Recent advances in LLM-based multi-agent systems have demonstrated remarkable capabilities in complex decision-making scenarios such as financial trading and software engineering. However, evaluating each individual agent’s effectiveness and online optimization of underperforming agents remain open challenges. To address these issues, we present HiveMind, a self-adaptive framework designed to optimize LLM multi-agent collaboration through contribution analysis. At its core, HiveMind introduces Contribution-Guided Online Prompt Optimization (CG-OPO), which autonomously refines agent prompts based on their quantified contributions. We first propose the Shapley value as a grounded metric to quantify each agent's contribution, thereby identifying underperforming agents in a principled manner for automated prompt refinement. To overcome the computational complexity of the classical Shapley value, we present DAG-Shapley, a novel and efficient attribution algorithm for Directed Acyclic Graph (DAG)-structured multi-agent workflows that leverages the inherent DAG structure of the agent workflow to axiomatically prune non-viable coalitions. By hierarchically reusing intermediate outputs of agents in the DAG, our method further reduces redundant computations, and achieving substantial cost savings without compromising the theoretical guarantees of Shapley values. Evaluated in a multi-agent stock-trading scenario, HiveMind achieves superior performance compared to static baselines. Notably, DAG-Shapley reduces LLM calls by over 80 percent while maintaining attribution accuracy comparable to full Shapley values, establishing a new standard for efficient credit assignment and enabling scalable, real-world optimization of multi-agent collaboration.

The convergence of unmanned aerial vehicles (UAVs) and the Internet of Things (IoT) is expected to enhance sensing coverage, connectivity, and resilience in distributed smart grids, especially in remote or infrastructure-sparse regions. In this UAV-assisted, IoT-enabled paradigm, UAVs act as aerial relays that collect, aggregate, and forward sensing data between ground devices and control centers. However, privacy-preserving data aggregation (PPDA) in such settings still faces key-escrow vulnerabilities, certificate management overhead, incomplete privacy protection, and high computational and energy costs, particularly for signature verification at UAV relays and decryption at control centers. To address these challenges, we propose LCE-PPDA, a lightweight, certificateless, and escrow-free PPDA scheme tailored for UAV-assisted, IoT-enabled smart grids. LCE-PPDA eliminates key escrow through joint key generation, adopts a hierarchical timing structure with macro-interval rekeying and micro-interval reporting, and supports ciphertext-level in-network aggregation with both individual and batch authentication at UAV relays. To ensure privacy with accountability, it integrates certificateless signatures, dynamic pseudonyms, and session-bound key masking, achieving end-to-end confidentiality, conditional anonymity, unlinkability, and accountable traceability. Formal analysis shows that LCE-PPDA achieves correctness and EUF-CMA security in the random-oracle model under the ECDLP assumption against both Type-I and Type-II adversaries. Performance evaluation further demonstrates that LCE-PPDA reduces computational, communication, and energy overheads compared with representative schemes, providing a scalable and lightweight foundation for secure, privacy-preserving data aggregation in UAV-assisted, IoT-enabled smart grids.

Continuous variable-quantum key distribution (CV-QKD) protocols attract increasing attentions in recent years because they enjoy high secret key rate (SKR) and good compatibility with existing optical communication infrastructure. Classical coherent receivers are widely employed in coherent states based CV-QKD protocols, whose detection performance is bounded by the standard quantum limit (SQL). Recently, quantum receivers based on displacement operators are experimentally demonstrated with detection performance outperforming the SQL in various practical conditions. However, potential applications of quantum receivers in CV-QKD protocols under turbulent channels are still not well explored, while practical CV-QKD protocols must survive from the atmospheric turbulence in satellite-to-ground optical communication links. In this paper, we consider the possibility of using a quantum receiver called multi-stage CD-Kennedy receiver to enhance the SKR performance of a quadrature phase shift keying (QPSK) modulated CV-QKD protocol in turbulent channels. We first derive the error probability of the multi-stage CD-Kennedy receiver for detecting QPSK signals in turbulent channels and further propose three types of multi-stage CD-Kennedy receiver with different displacement choices, i.e., the Type-I, Type-II, and Type-III receivers. Then we derive the SKR of a QPSK modulated CV-QKD protocol using the multi-stage CD-Kennedy receiver and post-selection strategy in turbulent channels. Numerical results show that the multi-stage CD-Kennedy receiver can outperform the classical coherent receiver in turbulent channels in terms of both error probability and SKR performance and the Type-II receiver can tolerate worse channel conditions compared with Type-I and Type-III receivers in terms of error probability performance.

As the application fields of permissioned blockchains broaden and the integration of related industries accelerates, there is a rising demand for permissioned blockchains to support scalable networks. This paper proposes a Partitioned, Parallel and Practicable permissioned blockchain, called as P3-Chain, which builds upon a multi-shard two-tier architecture. Its key design insight is to extend scalability in terms of consensus algorithm protocol, architecture, and scheduling. In particular, P3-Chain employs a dual-consensus algorithm with decoupled intra- and inter-shard operations, allowing them to run in parallel and asynchronously under practical scenarios. To resolve the conflicting transaction problem brought by this decoupled dual-consensus algorithm, P3-Chain incorporates a state-access locking mechanism. P3-Chain is implemented in Golang across multiple OSs, and it is evaluated on Hyperledger Caliper testbed, ensuring standardized and fair benchmarking. Through extensive experiments, the results indicate that P3-Chain can achieve TPS$3.3\times $that of FISCO,$3.3\times $that of partitioned FISCO,$2.7\times $that of Fabric,$2.3\times $that of AHL+,$2.2\times $that of SharPer and$7.4\times $that of Ethereum when system contains 32 nodes. Meanwhile, within the same experimental settings, P3-chain is scalable to 1024 nodes successfully, while Fabric and FISCO run with 64 nodes only. Furthermore, P3-Chain only sacrifice less than a 10% performance when the system scale expands$256\times $from 4 to 1024.

Permissioned blockchain systems provide a mutual-trust platform for data sharing and collaboration among organizations. However, performance bottlenecks limit their adoption in industrial applications requiring high transaction throughput and low latency. Recent advancements have focused on leveraging parallelism to improve performance, but transaction contention remains a significant challenge. The Optimistic Concurrency Control (OCC) mechanism, once widely used to manage transaction contention in permissioned blockchains, is valued for its simplicity and minimal design constraints. However, its reliance on the strategy of aborting conflicting transactions results in resource wastage and suboptimal performance, rendering it less favorable in recent research. This paper presents the Overpass Ledger (OPL), a high-performance permissioned blockchain system that utilizes an overpass-inspired workflow. To address transaction contention in such a highly parallelized workflow, the OCC mechanism is revisited and Re-Execution (ReX) is proposed, an enhanced OCC variant that efficiently re-executes conflicting transactions to eliminate transaction abortion and maximize resource utilization. By integrating ReX, OPL fully harnesses the advantages of parallel stage processing and concurrent transaction execution. Experimental results demonstrate that OPL achieves throughput improvements of$77\times$,$19\times$, and$4\times$compared to Hyperledger Fabric, BIDL, and FISCO BCOS, respectively, while maintaining consistently low latency.

