Cloud-assisted resource-constrained and widely used outdoor IoT devices deploying highly robust convolutional neural network (CNN) models to provide high-quality visual services have attracted widespread attention from industry and academia. Most existing methods suffer from two limitations: (i) large amount of parameter transmission, and (ii) low robustness in handling data affected by the environment. To this end, this paper proposes a transmission-friendly and robust CNN design method called NonLinear transformation generation Filter with NonLearnable hyperparameters, namely NL2Filter. In NL2Filter, some filters are first designated as seed filters, whose parameters are learnable, that is, updated as the model is trained. Other filters in this layer are generated based on the seed filters and nonlinear transformation function (NLF). The hyperparameters of the NLF in NL2Filter are randomly initialized and remain unchanged, so they can be saved and reproduced using random seed. After the cloud server trains the NL2Filter-CNN model, it only needs to send a small number of learnable parameters and random seed to reproduce the CNN model trained by the cloud server. Compared with sending the complete CNN model, sending only a small number of learnable parameters and random seed can significantly reduce the number of model parameters sent by the cloud server. On the other hand, NL2Filter groups seed filters and then uses NLFs with different hyperparameters to generate diverse filters on demand for each group, thereby improving the model's ability to capture feature diversity without increasing the complexity of the model, thereby improving the model's robustness in processing data affected by the environment. Experimental results on CIFAR-10, CIFAR-10-C, and Icons-50 datasets demonstrate that the proposed NL2Filter outperforms other state-of-the-art methods. Specifically, using the ResNet-101 architecture, on the CIFAR-10-C, NL2Filter's accuracy is about 3.9% higher than that of MonoCNN; On the Icons-50, NL2Filter's accuracy is about 2.7% higher than that of MonoCNN and about 2.3% higher than that of the standard ResNet-101.

This study aims to guide pre-service teachers in effectively utilizing Generative Artificial Intelligence (GAI) tools to enhance their Artificial Intelligence (AI) literacy. By ana-lyzing the impact of GAI on the AI literacy of pre-service teachers, this study proposes a “teacher-student-machine” triadic interactive teaching model based on self-organized learning and deep empowerment through GAI. Using an experimental class in a software engineering course as a case study, we construct an end-to-end experimental environment for the project-driven teaching lifecycle, covering requirement analysis, system design, development and implementation, as well as software testing and maintenance phases. GAI is used to support the completion of experimental tasks while fostering pre-service teachers' AI literacy in five core competencies: data literacy, digital communication and collaboration, critical thinking, computational thinking, and ethical literacy. The study demonstrates that this model effectively improves the AI literacy of pre-service teachers, provides a reference for innovating software engineering practice teaching, and offers a useful framework for the application of GAI in education.

This study investigates the trade-off between system stability and offloading cost in collaborative edge computing. While collaborative offloading among multiple edge servers enhances resource utilization, existing methods often overlook the role of queue stability in overall system performance. To address this, a multi-hop data transmission model is developed, along with a cost model that captures both energy consumption and delay. A time-varying queue model is then introduced to maintain system stability. Based on Lyapunov optimization, a dynamic offloading algorithm (LDSO) is proposed to minimize offloading cost while ensuring long-term stability. Theoretical analysis and experimental results verify that the proposed LDSO achieves significant improvements in both cost efficiency and system stability compared to the state-of-the-art.

Federated learning (FL) enables distributed deep learning model training without accessing private user data, but its performance is severely hampered by non-independent and identically distributed (Non-IID) user data and long-tailed distribution. Early hierarchical FL systems relying solely on federated averaging algorithms fail to handle complex real-world data distributions in practical deployments. To address these issues and leverage the strengths of both cloud and edge servers, we propose a semantic communication-assisted cloud-edge-client hierarchical FL system. In this system, clients transmit processed semantic information to nearby edge servers, which perform partial model aggregation over multiple rounds before uploading to the cloud for global aggregation. This approach not only mitigates Non-IID and long-tailed distribution issues but also enhances communication efficiency. Theoretical analysis defines the model's basic structure, and simulation experiments validate the architecture's advantages. Compared with traditional FL frameworks, the proposed system ensures training accuracy while reducing training time and terminal energy consumption in scenarios with complex data distributions and high communication delays.

