Video analytics (VA) has become essential in applications, yet it poses significant challenges related to privacy preservation, network bandwidth, and computational resources. With the increasing deployment of high-definition cameras, privacy concerns and resource constraints are becoming critical barriers to the widespread adoption of VA systems. Existing privacy-preserving techniques are often static, inefficient, and fail to adapt to dynamic, real-time scenarios. In this paper, we propose Parachute, a dynamic, resource-aware and privacy-preserving video analytics system that adaptively switches between a local mode and a collaborative mode in response to traffic conditions. The system uses local reinforcement learning to enable each individual camera to operate independently, and switches to multi-agent reinforcement learning for coordinated optimization when local resources become limited. Experiments on real-world datasets demonstrate that Parachute effectively balances detection accuracy and privacy protection, outperforming baseline methods under bandwidth constraints.

ABSTRACT Background Blockchain, as a decentralized and distributed ledger, shows significant application potential and commercial value in the Internet of Things (IoT). However, the dynamicity of IoT nodes introduces significant challenges to traditional blockchain systems. Specifically, in dynamic environments involving node joins/leaves, mobility, and state changes, traditional solutions often suffer from poor security, significant consensus latency, and severe storage overhead. Although blockchain for IoT has been extensively surveyed, prior reviews fail to provide a comprehensive evaluation because the existing literature considers security, consensus, and scalability only as separate issues, while overlooking node dynamics as a fundamental dimension. Objective This paper aims to provide a systematic investigation of blockchain adaptation to node dynamics in IoT and to establish a comprehensive evaluation perspective for analyzing the impacts of node dynamics on security, consensus, and scalability. Methods To this end, this paper systematically investigates these challenges through three contributions. Firstly, a unified taxonomy of node dynamics is proposed to analyze the impacts on security, consensus, and scalability. Secondly, it synthesizes representative solutions across trust evaluation, consensus algorithms, and scalability techniques. Thirdly, it introduces a three‐dimensional evaluation approach to comprehensively analyze these solutions in terms of performance, security, and adaptability. Results The evaluation results illuminate a clear trade‐off among these three dimensions, demonstrating that improving only one aspect often degrades the overall system balance in dynamic environments. Conclusion Finally, this paper introduces critical research limitations and future research directions to provide practical suggestions for effectively deploying blockchain in highly dynamic IoT environments.

The Internet of Vehicles (IoV) enhances road safety and supports autonomous driving through real-time communication, but current methods face key challenges: rigid resource allocation due to static architectures, communication failures in low-signal areas from infrastructure reliance, and passive defense mechanisms struggle to counter coordinated attacks, while high-latency encryption algorithms further compromise framework real-time performance. To address this, we propose a blockchain-based dynamically adaptive restructuring framework. It enables real-time IoV cluster restructuring by splitting overloaded IoVs to reduce communication overhead, or merging nearby IoVs to optimize resource utilization. In infrastructure-sparse zones, vehicles establish temporary multi-hop communication links based on relative mobility to ensure continuous connectivity. A multi-layered security mechanism integrates physical validation, event verification, and majority voting, achieving over 95% resistance to data tampering. Compared to Raft, PoS, and PBFT, our framework improves consensus speed by 27.06%–38.56%, and reduces transaction latency by 7%–35%, 15%–54%, and 27%–66%, respectively. It also maintains high robustness under dense traffic, high mobility, and weak signals, offering a proactive, adaptive security paradigm for intelligent transportation frameworks.

Transfer learning leverages existing models to help train new models, rather than training the new models from scratch. Unfortunately, realizing transfer learning in distributed cloud-edge networks faces critical challenges such as online training, uncertain network environments, time-coupled control decisions, and the balance between resource consumption and model accuracy. In this paper, targeting classification tasks, we study the settings of both homogeneous and heterogeneous transfer learning in cloud-edge networks via orchestrating model placement, data dispatching, and inference aggregation. We formulate non-linear mixed-integer programs of long-term cost optimization over consecutive time slots, and design polynomial-time online algorithms by exploiting the real-time trade-off between preserving previous control decisions and applying new control decisions. Our approaches produce new models by combining the existing pre-trained offline models and the online models that are continuously updated based on the inference results of data samples arriving in streams. We rigorously prove that our approaches only incur the number of inference mistakes no greater than a constant times that of the single best model in hindsight, and achieve constant competitive ratios for the total cost. Evaluations have confirmed the superior performance of our approaches compared to other state-of-the-art methods upon real-world data traces, under text classification transfer learning tasks.

