Decentralized Federated Learning (DFL) has been proposed to address the potential single point of failure issues in traditional federated learning. However, existing DFL frameworks face challenges such as significant computational and communication overhead, along with low node collaboration efficiency, which limits their applicability in lightweight scenarios. Moreover, privacy-preserving mechanisms in DFL frameworks further increase these inefficiencies, adding delays and resource consumption. In this paper, we propose a group-based decentralized federated learning framework (HEGD-FL) that integrates homomorphic encryption to enhance privacy protection. Considering the computationally demanding nature of the Paillier homomorphic encryption method, we optimize its exponentiation and modular operations. To strengthen privacy preservation, we introduce a client selection mechanism and a contribution mechanism, dynamically adjusting client roles to ensure fairness and security throughout the federated learning process. Experimental results show that HEGD-FL effectively ensures data privacy without compromising the model’s accuracy. At the same time, the group-based DFL framework design and encryption algorithm optimizations enhance system efficiency and broaden the framework’s applicability.

The rapid proliferation of some real-time applications (e.g., video surveillance) has driven enormous interest in maximizing information freshness, quantified by the age of information (AoI). For some computation-intensive updates such as images or videos, the real-time update processing requires intensive resources, which edge servers can provide in mobile edge computing (MEC). In this paper, we investigate information updating scheduling with multiple users in MEC. Due to the centralized algorithms’ limitations in distributed systems where users are self-interested, we investigate an efficient distributed scheduling algorithm. We model the information updating scheduling as an uncooperative game and propose a distributed algorithm to compute the unique Nash equilibrium. Considering the unavailability of some global network information, we propose a learning algorithm where each user learns how to make decisions based on observable information in a distributed manner. Extensive evaluation results show the efficiency of the proposed algorithms.

Age of information (AoI) is an important metric used to quantify the freshness of data. By utilizing resources of the edge server near the source nodes, mobile edge computing (MEC) can speed up the processing of information updates and ensure data freshness. However, the resources of the edge server are usually limited, so it is necessary to study the resource allocation strategy to ensure data freshness and optimize the profit of the edge server. In this paper, we propose a game-theoretic approach for resource allocation in mobile edge computing to guarantee information freshness. First, with the purpose of ensuring information freshness, we formalize the problem as minimizing the computational cost of source nodes and maximizing the profit of the edge server. Then, we introduce a two-stage dynamic game model to simulate the competitive process. We further transform the resource allocation problem into a knapsack problem and propose an iterative resource allocation algorithm based on dynamic programming. Experimental results show that the proposed algorithm can obtain a Nash equilibrium and maximize the profit of the edge server while ensuring information freshness.

Age of information (AoI) has been proposed to quantify data freshness. In some real-time applications such as surveillance systems, real-time analytics on source updates requires intensive computation resources and incurs high energy consumption. By providing computing resources at the network edge, mobile edge computing (MEC) can reduce update processing time and improve data freshness. In this paper, we investigate the age-optimal computation-intensive update scheduling for multiple sources in MEC-enabled IoT networks. Since the centralized algorithms may not apply due to the high computational complexity, we design an efficient decentralized scheduling mechanism for self-organized IoT networks. We provide a game-theoretic analysis and prove the existence of pure strategy Nash equilibrium. An efficient and decentralized algorithm based on the best-response dynamics is proposed to compute the equilibrium. We also provide the approximation ratio of the proposed algorithm. In particular, for the homogeneous source model, we show that the approximation ratio is at most 2.5. Extensive evaluation results show that the proposed decentralized algorithm is computationally efficient and closely approximates the centralized optimum in various settings.

