The integration of Low-Power Wide-Area Networks (LPWAN) with Unmanned Aerial Vehicles (UAVs) is a viable solution to address connectivity challenges in remote or disaster-affected areas. In this paper, we introduce and model a scheme where a UAV with a LoRaWAN interface interconnects two distant clusters of nodes.We present a mathematical performance evaluation of the network under varying spreading factors and packet arrival rates. Our results reveal that increasing packet arrival rates and spreading factors enhances success probability up to a certain threshold, beyond which congestion effects degrade network performance. Furthermore, we show the effect of UAV mobility on the network in multi-vacation scenarios. These insights contribute to optimizing UAV-assisted LPWAN deployments, particularly in IoT applications for emergency response and remote monitoring.

This study addresses the challenges faced by Vehicular Ad-Hoc Networks (VANETs) in intelligent transportation systems (ITS), including high vehicle mobility and dynamic network topologies that affect connectivity and data transmission reliability. We propose an integrated approach that combines Vehicular Clouds (VCs) with IEEE 802.11bd communication technology, modeling the vehicular cloud as an M/G/m queuing system. To align with IEEE 802.11bd's priority structure, we implement a priority scheme that limits task execution by each On-Board Unit (OBU) based on priority class. Additionally, the system enhances resource utilization through task delegation considering distance coverage and priority thresholds. Performance metrics are analyzed under varying traffic conditions, demonstrating the effectiveness of priority-based resource management to meet the evolving demands of ITS.

This paper introduces an advanced implementation of the Paxos consensus algorithm in which proposals have different priority levels so that higher priority proposals preempt lower priority ones to reduce latency. The operation of the protocol is modeled using preemptive priority queues, which allows us to evaluate system performance in a number of different scenarios. Our results indicate that the implementation of the Paxos algorithm in this manner offers tunable differentiation of completion times, making it suitable for a variety of applications, including permissioned blockchains in IoT scenarios.

Availability and/or performance of PBFT (Practical Byzantine Fault Tolerance) consensus service has been widely studied. However, the existing studies overlook the situation of software aging of honest nodes, which can degrade system performance over time. Rejuvenation techniques can mitigate the negative impact of aging. This paper aims to make a quantitative joint analysis of availability and performance (a.k.a performability) of PBFT consensus service in the scenario where honest nodes are susceptible to software aging and rejuvenation techniques are adopted for recovery. We propose a Semi-Markov process (SMP) based approach for model-based evaluation. Unlike traditional models that rely on exponential distributions, our approach allows the time intervals of all events to follow general distributions, thereby enable a more nuanced analysis of PBFT dynamics. We detail the modeling process and the derivation of metric formulas. We also carry out numerical analysis for the evaluation to assess the performability of PBFT consensus service.

Paxos is a well-known protocol for state machine replication and consensus in face of crash faults. However, it suffers from inefficiencies in request handling, particularly in scenarios where requests have different inherent priorities. To address this, we propose a priority-aware extension with priority upgrading similar to MultiPaxos, evaluate its performance using a queueing model, and show the improvement in performance metrics such as mean completion and waiting times. Our results demonstrate that integrating prioritization and priority upgrading mechanisms into Paxos reduces latency for high priority requests while ensuring fairness for lower priority ones. The aging-based priority upgrading approach maintains correctness of the consensus process while adding flexibility in managing time-sensitive distributed applications such as permissioned blockchains.

Sharding aims to improve scalability and performance of blockchain systems, but management of cross-shard transactions remains a challenge. In this paper we propose a dual-layer sharding framework in which all transactions are validated in their source shards, while cross-shard ones undergo additional verification at the global level before being distributed to their respective destination shards; validation at both levels uses Practical Byzantine Fault Tolerance (PBFT) consensus. The proposed scheme is analyzed using a queuing model, and results indicate that the scheme allows consistent performance whilst preserving key principles of security and decentralization.