To address the challenges of small target features being less prominent, susceptibility to background interference, and sample imbalance in road traffic sign detection, which leads to insufficient model detection accuracy, as well as the high complexity of current object detection models that struggle to operate efficiently on resource-constrained edge devices, we propose a traffic sign detection model based on GCA-YOLO. By adding small target detection layers and removing large target detection layers, the model enhances its small target detection capabilities and reduces its parameter size. The introduction of the T-BiFPN (Tiny-BiFPN) structure improves multi-scale feature fusion, while the C2f-CP module increases computational efficiency on edge devices. The GCA (Global Coordinate Attention) mechanism enhances feature extraction, and the Focaler-CIoU loss function enables the model to focus more on difficult samples and accelerate the convergence of bounding boxes. Experimental results show the superiorities of the proposed GCA-YOLO that compared to YOLOv8n, GCA-YOLO improves precision, recall, mAP@50, and mAP@50:95 by 8.6%, 6.1%, 8.7%, and 6.2%, respectively, while reducing the model's parameter count and size by 38.57% and 33.21%, respectively.

Using pruning techniques to prune redundant weights in large language models (LLMs) for model size reduction, enabling deployment on devices to deliver high-quality services, has garnered significant attention from industry and academia. However, most existing pruning methods suffer from two major problems: (1) they rely on operations with high computational complexity, such as Hessian matrix calculation, and (2) high pruning rate leads to a significant decrease in model accuracy. To this end, this paper proposes a low resource requirement and low accuracy loss post-training pruning approach, namely the compensated post-training pruning method (CPP) for on-device LLM services. First, CPP employs singular value decomposition on weight matrices, sorting the decomposed singular values in descending order. Based on the pruning ratio, it retains the principal eigenvector corresponding to the larger singular values. To maintain consistent output feature distributions, CPP applies orthogonal transformations to input data and weight matrices, leveraging the principle of matrix orthogonality invariance. Compared with pruning approaches based on the Hessian matrix, CPP does not need to iteratively calculate the second derivative, thereby avoiding a high computational overhead. Second, the CPP incorporates bias compensation to use valuable information in pruned weights to improve the accuracy of the model. It constructs mapping relationships between input features and pruned weights through tensor decomposition techniques to generate bias compensation terms. These terms fine-tune the output of each layer, reducing pruning-induced errors from ratio-based pruning, and consequently improving model accuracy. Finally, CPP is validated through comprehensive experiments on nine benchmark datasets (e.g., WikiText-2 and PIQA) using large language models (i.e., LLaMA and OPT). Specifically, compared with the FLAP, CPP demonstrates 6.68% reduction in average pruning time. At pruning ratios of 10% and 20%, CPP achieves 2. 14% and 1.45% reductions in perplexity, respectively, along with 1.22% to 3.43% improvement in zeroshot inference accuracy.

Task offloading can meet users’ demands for the latency and energy consumption by offloading tasks from resource-constrained Internet of Things devices to relatively resource-rich edge servers. Traditional task offloading usually makes use of fixed base stations or servers as edge servers. This would lead to limited range of services and increased costs due to large-scale deployment of edge servers. Therefore, deploying unmanned aerial vehicles (UAVs) as mobile edge servers for task offloading in complex terrains (e.g., forest, desert, etc.) is a worthwhile research problem. To this end, this article proposes a UAV-assisted task offloading mechanism. The mechanism aims to minimize the weighted sum of latency and energy consumption through jointly optimizing resource allocation, offloading decision, and UAV trajectory. We first transform the nonconvex optimization problem into convex optimization subproblems to obtain the optimal resource allocation. Second, we use an improved particle swarm optimization algorithm to find the optimal offloading decision. Finally, we present the deep determination policy gradient algorithm to optimize the UAV trajectory which is a kind of deep reinforcement learning algorithm. Through simulation experiments, we show that the proposed mechanism can efficiently reduce the weighted sum of latency and energy consumption.