Emerging as a promising technology to bridge the trust gap among multiple participants, blockchain has been envisioned to enable dynamic spectrum sharing in a decentralized manner. However, satellites with limited resources may struggle to support the frequent interactions required by blockchain networks. Additionally, due to the large coverage area of satellites, the differentiated spectrum sharing needs in various regions can make traditional blockchain approaches inadequate. In this paper, a two-tier multi-region blockchain-based dynamic spectrum sharing approach (TMB-DSS) is proposed. This approach enables regions to manage spectrum autonomously while jointly maintaining a unified blockchain ledger. Moreover, a theoretical framework using stochastic geometry is derived to evaluate the stability performance of TMB-DSS. Finally, numerical results are presented to validate the proposed approach.

With revolutionized various real-time and cyber-physical applications, today’s networked systems face more threats of cyberattacks due to their expansive networking attack surface. One promising trend is to deploy deep learning network (DNN) models to classify and identify cyberattacks at the network layer to identify either malicious traffic or anomaly traffic as alerts. However, deploying DNN models to classify network traffic will compromise the efficiency requirements of networked systems since high-performance DNN models are usually complex at inference. With more attack surfaces in today’s networked system, more complex DNN models are required to fulfill the feature extraction. In this paper, we propose a multi-model learning approach for both accurate and efficient traffic classification to address the above challenge. Our key insight is that network traffic is multi-model data including structured data (e.g., protocol text) and non-structured data (e.g., packets). By designing different lightweight feature extractors, we extract features of multi-modal data via language models and linear models respectively, and fuse features from heterogeneous network traffic data to classify and identify typical cyberattacks. Experimenting with showcases of adopting classic learning methods indicates that our approach can achieve an accurate and efficient network traffic classification to ensure the security of networked systems.

Efficient spectrum utilization is essential for large-scale Internet of Things (IoT) deployments, where massive heterogeneous devices and multiple service providers must coexist over constrained wireless resources. In contrast to traditional Static Spectrum Allocation (SSA), which assigns fixed bands to licensed users and can lead to under-utilization, Dynamic Spectrum Sharing (DSS) offers a promising solution by allowing flexible spectrum access across virtual operators without fixed allocation. However, traditional centralized DSS systems are often unsuitable for distributed IoT environments due to their complex management, limited scalability, and vulnerability to single-point failures. This paper proposes a decentralized and Byzantine-resilient DSS framework tailored for multi-operator IoT networks. We formulate a convex optimization problem to maximize the total wireless downlink transmission rate, which is decomposed into local subproblems and solved collaboratively by mobile virtual network operators (MVNOs). To ensure robustness against adversarial nodes, a coordinate-wise trimmed mean (CTM) method is adopted to filter corrupted updates during distributed optimization. Moreover, we introduce a blockchain-assisted mechanism to enforce global consistency and traceability. Smart contracts and consensus algorithms are used to synchronize parameter updates and prevent inconsistent message propagation from malicious participants. Simulation results demonstrate that the proposed framework effectively mitigates Byzantine behaviors and maintains near-optimal spectrum allocation under various attack models. The integration of blockchain further enhances the trustworthiness and auditability of decentralized DSS in adversarial IoT scenarios.

Model partitioning techniques, which decompose and collaboratively execute subtasks of deep neural networks (DNNs), have emerged as a critical strategy for enhancing distributed inference efficiency. However, in mobile edge computing (MEC), dynamic load fluctuations at edge nodes and the complexity of cross-node task dependencies make the delay minimization problem extremely challenging. Existing studies predominantly adopt a decoupled optimization framework that separately addresses partitioning-offloading and pipeline scheduling, neglecting their inherent cyclic state-dependent coupling. This oversight leads to suboptimal solutions, such as pipeline stagnation caused by mismatched computation and communication timestamps. To address these challenges, we propose a multi-endpoint directed acyclic graph (DAG)-driven cooperative optimization approach, enabling partitioning-offloading and pipeline scheduling in MEC. Specifically, the approach involves two core steps: 1)Dynamic pre-scheduling: We propose an improved DNN scheduling algorithm for constrained subtasks, which simulates node-level queuing delays and pipeline stalls under real-world constraints, translating runtime states into latency objectives. 2)Partitioning and offloading solution retrieval: Based on latency objectives, we introduce a novel multi-endpoint DAG structure and design a multi-node collaborative optimization retrieval algorithm, enabling adaptive partitioning-offloading remapping of subtasks. Experiments demonstrate the superiority of the proposed method over other advanced methods, reducing the time overhead by an average of 24% and 75% in two different scenarios, respectively. The resource code can be found at: https://github.com/aiheiheiheii/Partition_Scheduling.git.

Blockchain-assisted federated learning (BFL) can achieve decentralized storage and management of model data without relying on a central server. However, security issues caused by deliberate attacks in distributed systems and efficiency issues induced by heterogeneous computing consumption in resource-limited systems need to be urgently addressed in BFL. To address these issues, we propose a decentralized reputation management (DRM) mechanism for a trustworthy BFL (T-BFL) network, that explores, stores, and utilizes the endogenous reputation of distributed nodes to promote system security and efficiency. The proposed DRM includes three core modules, i.e., decentralized reputation evaluation, reputation-based model aggregation, and reputation-based blockchain consensus. Specifically, in the off-chain phase of T-BFL, the reputation value of each node is evaluated based on model quality, which other peer nodes can verify. This reputation value further determines the weight of global aggregation at each node. In the on-chain phase, the reputation of each node serves as the stake to dynamically adjust its consensus difficulty. Furthermore, we investigate the convergence rate of the T-BFL network under the poisoning attack, and dynamically optimize the energy allocation of local training, consensus, and communications by minimizing the upper bound of the global loss function. Extensive experiments are conducted to evaluate the performance of T-BFL on MNIST, Fashion-MNIST, and Cifar-10 datasets. The experimental results demonstrate that, compared with traditional BFL, T-BFL can achieve up to 56.12% accuracy improvement and$8.6\times $acceleration for reaching the target learning accuracy under the poisoning attack.

Isolatability and scalability are two critical issues faced by blockchain. Blockchain interoperability addresses isolatability between heterogeneous blockchains, while sharding-based blockchain achieves scalability by solving isolatability of homogeneous blockchains running in different shards. To ensure atomicity between different blockchains, they both need to overcome two significant problems: 1) how to handle cross-chain transactions without trusting any third parties; 2) how to enhance the resistance to double-spending attacks of participating blockchains. To this end, this work presents a two-tier multi-zone architecture, Distributed and Parallel Blockchain (DP-Chain), whereConsensus Zonein tier-1 features blockchain interoperability and sharding-based blockchain by allowing homogeneous or heterogeneous blockchains run in different zones, whileCoordination Layerin tier-2 allows them to interoperate as a Direct Acyclic Graph (DAG) without trusting any third parties. Meanwhile, the coordination scheme between the two tiers is designed to resist potential double-spending attacks. Then, stochastic models are used to captureDP-Chainconsensus process and analyze the probability of successful double-spending attacks. These analyses can help understandDP-Chaineasily and offer theoretical guidelines for further implementations. Finally, based on the proposed architecture, a practical system is realized with C++ and open in Github for testing. Extensive experiments show correctness and effectiveness of this design.