Vehicular cloud (VC) computing which integrates cloud computing principles with data communication through vehicular ad-hoc networks (VANETs) is the foundation for intelligent transportation systems (ITS). In this article, we propose a VC system where vehicles offload task computation to other vehicles in the vicinity through a threshold-based prioritization scheme, using IEEE 802.11bd communication technology to interconnect the vehicles. We model the VC as an$M/G/m$queuing system with task priority structure aligned to the priority structure of IEEE 802.11bd communications, taking into account distance coverage and priority thresholds that aim to enhance resource utilization across vehicles. Key performance metrics are analyzed under various traffic conditions. Our results demonstrate reliable and efficient priority-based resource management in the cloud network emphasizing the potential of this approach to meet the evolving demands of ITS.

Connected Autonomous Vehicles (CAVs) represent a transformative advancement in transportation, offering enhanced safety, improved traffic efficiency, and reduced environmental impact through intelligent driving. As CAVs operate without human intervention, they heavily rely on secure vehicle-to-vehicle (V2V) communication for cooperative perception and coordinated decision-making. These real-time inter-vehicle exchanges underpin safe coordination, dynamic decision-making, and collision avoidance. To ensure trust in such communication, robust and efficient authentication mechanisms are essential. However, existing schemes often fall short in terms of security resilience and operational practicality. In this paper, we propose a novel Certificateless Signature Scheme with Conditional Privacy-Preserving Authentication (CLSS-CPPA) tailored to CAV environments. The proposed scheme addresses three fundamental limitations in existing schemes: signature forgery vulnerabilities, single-authority dependency, and lack of dynamic revocation capability. Our approach employs distributed key generation to prevent signature forgery attacks, utilizes prefix tree structures for efficient dynamic key revocation, and implements dual-agency pseudonym management with mutual authority constraints to prevent single-entity power abuse. Lightweight cryptographic operations are also adopted to suit resource-constrained vehicular systems. Formal security analysis and extensive evaluations demonstrate that CLSS-CPPA enhances privacy preserving and reduces signing and verifying costs by 20%–90% compared to state-of-the-art schemes, making it a promising solution for real-world CAV deployments.

Existing research has demonstrated that autonomous driving policies based on deep reinforcement learning (DRL) are vulnerable to adversarial attacks, which poses challenges for the practical deployment of these policies. Designing effective adversarial attacks is a crucial prerequisite for building robust driving policies. In view of this, we propose a novel adversarial attack method, which can attack the DRL-based autonomous driving agents in a stealthy and efficient manner. This method models the attack as a mixed-integer optimization problem that aims to maximize the safety violations (e.g., collisions) of the agents while minimizing the number of attack steps. Then, a DRL-based adversary is devised in this method to solve the problem to automatically learn the optimal attack policy without domain knowledge. To further enhance the adversarys learning capability, this method incorporates attack-related information into its observations to provide more decisionmaking context and employ a trajectory clipping technique to enhance sample quality. Extensive evaluation results reveal that our method achieves a remarkable 105% enhancement in attack efficiency compared to existing methods.

Data provision, referring to data upload and data access, is one key phase in vehicular digital forensics. The unique features of driverless taxi (DT) bring new issues to this phase: I1) efficient verification of data integrity when diverse data providers (DPs) upload data; I2) DP privacy preservation during data upload; and I3) privacy preservation of both data and investigator (IN) under complex data ownership when accessing data. Considering that the existing works on digital forensics cannot address all these issues, we first propose a novel lightweight and privacy-preserving data provision (LPDP) approach consisting of three mechanisms: 1) privacy-friendly batch verification mechanism (PBVm); 2) data access control mechanism (DACm); and 3) decentralized IN warrant issuance mechanism (DIWIm). PBVm ensures scalable verification of data integrity to address I1. PBVm also ensures the DP privacy preservation in terms of the location privacy and unlinkability of data upload requests to address I2. Besides, DACm and DIWIm are combined to ensure data privacy preservation and the identity privacy of IN in terms of the anonymity and unlinkability of data access requests without sacrificing the traceability to address I3. Security analysis and performance evaluations validate LPDP’s capabilities in addressing the three issues.

