Mobile Edge Computing Server Research Articles

Federated Learning-Based Service Caching in Multi-Access Edge Computing System

Multi-access edge computing (MEC) brings computations closer to mobile users, thereby decreasing service latency and providing location-aware services. Nevertheless, given the constrained resources of the MEC server, it is crucial to provide a limited number of services that properly fulfill the demands of users. Several static service caching approaches have been proposed. However, the effectiveness of these strategies is constrained by the dynamic nature of the system states and user demand patterns. To mitigate this problem, several investigations have been conducted on dynamic service caching techniques that can be categorized as centralized and distributed. However, centralized approaches typically require gathering comprehensive data from the entire system. This increases the burden on resources and raises concerns regarding data security and privacy. By contrast, distributed strategies require the formulation of complicated optimization problems without leveraging the inherent characteristics of the data. This paper proposes a distributed service caching strategy based on federated learning (SCFL) that works efficiently in a distributed system with user mobility. An autoencoder model is utilized to extract features regarding the service request distribution of individual MEC servers. The global model is then generated using federated learning, which is utilized to make service-caching decisions. Extensive experiments are conducted to demonstrate that the performance of the proposed method is superior to that of other methods.

RS-DRL-based offloading policy and UAV trajectory design in F-MEC systems

For better flexibility and greater coverage areas, Unmanned Aerial Vehicles (UAVs) have been applied in Flying Mobile Edge Computing (F-MEC) systems to offer offloading services for the User Equipment (UEs). This paper considers a disaster-affected scenario where UAVs undertake the role of MEC servers to provide computing resources for Disaster Relief Devices (DRDs). Considering the fairness of DRDs, a max-min problem is formulated to optimize the saved time by jointly designing the trajectory of the UAVs, the offloading policy and serving time under the constraint of the UAVs' energy capacity. To solve the above non-convex problem, we first model the service process as a Markov Decision Process (MDP) with the Reward Shaping (RS) technique, and then propose a Deep Reinforcement Learning (DRL) based algorithm to find the optimal solution for the MDP. Simulations show that the proposed RS-DRL algorithm is valid and effective, and has better performance than the baseline algorithms.

Deep reinforcement learning‐based joint task offloading and resource allocation in multipath transmission vehicular networks

AbstractMobile edge computing (MEC) is increasingly being applied to task computation with the continuing increase of computing‐intensive applications in vehicular networks. The MEC can offload the tasks generated by the vehicle to the MEC server to mitigate the computational constraints on the mobile vehicle. However, most task offloading algorithms can not cope with the dynamic distribution of vehicles in vehicular networks. Moreover, most task offloading algorithms do not consider vehicular networks using multipath transmission schemes. Therefore, it remains a challenge to implement efficient joint task offloading and resource allocation (JTORA) algorithms in multipath transmission vehicular networks. In this paper, we introduce a novel cooperative transmission network architecture in multipath transmission vehicular networks. The computational tasks generated by the vehicles can be offloaded to MEC servers deployed near the base station or to cloud servers via multipath transmission. We formulate the task offloading and resource allocation problem as a multi‐objective optimization problem in a multipath transmission vehicular network. To achieve efficient task offloading and resource allocation under cooperative transmission network architecture, we propose a JTORA algorithm for multipath transmission vehicular networks based on deep reinforcement learning (DRL). JTORA converts the optimization problem into a DRL model and obtains a specific optimization policy through model training. Simulation results show that JTORA achieves lower average task delay and lower average task cost than other traditional heuristic algorithms.

A pipeline approach for privacy preservation against poisoning attacks in a Mobile Edge Computing environment

Mobile Edge Computing (MEC) has become increasingly popular due to its ability to provide real-time data processing and improved user experience. However, it also introduces potential security vulnerabilities, as malicious actors could exploit users’ position data stored in the MEC server. To address this challenge, it is essential to develop privacy-preserving schemes that are resistant to malicious activities, such as poisoning attacks. Previous works have not considered the evaluation of such schemes against certain types of poisoning attacks while ensuring sufficient utility, privacy, and scalability during the generation process of poisoning locations. Indeed, meeting the needs of the user or customers, keeping data secure and confidential, and having capacity to accommodate changes in demand are essential for any application or system to operate efficiently and securely. To address this limitation, we present a new pipeline approach for a privacy-preserving scheme against poisoning attacks in the MEC environment. We evaluate this method with a type of poisoning attack that ensures sufficient utility, privacy, and scalability during the generation process and assess its effectiveness in providing security and privacy. Our approach is evaluated on two data sets and the results are compared with three baseline approaches. The results show that our approach is more effective in detecting poisoning locations than the existing works without affecting the QoS metrics (bandwidth, resources consumption, latency and the storage) with should be consider when evaluating MEC method.