Federated learning (FL) emerges as a distributed training method in the Internet of Vehicles (IoVs), which promotes connected and automated vehicles (CAVs) to train a global model by exchanging models instead of raw data to protect data privacy. In this paper, consider the limitation of model accuracy and communication overhead in FL, as well as further verification in the real scenarios, we propose a directed acyclic graph (DAG) blockchain-based IoV system that comprises a DAG layer and a CAV layer for model sharing and training, respectively. Furthermore, a DAG blockchain-assisted asynchronous federated mutual learning (DAFML) algorithm is introduced to improve the model accuracy, which utilizes mutual distillation method to train a teacher-student model simultaneously. Moreover, a policy network will first be pre-trained by an expert data augmentation strategy through the DAFML algorithm via the behavior cloning, and be re-trained through the proposed proximal policy optimization (PPO) algorithm based autonomous driving framework. Finally, simulation results demonstrate that the proposed DAFML algorithm outperforms other benchmarks in terms of the model accuracy, distillation ratio and autonomous driving decision.

In the design of grant-free multiple access (GFMA) mechanisms, the number of active users (UEs) with heterogeneous delay tolerance is commonly assumed to be known, which is less practical in real-world implementations. In this paper, we propose a network state sensing assisted resource allocation algorithm for the GFMA systems, in which both the delay-sensitive unmanned aerial vehicles (UAVs) and the delay-tolerant terrestrial UEs simultaneously access to the base stations (BSs). Taking the limited radio resources and the practical assumption that the number of active UEs is not available at the BSs into account, we first design a Bayesian-based estimation algorithm that can determine the number of delay-sensitive UAVs and delay-tolerant terrestrial UEs in real-time. Based on the estimated result, we further develop a dynamic algorithm to judiciously allocate the limited radio resources according to the weights and number between the UAVs and the terrestrial UEs. Moreover, we dynamically adjust the access class barring factors to ensure the access delay requirements of the UEs. Through numerical results, we show how our proposed algorithm can achieve almost the same performance as that of the ideal algorithm assuming perfect information on the number of active UEs is available.

In the context of the burgeoning Industrial Internet of Things (IIoT), the proliferation of interconnected devices has created a reservoir of data resources distributed across diverse domains. However, due to the conflict between proprietary data and the use of data, it is a challenge to fully obtain data value in an efficient and legal way. To release the data value in an efficient and legal way, blockchain is considered a promising technology for data security and privacy, which has been widely introduced to cross-domain data governance. In this paper, we propose a blockchain-assisted cross-domain data sharing (BCDS) in IIoT. Specifically, by deploying the permissioned blockchain, we design a zero-knowledge proof scheme to verify data ownership under the criterion of confidence and anonymity. Besides, to prevent the thrid-party from decrypting data, we design a key agreement protocol to ensure that only recipient is authorized to decrypt data based on private key. Furthermore, we theoretically analyze the security performance of schemes. Extensive experiments in simulation computer systems and testbed deployment are conducted to demonstrate the effectiveness and efficiency of the proposed scheme.

To enable intelligent and low-cost End-to-End (E2E) network service deployment and Service Level Agreement (SLA) quality management in the two-level Cloud Radio Access Network (C-RAN), this paper studies a DTN-based SLA quality closed-loop management scheme, which mainly includes acquisition module, base module, deployment module, and monitoring module. The deployment module is responsible for constructing the service deployment optimization model with the goal of minimizing the average E2E delay of packets, and quickly obtain deployment decisions through a Weighted GraphSAGE (WGraphSAGE)-assisted Double Deep Q-network (DDQN)-based two-stage service deployment (WDTSD) algorithm. The monitoring module uses the state monitoring model based on Bayesian Convolutional Neural Network (BCNN) to complete the abnormal detection of physical devices. The modular closed-loop interaction provides a virtual environment for network service deployment, verification, monitoring, and policy revision, achieving SLA quality assurance. Extensive results validate the effectiveness of the WDTSD algorithm, state monitoring model, and DTN. WDTSD outperforms existing solutions in terms of memory overhead, computing speed, E2E delay, and service access ratio. The state monitoring model has better performance in indicators such as accuracy. The results under different data acquisition periods show that the service deployment effect is better when the DTN is closer to the physical network.

Decentralized federated learning (DFL) has emerged as a conducive paradigm, facilitating a distributed privacy-preserving data collaboration mode in mobile edge computing (MEC) systems to bolster the expansion of artificial intelligence applications. Nevertheless, the dynamic wireless environment and the heterogeneity among collaborating nodes, characterized by skewed datasets and uneven capabilities, present substantial challenges for efficient DFL model training in MEC systems. Consequently, the design of an efficient collaboration strategy becomes essential to facilitate practical distributed knowledge sharing and cost reduction for MEC. In this paper, we propose an adaptive decentralized federated learning framework that enables heterogeneous nodes to learn tailored collaboration strategies, thereby maximizing the efficiency of the DFL training process in collaborative MEC systems. Specifically, we present an effective option critic-based collaboration strategy learning (OCSL) mechanism by decomposing the collaboration strategy model into two sub-strategies: local training strategy and resource scheduling strategy. In addressing inherent issues such as large-scale action space and overestimation in collaboration strategy learning, we introduce the option framework and a dual critic network-based approximation method within the OCSL design. We theoretically prove that the learned collaboration strategy achieves the Nash equilibrium. Extensive numerical results demonstrate the effectiveness of the proposed method in comparison with existing baselines.

Ensuring secure access to cellular networks is of paramount importance, in which system information (SI) protection plays a crucial role at the initial access stage. While the 3rd generation partnership project (3GPP) released many standardizations to enhance SI protection for preventing users from false base station (FBS) attacks, most of them are centralized solutions which are vulnerable to potential attacks and single-point failures. To address the aforementioned issues, a blockchain-enabled SI protection (BeSI), as a compatible and effective secure access scheme, is developed in this work, which aims at guaranteeing the authenticity and reliability of SI by considering the features of blockchain in immutability, traceability, and decentralization. Then, we derive a mathematical framework to justify the superiority of using blockchain in SI protection. Moreover, by resorting to a Poisson point process as the geographical model for both base stations and FBSs, we thus theoretically analyze the security gain of blockchain and understand the impact of network parameters including redundancy rate, number of confirmation blocks, and the density of base stations. Finally, numerical results are demonstrated to validate the effectiveness of BeSI.