Vehicle Edge Computing (VEC) can effectively improve the efficiency of vehicle task calculation and offloading by integrating edge computing and the Internet of Vehicles. However, current research in VEC mostly focuses on device collaboration within single-hop or two-hop ranges, limiting the additional performance gain and load balancing provided by multi-hop device collaboration. In this study, an Edge Digital Twin assisted Multi-hop Task Offloading Scheme (EDT_MTOS) is proposed to enhance the execution efficiency of vehicle tasks by establishing an edge digital twin layer for virtual mapping of vehicles and edge servers. Firstly, the multi-hop task offloading problem is transformed into a cost optimization problem related to delay and energy consumption under the constraint of load balancing. Secondly, a dynamic collaboration knowledge graph based on digital twin knowledge mapping is introduced to select collaborative device sets for the upload and return links within the multi-hop range. Then, a value iteration algorithm based on the maximum link quality is proposed to realize the dynamic collaboration knowledge graph. Furthermore, a task offloading solution algorithm is proposed based on Dynamic collaboration Knowledge Graph and Double Deep Q-Network (DKG_DDQN). Finally, the simulation results demonstrate that the proposed offloading algorithm can reduce vehicle task processing costs by 33.77% and 26.53% in an idle scenario, and by 27.60% and 26.21% in a busy scenario, compared to state-of-the-art collaborative algorithms such as COOR and DRL-COMV, respectively.

In cell-free massive multiple-input multiple-output (CF-mMIMO) systems, seamless communication coverage is achieved through the dense deployment of numerous access points (APs), significantly enhancing spectral efficiency (SE) for users and overall system capacity. However, the implementation of this approach demands substantial deployment costs and unavoidably necessitates the use of non-ideal hardware. This paper investigates the achievable rate of users in the uplink CF-mMIMO systems that employ nonlinear power amplifiers (PAs) and low-resolution analog-to-digital converters (ADCs) at user equipment (UE) and APs, respectively. In particular, we derive a closed-form expression for the achievable uplink user rate and conduct a comprehensive analysis of various factors, including the number of APs, UE density, number of AP antennas, and ADC resolution. To mitigate the interference among UEs and maximize the sum rate, we propose a graph neural network (GNN) assisted actor-critic algorithm (DMAGNN-AC) for power allocation. The established framework overcomes the representation bottleneck of DRL in high-dimensional unstructured state spaces and provides physically interpretable feature embeddings. In comparison to the full power output, the proposed power allocation scheme is capable of doubling the rate. Furthermore, to address the detrimental impact of low-resolution ADCs on the rate, we develop an enhanced algorithm, multi-agent deep Q-integrated network (MADQIN), which optimizes the allocation strategy of ADC resolutions. Finally, the effectiveness of the proposed schemes is validated by the presented simulation results.

As one of the most critical tasks currently, it is essential to excavate the status update performance. In this paper, we combine access control and pilot design to minimize average age of information (AoI) in cell-free (CF) massive multiple-input multiple-output (MIMO) system with grant-free (GF) random access, where the positions of both the access points (APs) and machine-type communication devices (MTCDs) are geographically distributed as independent Poisson point processes (PPPs). Based on the establish two-disk computation model, we frist formulate the access successful probability by leveraging the stochastic geometry for GF transmission. Then, the closed-form expression of the average AoI is approximately derived. The derived results enable us precisely quantify the impact of network parameters on the system-level performance. To minimize the average AoI, a joint access control and pilot design scheme is proposed. Finally, simulation results are provided to furnish invaluable insight into system performance and certificate the validity of the proposed algorithm.