User-initiated mobile data trading, where mobile devices trade their mobile data quota via personal hotspots, is a promising approach to improve resource utilization. Most existing works only consider the data size, while ignoring the data rate and temporal requirements. To fill this void, we propose a novel data trading marketplace, where mobile users trade Internet access continuously for a time period with a specific data rate with neighboring mobile devices. Each request is characterized by an arrival time, departure time, the demanded data rate, and a value for getting services. To achieve the most system efficiency, we formulate an integer linear programming problem to maximize the total social welfare, which takes the data rate and temporal requirements into account. We next consider two request models: 1) a homogeneous request model and 2) a heterogeneous request model. In the homogeneous request model, all the requests demand the overall system lifetime, and we propose a computationally efficient auction that makes allocation decisions for all the requests simultaneously. In the heterogeneous request model, all the requests require different Internet access periods and dynamic arrive. Upon requests’ arrival, the system must make real-time allocations without the availability of future information. To jointly deal with requesters’ multidimensional private information (i.e., the arrival/departure time, demanded data rate, and the value), and the uncertainty about future arrival requests, we propose a multi-round online auction. Theoretical analysis shows that both the auctions satisfy the desired properties, including individual rationality, truthfulness, and computational efficiency. Simulation results show the efficiency of the proposed auctions.

Device-to-device (D2D) content sharing holds great promise to alleviate the growing strain on cellular networks, as it offloads popular content data onto direct peer-to-peer links. However, it is still unexplored how content sharing could benefit from utilizing multihop rather than the conventional single-hop D2D communications. As a step towards this end, this paper proposes a game theoretic approach to enable D2D content sharing with multihop communication capabilities. Given a subset of participants, a Nash bargaining game is modeled to provide the routing and pricing graphs, where a novel incentive mechanism is adopted to stimulate cooperation. By iteratively evaluating the solution of the Nash bargaining subgame, participants that include content sources and transmission relays are determined, which ensures that all participants make contributions to the content sharing process. An additional procedure termed pricing plan is introduced to make sure that the final pricing graph is practical and feasible in terms of D2D communication. Experimental results are presented to demonstrate that the proposed game theoretic approach could not only jointly deal with the participants selection, routing, and pricing problems in multihop D2D content sharing, but also effectively restrict utilities and transmission resources to only contributive participants.

With the development of edge computing (EC), more and more tasks are offloaded to edge servers (ESs). However, faced with a huge number of users offloading tasks to ESs, how to allocate resources reasonably and reduce the response time of the system are problems worth studying. In this paper, we design an online auction algorithm to deal with those two issues at the same time. We first introduce four task value functions to model the sensitivity to the delay of different tasks. Then, we construct a three-layer EC model. Based on it, we define the resources allocation problem as a social welfare (SW) maximization problem, which is NP-hard. To solve this problem, we utilize the master-dual technique to transform it into an online auction problem. Finally, an algorithm considering task classification is proposed, which realizes both resource allocation and latency reduction in a polynomial time. Experiment results show that our approach reduces the scheduling latency by an average of 38% while maintains SW at the same time.

Caching has become more and more important in mobile video delivery with the development of communication technology, which can reduce redundant data transmissions and decrease costs. In this paper, we consider a multiple bitrate caching system consisting of a network service provider (NSP) in charge of edge cloud with caching and processing capacity, a set of video providers (VPs), and multiple mobile users. The NSP leases the storage space of the edge cloud to VPs and VPs place their videos in edge cloud to provide better services for mobile users. The competition between NSP and VPs is modeled as a Stackelberg game. We first solve the knapsack problem to find the optimal cache placement strategy for each VP based on the allocated space to maximize its profit and then adjust the allocation strategy to maximize the profit of NSP. Moreover, we develop a dynamic programming algorithm and a multiple bitrate caching algorithm to find the Stackelberg equilibrium (SE). The numerical results show the effectiveness of proposed algorithms on pricing and cache placement.