Driverless taxis (DTs) are gaining attention for their potential to improve urban transportation efficiency. However, unforeseen incidents caused by unsupervised users and the personalized needs of passengers in DTs highlight the need for authenticating user identities and attributes. Additionally, protecting user privacy while enabling rapid traceability of malicious users remains a challenge for the widespread adoption of DTs. This paper proposes a novel Attribute-based Anonymity Enhanced (A2E) authentication scheme for users to access DT services. The security capabilities of A2E include: 1) A2E is attribute-based authentication, which is achieved by designing a user attribute credential. Meanwhile, this attribute credential also satisfies unlinkability. And 2) A2E has enhanced anonymity, which is achieved by designing a decentralized credential issuance mechanism, safeguarding user attributes from association with anonymous identities. Moreover, this mechanism provides traceability and non-frameability to users. From the performance aspect, A2E causes low overhead when tracing malicious users and updating credentials. Besides, both scalability and lightweight are satisfied, which contributes to A2E’s practicability. We conduct security and performance analysis to validate these capabilities.

Paxos is a well known protocol for state machine replication and consensus in face of crash faults. However, it suffers from inefficiencies in request handling, particularly in scenarios requiring preemptive prioritization. To address this, we propose a priority-aware extension similar to MultiPaxos and evaluate its performance using a queuing model, and show the improvement in performance metrics such as mean completion and waiting times. Our results demonstrate that integrating prioritization mechanisms into Paxos reduces latency for high-priority requests while ensuring fairness. The aging-based approach maintains correctness of the consensus process while adding flexibility to manage time-sensitive distributed applications such as permissioned blockchains.

Ethereum 2.0 uses a Proof-of-Stake-based consensus which aims to minimize the impact of malicious validators by decentralizing the voting protocol. In this paper we investigate the lifecycle of a validator in a consensus protocol similar to Ethereum 2.0 but with simplifications introduced for tractability. In particular, the protocol operates with near-single slot finality and includes the impact of behaviors such as truthful and false voting, abstention from voting, voluntary exit from the validator committee, and return to the committee upon depositing the required stake. Using probabilistic techniques and a Markov chain model, we examine the impact of all those factors on consensus probability. Our results indicate that the probability of truthful voting has a predominant effect on consensus, although the interplay between probabilities of voluntary exit and waiting before returning to the committee also plays an important role. We also investigate the process of fork resolution and model the behavior of the blockchain in the presence of multiple tips, and we show that probability of truthful voting is equally important in this case as higher values accelerate fork resolution.

Federated Learning (FL), a privacy-oriented distributed ML paradigm, is gaining great interest in the Internet of Things because of its capability to protect participants’ data privacy. Studies have been conducted to address the challenges of communication efficiency and privacy-preserving, which exist in standard FL. However, they cannot achieve the goal of making a tradeoff between communication efficiency and model accuracy while guaranteeing privacy. This paper proposes a Conditional Random Sampling (CRS) method and implements it into the standard FL (CRS-FL) to tackle the above-mentioned challenges. CRS explores a Poisson-sampling-based stochastic coefficient to achieve a higher probability of obtaining zero-gradient unbiasedly and then decreases the communication overhead effectively without model accuracy degradation. Moreover, we dig out the relaxation Local Differential Privacy (LDP) guarantee conditions of CRS theoretically. Extensive experiment results indicate that (1) in communication efficiency, CRS-FL performs better than the existing methods in metric accuracy per transmission byte without model accuracy reduction in more than 7% sampling ratio (# sampling size / # model size); (2) in privacy-preserving, CRS-FL achieves no accuracy reduction compared with LDP baselines while holding the efficiency, even exceeding them in model accuracy under more sampling ratio conditions.

Traditional covert transmission (CT) approaches have been hindering CT application while blockchain technology offers new avenue. Current blockchain-based CT approaches require off-chain negotiation of critical information and often overlook the dynamic updating of session keys, which increases the risk of message and key leakage. Additionally, in some approaches the covert transactions exhibit obvious characteristics that can be easily detected by third-parties. Moreover, most approaches do not address the issue of decreased reliability of message transmission in blockchain attack scenarios. Bitcoin-and Ethereum-based approaches also have the issue of transaction linkability, which can be tackled by Monero-based approaches because of the privacy protection mechanisms in Monero. However, Monero-based CT has the problem of sender repudiation. In this paper, we propose a novel$M$onero-$B$ased CT approach (MBCT), which enables on-chain session key dynamically updating without off-chain negotiation. MBCT can assure confidentiality of on-chain session key, non-repudiation of transmission parties, reliability of message transmission under blockchain attack, unlinkability and obscurity of covert transactions. They are achieved by the three components in MBCT, namely, a sender authentication method, a dynamically on-chain session key updating method and a state feedback method. We implement MBCT in Monero-0.18.1.0 and the experiment results demonstrate its high embedding capacity of MBCT.