The integration of vehicle edge computing (VEC) and microservice architectures improves real-time data processing and computational optimization in the Internet of Vehicles. Specifically, in high-traffic areas, the dynamic deployment and request routing of microservices with complex data dependencies within vehicle clusters can effectively reduce the computational load on edge devices. However, existing research has primarily focused on efficiently utilizing vehicular resources, overlooking the dynamic nature of vehicular cluster networks and the additional communication costs arising from data dependencies between microservices. Therefore, we propose a joint service deployment and request routing problem for vehicle collaboration. We first design a vehicle-road collaborative service framework assisted by temporary vehicle workers, expanding available resources and coverage by deploying microservice instances on selected temporary vehicle nodes. Second, recognizing the dependency between service deployment and request routing, we propose a dual-timescale service-deployment and request-routing policy. On a long timescale, a microservice-aware deployment method optimizes request selection and response time. On a short timescale, we propose a decentralized, swarm intelligence-based collaborative request routing method that constructs a response threshold model through agent interaction, thereby enhancing the collaborative optimization capability of the system. Finally, experimental results using real datasets show that our method outperforms other approaches in reducing request response time when communication costs are taken into account.

Aiming at solving the problems of high latency, data transmission bandwidth limitation and privacy security faced by traditional object detection methods in the Internet of Things, a cloud-edge collaborative system for object detection based on KubeEdge is proposed. It takes advantage of cloud-edge collaboration technology and edge computing platform to perform tasks on edge devices to achieve faster response times. Firstly, through the deployment of KubeEdge edge computing platform, the cloud edge collaboration function is realized. Then, the object detection model is trained on the cloud server, and the trained model is deployed on the edge device to perform the model inference task. Finally, the edge device transmits the inference results to a cloud server, which stores the results for further analysis. The system has significant advantages in realizing low delay calculation, collaborative assurance, and privacy protection, etc. Taking mask detection as an example, it validated the practicality and reliability of the system, which provides strong support for the application of cloud-edge collaborative technology in the field of object detection and holds significant importance in meeting the growing demands of edge computing.

Device to Device(D2D) caching employs direct communication technology to facilitate content sharing among adjacent users, effectively harnessing the storage resource of numerous terminal devices. Integrating user request diversity, resource limitation and content recommendation on user request probability, a joint optimization strategy for recommendation and collaborative cache is proposed to maximize the cache hit rate in D2D network. Firstly, the concept of regional user request probability is introduced, and the impact of content recommendation on user request probability is analyzed. And then, an initial regional user request model based on multi-level round-by-round iteration heuristic and long short-term memory algorithm is proposed to predict content popularity within collaborative clusters. Finally, combining recommendation and collaboration, the regional user request model is reconstructed, and an optimization algorithm is proposed to address the problem of content caching in D2D networks. Compared with other studies under different recommendation acceptance probabilities and cache capacities, the results show that the proposed algorithm improves the local cache hit rate by at least 24% and 9%, respectively.

As the size of data clusters grows, the issues of storage redundancy, data availability, and the cost of edge caching systems become the focus of attention. Although the multi-copy strategy improves data availability, its redundancy cost is high and does not fully consider file data characteristics. Erasure codes techniques have been widely studied as an alternative, but the selection of a suitable coding scheme remains a challenge. To this end, we propose a hybrid coding caching scheme. Firstly, we predict data types using machine learning methods to ensure that the most valuable content is stored. Secondly, for the characteristics of the multi-copy strategy, low-density parity check (LDPC), and Reed-Solomon coding (RS), we propose an adaptive data partitioning strategy (ADPRF) to select appropriate coding schemes for different data. Finally, by combining the ideas of dynamic programming and heuristic algorithms, we propose a redundancy-aware edge collaborative caching algorithm (RPCO) with joint optimization of energy and cost to determine the optimal set of collaborative nodes and the optimal cache locations for files. Compared with the traditional responsive caching scheme, this algorithm can reduce the system cost by 13.8% and access latency by 11.5%.