A computation offloading method with distributed double deep Q‐network for connected vehicle platooning with vehicle‐to‐infrastructure communications

AbstractCurrent connected vehicle applications, such as platooning require heavy‐load computing capability. Although mobile edge computing (MEC) servers connected to the roadside intelligence facility can assist such separable applications from vehicles, it is a challenge to coordinate the allocation of subtasks among vehicles and MEC servers on the premise of ensuring communication quality. Therefore, an offloading algorithm is proposed based on a double deep Q‐network to solve the placement of subtasks for vehicle to infrastructure and vehicle to vehicle cases. This algorithm considers the randomness of task generation and is model‐free. The MEC server can assist the vehicle in training the neural network and storing relevant state transitions. To improve the performance of the algorithm, the decaying greedy policy is incorporated for faster convergence. The simulation results showed that this algorithm performed well in reducing the dropped subtask rate, average time delay, and total energy consumption.

Joint fiber and MEC deployment for sparsely populated areas

The deployment of multi-access edge computing (MEC) networks gives rise to the MEC placement problem, which deals with finding the right server locations to reduce the cost and guarantee network performance. Multiple papers have been presented to solve this problem, but they are usually oriented to urban areas where short distances and high-quality network infrastructure are assumed. When this problem must be solved for sparsely populated areas, like rural environments, the connectivity is not always granted and the deployment of such connectivity using fiber technologies should be included in the problem. In contrast to urban areas, where the density of users is high and therefore the main problem is capacity, in sparsely populated areas, the problem lies in how to cost-effectively plan the MEC sites and the interconnecting network while meeting the delay constraints of the services offered through that network. This paper proposes a technique to solve the MEC placement problem considering the joint deployment of the optical network required to interconnect the base stations and the MEC servers. It consists of a three-phase scheme, which combines a spanning tree topology, for fiber deployment, with the use of mixed integer linear programming (MILP) formulations to minimize MEC servers and MEC data centers (MEC-DCs). We have applied the technique in a case study for a province in Spain (Valladolid, 8110km2 ), obtaining a reduction of around 50% of the total cost when compared to a previous work. In addition, a clustering method is proposed to improve the scalability of the model for large scenarios. A simulation study is also presented to demonstrate the performance of the proposal assuming a 94,226km2 region (Castilla y León) with 1576 base stations.

Enhancing Data Freshness in Air-Ground Collaborative Heterogeneous Networks through Contract Theory and Generative Diffusion-Based Mobile Edge Computing.

Mobile edge computing is critical for improving the user experience of latency-sensitive and freshness-based applications. This paper provides insights into the potential of non-orthogonal multiple access (NOMA) convergence with heterogeneous air-ground collaborative networks to improve system throughput and spectral efficiency. Coordinated resource allocation between UAVs and MEC servers, especially in the NOMA framework, is addressed as a key challenge. Under the unrealistic assumption that edge nodes contribute resources indiscriminately, we introduce a two-stage incentive mechanism. The model is based on contract theory and aims at optimizing the utility of the service provider (SP) under the constraints of individual rationality (IR) and incentive compatibility (IC) of the mobile user. The block coordinate descent method is used to refine the contract design and complemented by a generative diffusion model to improve the efficiency of searching for contracts. During the deployment process, the study emphasizes the positioning of UAVs to maximize SP effectiveness. An improved differential evolutionary algorithm is introduced to optimize the positioning of UAVs. Extensive evaluation shows our approach has excellent effectiveness and robustness in deterministic and unpredictable scenarios.