In this paper, we describe a mechanism to improve the speed of the Practical Byzantine Fault Tolerance (PBFT) consensus protocol in a multi-leader setting by overlapping the COMMIT and PREPREPARE stages where applicable. We analyze the performance of the proposed mechanism using a probabilistic model and show that it achieves high throughput at high block arrival rates, with cycle time approaching one maximum round-trip time between the nodes on the consensus committee. The mechanism is suitable for blockchain applications since invalid blocks are detected and rejected directly by the consensus protocol without the need to retract the decisions already reached.

This study investigates the performance of IEEE 802.11bd channel bonding, both with and without fallback, when Next-Generation Vehicle (NGV) devices request and send multiple packets using Transmission Opportunity (TXOP) allocation in a mixed environment with legacy, non-NGV devices that do not support TXOP. To this end, we use probabilistic modeling and Markov chain analysis. Our results show that the effectiveness of TXOP depends on the priority level assigned to each NGV traffic class. Overall, low-priority traffic classes experience marginal improvements, while higher-priority classes benefit moderately from the use of TXOP.

Implementing a consensus protocol in a Proof-of-Stake context requires a delicate tradeoff between different system parameters. Ethereum 2.0, probably the most popular PoS system today, uses a large number of validators to achieve decentralization, but long time windows, during which both blocks and attestations for those blocks are considered valid, open up the possibility for a number of attacks that target the process of consensus. A possible remedy would be to try to achieve single-slot finality similar to that obtained in Practical Byzantine Fault Tolerance (PBFT). In this paper, we develop a Markov chain model of validator lifecycle in an Ethereum 2.0-like system with single-slot finality which includes penalties and rewards, as well as the possibility of voluntary exit and waiting to rejoin the validator pool. Using the model, we obtain the probability of achieving consensus as the function of probabilities of different events, most notably the probability of truthful voting by the validator. Our results indicate that consensus is rather sensitive to false voting, and that low probability of waiting and low probability of voluntary exit help improve the probability of consensus.

Federated Learning (FL), as a privacy-oriented distributed machine learning paradigm, can obtain a well-trained global model without private dataset transferring. Nevertheless, FL is subject to severe security threats of adversarial examples (AEs) with unnoticeable perturbations, generated by white-box attacks in honest-but-curious FL participants. Adversarial training is an effective solution to enhance the robustness of the model by identifying AEs as correct samples. However, the AE training efficiency is crucial in realistic scenarios of adversarial training, such as autonomous driving. Researchers have proposed the Fast Gradient Sign Method (FGSM) and its improvement to generate AEs rapidly. In this paper, we propose a novel optimizer-based FGSM, FastAdaBelief-based FGSM (FAB-FGSM), in order to generate AEs more efficiently and effectively. Benefitting from time-vary coefficients and a vanishing factor, FAB-FGSM realizes a more adaptive iteration step size than AdaBelief-based FGSM (AB-FGSM) and Adam-based FGSM (AI-FGSM). We explore the probable causes by recalling the theoretical analysis of three optimizers. Extensive experiment results demonstrate that compared to AB-FGSM and AI-FGSM, our FAB-FGSM achieves the fastest convergence and the best attack success rate in four target models, including Inception v3, Inception v4, Inception ResNet v2, ResNet-101.

Both blockchain and oracle are among key technologies which are leveraged in Web 3.0 to empower the internet industry. Oracle aims to provision blockchain with real world data and support external connectivity for closed blockchain systems. Compared with a centralized oracle system, a distributed oracle system can tackle the issues of single point failure and untrusted data. This paper explores analytical modeling techniques to quantitatively study the dependability (availability and reliability) of the distributed oracle system with arbitrary number of heterogeneous oracle nodes. We first develop a Markov model to describe oracle system dynamics. Then we derive both the formula of system availability and the formula of mean time to failure (MTTF) to study the system reliability. The experimental results indicate 1) the system availability is mainly affected by mean node failure time when it is smaller than 10 days, 2) the system recovery ability has critical impact on availability, and 3) MTTF can be significantly improved by introducing more nodes.