A critical security issue in the blockchain-aided cooperative spectrum sensing (B-CSS) network is that, blockchain cannot guarantee the reliability of off-chain data source, even though the data has been recorded on the chain. Furthermore, the performance optimization of the B-CSS networks is constrained by an underlying tradeoff between throughput and security. Driven by these issues, we first develop a novel B-CSS framework with a decentralized reputation management (DRM) mechanism, wherein nodes not only collaborate to detect the availability of target spectrum off the chain, but also act as the blockchain nodes to maintain global decisions on the chain. In the off-chain phase, the DRM mechanism can enhance the trustworthiness of CSS by evaluating each node's reputation according to its contribution to the global detection. Furthermore, in light of the on-chain throughput-and-security tradeoff, verifiable reputation can be utilized as the consensus stake to adjust the difficulty level of block generation. Then, given the on-chain reputation consensus, we maximize the average throughput of the proposed framework by jointly optimizing the block size, sensing time, and block generation time. Simulation results demonstrate the optimized performance of the proposed framework. Moreover, compared with the baseline schemes, our proposal is more robust to the threat of malicious attacks such as data-tampering attack and collusion attack.

To reduce the delay and network congestion for content delivery in wireless networks, proactive caching scheme has attracted lots of attentions from both academia and industry. However, traditional caching prediction methods require to collect user data in a centralized server, which is becoming unreliable and impractical due to regulatory restrictions. To circumvent this issue, deploying caching prediction system in a federated learning (FL) fashion becomes a promising solution. However, there still exist privacy risks, and even worse, the FL is vulnerable to low-cost attacks. To solve this problem, a novel federated prediction system (FPS) is studied to provide high robustness and privacy. Firstly, to keep a balance between further enhancing privacy protection and alleviating the performance degradation caused by additional protection schemes, we propose an edge-assisted, robust and privacy-preserving FPS framework based on the local differential privacy (LDP) scheme. Secondly, to mitigate the impact of heterogeneous data, we add a regularization term to the local loss function. Furthermore, an attention-based aggregation scheme is proposed to defend against Byzantine attacks during the training process. Finally, the experiment results are provided to show the superiority of our proposed algorithm in terms of prediction accuracy and robustness.

Blockchain is envisioned as one of the promising technologies to address trust concern brought by mobile crowdsensing (MCS), due to its auditability, immutability and decentralization. Nevertheless, blockchain cannot fundamentally guarantee that the valuable sensed data outside the chain can enter the chain, although data integrity and consistency can be ensured once it is confirmed inside the chain. In addition, simply applying blockchain in MCS while ignoring possible abnormal saboteurs hidden in numerous devices may mislead the normal operation of blockchain, resulting in untrustworthy interactions. Consequently, it is highly desirable to build a trust-preserving mechanism (TPM) to fully enjoy the benefits of using blockchain in MCS. To this end, we first resort to a probabilistic trust assessment inferred from the interaction outcomes in blockchain, to incentivize participants to maintain the trustworthiness of interactions. By inferring trust to aid decision-making, trust decision is further made, including leader election and transaction data generation, to filter untrusted nodes from participating in blockchain process. Finally, extensive simulations are conducted to validate the effectiveness and efficiency of TPM, and improve the performance in terms of contribution rate, consensus accuracy and system stability.

Due to the distributed characteristics of federated learning (FL), the vulnerability of the global model and the coordination of devices are the main obstacle. As a promising solution of decentralization, scalability, and security, leveraging the blockchain in FL has attracted much attention in recent years. However, the traditional consensus mechanisms designed for blockchain-like proof of work (PoW) would cause extreme resource consumption, which reduces the efficiency of FL greatly, especially when the participating devices are wireless and resource-limited. In order to address device asynchrony and anomaly detection in FL while avoiding the extra resource consumption caused by blockchain, this article introduces a framework for empowering FL using direct acyclic graph (DAG)-based blockchain systematically (DAG-FL). Accordingly, DAG-FL is first introduced from a three-layer architecture in detail, and then, two algorithms DAG-FL Controlling and DAG-FL Updating are designed running on different nodes to elaborate the operation of the DAG-FL consensus mechanism. After that, a Poisson process model is formulated to discuss that how to set deployment parameters to maintain DAG-FL stably in different FL tasks. The extensive simulations and experiments show that DAG-FL can achieve better performance in terms of training efficiency and model accuracy compared with the typical existing on-device FL systems as the benchmarks.

Blockchain, as a constantly evolving Peer-to-Peer (P2P) distributed ledger technology with characteristics, such as decentralization, security, interoperation, and trust establishment, can potentially lower the costs of the underpinning infrastructure and maintenance compared with conventional centralized systems. Consequently, the distributed structure of blockchain is naturally suitable for the Internet of Things (IoT), which can be used to build secure and trusted IoT. Despite the advances made in applying blockchain to IoT in the past few years, some research challenges remain to be addressed, including the poor scalability, heterogeneous IoT devices, and the impact of integration on network performance.

To securely integrate Mobile Edge Computing (MEC) into Wireless Blockchain Network (WBN), this paper proposes a framework for blockchain based offloading strategy, where blockchain nodes are categorized as blockchain users and blockchain miners from a motivation perspective. Particularly, aiming at improving the motivation ability, a block generation process is first designed for blockchain miners’ short transaction processing time lower bound. Then, to further maximize the utilities of both blockchain users and blockchain miners, an optimization problem is formulated to determine an optimal strategy which involves a trade-off between the fast transaction confirmation rate required by blockchain users and the transaction fees obtained by blockchain miners. A Stackelberg game is introduced to model the interaction between the blockchain users and miners. Meanwhile, a distributed algorithm is designed to converge this strategy in an iterative manner based on the buyer-seller negotiation. Additionally, double-spending attack and selfish mining attack are analysed to examine their impact on the system performance in terms of confirmation delay and throughput while guaranteeing the high security level. Finally, extensive experiments have been conducted to show the rightness and effectiveness of the proposed equilibrium-based strategy and mathematical analysis, and some insights are discussed for the further guide as well.

In order to improve mobile users’ service experience, mobile cloud computing (MCC) is promoted. Although MCC can alleviate the burdens of Smart mobile devices (SMDs) by offloading computation-intensive applications to the cloud, it also aggravates computing and storage overheads in cloud centers and bandwidth overhead on wireless links for offloading workloads of mobile users. Therefore, we should carefully design the offloading policy to decrease these overheads while easing the burdens of SMDs. To this end, we investigate the offloading policy in heterogeneous wireless networks. In this paper, a queue model is built to formulate the mobile users’ workload offloading problem and Lyapunov optimization framework is proposed to make trade-off between system offloading utility and queue backlog. For deterministic WiFi connections, a Lagrangian optimization method is proposed to decide the optimal offloading workloads. Furthermore, considering random WiFi connection durations, a multi-stage stochastic programming method is proposed. The experimental results show effectiveness of the Lagrangian optimization offloading method for deterministic WiFi connection and the multi-stage stochastic programming method for random WiFi connection.