Key security measurement method of authentication based on mobile edge computing in urban rail transit communication network

In the rail transit industry, the communication network is also widely used in various professions. Rail transit stations have the characteristics of narrow space, high degree of airtightness, limited sight lines, and uneven distribution of personnel in various periods. Although the security degree of rail transit entrances and exits is relatively reliable compared with that of stations, the anti-risk ability is fragile, and the consequences are often disastrous. To solve the problems of low-security access rate, long response time and ineffective identification of attack components of urban rail transit communication platform users, a security measurement method based on mobile edge computing (MEC) for the authentication key of urban rail transit communication network is proposed. Firstly, the wireless communication service of the urban rail transit platform is analyzed, and the data key protocol is constructed to obtain the hyper-chaos key pair of wireless communication information. Then, the double chaotic mapping model is applied to determine the key form and input parameters, and the bit key stream is obtained, which is subjected to exclusive operation with the plaintext for key decryption. Next, the block matching structure is used to analyze the associated eigenvalues that affect the security risk of urban rail transit communication networks. Meanwhile, the MEC server is used to accelerate the extraction of its feature values. Finally, in the environment of MEC, the paper constructs a risk identification model, evaluates the security strength of the authentication key, controls the threshold value, analyzes the impact factors and weights of communication network security, and completes the traceability measurement of communication network security risk. Experimental results show that the security access times of the MEC platform are senior, the maximum attack component obtained is 23500, which is consistent with the actual value, and the global maximum response time is 0.30 ms.

Optimal dispatch of distributed renewable energy and energy storage systems via optimal configuration of mobile edge computing

AbstractThe access of distributed units leads to the rapid increase of power network information services, which brings great problems to the centralized dispatch of power system. To improve the efficiency of data processing and the flexibility of each unit dispatching, first, the areas are divided according to the load characteristics. An operating framework of distributed power system is presented based on offload strategy of mobile edge computing (MEC) and optimal allocation of computational quantity. Second, a novel hierarchical dispatching model is established based on the optimal configuration of MEC. In each area, the upper model takes the total energy consumption of the MEC server and the operating cost of the power system as the objective to calculate the output of each distributed unit and the computational quantity under the optimal offload strategy. The lower model ensures the complete accommodation of renewable energy and the optimal economic operation of the whole area through the introduction of electric vehicles (EVs) and secondary operation of energy storage systems under the constraint of MEC server computing delay. Third, the screening rules of the MEC system computing mode and the simulation strategies of EVs in different areas are proposed. Finally, the effectiveness and superiority of the model are verified by simulation experiments.

Task Migration and Resource Allocation Scheme in IoV With Roadside Unit

Mobile Edge Computing (MEC) has attracted attention for its short-range and low-latency computing services for the Internet of Vehicles (IoV). However, in the Vehicle-RSU-MEC environment, incomplete task migration is a problem when Vehicle Users(VUs) are moving at high speeds around the Road Side Units (RSUs). Additionally, during peak traffic periods or road congestion, completed tasks compete for MEC server computing resources, leading to load imbalance. Therefore, making reasonable migration and offloading decisions is an important challenge. To address this challenge, the paper proposes a Cooperative Offloading strategy to jointly optimize offloading Decisions and Allocation of computing resources (CODA) step by step. First, to solve the problem of incomplete task migration, CODA proposed a Greedy-Based Task Completion Migration (GBTCM) algorithm. The algorithm calculates the required RSU set for each task to achieve complete migration, and greedily searches for the optimal migration target in the corresponding set to reduce task transmission latency. Second, after completing the task migration, CODA proposed a Distance-Based Computing Resource Allocation (DBCRA) algorithm to achieve load balancing for MEC servers. The algorithm prioritizes distance and finds MEC servers with sufficient computing resources to achieve better load balancing performance. Experimental results have shown that CODA is a low-complexity algorithm applicable to IoV, which can make reasonable and rapid decisions for task migration and offloading. Compared to three other benchmarks, CODA exhibits higher effectiveness and superiority.

Distributed Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching in Internet-of-Vehicles

Mobile edge computing (MEC) is becoming popular in many civilian applications. However, due to the broadcast nature of wireless connections, traditional MEC is open to eavesdropping attacks that endanger mobile users’ information confidentiality. Friendly jamming (FJ), as one of the physical layer security techniques, can efficiently protect users from such threats by degrading attackers’ wiretap channel capacities. In this paper, we propose an FJ-based physical layer security (PLS) mechanism to safeguard the confidentiality of data uploading in MEC services. We design a comprehensive security scheme that uses FJ from both base station (BS) and nearby mobile users to cover upload transmission. Accordingly, the eavesdropping risk zone (ERZ) is formulated as an area under high secrecy outage probability (SOP). To promote FJ from users, we further introduce Device-to-device (D2D) communication-based FJ, which is inspired by non-orthogonal multiple access (NOMA) techniques. Then, we formulate, simplify, and solve the problem by optimizing the MEC secrecy performance under the required risk, with efficiencies in data uploading, energy consumption, and incentive delivery. Furthermore, we tackle the challenge of efficient incentive design under information asymmetry by introducing Contract Theory and providing differentiated rewards. By simulations, we evaluate the mechanism in terms of secrecy performance and incentive efficiency under information asymmetry, and the results accordingly show the effectiveness of the proposed security mechanism.