Abstract Fully homomorphic encryption (FHE) has experienced significant development and continuous breakthroughs in theory, enabling its widespread application in various fields, like outsourcing computation and secure multi-party computing, in order to preserve privacy. Nonetheless, the application of FHE is constrained by its substantial computing overhead and storage cost. Researchers have proposed practical acceleration solutions to address these issues. This paper aims to provide a comprehensive survey for systematically comparing and analyzing the strengths and weaknesses of FHE acceleration schemes, which is currently lacking in the literature. The relevant researches conducted between 2019 and 2022 are investigated. We first provide a comprehensive summary of the latest research findings on accelerating FHE, aiming to offer valuable insights for researchers interested in FHE acceleration. Secondly, we classify existing acceleration schemes from algorithmic and hardware perspectives. We also propose evaluation metrics and conduct a detailed comparison of various methods. Finally, our study presents the future research directions of FHE acceleration, and also offers both guidance and support for practical application and theoretical research in this field.

The recent IEEE 802.11bd communication technology allows next-generation vehicle (NGV) devices to make use of wide channels using channel bonding, both with and without fallback, in addition to multiple packet transmission using transmission opportunity (TXOP) allocation. To evaluate its performance in an environment which also contains legacy or non-NGV devices that do not support TXOP allocation, we develop a detailed analytical model through probabilistic modeling and Markov chain analysis. The model includes data rate scaling with distance from the roadside unit (RSU). Performance analysis provided by the model shows that enhanced distributed channel access (EDCA), which is available for both NGV and legacy devices, provides noticeable prioritization of traffic; that TXOP allocation leads to better performance by an amount which depends on the traffic priority level; and that NGV devices that use fallback experience better performance than either legacy devices or NGV devices that do not use fallback.

Unmanned aerial vehicles (UAVs) are being broadly employed to assist in efficient data collection for Internet of Things (IoT) networks. Studies have been conducted to ensure the effectiveness and safety of UAVs in the data collection process. However, they only considered part of the challenges of energy consumption, collision avoidance, and mobility of IoT devices. In this article, we study a UAV path planning optimization problem for UAV-assisted data collection to maximize the amount of collected data. Different from these existing works, this optimization problem not only considers all these challenges, but also considers the kinematic and communication constraints. Moreover, in this problem, the duration required for the UAV to complete the mission is unknown, makes it more challenging to solve this problem through traditional optimization methods. We thus formulate the problem as a partially observable Markov decision process (POMDP) with a continuous action space and propose a proximal policy optimization-based algorithm to address it. Experiment results demonstrate that our algorithm has significant advantages over other baseline algorithms in terms of success rate, data collection rate, and collision rate.

Recently, big data has seen explosive growth in the Internet of Things (IoT). Multi-layer FL (MFL) based on cloud-edge-end architecture can promote model training efficiency and model accuracy while preserving IoT data privacy. This paper considers an improved MFL, where edge layer devices own private data and can join the training process. iMFL can improve edge resource utilization and also alleviate the strict requirement of end devices, but suffers from the issues of Data Reconstruction Attack (DRA) and unacceptable communication overhead. This paper aims to address these issues with iMFL. We propose a Privacy Amplification scheme on iMFL (PA-iMFL). Differing from standard MFL, we design privacy operations in end and edge devices after local training, including three sequential components, local differential privacy with Laplace mechanism, privacy amplification subsample, and gradient sign reset. Benefitting from privacy operations, PA-iMFL reduces communication overhead and achieves privacy-preserving. Extensive results demonstrate that against State-Of-The-Art (SOTA) DRAs, PA-iMFL can effectively mitigate private data leakage and reach the same level of protection capability as the SOTA defense model. Moreover, due to adopting privacy operations in edge devices, PA-iMFL promotes up to 2.8 × communication efficiency than the SOTA compression method without compromising model accuracy.