Beyond fifth-generation (B5G) and future networks face the challenges of spectral, energy and cost efficiency for large-scale machine-type communications. Recently, emerging ambient backscatter communication (AmBC) technology provides a promising paradigm for the development of green Internet of Things (IoT) networks in B5G era. Unlike existing work on AmBC which mostly focuses on physical layer with relatively ideal model, i.e., classic three-nodes model composed of radio frequency (RF), backscatter device (BD) and IoT device, this paper studies the access control strategy, including coefficient design and device association, from the perspective of networking. Assuming whether channel information is available a-priori, we propose online and offline access control strategies respectively. For offline access control strategy, we leverage the difference of two convex functions approximation (DCA) and dual decomposition to transform the non-concave optimization problem into the concave one, and design a distributed access control strategy called DCA-S. Furthermore, for the case that channel information is assumed to be unknown in advance due to the dynamics of primary and backscatter networks, we design a combinatorial multi-armed bandit (CMAB) access control strategy (CMAB-S). Numerical results show that the proposed DCA-S and CMAB-S can achieve significant performance improvement of the system in both cases of available and unavailable channel information compared with benchmark schemes.

Due to the distributed characteristics of Federated Learning (FL), the vulnerability of global model and coordination of devices are the main obstacle. As a promising solution of decentralization, scalability and security, leveraging blockchain in FL has attracted much attention in recent years. However, the traditional consensus mechanisms designed for blockchain like Proof of Work (PoW) would cause extreme resource consumption, which reduces the efficiency of FL greatly, especially when the participating devices are wireless and resource-limited. In order to address device asynchrony and anomaly detection in FL while avoiding the extra resource consumption caused by blockchain, this paper introduces a framework for empowering FL using Direct Acyclic Graph (DAG)-based blockchain systematically (DAG-FL). Accordingly, DAG-FL is first introduced from a three-layer architecture in details, and then two algorithms DAG-FL Controlling and DAG-FL Updating are designed running on different nodes to elaborate the operation of DAG-FL consensus mechanism. The extensive simulations show that DAG-FL can achieve the better performance in terms of training efficiency and model accuracy compared with the typical existing on-device federated learning systems as the benchmarks.

The design of sharding aims to solve the scalability challenge in a blockchain network. Typically, by splitting the whole blockchain network into smaller shards, the transaction throughput can be significantly improved. However, distributing fewer attesting nodes for transactions in a shard may cause higher security risks. This paper analyzes the security level of transaction verification in different types of shards and transactions. The analyzed result indicates that the size of shards and validating nodes number may influence the transaction security in shards. And the random distribution of attesting nodes can reduce such influence and improve the reliability of consensus in shards.

Despite the advantages of Federated Learning (FL), such as devolving model training to intelligent devices and preserving data privacy, FL still faces the risk of the single point of failure and attack from malicious participants. Recently, blockchain is considered a promising solution that can transform FL training into a decentralized manner and improve security during training. However, traditional consensus mechanisms and architecture for blockchain can hardly handle the large-scale FL task due to the huge resource consumption, limited throughput, and high communication complexity. To this end, this paper proposes a two-layer blockchain-driven FL framework, called as ChainsFL, which is composed of multiple Raft-based shard networks (layer-l) and a Direct Acyclic Graph (DAG)-based main chain (layer-2) where layer-l limits the scale of each shard for a small range of information exchange, and layer-2 allows each shard to update and share the model in parallel and asynchronously. Furthermore, FL procedure in a blockchain manner is designed, and the refined DAG consensus mechanism to mitigate the effect of stale models is proposed. In order to provide a proof-of-concept implementation and evaluation, the shard blockchain base on Hyperledger Fabric is deployed on the self-made gateway as layer-l, and the self-developed DAG-based main chain is deployed on the personal computer as layer-2. The experimental results show that ChainsFL provides acceptable and sometimes better training efficiency and stronger robustness comparing with the typical existing FL systems.

To meet low latency and real-time monitoring demands of IoT-eHealth, fog computing is envisioned as a key technology to offer elastic computing resource at the edge of networks. In this context, eHealth devices can offload collected healthcare data or computational expensive tasks to a nearby fog server. However, the mobility of the eHealth devices may make the connection between them to fog servers uncertain, resulting in possible migration between fog servers. In order to evaluate the impact of this uncertainty on decision-making for offloading and resource allocation, we formulate the task offloading problem as a Multi-Stage Stochastic Programming (MSSP), with aim of minimizing the total latency of offloading to determine whether to offload or not, how much workload to offload, how much computing resource to allocate, as well as whether to migrate or not. Different from the previous MSSP based work focusing on the workload assignment only, the proposed MSSP examines joint decisions of offloading, resource allocation, and migration, advancing the understanding of the interactions among these decisions. Furthermore, to reduce the computational complexity of MSSP, we design an efficient sub-optimal offloading policy based on Sample Average Approximation, called SAA-MSSP. We conduct extensive simulation experiments to validate the effectiveness of SAA-MSSP. The results show that SAA-MSSP can converge to a near-optimal solution quickly.

Practical Byzantine Fault Tolerance (PBFT) consensus mechanism shows a great potential to break the performance bottleneck of the Proof-of-Work (PoW)-based blockchain systems, which typically support only dozens of transactions per second and require minutes to hours for transaction confirmation. However, due to frequent inter-node communications, PBFT mechanism has a poor node scalability and thus it is typically adopted in small networks. To enable PBFT in large systems such as massive Internet of Things (IoT) ecosystems and blockchain, in this article, a scalable multi-layer PBFT-based consensus mechanism is proposed by hierarchically grouping nodes into different layers and limiting the communication within the group. We first propose an optimal double-layer PBFT and show that the communication complexity is significantly reduced. Specifically, we prove that when the nodes are evenly distributed within the sub-groups in the second layer, the communication complexity is minimized. The security threshold is analyzed based on faulty probability determined (FPD) and faulty number determined (FND) models, respectively. We also provide a practical protocol for the proposed double-layer PBFT system. Finally, the results are extended to arbitrary-layer PBFT systems with communication complexity and security analysis. Simulation results verify the effectiveness of the analytical results.

Keeping patients' sensitive information secured and untampered in the e-Health system is of paramount importance. Emerging as a promising technology to build a secure and reliable distributed ledger, blockchain can protect data from being falsified, which has attracted much attention from both academia and industry. However, with limited computational resources, medical IoT devices do not have efficient ability to fulfill the functionalities as a full node in wireless blockchain network (WBN). Facing this dilemma, Mobile Edge Computing (MEC) brings us dawn and hope through offloading the high resource demanding blockchain functionalities at the IoT devices to the MEC. However, aiming to maximize the mining profit, most of existing offloading strategies have ignored the other needs of wireless devices, e.g., faster transaction writing. In this paper, according to different needs, blockchain nodes are firstly divided into two categories. One is blockchain users whose needs are faster transaction uploading, the other is blockchain miners whose goals are maximum revenue. Then, to maximize both the utilities of blockchain users and blockchain miners, a Stackelberg game is introduced to formulate the interaction between them. From the simulation results, this game is proved to converge to a unique optimal equilibrium.