Joint Resource Scheduling for UAV-Enabled Mobile Edge Computing System in Internet of Vehicles

The sudden outbreak of COVID-19 brings many unpredictable situations to human travel, such as temporarily closed highways, parking lots, etc. The scenarios mentioned above will lead to a large backlog of vehicles, and the requirements of Internet of vehicle (IoV) applications increase sharply in a period of short time correspondingly. Mobile edge computing (MEC) is a key enabling technology that can guarantee the diverse requirements of IoV applications through the optimization of resource scheduling. However, the sharp increasing in requirements of IoV applications caused by the congestion of highways or parking lots still bring great challenges to the deployment of traditional MEC. Therefore, in this paper, we construct an unmanned aerial vehicle (UAV) enabled MEC system, in which the data generated from IoV applications is processed by offloading to UAVs with MEC servers to ensure the efficiency of data processing and the response time of IoV applications. In order to approximate real-world UAV enabled MEC system, we consider the stochastic offloading and downloading processing time. Moreover, the priority constraints of sensors from the same vehicle are taken into consideration since they have different importance degrees. Then, we propose an Markov network-based cooperative evolutionary algorithm (MNCEA) to search out the optimal UAV scheduling solution to guarantee the shortest response time, in which the solution space is divided into multiple sub-solution spaces with the help of MN structure and parameters. Finally, we construct multiple simulation experiments with different probability distributions to simulate uncertainty factors. The simulation results verify the validity of MNCEA compared with the state-of-the-art methods, which is reflected by the shortest response time of requirements of IoV applications

Multi-objective optimization for NOMA-based mobile edge computing offloading by maximizing system utility

Mobile Edge Computing (MEC) is a technology for the fifth-generation (5G) wireless communications to enable User Equipment (UE) to offload tasks to servers deployed at the edge of network. However, taking both delay and energy consumption into consideration in the 5G MEC system is usually complex and contradictory. Non-orthogonal multiple access (NOMA) enable more UEs to offload their computing tasks to MEC servers using the same spectrum resources to enhance the spectrum efficiency for 5G, which makes the problem even more complex in the NOMA-MEC system. In this work, a system utility maximization model is present to NOMA-MEC system, and two optimization algorithms based on Newton method and greedy algorithm respectively are proposed to jointly optimize the computing resource allocation, SIC order, transmission time slot allocation, which can easily achieve a better trade-off between the delay and energy consumption. The simulation results prove that the proposed method is effective for NOMA-MEC systems.

Collaborative computation offloading for scheduling emergency tasks in SDN-based mobile edge computing networks

Due to the growing demand for time-intensive tasks, several tasks coexist, some of which last for a long time or only occur once in a while, such as emergencies. Emergencies such as natural disasters, accidents or medical emergencies require immediate attention and often demand real-time execution. Such tasks are the most difficult to plan for and foresee. Planning, resource allocation, and clear communication between all involved parties and network layers are crucial for scheduling emergency tasks. In the extensive literature, when an emergency task triggers, it will be handled like any other regular task. If the deadline is missed, a catastrophe will ensue. They ignored the impact of insufficient mobile edge computing (MEC) capacity and network congestion, which might result in execution delays. Collaborative offloading, which involves distributing computation tasks across multiple servers, shows promise in enhancing MEC network performance, particularly in emergency scenarios. In this context, efficient task scheduling plays a vital role in minimizing the total execution time of regular tasks while meeting the deadlines of emergency tasks. Our proposed scheme utilizes a collaborative offloading approach to schedule emergency tasks in MEC networks, leveraging the computing capacity of edge-deployed MEC servers. By utilizing the software-defined networking (SDN)’s global view of the network, task requests are collected and allocated to suitable MEC servers capable of meeting the demands. To address key challenges, our scheme propose four scheduling algorithms to address the following issues: (i) ensuring tasks are assigned to the nearest MEC server with sufficient computational resources, (ii) controlling a threshold to prevent network congestion, (iii) selecting an optimal collaborative MEC server for executing overloaded tasks based on collaborative offloading decisions, and (iv) allocating resources for emergency tasks when triggered to meet urgent deadlines by stealing resources from regular tasks without compromising their deadlines. Extensive simulations were conducted to assess the effectiveness of the proposed scheme. The results clearly illustrate its enhanced performance in terms of the total execution time of regular tasks and the ability to meet deadlines for emergency tasks.