Federated learning (FL) as a secure distributed learning framework gains interests in Internet of Things (IoT) due to its capability of protecting the privacy of participant data. However, traditional FL systems are vulnerable to free-rider (FR) attacks, which causes unfairness, privacy leakage and inferior performance to FL systems. The prior defense mechanisms against FR attacks assumed that malicious clients (namely, adversaries) declare less than 50% of the total amount of clients. Moreover, they aimed for anonymous FR (AFR) attacks and lost effectiveness in resisting selfish FR (SFR) attacks. In this article, we propose a parameter audit-based secure and fair FL scheme (PASS) against FR attack. PASS has the following key features: 1) prevent from privacy leakage with less accuracy loss; 2) be effective in countering both AFR and SFR attacks; and 3) work well no matter whether AFR and SFR adversaries occupy the majority of clients or not. Extensive experimental results validate that PASS: 1) has the same level as the state-of-the-art method in mean square error against privacy leakage; 2) defends against AFR and SFR attacks in terms of a higher defense success rate, lower false positive rate, and higher F1-score; and 3) is still effective where adversaries exceed 50%, with F1-score 89% against AFR attack and F1-score 87% against SFR attack. Note that PASS produces no negative effect on FL accuracy when there is no FR adversary.

In this paper, we analyze the performance of Ethereum data distribution network using a probabilistic model which allows accurate modeling of data propagation but also of forking, which happens when the blockchain maintained by the network temporarily splits into multiple versions due to a disagreement over the validity of a particular block. We also investigate the duration of inconsistent states of the ledger, which refers to the amount of time that the network remains split or partitioned. Finally, we model the block withholding attack and block slowdown attack, and analyze their impact on network performance of the network in terms of quality indicators such as block delivery time, the duration of ledger inconsistency, and forking probability. We also propose countermeasures for the block withholding attack.

Delegated Proof of Stake (DPoS) is a high throughput, low power consumption consensus mechanism is which elected participants or witnesses vote to accept blocks to be included in the blockchain ledger. However, DPoS is prone to centralization of voting power which can introduce bias and degrade the usability of the blockchain. In this work, we propose the concept of virtual stake which measures the truthfulness of witness voting throughout the round. Virtual stake at the end of a round is used as incentive for the next election of witnesses, but electors have a discretionary right to cast a portion of their votes for candidate witnesses regardless of their past behavior. Virtual stake is also used to guide the process of selecting the leader(s) to propose blocks for voting in PBFT cycles within the round, which prevents mis.behaving witnesses from submitting blocks. We describe and solve the analytical model of witness behavior, assuming that witnesses can be categorized into behavioral classes with different probability of truthful voting, false voting, and abstention. Our results show the impact of class populations, and voting behavior on virtual stakes, distribution of votes, and, most importantly, on overall consensus probability. The impact of centralization and witness misbehavior in voting can be countered by an increase of voting groups and decrease of round size, although further control of voting process during a round may be necessary to maintain the desired performance level of the blockchain system.

GHOST, like the longest-chain protocol, is a chain selection protocol and its capability in resisting selfish mining attack has been validated in imperfect (delay-existing-) blockchains of Bitcoin and its variants (Bitcoin-like). This paper explores an analytical-model-based approach to investigate the impact of stubborn mining attack in imperfect GHOST Bitcoin-like blockchains. We first quantify chain dynamics based on Markov chain process and then derive the formulas of miner revenue and system throughput. We also propose a new metric, “Hazard Index”, which can be used to evaluate attack threat severity and also assist the adversary in determining whether it is profitable to conduct an attack. The experiment results show that 1) An adversary with more than 30% computing power can get huge profit and extremely downgrade system throughput by launching stubborn mining attack. 2) An adversary should not launch stubborn mining attack if it has less than 25% computing power. 3) Stubborn mining attack causes more damage than selfish mining attack under GHOST. Our work provides insight into stubborn mining attack and is helpful in designing countermeasures.

In this paper, we develop a detailed analytical model of IEEE 802.11bd channel bonding with fallback using an M/G/1 queuing model and discrete time Markov chain model for both Next Generation Vehicle (NGV) devices and older legacy (non-NGV) devices. We compute a number of performance metrics and show that the channel bonding mechanism of IEEE 802.11bd provides clear advantages over existing standards.

