliferation of smart devices and traffic-intensive applications. An alternate solution is to perform the data offloading, where a portion of data can be offloaded from primary links and transferred using opportunistic terminal-to-terminal (T2T) network that relies on direct communication between mobile users, without any need for an infrastructure backbone. However, such approach may lead to data loss and delay if dynamics of time-varying topology and mobility of nodes is not taken care of. To address this challenge, we propose three prediction-based offloading schemes that exploit the mobility patterns and temporal contacts of nodes to predict future data transfer opportunities. We have utilized the High-level Petri Nets to model and formally analyzed the communication processes of the proposed schemes. The new symbolic model verifier (NuSMV) has been employed for the verification of the three schemes against the identified constraints. The verification results affirm the correctness and scalability of the models. The protocols are evaluated with performance metrics, such as the delivery ratio, latency, and overhead. Our results indicate significant improvement in performance compared to existing approaches.

A large number of connected sensors and devices in Internet of Things (IoT) can generate large amounts of computing data and increase massive energy consumption. Real-time states monitoring and data processing of IoT nodes are of great significance, but the processing power of IoT devices is limited. Using the emerging mobile edge computing (MEC), IoT devices can offload computing tasks to an MEC server associated with small or macro base stations. Moreover, the use of renewable energy harvesting capabilities in base stations or IoT nodes may reduce energy consumption. As wireless channel conditions vary with time and the arrival rates of renewable energy, computing tasks are stochastic, and data offloading and renewable energy aware for IoT devices under a dynamic and unknown environment are major challenges. In this work, we design a data offloading and renewable energy aware model considering an MEC server performing multiple stochastic computing tasks and involving time-varied wireless channels. To optimize data transmission delay, energy consumption, and bandwidth allocation jointly, and to avoid the curse of dimensionality caused by the complexity of the action space, we propose a joint optimization method for data offloading, renewable energy aware, and bandwidth allocation for IoT devices based on deep reinforcement learning (JODRBRL), which can handle the continuous action space. JODRBRL can minimize the total system cost(including data buffer delay cost, energy consumption cost, and bandwidth cost) and obtain an efficient solution by adaptively learning from the dynamic IoT environment. The numerical results demonstrate that JODRBRL can effectively learn the optimal policy, which outperforms Dueling DQN, Double DQN (DDQN), and greedy policy in stochastic environments.

Data offloading through vehicular ad hoc networks (VANETs) is one of the most promising methods for overcoming the overload problem in cellular networks. As data delivery by service providers consumes resources such as bandwidth, storage and power, the incentive scheme with the optimal pricing strategy must be identified. In the literature, most incentive schemes focus on offloading through fixed nodes, such as roadside units (RSUs). It remains very challenging to motivate a moving vehicle to help other users deliver their data due to the high mobility of vehicles. Game theory is a widely adopted method for analyzing pricing issues in wireless networks. Therefore, this paper proposes an optimal pricing strategy that uses the Stackelberg game to model the interaction between a service provider and a service requester. Then, the Stackelberg equilibrium is derived under the corresponding conditions. Next, an algorithm is proposed for selecting the service provider that offers the lowest price based on the results of the Stackelberg equilibrium. Finally, the simulation results demonstrate that the proposed algorithm can effectively reduce the downloading time of a task while maximizing the utilities of both the service provider and the requester.

With the emergence of affordable smart mobile devices (such as smartphones and tablets) running innovative applications have severely overloaded the cellular network. To cope with this issue, there have been many efforts to offload the traffic from the cellular network to other complement networks, for instance, Wi-Fi and device-to-device (D2D) communications. In this paper, we formulate the traffic offloading issue as a link prediction problem in opportunistic D2D network, which is targeted to alleviate the overburdened cellular network traffic and reduce the delay time. Considering the complexity of realistic networks, we employ three indexes of link prediction: common neighbors, Katz, and LRW index. To measure the performance of our proposed algorithm, we analyze it is offloading traffic capacity along with delay minimization among users in different networks. It is demonstrated that our proposed link prediction solution can efficiently offload up to 80% of the cellular traffic.