The rising demand for blockchain interoperability is accelerating advancements in cross-chain bridge technologies, which are crucial for a seamless information transfer in multi-blockchain ecosystems. Existing blockchain bridges are typically classified into two categories: custodial and non-custodial. Custodial bridges use a trusted third party for easier and faster transactions but depend on custodian trust, while non-custodial bridges enhance transparency and control with smart contracts but increased complexity and latency. Currently, no bridge design successfully combines the benefits of both while avoiding their drawbacks. This paper presents EquiLink, a semi-custodial bridge that combines the benefits of both custodial and non-custodial methods. EquiLink employs a smart contract, known as the EquiLink Service, to initiate cross-chain transfers. It then uses the EquiLink Network, a system composed of remote-attested Trusted Execution Environments (TEEs), to verify and issue these transfers between two blockchains. Any eligible participants validated through remote attestation can join the EquiLink Network and contribute to the bridge’s functionality. Additionally, participants are regulated by an economic model, providing an extra layer of security through economic incentives. This semi-custodial bridge enhances transparency and control for users. Meanwhile, it mitigates the risks associated with centralized custody and decentralization. In the evaluation, EquiLink is resilient against both replay and physical attacks. Additionally, it operates efficiently, reducing transaction costs by 14.1% and latency by 18.9%

In the pursuit of data privacy and reduced latency, the adoption of edge intelligence has surged. Meanwhile, the enormous increase in AI has resulted in significant energy consumption. Edge intelligence plays a crucial role in Energy Demand Response (EDR). However, existing edge intelligence falls short of meeting the demands of co-locating training and inference tasks while satisfying EDR. Specifically, the intertwinement between balancing energy consumption, system delay and model accuracy, and uncertain future inputs adds to the challenge of designing an online sustainable system for co-located training and inference tasks. To address these challenges, we propose a novel two-timescale system for co-locating training and inference EDR. Our approach satisfies EDR by strategically planning training schedules on macro-timescales and migrating inference requests between heterogeneous edges on micro-timescales while minimizing long-term cost. We introduce a novel online polynomial time algorithm that first breaks down the problem into two subproblems, which are subsequently solved using an online-learning-based fractional algorithm and a randomized roun ding algorithm, respectively. Rigorous analysis demonstrates that our approach achieves both sublinear dynamic regret and sublinear dynamic fit. Extensive trace-driven evaluations validate the practical superiority of our approach over multiple existing methods, highlighting its effectiveness in real-world scenarios.

Sharding is a promising solution for improving blockchain scalability by distributing the workload across smaller groups of nodes called shards. However, it presents significant challenges, such as balancing tradeoffs between transactions per second (TPS), cross-shard transactions (CSTx), and confirmation latency in dynamic, online environments. While increasing the number of shards enhances TPS within individual shards, it also raises CSTx frequency, leading to higher failure probabilities, increased communication overhead, and prolonged confirmation times. Moreover, managing the number of shards and their configurations under one-sided feedback scenarios adds further complexity. To address these challenges, we model sharding as an online one-sided feedback optimization problem, focusing on maximizing long-term utilities. We introduce a polynomial-time online algorithm that adapts selection probabilities based on feedback information to address this NP-hard problem. Through rigorous analysis, we demonstrate that our approach achieves dynamic regret that grows sub-linearly over time. Extensive evaluations on real-world datasets confirm that our method outperforms existing baseline algorithms in terms of practical performance.

To make edge AI inference carbon-neutral, we perform a comprehensive mathematical and algorithmic study on the complex online management of AI model selection and placement with carbon allowance trading. This work is non-trivial due to the critical challenges such as the unknown stochastic distributions and arrivals of inference data, the exploration-exploitation tradeoff with model switching cost, and the uncertain, time-varying allowance prices and system environments. We first model a long-term stochastic cost optimization problem to capture these challenges. Then, we design a novel learning-centric decomposition-based online algorithmic framework which, on the one hand, samples and places the models repeatedly to minimize the expected inference loss with bounded model switches, and on the other hand, buys and sells carbon allowances cost-efficiently in real time toward carbon neutrality without relying on future allowance prices and system emissions. We further formally prove multiple performance guarantees of our algorithms in terms of sub-linear regret and fit. Finally, we conduct trace-driven evaluations to confirm the substantial advantages of our approach compared to baselines and state-of-the-arts in practice.