When offloading computing tasks of sensory data to the edge network, it is necessary to consider whether the idle resources such as CPU frequency and memory, meet the task processing requirements. However, even if edge collaboration is used to improve offloading performance, most studies assume homogeneity in hardware configuration across all edge servers, discarding the impact of the differentiated resource allocation among heterogeneous edge servers. Therefore, resource allocation and offloading decisions in a collaborative heterogeneous edge network are comprehensively considered in this study. Firstly, the offloading problem of heterogeneous edge servers is expressed as a joint optimization problem associated with delay and energy consumption constrained by CPU frequency and storage resources. Secondly, dynamic collaboration clusters are constructed based on distance, position and workload correlation to identify distinct collaboration regions and balance the load within edge servers. And then, a distributed alternating direction multiplier method (ADMM) based on constraint projection and variable splitting is proposed to solve the optimization problem. Additionally, a cooperative path selection algorithm, which takes into account length and throughput of return paths, is proposed to alleviate network congestion and minimize energy consumption loss. Finally, the proposed algorithm for Collaborative offloading and resource Configuration (CooCo) is demonstrated to be effective and rapidly converging based on a real dataset from Shanghai Telecom. The simulation results also show that compared to the DRAOA, no-cooperation, single-hop, and other state-of-the-art collaborative algorithm, CooCo can significantly reduce the sum of the system costs by 26%, 35%,11% and 8%, respectively.

Mobile devices commonly offload latency-sensitive applications to edge servers to meet low-latency requirements. However, existing studies overlook dependency and application hit ratio considerations, hindering effective offloading for multi-applications and multi-tasks. To this end, this article proposes a Dependent task offloading and Service placement Optimization (DSO) method to maximize the application hit ratio, thereby providing high-quality service. The proposed DSO includes Improved Multi-Agent Q-Learning (IMAQL) and greedy algorithms. IMAQL optimizes service placement via Q-learning, while the greedy algorithm schedules task offloading. Extensive experiments on public datasets demonstrate that the DSO method enhances the application hit ratio by 4.7% to 11.7% and reduces the completion time by about 3.4% to 4.9% compared to alternative approaches.

Task offloading offloads latency-sensitive and computation-intensive applications from resource-constrained terminal devices to relatively resource-rich edge servers to meet users’ demands for latency and energy consumption, which has attracted extensive attention from academia and industry. However, most of the existing researches only considers offloading dependent tasks within a single application or multiple independent applications, while ignoring the dependencies between applications. To this end, this paper proposes an offloading strategy for distributed dependent applications under the condition of limited computing and cache resources. The goal of the proposed strategy is to minimize the weighted sum of latency and energy to complete all applications while solving the offloading and resource allocation problems of dependent applications. However, the dual dependencies between applications and tasks within the application complicate offloading tasks. To accommodate this issue, we represent the dual dependencies as a directed acyclic graph. Then, we design the offloading strategy as follows: First, we transform the formulated non-convex problem into convex optimization subproblems. Second, we iteratively calculate the task priority and obtain the optimal offloading decision of the task according to the priority. Finally, we perform validation on real datasets. Compared with several state-of-the-art methods, our proposed strategy can significantly reduce the weighted sum of latency and energy.

Content offloading in mobile edge computing is one of the effective ways to alleviate network congestion. This paper studies the content offloading problem in buffer-constraint edge scenarios. First, the content offloading problem is transformed into the maximum delivery rate and minimum transmission delay problem integrating the heterogeneous contact rate between users and small base stations. Second, a Lagrangian multiplier method is employed to compute the optimal numbers of offloading copy for each file, and then a greedy algorithm which can achieve the optimality with a probability 1 - 1/e is proposed, followed by a distributed algorithm with the same performance as the greedy one. Finally, the proposed schemes are compared with the classical algorithms, and the simulation results show that they enhance the content delivery rate and reduce the transmission delay in different popularity distributions and buffer sizes.

This article studies the capacity of edge caching systems. Capacity is analyzed from the perspective of node mobility, caching, content popularity, etc., neglecting the influence of edge node cooperation on system performance. However, cooperation among edge nodes has been shown to substantially improve the system performance at the expense of a cooperation cost. Therefore, we attempt to maximize the capacity of mobile edge computing (MEC) subject to a budget constraint on the cooperation cost. We first transform the capacity maximization problem into a transmission distance minimization problem; then, we explore the average transmission distance of each source-to-destination pair under the assumption that the locations of the edge nodes follow a Poisson point process (PPP). We find that both the average transmission distance and the cooperation cost are associated with a key parameter, i.e., the number of content copies. We use the Lagrangian multiplier method to calculate the optimal copy number and propose a file allocation algorithm to store these copies. Finally, we analyze the influence of various parameters on the system capacity. The numerical results verify the efficiency of our solution compared with classic works.