Blockchain-enabled wireless network has been recognized as an emerging network architecture to be widely employed into the Internet of Things (IoT) ecosystems for establishing trust and consensus mechanisms without the involvement of a third party. However, the uncertainty and vulnerability of wireless channels among the IoT nodes may pose a serious challenge to facilitate the deployment of blockchain in wireless networks. In this paper, we first present a generic system model for blockchain enabled wireless networks with multiple communication connections, where the number of communication connections between a client IoT node and the blockchain full nodes can be any arbitrary positive integer to satisfy different security requirements. Based on the proposed spatial-temporal network model, we theoretically calculate the transmission successful probability and the required communication throughput to support a wireless blockchain network. Finally, simulation results validate the accuracy of our theoretical analysis.

The impact of communication transmission delay on the original blockchain, has not been well considered and studied since it is primarily designed in stable wired communication environment with high communication capacity. However, in a wireless scenario, due to the scarcity of spectrum resource, a blockchain user may have to compete for wireless channel to broadcast transactions following media access control (MAC) mechanism. As a result, the communication transmission delay may be significant and pose a bottleneck on the blockchain system performance and security. To facilitate blockchain applications in wireless industrial Internet of Things (IIoTs), this article aims to investigate whether the widely used MAC mechanism, carrier sense multiple access/collision avoidance (CSMA/CA), is suitable for wireless blockchain networks or not. Based on tangle, as an example to analyze the system performance in term of confirmation delay, transaction per second and transaction loss probability by considering the impact of queueing and transmission delay caused by CSMA/CA. Next, a stochastic model is proposed to analyze the security issue taking into account the malicious double-spending attack. Simulation results provide valuable insights when running blockchain in wireless network, the performance would be limited by the traditional CSMA/CA protocol. Meanwhile, we demonstrate that the probability of launching a successful double-spending attack would be affected by CSMA/CA as well.

With the exponentially increasing number of mobile devices, crowdsensing has been a hot topic to use the available resource of neighbor mobile devices to perform sensing tasks cooperatively. However, there still remain three main obstacles to be solved in the practical system. First, since mobile devices are selfish and rational, it is natural to provide cooperation for sensing with a reasonable payment. Meanwhile, due to the arrival and departure of sensing tasks, resource should be allocated and released dynamically when sensing task comes or leaves. To this end, this paper designs a game theoretic approach based incentive mechanism to encourage the “best” neighbor mobile devices to share their own resource for sensing. Next, in order to adjust resource among mobile devices for the better crowdsensing response, an auction based task migration algorithm is proposed, which can guarantee the truthfulness of announced price of auctioneer, individual rationality, profitability, and computational efficiency. Moreover, taking into account the random movement of mobile devices resulting in the stochastic connection, we also use multi-stage stochastic decision to take posterior resource allocation to compensate for inaccurate prediction. The numerical results show the effectiveness and improvement of the proposed multi-stage stochastic programming based distributed game theoretic methodology (SPG) for crowdsensing.

Direct Acyclic Graph (DAG)-based ledger and the corresponding consensus algorithm has been identified as a promising technology for Internet of Things (IoT). Compared with Proof-of-Work (PoW) and Proof-of-Stake (PoS) that have been widely used in blockchain, the consensus mechanism designed on DAG structure (simply called as DAG consensus) can overcome some shortcomings such as high resource consumption, high transaction fee, low transaction throughput and long confirmation delay. However, the theoretic analysis on the DAG consensus is an untapped venue to be explored. To this end, based on one of the most typical DAG consensuses, Tangle, we investigate the impact of network load on the performance and security of the DAG-based ledger. Considering unsteady network load, we first propose a Markov chain model to capture the behavior of DAG consensus process under dynamic load conditions. The key performance metrics, i.e., cumulative weight and confirmation delay are analysed based on the proposed model. Then, we leverage a stochastic model to analyse the probability of a successful double-spending attack in different network load regimes. The results can provide an insightful understanding of DAG consensus process, e.g., how the network load affects the confirmation delay and the probability of a successful attack. Meanwhile, we also demonstrate the trade-off between security level and confirmation delay, which can act as a guidance for practical deployment of DAG-based ledgers.

In Mobile Edge Computing based Internet of Vehicle (MEC-IoV), how to assign suitable task to MEC to satisfy the requirement under the constraints is the most important issue. Usually, in the practical system, due to the random movement and changing traffic load, the connection between vehicle user and MEC server is uncertain, and this makes it difficult to assign the suitable task in offloading. Considering the uncertainty and the corresponding impact, this paper proposes a stochastic programming methodology to make offloading policy. To this end, the task offloading problem is formulated as a Multi-Stage Stochastic Programming (MSSP) minimizing the expected cost function caused by offloading while considering the energy consumption. The experimental results show that the proposed MSSP method for offloading decision-making can effectively reduce the total cost in stochastic scenarios.

Recently, as the development of artificial intelligence (AI), data-driven AI methods have shown amazing performance in solving complex problems to support the Internet of Things (IoT) world with massive resource-consuming and delay-sensitive services. In this paper, we propose an intelligent resource allocation framework (iRAF) to solve the complex resource allocation problem for the collaborative mobile edge computing (CoMEC) network. The core of iRAF is a multitask deep reinforcement learning algorithm for making resource allocation decisions based on network states and task characteristics, such as the computing capability of edge servers and devices, communication channel quality, resource utilization, and latency requirement of the services, etc. The proposed iRAF can automatically learn the network environment and generate resource allocation decision to maximize the performance over latency and power consumption with self-play training. iRAF becomes its own teacher: a deep neural network (DNN) is trained to predict iRAF's resource allocation action in a self-supervised learning manner, where the training data is generated from the searching process of Monte Carlo tree search (MCTS) algorithm. A major advantage of MCTS is that it will simulate trajectories into the future, starting from a root state, to obtain a best action by evaluating the reward value. Numerical results show that our proposed iRAF achieves 59.27% and 51.71% improvement on service latency performance compared with the greedy-search and the deep Q-learning-based methods, respectively.

Blockchain has shown a great potential in Internet of Things (IoT) ecosystems for establishing trust and consensus mechanisms without involvement of any third party. Understanding the relationship between communication and blockchain as well as the performance constraints posing on the counterparts can facilitate designing a dedicated blockchain-enabled IoT systems. In this paper, we establish an analytical model for the blockchain-enabled wireless IoT system. By considering spatio-temporal domain Poisson distribution, i.e., node geographical distribution in spatial domain and transaction arrival rate in time domain are both modeled as Poisson point process (PPP), we first derive the distribution of signal-to-interference-plus-noise ratio (SINR), blockchain transaction successful rate as well as overall throughput. Based on the system model and performance analysis, we design an algorithm to determine the optimal full function node deployment for blockchain system under the criterion of maximizing transaction throughput. Finally, the security performance is analyzed in the proposed networks with three typical attacks. Solutions such as physical layer security are presented and discussed to keep the system secure under these attacks. Numerical results validate the accuracy of our theoretical analysis and optimal node deployment algorithm.