The extensive coverage of satellite constellations has rendered the satellite–terrestrial integrated network (STIN) a pivotal solution for communication and computation services in internet of vehicles (IoVs) rescuing in remote or disaster areas with limited terrestrial networks. To optimise network resource utilisation and service quality, the integration of the service function chain (SFC) into STIN‐enabled IoV rescuing systems has become essential. However, traditional SFC‐based STIN systems encounter challenges in flow scheduling flexibility, stemming from the sequential execution of subtasks on satellites equipped with virtual network functions (VNFs). This leads to a trade‐off between data volume reduction and the additional communication and computation energy costs incurred in the orbit. To address this issue, this paper introduces a space ground collaborative SFC (SGC‐SFC) flow scheduling strategy. This strategy enables the execution of subtasks on either VNF‐equipped satellites or the ground vehicle formation, contingent on network conditions. Firstly, we carry out a computation–space–time graph (CSTG) model specifically for the STIN‐enabled IoV rescuing system with SFC. This model integrates the computational layer into the space–time graph (STG), accurately capturing the data volume reduction characteristics and sequential execution constraints of SFC in the STIN‐enabled IoV rescuing system. Secondly, a SGC‐SFC flow scheduling algorithm is designed to identify a set of feasible paths with minimal energy cost and maximum processable data volume. Simulation results validate the effectiveness and robustness of our proposed SGC‐SFC under diverse conditions.

With advancements of in‐vehicle computing and Multi‐access Edge Computing (MEC), the Internet of Vehicles (IoV) is increasingly capable of supporting Vehicle‐oriented Edge Intelligence (VEI) applications, such as autonomous driving and Intelligent Transportation Systems (ITSs). However, IoV systems that rely solely on vehicular sensors often encounter limitations in forecasting events beyond current roadways, which are critical for regional transportation management. Moreover, the inherent temporal dependency in VEI application data poses risks of interruptions, impeding the seamless tracking of incremental information. To address these challenges, this paper introduces a joint task offloading and resource allocation strategy within an MEC environment that collaboratively integrates vehicles and Roadside Infrastructure Sensors (RISs). The strategy carefully considers the Doppler shift from vehicle mobility and the Tolerance for Interruptions of Incremental Information (T3I) in VEI applications. We establish a decision‐making framework that actively balances delay, energy consumption, and the T3I metric by formulating the task offloading as a stochastic network optimization problem. Utilizing Lyapunov optimization, we dissect this complex problem into three targeted subproblems that include optimizing local computational capacity, MEC computational capacity and comprehensive offloading decisions. To tackle the efficient offloading, we develop algorithms that separately optimize offloading scheduling, channel allocation and transmission power control. Notably, we incorporate a Potential Minimum Point (PMP) algorithm to boost parallel processing and simplify computational scale through matrix decomposition. Evaluations of our algorithm show that it excels in both complexity and accuracy, with accuracy improvements ranging from 74.3% to 114.0% in asymmetric resource environments. Simulation and experimental studies on offloading performance validate the effectiveness of our framework, which significantly balances network performance, reduces latency, and improves system stability.

While ensemble methods can tackle concept drifts, obtaining pretrained models and conducting ensemble learning upon streamed data impose fundamental challenges, including the dynamic balance between system overhead and inference accuracy in uncertain system environments, and the interlacement between desired economic properties and long-term participation. In this paper, we propose the joint optimization which enables service providers to obtain models via repetitive auctions from the model providers and conduct ensemble methods online in a cost-efficient manner. We design polynomial-time online algorithms to solve the underlying non-linear mixed-integer social cost minimization problem, involving bid selection, payment allocation, model hosting, and ensemble model-weight adaption. We further rigorously prove the performance guarantees with our approach, such as the sub-linear dynamic regret for the bidding cost, the sub-linear dynamic fit for the long-term participation constraint, the truthfulness and the individual rationality for the auctions, the upper bound for ensemble inference loss, and the parameterized-constant competitive ratio for the long-term social cost. Through extensive trace-driven evaluations under real-world settings, we have validated the significant advantages of our approach over multiple baselines and state-of-the-art algorithms.