Structured data is widely used in big data storage and analytics but only a few Structured Data Symmetric Searchable Encryption (SD-SSE) schemes were designed. Moreover, they at least have two security issues: lack of both forward security and keyword privacy. In addition, the existing various SSE schemes designed for unstructured data cannot be applied to structured data. The paper proposes a Secure and Efficient SSE Scheme (SES2) for structured data. SES2 can not only address the above two security issues but also is more efficient than the existing Structured Data SSE (SD-SSE) schemes. Forward security is achieved by using a new key to generate the related index when the data is updated. Keyword privacy is assured by adding noise to the query trapdoor. Efficiency is improved by generating indexes with Bloom filter in a more efficient way.

In this paper, we analyze the performance of Ethereum data distribution network using a probabilistic model which allows accurate modeling of data propagation. We then apply the model to analyze the performance of data propagation in Ethereum when using geth protocol under block withholding attack, Eclipse attack, and block slowdown attack. We also propose countermeasures for the block withholding attack and Eclipse attack.

Practical Byzantine Fault Tolerance (PBFT) is widely used despite its limitations. In this paper we extend a previously described multiple entry version of PBFT with fast cycle capability. In this approach, multiple data blocks are proposed by different nodes or replicas, but instead of choosing only one to undergo the consensus procedure, replicas collect and order the proposals which are then accepted through a simplified consensus cycle. We analyze the operation of this scheme using a probabilistic model and show that consensus performance actually improves over that of the original PBFT as the traffic load increases. In this manner, the proposed scheme appears to be well suited for blockchain-based Internet of Things (IoT) applications.

In this paper we propose a gated cycle scheme for prioritized block access in a PBFT-like consensus mechanism suitable for blockchain-based IoT applications. Blocks submitted by clients are processed in cycles of variable length, in the order determined according to their priorities which can be assigned according to the block length or orderer's stake. We investigate the performance of this scheme using an analytical model and show that it indeed allows for clear differentiation of traffic of different priorities whilst retaining good performance, esp. under higher traffic load, compared to the original PBFT.

With the popularization of emerging technologies such as artificial intelligence, 5G and beyond, extended reality and blockchain, the next generation Internet is rapid expansion. “Metaverse” as an evolving paradigm of next-generation Internet, can be recognized as a fully immersive, hyper spatiotemporal and self-sustaining virtual shared space, and its concept is continuous development and evolution. It is moving from imagination to the coming reality, but it is still far from being realized. One of reasons is that distinct sub-metaverses deploying their services on heterogeneous blockchains results in major problems for interoperability, preventing the implementation of seamless integrated metaverse. Facing the challenge, this paper proposes a decentralized identifiers (DIDs) assisted secure cross-metaverse authentication scheme for MEC-enabled metaverse, which is based on a novel designed infrastructure build on MEC and blockchain. In addition, the proposed scheme adopts DIDs, which can not only achieve the secure cross-metaverse authentication, but also increase the decentralization of the metaverse. In addition, the adoption of ID-based aggregate signature can reduce the overhead of computation, communication and storage.

Bitcoin is the largest PoW blockchain, which currently uses the longest-chain protocol for chain selection and is vulnerable to various attacks like stubborn mining attack. As a variant of selfish mining attack, stubborn mining attack usually has 7 types of strategies, each of which does damage to the blockchain system. GHOST is another chain-selection protocol, which has been demonstrated to make the blockchain system more secure than the longest-chain protocol under selfish mining attack. There were studies on stubborn mining in perfect GHOST blockchains and they only studied two types of stubborn mining strategies. But it is a fact of life that the blockchain is an imperfect network due to ubiquitous network congestion and/or attacks. This paper aims to explore a simulation-based approach to quantitatively evaluate the threat capability of all 7 stubborn mining strategies. We first develop all stubborn strategies in imperfect GHOST blockchains. Then we evaluate miner revenues and system throughput over different network conditions. The results show that the lead-fork-stubborn strategy is the dominant strategy for attackers when they have more than 33% total computing power. The stubborn attackers with less than 20% total computing power lose their revenue whichever stubborn mining strategy is used. The blockchain with high network quality still has the risk of significant throughput downgrade. Our work can help the stubborn mining attack detection and secure blockchain system design.

In this paper, we investigate the performance of IEEE 802.11bd networks through a discrete Markov chain and E-limited M/G/1 queuing models. We then use the resulting model to compare the performance of Next-Generation Vehicle (NGV) that use channel bonding without fallback and legacy (non-NGV) devices through performance metrics such as successful transmission probability, medium access probability, and access delay.