Proactive Service Caching in a MEC System by Using Spatio-Temporal Correlation among MEC Servers

Optimizingthe cache hit rate in a multi-access edge computing (MEC) system is essential in increasing the utility of a system. A pivotal challenge within this context lies in predicting the popularity of a service. However, accurately predicting popular services for each MEC server (MECS) is hindered by the dynamic nature of user preferences in both time and space, coupled with the necessity for real-time adaptability. In this paper, we address this challenge by employing the Convolutional Long Short-Term Memory (ConvLSTM) model, which can capture both temporal and spatial correlations inherent in service request patterns. Our proposed methodology leverages ConvLSTM for service popularity prediction by modeling the distribution of service popularity in a MEC system as a heatmap image. Additionally, we propose a procedure that predicts service popularity in each MECS through a sequence of heatmap images. Through simulation studies using real-world datasets, we compare the performance of our method with that of the LSTM-based method. In the LSTM-based method, each MECS predicts the service popularity independently. On the contrary, our method takes a holistic approach by considering spatio-temporal correlations among MECSs during prediction. As a result, our method increases the average cache hit rate by more than 6.97% compared to the LSTM-based method. From an implementation standpoint, our method requires only one ConvLSTM model while the LSTM-based method requires at least one LSTM model for each MECS. Thus, compared to the LSTM-based method, our method reduces the deep learning model parameters by 32.15%.

Computation Offloading and Beamforming Optimization for Energy Minimization in Wireless-Powered IRS-Assisted MEC

Intelligent reflecting surface (IRS) has been recently exploited as a symbiotic radio (SR) technology to improve energy-and spectral-efficiencies in wireless systems. In this paper, we consider a symbiotic IRS-assisted mobile edge computing (MEC) system that allows edge users to first harvest RF power from a hybrid access point (HAP) and then offload its computational workload to the MEC server associated with the HAP. We aim to minimize the HAP’s energy consumption by jointly optimizing the users’ offloading schemes, the HAP’s active beamforming, and the IRS’s passive beamforming strategies. We propose an optimization-driven hierarchical deep deterministic policy gradient (OH-DDPG) framework to decompose the energy minimization problem into the optimization and the learning sub-problems, respectively. The outer-loop DDPG learning method adapts the IRS’s passive beamforming strategy, while the inner-loop optimization deals with the other control variables with reduced dimensionality. Moreover, to improve the learning efficiency, we extend OH-DDPG to the multi-agent scenario. In particular, the HAP first estimates the users’ offloading strategy by the inner-loop optimization and shares it with all user agents. Then, each user agent refines its offloading decision using the DDPG algorithm independently. This can avoid signaling overhead among users and improve the multi-user learning efficiency. Simulation results show that the proposed OH-DDPG and the multi-user extension can achieve significant performance gains compared to the conventional model-free learning algorithms.

Multi-mobile vehicles task offloading for vehicle-edge-cloud collaboration: A dependency-aware and deep reinforcement learning approach

The wide application of edge cloud computing in the Internet of Vehicles (IoV) provides lower latency, more efficient computing power, and more reliable data transmission services for vehicle applications. Realistic vehicle applications frequently consist of multiple tasks with dependencies. Efficiently and quickly scheduling individual tasks for multiple vehicle applications to reduce latency and energy consumption is challenging. Our proposed approach leverages Deep Reinforcement Learning (DRL) to develop a task scheduling strategy that ensures real-time and efficient operations. We maximize the utilization of available resources by harnessing the computational capabilities of vehicles, multiple MEC servers, and a cloud server. Specifically, we model the dependencies of tasks using a Directed Acyclic Graph (DAG) and design dynamically adjustable weights for delay and energy consumption. Transforming the task offloading problem in a vehicle-edge-cloud environment, which considers dependencies, into a Markov Decision Process (MDP) enables us to tackle it effectively. To obtain optimized offloading decisions quickly, we employ Double Deep Q-Network (DDQN) along with specially designed mobility management strategies. A penalty mechanism is introduced in DDQN to impose penalties when the vehicle application is delayed beyond its deadline. Simulation results show that the proposed scheme can significantly decrease the latency and energy consumption of multi-applications compared to the other three schemes and ensure the successful execution of tasks.