In the foreseeable Intelligent Transportation System, Intelligent Connected Vehicles (ICVs) will play an important role in improving travel efficiency and safety. However, it is challenging for ICVs to support the resource-hungry autonomous driving applications due to the limitation of hardware computing power. Fortunately, the emergence of Multi-access Edge Computing helps overcome this limitation effectively. This paper addresses the vehicle-to-edge server computation offloading conundrum by optimizing the trade-offs in partial offloading and resource allocation. Proposing a distributed approach, this study confronts the multi-variable non-convex challenge directly by decoupling variables and deriving constraint-based bounds that guide the decisions for offloading and allocation. A novel low-complexity distributed algorithm is introduced that not only tends toward optimal but also demonstrates superior real-time applicability and efficiency, illustrated through enhanced performances both in simulated trials and genuine vehicular edge computing settings. The algorithm’s practical effectiveness addresses a notable gap between the theoretical models for computation offloading and actual real-life execution, reinforcing the soundness and relevance of the proposed method. Furthermore, its advanced integration with federated learning frameworks marks a leading-edge application, substantiating significant enhancements in computational efficiency and robustness.

Distributed edge computing systems that participate in Emergency Demand Response (EDR) programs can adjust workload across heterogenous edges to reduce total energy consumption. Unfortunately, this approach may not always reduce sufficient energy as required by EDR. In this paper, we propose to leverage Electrical Vehicles (EVs) and Vehicle-to-Grid (V2G) techniques to provide energy to the edge system, and design an auction mechanism to incentivize EVs to discharge energy for the edges. Yet, we face critical challenges, such as the uncertainty of EV bid arrivals, the restriction of discharging deadlines, and the desire to achieve required economic efficiency. To overcome such challenges, we design a novel online approach,$E^{3}$DR, of multiple algorithms that decompose our original NP-hard social cost minimization problem into two subproblems, solve the first subproblem via reformulation, the primal-dual optimization theory, and a careful payment design, and solve the second subproblem via standard solvers. We have rigorously proved that our approach finishes in polynomial time, achieves truthfulness and individual rationality economically, and leads to a parameterized competitive ratio for the long-term social cost. Through extensive evaluations using real-world data traces, we have validated the superior practical performance of our approach compared to existing algorithms.

Transfer learning does not train from scratch but leverages existing models to help train the new model of better accuracy. Unfortunately, realizing transfer learning in distributed cloud-edge networks faces critical challenges such as online training, uncertain network environments, time-coupled control decisions, and the balance between resource consumption and model accuracy. We formulate distributed transfer learning as a non-linear mixed-integer program of long-term cost optimization. We design polynomial-time online algorithms by exploiting the real-time trade-off between preserving previous decisions and applying new decisions, based on primal-dual one-shot solutions for each single time slot. While orchestrating model placement, data dispatching, and inference aggregation, our approach produces new models via combining the existing offline models and the online models being trained using weights adaptively updated based on inference upon data samples that dynamically arrive. Our approach provably incurs the number of inference mistakes no greater than a constant times that of the single best model in hindsight, and achieves a constant competitive ratio for the total cost. Evaluations have confirmed the superior performance of our approach compared to alternatives on real-world traces.

Due to the enormous energy consumption and the wide geographic distribution, Electrical Vehicle (EV) charging stations are believed to have great potential in Emergency Demand Response (EDR) participation. However, EDR limits the electricity drawn from the power grid by the charging station, and can pose threats to satisfying EVs’ charging demand. In this paper, in order to complement the charging station’s energy supply to meet the dynamic EV charging demand, we formulate an online EV charging scheduling problem under EDR as a non-linear mixed-integer program, and propose a novel polynomial-time online algorithm and auction mechanism to jointly incentivize EVs with energy to sell their energy and utilize the charging station’s local generator to produce energy. Our approach conducts an auction in each single round based on a primal-dual method and ties these auctions over time to optimize the system’s long-term social cost, while accommodating the local generator’ on/off-state control, each EV bidder’s cumulative energy budget constraint, and the power grid’s EDR energy cap. Our approach achieves the economic properties of truthfulness, individual rationality, and computational efficiency simultaneously for each auction, and a parameterized-constant competitive ratio for the long-term social cost. By rigorous theoretical analysis and trace-driven experimental studies, the results exhibit that our approach outperforms multiple alternative algorithms regarding the social cost, attains the economic properties, and also executes efficiently in practice.