In future mobile networks, more and more machine-type communication (MTC) devices with different service requirements will be deployed. To meet the massive access needs of MTC, this paper develops a cluster-based congestion-mitigating access scheme (CCAS), with aim to mitigate the severe collision of MTC devices (MTCDs) that access to the base station (BS) concurrently. To this end, we first design a modified spectral clustering algorithm to group MTCDs into different clusters based on their locations and service requirements. Then, a device called MTC gateway (MTCG) is chosen by two steps to assist transmitting data for MTCDs in each cluster. In the data transmission process, MTCG is in charge of aggregating packets generated by MTCDs in a cluster and forwarding them to BS when the number of buffered packets reaches a certain threshold. To model the aggregation and forwarding process of each MTCG, we use queuing theory to analyze the access performance in terms of collision probability and access delay. In addition, we also implement simulations to further validate the accuracy of our analytical model and the effectiveness of CCAS. Numerical results, which are consistent with the theoretical values, show that the proposed CCAS can significantly decrease collision probability, and increase the number of successfully received packets of the system without increasing average access delay.

The resource reservation is one of the key techniques to ensure the quality of service (QoS) of a multimedia application. In mobile cloud computing (MCC), the resource reservation and allocation (RRA) in advance can significantly reduce the total provisioning cost of cloud service providers. However, the uncertain features of mobile users' demands for resources make RRA challengeable. In MCC, the QoS of a mobile application, such as voice IP or video, is determined by both of the radio resource (RR) and the cloud virtual machine resource (VMR) allocated to the mobile application, so we should jointly allocate these two types of resources. In this paper, RRA with uncertain demands of mobile users is formulated as a robust optimization model. Logarithmic utility functions are defined to capture the mobile users' satisfaction, which show how to match the allocations between RRs and VMRs according to the resource demands of the mobile applications. Then, a robust joint resource reservation and allocation algorithm in MCC (JRRA-MCC) is proposed to realize the optimal provisioning of RRs and VMRs. Simulation results show that the proposed JRRA-MCC can minimize the total resource provisioning cost of cloud service providers and enhance the resource utilization efficiently.

Offloading has been widely adopted as an effective technique to overcome the processing and computation limitation in mobile networks. In this work, we consider the problem of offloading in a mobile ad hoc environment in order to improve the processing capability and power efficiency. We formulate this as an incentive-based workload assignment problem. For maximizing the individual utility, the buyer/seller game is formulated to model the interactions among mobile devices for offloading. We derive the Stackelberg Equilibrium solution is to determine the workload assignment and power allocation. Based on this, we design distributed allocation algorithms, and the results from experiment verify the effectiveness of our proposal.

Coordinated Multi-Point (CoMP) is an effective way to improve user performance in next-generation wireless cellular networks, such as 3GPP LTE-Advanced(LTE-A). The base station cooperation can reduce interference, and increase the signal to interference and noise ratio (SINR) of cell-edge users and improve the system capacity. However, the base station cooperation also adds additional power consumption for signal processing and sharing information through back-haul links between cooperative base stations. As such, CoMP may potentially consume more energy. This paper studies such energy consumption issue in CoMP, presents a semi-dynamic CoMP cluster division algorithm based on energy efficiency (SCCD-EE) that can effectively adapt to users' real-time interference, and employs the idea of Maximal Independent Set (MIS) to solve the problem of cluster overlapping. To verify the feasibility of the proposed algorithm, this paper performs comprehensive evaluations in terms of energy efficiency and system capacity. The simulation results show that the proposed semi-dynamic cluster division algorithm can not only improve the system capacity and the quality of service (QoS) of cell-edge users, but also achieve higher network energy efficiency compared with static cluster methods and Non-CoMP approaches. Copyright © 2016 John Wiley & Sons, Ltd.

In traditional wireless network infrastructure, multiple wireless networks with various access points (APs) would be deployed in the same area. Although this deployment can easily provide service for mobile user equipment (MUE), any AP only allows the authorized MUEs to access, and thus some wireless networks might be overloaded and others might be lightly loaded. As a result, resource allocation would be inefficient. Using wireless network virtualization, an infrastructure provider (InP) can deploy only a single physical AP in the same area. This AP, which is controlled by a network operator (NO), is shared by multiple service providers (SPs) coexisting in the same AP. In the framework of wireless network virtualization, NO is in charge of resource allocation for the whole system and SP focuses on the access, connection and resource requirement of MUEs (such as the desired transmission power in downlink). In this paper, a Game theory based Two Steps Power Allocation scheme for wireless network virtualization, called G2SPA, is proposed, which designs a Stacklberg Equilibrium price strategy based on the interactions between SP and MUE, and then performs McAfee based auction to reallocate resource. The numerous experimental simulation results show that the rightness and effectiveness of G2SPA.

Cooperative spectrum sharing increases the spectrum efficiency and improves the performance of primary users (PUs) in cognitive radio domain. This paper proposes an energy-aware dynamic spectrum sharing framework, named Cooperative Spectrum Sharing with Energy-save(CSSE), which maximizes energy saving while ensures communication QoS (i.e. transmission rate) of primary transmitter (PT). In CSSE, the PT leverages a proper set of secondary transmitters (STs) as cooperative relays for its transmission and releases a proportion of bandwidth to the cooperative STs. Under the restriction of energy budget, each ST decides its power density allocation (including relaying power density and transmit power density for its own transmission) to maximize its transmission rate. Taking the users' selfishness and intellectuality into consideration, we formulate the above optimal problem as a Stackelberg game (SG) and prove that a Unique Nash Equilibrium (UNE) point exists among the non-cooperative STs. Theoretical analysis and simulation results show that the PU can obtain maximum benefit. Meanwhile, the relaying STs can get acceptable benefits under CSSE.

Abstract Cooperative communications can obtain spatial diversity, high channel capacity, and reliable transmission without multiple antennas, and thus, it has become a hot topic in recent years. Different from existing research, this paper pays attention on cooperative media access control (MAC) mechanism, which considers both physical gain and MAC overhead caused by cooperation. To this end, a dynamic cooperative MAC mechanism for wireless networks, called DCMAC, is proposed. DCMAC can obtain the useful channel state information through broadcasting characteristic of wireless channel, choose the suitable helpers to relay data with our proposed helpers selection algorithm, and reserve wireless channel efficiently and dynamically. Numerical results show the effectiveness of DCMAC to improve the system performance.