The mobile phone is currently the most wid- ely used and most convenient photographic device. Like many optical imaging devices the camera of mobile ph- one are far from flawless, such as limited field of view and depth of focus in photos. Due to its limited computing power, it is not possible to use a mobile phone to fuse images. In addition, the multimedia communication need a good network connection and bandwidth. In this paper, we solve this problem by proposing an image fusion strategy and mobile data offloading method based on cloud computing platform. We design a cloud computing environment to fuse the images captured by the mobile phone where mobile terminal uploads and downloads images to the cloud through 5G signal. And a continuous time Markov model is applied to offload the mobile network data. We fuse the original images to all-in-focus image and 3D stereo image based on Non Subsampled Contourlet Transform and Detail-preserving & Content-aware Variational multiple images fusion method, respectively. The experimental results show that the proposed image fusion and data offloading method achieves satisfactory image quality and execution performance. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) paradigm is designed to meet the user requirements by providing cloud services at the edge of the user network. Blockchain technology with the Edge Computing (EC) paradigm is reliable in delivering the edge services depending on user requirements and improving the distributed management of resources at ease. In this article, blockchain-assisted data offloading for Availability Maximization (BDO-AM) is introduced. This proposed approach is presented to thwart the non-probabilistic (NP) hardness problem of data availability due to prolonging backlogs. This approach classifies the different instances of data availability and delivery for the edge-connected end-user services/applications. The classification is preceded by using Naïve Bayes’ classification to identify the offloading instances to prevent unnecessary backlogs. The probability of data transmission, delivery, and offloading are independently analyzed for their likelihood, and the appropriate available time instances are allocated in a distributed manner. This validation helps to maximize data delivery by reducing the data drops and service delays. [ABSTRACT FROM AUTHOR]

Offloading among multiple radio access technologies (RATs) is an effective solution to tackle some of the key challenges faced by 5G wireless networks. While making an offloading decision, in user-centric offloading schemes, the network context is not taken into account, which limits the full utilization of the potential offered by these techniques. Therefore, in this work, we develop a data offloading framework wherein a user defines a tuple, which includes a request for data service and the associated delay limit, and the user equipment (UE) translates this tuple into an optimal offloading policy by taking into account the network context. The data offloading problem is formulated by assuming a finite horizon Markov decision process (MDP), and an analytical data offloading model is derived by using stochastic geometry for modeling the spatial domain of a multi-RAT wireless network, and by employing Markov process for capturing mobility of users. In order to validate the performance of derived model, we also solve the offloading problem by using policy iteration (PI) algorithm. The results show that the analytical algorithm outperforms the iterative algorithm. We also benchmark the proposed framework against standard data offloading methods. [ABSTRACT FROM AUTHOR]

Mobile Ad-hoc Network (MANET) is the dynamic infrastructure-less network that contains a heterogeneous wireless mobile devices. There are several issues that can occur in MANET such as limited bandwidth, dynamic topology, routing overhead, and battery constraints. In this paper, the heterogeneous MANET comprising of different MAC layer protocols are considered. The problems faced by the Heterogeneous Secure-dEed-Reflection-Inducement-eState (He-SERIeS) algorithm are scalability and offloading. In order to resolve these challenges of the existing SERIeS algorithm, a He-SERIeS algorithm is designed. The Cluster Head (CH) is elected using the on-demand clustering algorithm and the nodes with similar requirements are clustered into a distinct cluster using the similarity measure algorithm. The auxiliary key management schemes such as MD5 and SHA1 are used to predict the faulty nodes. The performance of the proposed He-SERIeS algorithm is analyzed with the existing algorithms such as Random Cache (RC), Centrality based Cache (CC), Set Cover Based Caching (SCBC) and SERIeS algorithm. The metrics used for comparison are throughput, Packet Delivery Ratio (PDR), end-to-end delay, replication overhead and offloading ratio. The proposed scheme achieved 92.8% PDR, a high throughput of 96.6% and a high offloading ratio of 0.532, less replication overhead, and end-to-end delay.

Rapid developments in network technologies and mobile devices improve our lives, but the resultant fast-growing traffic leads to a lack of cellular network resources and breaks the balance between profit and costs for network providers. Data offloading can relieve the loads on network infrastructure and shift data to a complementary wireless technology, thereby increasing maximum throughput and extending network coverage. In this paper, we propose a terminal-to-terminal-based offloading method called Virtual Bank with movement prediction (VBMP), which uses an opportunistic network to achieve offloading. We establish a virtual trading system to manage information transmission. Meanwhile, node movement prediction and node subset selection via a submodular function are used to select the relay nodes and to discover the dynamic topology of mobile nodes. Theoretical analysis and simulation results demonstrate that VBMP effectively improves data offloading.

Recently, for satisfying users' various mobile Internet service requests for data exchange anytime and anywhere even in their moving vehicles, the generated mobile data traffic has been rapidly increasing and has become a serious burden on current cellular networks. To partially address such a serve challenge, vehicular ad hoc networks (VANETs) have emerged as an effective approach for enhancing vehicular services and applications by equipping vehicles with wireless and processing capabilities. In this paper, we survey the recent advances in the data offloading techniques through VANETs. Particularly, based on the communication patterns among vehicles and infrastructures, we classify these techniques into three categories, i.e., data offloading through vehicle-to-vehicle communications, vehicle-to-infrastructure communications, and vehicle-to-everything communications. Besides, we present a detailed taxonomy of the related techniques by discussing the pros and cons for various offloading techniques for different problems in VANETs. Finally, some opening research issues and challenges are outlined to provide guidelines for future research work.