Mobile users are often reluctant to participate in federated learning to train models, due to the excessive consumption of the limited resources such as the mobile devices’ energy. We propose an auction-based online incentive mechanism, FLORA, which allows users to submit bids dynamically and repetitively and compensates such bids subject to each user’s long-term battery capacity. We formulate a nonlinear mixed-integer program to capture the social cost minimization in the federated learning system. Then we design multiple polynomial-time online algorithms, including a fractional online algorithm and a randomized rounding algorithm to select winning bids and control training accuracy, as well as a payment allocation algorithm to calculate the remuneration based on the bid-winning probabilities. Maintaining the satisfiable quality of the global model that is trained, our approach works on the fly without relying on the unknown future inputs, and achieves provably a sublinear regret and a sublinear fit over time while attaining the economic properties of truthfulness and individual rationality in expectation. Extensive trace-driven evaluations have confirmed the practical superiority of FLORA over existing alternatives.

The secure exchange of messages between different communication devices is a major issue of Internet-of-Things (IoT) applications in future smart cities. Current security mechanisms focus on multiple antennas technology, such as spatial modulation (SM), but in space shift keying (SSK), there is still a space to explore. In this article, we propose a secrecy-enhancing SSK scheme for IoT applications by applying security technologies in the physical layer wherein the number of transmit antennas is arbitrary rather than the value of power of two. In this scheme, the security performance of the communication system is improved by using two technologies, namely, artificial noise (AN) and antenna selection. We assume that the application scenario of the SSK system is under the classic eavesdropping model. First, we design ANs according to the channel state information (CSI) to interrupt the eavesdropper and benefit the legitimate receiver via the appropriate cancellation technology. Second, the antenna selection method is designed based on the signal to leakage noise ratio (SLNR) to further boost the secrecy performance by expanding the mutual information difference between the main channel and the eavesdropping channel. Results from our simulations indicate that by the use of the proposed scheme, significant secrecy enhancing can be achieved in terms of bit error ratio (BER) and secrecy rate (SR) when compared with existing schemes. This achieved secrecy enhancing can benefit the suitable IoT communication applications in the smart city environment to avoid the leakage of data transmission.

Bad repeatability is a defect of on-road V2X (Ve-hicle to Everything) related test. The outside electromagnetic environment is un-controllable and the test outputs perform very un-stable. An obvious advantage of in-chamber test is the controllability of test environment parameters, which could mend the defect of on-road field test. In this paper, an in-chamber test scheme is designed for V2X related test. Corresponding operation conditions are analyzed, and test cases are defined in detail. Moreover, the test parameter setting method is given. A typical application, FCW, is test to verify the effectiveness of proposed scheme. Test results show that the output of in-chamber test is stable, which illustrate a good repeatability.

Recent advances in cooperative multiple-input-multiple-output (CMIMO) techniques have encouraged interest in the development of intelligent transportation systems (ITS). They have the potential for use in the infrastructure to vehicle (I2V) and infrastructure to infrastructure (I2I) communications in ITS networks where the energy consumption of wireless sensor nodes embedded on the road infrastructure is constraint. Therefore, how to reduce the energy consumption becomes a hot research topic . In this paper, applications of cooperative communications in ITS networks are proposed for reducing the total energy consumption . At first, the ITS model is established based on the cooperative multiple-input-multiple-output spatial modulation (CMIMO-SM). A detailed energy consumption analysis of the proposed scheme compared with the traditional single-input-single-output (SISO) based scheme is then presented. The comparison conducted between these communication schemes helps us select the optimal one for energy reduction in energy constrained ITS networks. Additionally, under the guidance of the proposed scheme we consider the multi-hop transmission scenario where the energy efficiency improvement is achieved by finding the optimal hop number with the equal hop-length scheme. As a result, we analyze the energy consumption in different situations, and discuss the requirements on the hop-length and hop number in ITS networks. It shows that the optimal results are dependent on the ITS scenarios and choosing the appropriate transmission scheme will provide a good energy consumption performance in ITS.