ADRLO: Adaptive deep reinforcement learning-based offloading for edge computing

Computation offloading, as an efficient and promising computing paradigm for mobile edge computing (MEC), provides optimal computation allocation between MEC servers and Internet of Things (IoT) devices. However, in modern wireless communications, most of the computational tasks are latency-sensitive, presenting an efficiency challenge for offloading services. Moreover, to make the best use of computation resources, it remains an issue to ensure stable optimal offloading for the allocation of computation resources. In this paper, a novel adaptive deep reinforcement learning-based offloading (ADRLO) framework is developed to achieve a stable optimal computation revenue with low execution latency for MEC offloading. Structurally, we implement a residual connected deep neural network (DNN) to generate a series of offloading strategies and use them to obtain the global system computation rate. Especially, the strategy that achieves the maximum computation rate is then selected as experience to support the DNN training, forming a closed-loop deep reinforcement learning (DRL) mechanism. To further enhance the efficiency and stability of the MEC offloading, an adaptive regulator is presented for the framework parameters including the size of action space and the DNN learning rate. Numerical results show that, compared to existing state-of-the-art approaches, our proposal achieves optimal computation revenue, exhibits better convergence performance, and significantly reduces the execution latency by almost 50%.

Ellipsoidal Trajectory Optimization for Minimizing Latency and Data Transmission Energy in UAV-Assisted MEC Using Deep Reinforcement Learning

Due to their flexible deployment and movement capability, unmanned aerial vehicles (UAVs) are being utilized as flying mobile edge computing (MEC) platforms, offering real-time computational resources and low-latency data processing for a wide range of applications. This article aims to explore a UAV-assisted MEC system where multiple UAVs provide MEC services to mobile devices (MDs) using an ellipsoidal trajectory. Depending on the position, size, and orientation of the ellipsoidal trajectories, the coverage area of the UAV, the energy consumption, and the task transmission latency of MDs change. This has rarely been investigated in the existing works. Furthermore, unlike other studies, we consider that each MD has varying task offloading rates, which, together with varying user densities, makes the problem more challenging. Therefore, we formulate an optimization problem that finds the center position, major radius, minor radius, and rotation angle of the ellipsoidal trajectory of UAV-assisted MEC servers, to minimize the total transmission latency and energy consumption of mobile devices while taking into account the required data transmission rate, task transmission time, and energy consumption constraints. Then, we transform this optimization problem into a Markov decision process and propose a deep Q-learning-based ellipsoidal trajectory optimization (DETO) algorithm, to resolve it. The results from our simulations demonstrate that DETO efficiently computes the optimal position and trajectory for each UAV, and can achieve better performance compared to other baselines, leading to the reduced data transmission latency and energy consumption of mobile devices across a range of simulation scenarios.

Empowering smart cities: High-altitude platforms based Mobile Edge Computing and Wireless Power Transfer for efficient IoT data processing

This work presents an efficient framework that combines High Altitude Platform (HAP)-based Mobile Edge Computing (MEC) networks with Wireless Power Transfer (WPT) to optimize resource allocation and task offloading. With the proliferation of smart sensor nodes (IoT) generating real-time data, there is a pressing need to overcome device limitations, including finite battery life and computational resources. Our proposed framework leverages HAP-based MEC servers, offering on-demand computation and communication resources without extensive physical infrastructure. Additionally, WPT, through terrestrial networks, addresses IoT device battery constraints by enabling energy harvesting from nearby access points. The primary focus is joint optimization, aiming to maximize computing bits while minimizing energy consumption under system constraints. Given the optimization problem’s complexity, we employ a decomposition approach, breaking it into sub-problems. The first part handles mode selection and task segmentation, determining optimal placement and mode selection variables. The second part addresses resource allocation, optimizing transmission power, offloading time, energy harvesting time, and device computational resources. Numerical results demonstrate the framework’s effectiveness compared to relevant benchmark schemes. This approach holds promise for enhancing IoT device performance and energy efficiency in smart city applications.