Node buffer size has a big influence on performance of Mobile Opportunistic Networks (MONs). This is mainly because each node should temporarily cache packets to deal with the intermittently connected links. In this paper, we study fundamental bounds on node buffer size below which the network system can not achieve the expected performance. Given the condition that each link has the same probability p to be active, and q to be inactive during each time slot, there exits a critical value pcfrom a percolation perspective. If p > pc, the network is in the supercritical case, there is an achievable upper bound on the buffer size of nodes, independent of the inactive probability q. When pc, the network is in the subcritical case, and there exists a closed-form solution for buffer occupation, which is independent of the size of the network.

Mobile opportunistic network (MON) is a new paradigm which exploits node contacts to forward data, enabling numerous and impressive applications. The data copy spraying scheme is a challenging problem in MON, due to the mobility of nodes and lack of global knowledge, it hence captures great interests from research communities. Traditional algorithms allocate data copies with node's statistical information and neglect the temporal contact feature, resulting in a poor delivery performance. We propose AS, an adaptive data copy spraying scheme in MON. AS adjusts the number of copies dynamically based on the temporal contact feature among nodes. Theoretical analysis verifies that AS achieves a lower mean delivery delay than SprayWait, one of the state-of-the-art works. Simulation results show that AS improves the packet delivery ratio simultaneously.

Routing in opportunistic networks is difficult due to the intermittently connected environment and lack of global view on network topology. In such scenarios, nodes attempt to transmit packets in a store-carry-and forward manner. The main issue is which forwarding mechanism achieves the best trade off between successful packet delivery ratio and cost. We address this challenge by proposing the Fuzzy Forwarding (FF) approach, in which packets are preferred jointly considering the knowledge of performance metrics (e.g., the mean delivery delay and average number of hops per packet) and that of node contacts (e.g., durations, times and locations). FF gives higher priority to packets, as compared to the heuristic knowledge, if they have shorter TTL (time to live) and smaller number of hops, and nodes desiring to relay them have higher delivery probability to destination. Furthermore, to deal with the incompleteness and uncertainty of such knowledge, a fuzzy logic engine is developed for the computation of packet preference. The trace-driven simulation results demonstrate that FF achieves significantly better cost compared to the state-of-the-art works, while keeping better delivery ratios and mean delivery delays under different TTL requirements.

Performance of data forwarding in opportunistic networks benefits considerably if one can make use of human mobility in terms of social structures. However, it is difficult and time-consuming to calculate the centrality and similarity of nodes by using solutions for traditional social networks, this is mainly because of the transient node contact and the intermittently connected environment. In this paper, we are interested in the following question: Can we explore some other stable social attributes to quantify the centrality and similarity of nodes? Taking GPS traces of human walks from the real world, we find that there exist two known phenomena. One is public hotspot, the other is personal hotspot. Motivated by this observation, we present Hoten (HOTspot ENtropy), a novel forwarding metric to improve the performance of opportunistic routing. First, we use the relative entropy between the public hotspots and the personal hotspots to compute the centrality of nodes. Then we utilize the inverse symmetrized entropy of the personal hotspots between two nodes to compute the similarity between them. Third, we exploit the entropy of personal hotspots of a node to characterize its personality. Besides, we propose a method to ascertain the optimized size of hotspot. Finally, we compare our routing strategy with state-of-the-art works through extensive trace-driven simulations, the results show that Hoten largely outperforms other solutions, especially in terms of packet delivery ratio and the average number of hops per message.