The Internet of Things (IoT) is visioned to connect everything in the world by a common networking technique. In spite that Internet protocol is the most prevailing networking solution, it is difficult to adapt to IoT which is born with scalability, heterogeneity, and dynamics. Thanks to the content-centric communication paradigm, it exerts data-naming strategy to incorporate the heterogeneity and dynamics so as to apply for IoT, composing Named Data Networking (NDN) of Things. However, the paradigm also introduces new types of security attacks. In this paper, the NDN of Things architecture will be illustrated and the security analysis is conducted. Furthermore, the potential security attacks of NDN of Things are categorized and the performance impact caused by security attacks is evaluated. Finally, the solutions for NDN of Things security attacks are discussed and a blockchain solution is illustrated.

While large layer-2 networks are widely accepted as the network fabric for modern data centers and network virtualization is required to support multi-tenant cloud computing, existing network virtualization solutions are not specifically designed for layer-2 networks. In this paper, we designSVDC, a highly-scalable and low-overhead virtualization architecture for large layer-2 data center networks. By leveraging the emerging software defined networking (SDN) framework, SVDC decouples the global identifier of a virtual network from the identifier carried in the packet header. Hence, SVDC can scale to a great number of virtual networks with a very short tag in the packet header, which is never achieved by previous network virtualization solutions. SVDC enhances MAC-in-MAC encapsulation in a way that packets with overlapped MAC addresses are correctly forwarded even without in-packet global identifiers to differentiate the virtual networks they belong to. Besides, scalable and efficient layer-2 multicast and broadcast within virtual networks are also supported in SVDC. With extensive simulations and experiments, we show that SVDC is better than existing solutions in many aspects, particularly isolating virtual networks with high scalability and higher network goodput due to minimal packet header overhead.

Nowadays social networking services (SNS) have become an important part of our daily-life. Thanks to the rapid development of mobile computing technologies, more and more people start to use mobile devices, e.g., smartphones and tablets, to access the SNS. Different from the traditional way, i.e., using the desktop PCs to access the web interface of the SNS, using mobile devices will introduce more flexibility. However, there is a lack of a comprehensive study to evaluate the effects of using mobile devices to access SNS. In this paper, we study the Twitter social network by comparing between the emerging mobile users and traditional web users. We have three major findings. First, by examining the tweet posting behavior, we find that mobile users are more active for posting shorter tweets, while less active for retweeting. Second, by referring to the social structure, we can see that the social network composed by mobile users are less clustered, while the radius and diameters of mobile social graph are shorter. Finally, we introduce one classic simulation scenarios, which is information diffusion in SNS. The simulation results indicate that the mobile effects actually decrease the scope of information diffusion as they are less active for message forwarding.

The data caching in Device-to-Device (D2D) networks enables the quick data access in mobile networks. The D2D channels allows content sharing when two devices are in close proximity which can help improve resource utilization and network capacity. Due to the selfish nature of users, they wish to get as much replication as possible in the opportunistic connections, seeking to maximize their own profit. However, caching resources for other nodes may lead cost to the node who serves as cache. It lacks incentives for mobile nodes to cache for other peers in D2D network. In this paper, we use an incentive method to make mobile nodes cache for others and aim to minimize the total cost of getting object data in the network. The total cost is occurred by the cache placement of cache nodes and accessing cost of the other nodes. We consider the social ties and physical distance as the factors for the cost. We model the data cache problem as a socially-aware payment game, and we introduce a hierarchical caching scheme to incentive nodes to cache, which use the user relationship to construct the cost function. In order to model user relationship, we divide the network into three categories in perspective a node: self, friends and strangers. We obtain the Nash equilibrium of the game and propose a heuristic algorithm to solve the cache placement problem. The extensive simulation results show that our algorithm gain significant cache benefit.