Collaborative computation offloading in mobile edge computing where edge users offload tasks opportunistically to resourceful neighboring mobile devices (MDs), offers a promising solution to satisfy low-latency requirements. However, most existing works assume that those MDs volunteer to help edge users without an incentive mechanism. In this article, we propose an auction-based incentive mechanism, where users and MDs participate in the system dynamically. Our auction mechanism runs in the online fashion and optimizes the long-term system welfare without knowledge of future information, e.g., task start time, task length, resource demand, and valuation, etc. We prove that the proposed online mechanism achieves the desired properties, including individual rationality, truthfulness, and computational tractability. Moreover, the theoretical competitive ratio shows that our online mechanism achieves near-optimal long-term social welfare close to the offline optimum. Extensive experiments based on real-world traces demonstrate the efficiency of the proposed online mechanism.

Data rate trading, in which mobile devices trade their real-time data transmission rates to achieve cooperative mobile networks access, not only can meet the increasing data access demands of users but also can reduce the pressure on cellular networks. However, there is no directly available mechanism for data rate trading. In this paper, we propose a truthful online auction mechanism for data rate trading in mobile networks. In the designed auction, the data rate buyers submit their realtime data access requests, including the rate requirement, access time and payment. The auctioneer, which may be the network operator, assigns data rate requests to appropriate sellers who leverage their surplus cellular data plan or other networks to complete the data rate requests and benefit from them. To achieve this model, we first formulate the social welfare maximization problem in data rate trading as an integer linear programming and show its NP-hardness. Then, we resort to the Lagrangian relaxation technique to design an online approximation algorithm to assign data rate requests and compute the corresponding payments in polynomial time. Theoretical analysis and simulation experiments show that the proposed auction mechanism obtains a good competitive ratio and satisfies the desired properties, including individual rationality, truthfulness, and computational efficiency.

Nowadays, multiple heterogeneous cellular networks coexist simultaneously, and mobile devices can freely select the appropriate network for data communication. Our measurement studies show that a barrier exists between heterogeneous cellular networks and applications. Triggering handovers between various cellular networks can break the barrier and present the promise of saving energy for cellular data communication. However, most existing network handover triggering methods do not adequately incorporate the characteristics of applications and may cause unnecessary handovers. In this paper, we propose an application-based handover triggering method called AHT. According to the applications that are used by users, AHT triggers handovers between high-performance and energy-efficient cellular networks, thus conserving energy. Based on the user experience (UX) requirements, AHT classifies applications into UX- sensitive and insensitive ones. AHT determines whether and when to switch to the high-performance network (e.g., LTE) in accordance with the predicted UX-sensitive application and estimated usage time, and triggers handovers to the energy- efficient network (e.g., UMTS) through an idle timer. We evaluate the performance of AHT with real application usage traces. Experimental results show that AHT saves up to 60.7% and 32.8% energy compared with the pure LTE network transmission and the screen-based handover triggering scheme Intelli3G, respectively.

Device-to-device (D2D) content sharing holds great promise to alleviate the growing strain on cellular networks, as it offloads popular content data onto direct peer-to-peer links. However, it is still largely unexplored how content sharing could benefit from utilizing multi-hop rather than conventional single-hop D2D communications. As a step towards this end, this paper proposes a generalized two-level Stackelberg game theoretic framework to enable content sharing with multi-hop D2D communication capabilities. At the lower level, a Nash bargaining subgame is proposed to provide the routing and pricing graphs, where a novel incentive mechanism is adopted to stimulate cooperation. At the upper level, the set of participants is decided to ensure all participants contribute to the content sharing. An additional pricing plan is introduced to make sure that the final pricing is practical and feasible. Numerical results are presented to demonstrate that the proposed game theoretic framework could not only jointly deal with participants selection, routing and pricing in D2D content sharing, but also effectively restrict utilities and transmission resources to only contributive participants.