Performance modeling of epidemic routing is challenging because of the unguaranteed end-to-end connectivity and lack of global information in delay-tolerant scenarios. Existing works analyze the scaling law of epidemic protocol based on the assumption that each node has the same infectivity. Whereas, the most recent work indicates that the distribution of infected nodes has spatial-temporal correlation rather than homogeneity, i.e., nodes in different locations have different infectivities, which leads to defectiveness of the existing solutions. In this paper, by exploring the reason behind this difference, we try to relax the assumption and rebuild the model for epidemic routing. We first introduce the concept of infection rate to reflect the infectivity of infected nodes. Second, we propose an effective method to compute the average infection rate and use it to derive a generic scaling law. Third, we give an explicit expression for the generic scaling law, which provides us with upper bound. We finally compare our model with the existing works through theoretical analysis and simulations. The results show that our model has a closer match than those of the existing works and gets some insights into the spatial distribution of infection process.

In this paper we study multi-copy routing schemes in opportunistic networks. Compared to single-copy protocols, the multi-copy schemes expedite the mean delivery delay while consuming more resources and exposing lower packet delivery ratio in resource-constrained system. To enhance the system capacity, most recent works explore the impact of social structure on network performance. Their results indicate that integrating the social relationship into opportunistic routing can greatly improve the performance metrics especially in term of packet delivery ratio. Considering this fact, we propose Hug, a “HUman Gathering point” assisted routing for delay-tolerant environments and evaluate it with the other two earlier presented Spray-wait and Epidemic routing protocols through simulations and theoretical analysis. Our results show that Hug achieves higher packet delivery ratio and lower communication overhead than Spray-wait and Epidemic routing while keeping a reasonable delay.

Probabilistic forwarding methods have been exploited in opportunistic networks to reduce the overhead of epidemic routing. However, most existing methods make all the nodes forward messages with the same probability (i.e., equal scheme), which causes the energy unbalance of nodes. To guarantee the energy balance of nodes and prolong the network lifetime, we design a differentiated scheme, i.e., different nodes are assigned with different forwarding probabilities based on their respective energies. We model the message dissemination based on the differentiated scheme, and formulate two optimization problems: maximize the message deliver probability under the constraint on the total energy consumption, and based on this, maximize the network lifetime under the constraint on the energy consumption of each node. By solving these two optimization problems, we derive the optimal differentiated forwarding probabilities by theoretical analysis. Our simulation results show that our designed differentiated scheme can guarantee the message deliver probability and extend the network lifetime, compared with the equal scheme.

We evaluate the performance of epidemic protocol in opportunistic networks. Early works follow an independent model which assumes that different messages in the network disseminate independently. Whereas, most recent work has pointed out that the same event can be detected by multiple nodes at different locations or different moments. Hence, the messages sensed by different nodes, indicating the same event, have spatial-temporal correlations. It is necessary to build an accurate mathematical model for reflecting the message dissemination process of epidemic protocol for further designing or optimizing the family of flooding protocols. However, the independent model does not provide good performance estimates in this situation. In this paper, we try to solve the problem by using a correlated model which takes into account the correlations among messages and permits the different messages denoting the same event to be aggregated in their propagation processes, and the numerical results show a close match with our theoretical analysis. Compared to the previous work, our correlated model, on the one hand, allows a network engineer to implement such a system with optimized performance confidently in an intermittently connected environment, on the other hand, our work offers a fresh insight into the spatial-temporal correlations of the messages and achieves good performance metrics in scalability.

Routing is one of the challenging tasks in Delay Tolerant Networks (DTNs), due to the lack of global knowledge and sporadic contacts between nodes. Most existing works take greedy mechanism to forward messages, i.e., only nodes which have higher quality metrics than current carriers can be selected as relays to final destinations. In this work, we explore the influence of strangers on routing performance under a more challenging scenario of pure darkness. We first present a method to identify the relationship between nodes (i.e., stranger or friend). Second, we explore the optimized number of strangers we can employ. Third, we propose a novel routing scheme which are called STRON in this paper by taking both the STRangers and their Optimized Number into account. We finally compare our routing scheme with the greedy mechanism through synthetical and trace-driven simulations, the results show that our routing strategy achieves a better performance, especially in terms of combined overhead/packet delivery ratio and the average number of hops per message.