The performance gain of cooperative communications depends heavily on the selection of relay. Most of existing relay selection methods aim at maximizing cooperative gain by selecting appropriate relay, without taking into account the adverse effect brought by cooperative communications: extra interferences introduced by relay transmission (called cooperation interference). Thus the derived performance gain could be inaccurate and/or the selected relay may be not optimal. In this paper, we address the assignment of relays for multiple communication sessions using cooperative communications in a wireless network. We first thoroughly investigate the adverse effect brought by using relays, and derive the cooperation gain with consideration of cooperation interference. Based on the insights of our investigation, we propose a method of assigning relays to individual transmission flows while taking into account cooperation interference in cooperative communications. In order to tradeoff the advantage and adverse effect caused by relay transmissions, we use an auction approach to address relay assignment of cooperative communications. Specifically, we propose a Single round double Auction Scheme (SAS) for centralized wireless network and a Multiple rounds sequential Auction Scheme (MAS) for decentralized wireless network for relay assignment. We conduct extensive simulation experiments to validate the effectiveness of SAS and MAS. The significance of the impact of cooperation interference, improvement of system throughput and energy efficiency are demonstrated by numerical results.

In wireless networks, the retransmission should be performed by the sender to recover lost packets at the receiver. The conventional retransmission scheme only includes one lost packet in a retransmission, which results low retransmission efficiency. However, with the help of broadcasting nature of wireless channel and the network coding (NC) technique, we can encode N packets lost at N destinations in one retransmission to effectively recover the lost packets. Some work has been conducted on using NC to retransmit lost packets in the single-sender multiple-receiver (SSMR) wireless network scenarios. In this paper, we combine NC and retransmission in multiple-sender multiple-receiver scenarios, which is a more realistic wireless network scenario. To this end, a Probabilistic Network Coding Retransmission Mechanism (PNCRM) is proposed for MSMR wireless networks. The analysis and numerical results validate the effectiveness of PNCRM.

Inter-user interference degrades the reliability of data delivery in Wireless Body Sensor Networks (WBSNs) in dense deployments when multiple users wearing WBSNs are in close proximity to one another. The impact of such interference in realistic WBSN systems is significant but has not been well explored. To this end, we investigate and analyze the impact of inter-user interference in practical WBSN systems based on TelosB platform. We capture packet delivery ratio (PDR) and throughput considering unslotted carrier sense multiple access with collision avoidance (unslotted CSMA/CA) and slotted CSMA/CA modes in IEEE 802.15.4 MAC. Our experimental results show that the unslotted CSMA/CA is only effective in light inter-user interference scenarios. Comparably, the slotted CSMA/CA can provide dramatic performance improvement (2.7 times higher in PDR and 1.7 times higher in throughput on average), when severe inter-user interference occurs in WBSN deployment.

Cooperative transmission (CT) is a promising technique to improve transmission rate and throughput in wireless networks, and relay node (RN) which could provide a good two-hop channel plays a key role in CT mode. As a result, most of existing work take the advantage of benefit of RN in CT mode, but do not fully recognize its potential adverse effect. In this paper, we investigate the adverse impact called flow-level cooperation interference (FCI) incurred by RN in CT mode, therefore, CT mode may not be beneficial as expected. To this end, we describe our insight and analyze the reason of FCI in wireless networks. To understand and solve FCI, we formulate this problem with a game-theoretic approach, and thus two methods which are named Nash equilibrium cooperative transmission strategy (NECTS) and Bayesian Nash equilibrium cooperative transmission strategy (BNECTS) are proposed, respectively. Our numerical results validate our analytical approach and demonstrate the effectiveness of our proposed NECTS and BNECTS.

Opportunistic networks are the emerging networks featured by partitions, long disconnections, and topology instability, where the message propagation depends on the cooperation of nodes to fulfill a “store-carry-and-forward” fashion. But due to constrained energy and buffer, some nodes may behave selfishly, which will involve damage to the existing routing approaches and seriously degrade the performance of opportunistic networks. Aiming at the above problem, this paper proposes a novel bargaining based incentive protocol (BIP) for opportunistic networks, which exploits two-person bargaining model and allows a node to pay and charge according to its state and the attributes of messages. In addition, the proposed BIP protocol can tackle the issue of blind cooperation when the resources are very scarce. Extensive simulation results demonstrate the effectiveness and the practicality of the proposed BIP protocol in terms of high delivery ratio, low energy consumption and small average delay.

In Long Term Evolution (LTE) networks, Energy-Efficient (EE) and Mobility Load Balancing (MLB) are two important functions to optimize the network performance. In order to deal with the energy consumption in downlink LTE system for unbalanced low load scenarios, we built an effective EE resource allocation optimization model and employed a low complexity method to achieve the goal of the optimization in this paper, named EE-VBEM. Simulation results show that EE-VBEM can reduce the overall downlink energy consumption significantly and enhance the spectrum efficiency effectively in unbalanced low load networks.

Relay node (RN) plays a key role in cooperative communications and RN selection may substantially affects the performance gain. In this paper we address the issue of RN selection while taking into account Medium Access Control (MAC) overhead, which is incurred by not only handshake signaling but also frame retransmissions due to transmission error. We use a theoretical model to analyze the cooperation performance gains of cooperative MAC mechanism, and are thus able to select the optimal relay node. We derive the network saturation throughput of the designed MAC with our RN selection algorithm. Numerical results validate the effectiveness of our analytical model and show that our designed MAC significantly outperforms existing cooperative MAC mechanisms which do not consider retransmission MAC overhead.

The essence of spectrum allocation is to find an appropriate distribution of spectrum bands among users so that they can coexist. Thus, spectrum allocation can avoid interference that caused by two interference SUs using the same channel. Different spectrum allocation can lead to different network topologies and consequently have effect on the network connectivity. The aim of this paper is twofold: 1) based on the Laplacian spectrum of graphs, we represent the cognitive radio Ad-Hoc network connectivity with knowledge of the effects of the different labeling rules of spectrum allocation; 2) we give a deep analysis of the network connectivity and the impact that different parameters have on it. The results show that the different labeling rules of Color-Sensitive Graph Coloring have different influence on the network connectivity. For the different labeling rules of Color-Sensitive Graph Coloring, we can obtain the different relationship between the probability of primary users' activity and the network connectivity.

Cognitive radio has been suggested as an efficient method for secondary users to promote the efficient utilization of spectrum. Meanwhile, Cooperative relay allows different users or nodes to share resources and to create collaboration through distributed transmission in a wireless networks. The combination of Cognitive radio with cooperative communication could significantly improve the system performance in cognitive radio ad-hoc networks (CRAHNs). In this paper, we discuss how to use cooperative relay to increase the transmission rate in CRAHNs. We first give a new distributed relay selection algorithm, it considers several aspects including channel gain, channel available probability and spectrum heterogeneity of secondary nodes. A cooperative MAC protocol, Cooper-MAC, is then proposed for CRAHNs which enables secondary users to negotiate channels and relays. Simulation results demonstrate the effectiveness of the Cooper-MAC.

Cooperative communication can obtain spatial diversity without using multiple antennas, and thus achieve more reliable transmission or consume less power. Accordingly, a new cooperative MAC mechanism in wireless networks, the DCMAC, is proposed in this paper. The DCMAC makes full use of the broadcasting characteristics of wireless channel to obtain channel information, chooses the most suitable cooperative nodes, and reserves wireless channel efficiently. Evaluation results show that DCMAC can choose the most suitable cooperative nodes to improve system performance.