The growing demand on the performance of transparent computing systems requires good cache schemes in order to overcome the prolonged network latency. However, evaluating cache schemes, especially measuring the performance of a transparent computing system with particular cache scheme remains challenging. This is because neither method is available to evaluate the effectiveness and efficiency of the cache schemes in transparent computing, nor the simulator has been developed to measure the system performance under particular cache schemes. In this paper, we propose TranSim, a full-featured, high-performance simulation framework for transparent computing. For the first time, TranSim introduces a methodology to evaluate the performance of multi-level cache hierarchies in transparent computing under different cache configurations and cache replacement policies. TranSim can also demonstrate the behavior and performance of the entire transparent computing system rather than only the cache miss/hit rate. Using TranSim, the system designer can quickly evaluate the effectiveness of cache schemes along with the system performance. We construct several experiments to evaluate the effectiveness and efficiency of TranSim. Results show that TranSim can accurately output the performance of both the cache hierarchy and the entire transparent computing system.

Elasticity has now become the elemental feature of cloud computing as it enables the ability to dynamically add or remove virtual machine instances when workload changes. However, effective virtualized resource management is still one of the most challenging tasks. When the workload of a service increases rapidly, existing approaches cannot respond to the growing performance requirement efficiently because of either inaccuracy of adaptation decisions or the slow process of adjustments, both of which may result in insufficient resource provisioning. As a consequence, the Quality of Service (QoS) of the hosted applications may degrade and the Service Level Objective (SLO) will be thus violated. In this paper, we introduce SPRNT, a novel resource management framework, to ensure high-level QoS in the cloud computing system. SPRNT utilizes an aggressive resource provisioning strategy which encourages SPRNT to substantially increase the resource allocation in each adaptation cycle when workload increases. This strategy first provisions resources which are possibly more than actual demands, and then reduces the over-provisioned resources if needed. By applying the aggressive strategy, SPRNT can satisfy the increasing performance requirement in the first place so that the QoS can be kept at a high level. The experimental results show that SPRNT achieves up to 7.7× speedup in adaptation time, compared with existing efforts. By enabling quick adaptation, SPRNT limits the SLO violation rate up to 1.3 percent even when dealing with rapidly increasing workload.

Accurately determining the geographic location of an Internet host is important for location-aware applications such as location-based advertising and network diagnostics. Despite their fast response time, widely used database-driven geolocation approaches provide only inaccurate locations. Delay measurement based approaches improve the estimation accuracy but still suffer from a limited precision (about 10 km) and a long response time (tens of seconds) to localize a single PC, which cannot meet the demand of precise and real-time geolocation for location-aware applications. In this paper, we propose a new geolocation approach, Checkin-Geo, which exploits geolocation resources fundamentally different from existing database-driven (using DNS, Whois, etc.) or network delay measurement based approaches. In particular, we leverage the location data that users are willing to share in location-sharing services and logs of user logins from PCs for real-time and accurate geolocation. Experimental results show that compared to existing geolocation techniques, Checkin-Geo achieves 1) a median estimation error of 799 meters (an order of magnitude smaller than existing approaches), and 2) a negligible response time, which are promising for accurate location-aware applications.

In cellular networks, inactivity timers are used to control the release of radio resources. However, during the timeout period of inactivity timers, known as the tail time, a large proportion of energy in user devices and a considerable amount of radio resources are wasted. In this paper, we propose TailTheft, a scheme that leverages the tail time for batching and prefetching to reduce energy consumption. For network requests from a number of applications that can be deferred or prefetched, TailTheft provides a customized application programming interface to distinguish requests and then schedules delay-tolerant and prefetchable requests in the tail time to save energy. TailTheft employs a virtual tail time mechanism to determine the amount of tail time that can be used and a dual queue scheduling algorithm to schedule transmissions. We implement TailTheft in the Network Simulator with a model for calculating energy consumption that is based on parameters measured from mobile phones. We evaluate TailTheft using real application traces, and the experimental results show that TailTheft can achieve significant savings on battery energy (up to 65%) and dedicated radio resources (up to 56%), compared to the default policy.