In this paper, we propose a hierarchical architecture based on software defined networking (SDN) to manage the physical resources in vehicular ad-hoc networks (VANETs), namely sdnMAC. First of all, a novel roadside unit (denoted by ROFS) is designed, which is an OpenFlow switch equipped with a wireless interface. Then, a hierarchical architecture is proposed for sdnMAC, consisting of two tiers, one is the management of the ROFSs by the Controller, the other is management of vehicles by ROFSs. Due to the cooperative share of slots information, sdnMAC can provide pre-warning of collisions and agility to topology change and varying densities of vehicles.

Advertising among vehicles has become popular with the proliferation of vehicular ad-hoc networks (VANETs). Since the price of the advertisements broadcast in such networks decay over time, distributing advertisements with a high price value to more private vehicles can generate more revenues to the sellers. In this paper, we consider a bus-based priced advertising scenario in a VANET, in which the buses act as the sources of advertisements and broadcast advertisements to private vehicles running within their communication range. Meanwhile, in the area where no bus exists, private vehicles share their advertising segments. The manner in which the buses and the private vehicles distribute and share advertisements in the network so as to draw the largest benefit is addressed in our formulated problem. To solve this problem, a bus-based priced advertisement dissemination scheme dubbed Bus-Ads is proposed by using coalition formation game. First, a bus-broadcast method is presented to enable each bus to distribute the priced advertising segments with the largest potential benefit to surrounding private vehicles. Second, we apply coalition formation game to guide private vehicles to construct broadcast coalitions for efficient advertisement sharing. Simulation results demonstrate that our proposed Bus-Ads method can achieve about twice the total benefits compared with that of the non-coalition-based approach.

Opportunistic Mobile Social Networks (MSNs) as a kind of social network in which nodes are opportunistically connected. Many data routing strategies have been proposed for opportunistic MSNs. Most of them apply a utility for relay selection that a node with a higher utility value is selected as relay. However, such utility-based routing strategies run into dead end problem in which the data is stuck into a node with local maximal utility value. In this paper, we propose a virtual routing method named VIRO, which exerts conformal mapping to convert the local topology of dead end into a virtual geometric map to guarantee data dissemination to the next relay. We first convert the utility into geometric planner, and then conduct the discrete Ricci Flow bypass the gap between the dead end and next relay. Extensive experiment results suggest VIRO can reduce the ratio of dead end effectively so that the data delivery ratio is enhanced up to 42% by increasing only a slight amount of delay and cost.

Data access is an important and challenging issue in Mobile Social Networks (MSNs), and cooperative caching is an effective technique to improve the access performance. Most of current research efforts in data access of MSNs focus on improving the access performance while neglecting the fair treatment of users. Because fairness is considered as a major incentive for peer-to-peer service especially in infrastructure-less wireless networks, in this paper we propose a novel approach to support fairness aware cooperative caching scheme in MSNs. Through capturing close friend set of each node, we cache data prior at nodes which are overlapped by most nodes' close friend sets. Then we derive the optimal cooperative scheme by using the minimum dominating set, which is an NP-Complete problem, and we design a heuristic algorithm to handle it. Experimental results show that our scheme can effectively improve data access fairness as well as maintain nearly the same access performance compared to existing cooperative caching schemes.

Large military ad hoc networks are often characterized by the interconnection of heterogeneous domains. The same trend is emerging in civilian MANETs (e.g., search and rescue, vehicular networks). In these networks it is important to be able to efficiently propagate information across domains in multicast mode (e.g., situation awareness dissemination, commands, streams). Several multicast protocols have been developed for single domain MANET. However, few can be extended to inter-domain operation. In fact, multicast routing across different MANET domains faces the challenges of node motion, topology changes, dynamic gateway election and, possibly, connectivity interruption. To overcome these challenges, especially to achieve routing scalability and at the same time maintains efficient routing, this paper proposes the Geo-assisted Multicast Inter-domain Routing (GMIDR) protocol based on geographical assistance and cluster technology. Intensive simulation results show that the GMIDR protocol is scalable and stable with various numbers of multicast group members, and it outperforms other multicast protocols. A military use case scenario simulation shows that GMIDR can be utilized efficiently in the large scale networks crossing multiple domains. Geocast by applying GMIDR shows the flexibility of the protocol.