Abstract Internet of Things (IoT) jobs not only require computational resources but also are delay-sensitive and security-sensitive. Edge computing emerges as a promising paradigm to improve the quality of experience for IoT users. Edge computing faces many security threats, perhaps even more than traditional data centers. With a growing amount of data offloaded to Edge Data Centers (EDCs), the EDC performance needs to be considered and evaluated carefully for improving the vulnerable EDC resource utilization while satisfying IoT job requirements. This paper develops an analytical model, which can capture the dynamics of an EDC system with the following features: (i) The system is under heterogeneous workloads; (ii) the system is subject to attacks, which prevent equipment units in the system from providing service and (iii) the jobs in the system are delay-sensitive. Namely, the job processing fails before the processing is completed. Based on the proposed model, we develop formulas for performance and profit metrics and conduct a series of simulation experiments to verify the correctness and accuracy of our model. Finally, through our model, we evaluate the performance of the EDC, and we offer solutions for EDC administrators to maximize profit.

This article investigates a hierarchical aerial computing system, where both high-altitude platforms (HAPs) and unmanned aerial vehicles (UAVs) provision computation services for ground devices (GDs). Different from the existing works which ignored UAV task offloading to HAPs and suffered long transmission delay between HAPs and GDs, in our system, UAVs are responsible for collecting the tasks generated by GDs. Considering limited resources and constrained coverage, UAVs need to cooperatively allocate their resources (including spectrum, caching, and computing) to GDs. After collecting GD tasks, UAVs are allowed to offload part of these tasks to the HAP, in order to minimize task processing delay and then better satisfy GD delay requirement. Our objective is to maximize the amount of computed tasks while satisfying tasks’ heterogeneous Quality-of-Service (QoS) requirements through the joint optimization of UAV resource allocation and task offloading. To this end, a joint optimization problem is first formulated as a partially observable Markov decision process (POMDP) under the constraints of available resources, UAV energy, and collision avoidance. Then, we design a multiagent proximal policy optimization (MAPPO)-based algorithm to solve the optimization problem. By introducing the centralized training with decentralized execution framework, UAVs acting as agents can cooperatively make decisions on GDs association, resource allocation, and task offloading according to their local observations. In addition, state normalization and action mask are also adopted to improve training efficiency. Experimental results verify the efficiency of the proposed algorithm and the system performance is also analyzed by the numerical results.

The consensus algorithm is an essential ingredient of any blockchain system. Many different consensus mechanisms, such as practical Byzantine fault tolerance (PBFT), Proof-of-Work (PoW), Proof-of-Stake (PoS), and their many derivatives, have been proposed over the years, but the complementary problems of performance and resilience to malicious behavior of the nodes have yet to be resolved in a satisfactory manner. In this work, we propose a consensus mechanism that integrates PoS with PBFT, which can effectively deal with dishonest nodes, both individual validators and leaders, while maintaining high performance. Our model incentivized truthful behavior by using trust score and reward mechanisms as crucial components of the block validation and ordering processes. The performance of the proposed scheme is evaluated using an analytical model that employs a semi-Markov process, defined by an ergodic multidimensional Markov chain with a finite number of states. The results show the efficiency of the proposed model in consensus-based decision making, even under a high likelihood of dishonest node behavior.

Fork after withholding (FAW) attack is an easy-to-conduct attack in the Bitcoin system and it is hard to be detected than some attacks like selfish mining and selfholding attacks. The previous studies about FAW attack made some strong assumptions, such as no propagation delay in the network.

We provide a comprehensive analytical model for block distribution in the Ethereum distribution network. We begin with a model for node connectivity based on reported measurements' and model the distribution of blocks using a Jackson network together with a priority M/G/1 queuing system. Our results show that the probability distribution of block response time is close to exponential distribution, while block delivery time exhibits a hypo-exponential distribution. Both distributions exhibit a thicker tail than the exponential distribution due to the peculiarities of the gossip-like protocol used to propagate blocks and transactions in Ethereum.

In this work we combine Delegated Proof of Stake (DPoS) consensus technique with multiple entry Practical Byzantine Fault Tolerant voting in a permissioned blockchain network. Voting is organized in rounds that contain a number of PBFT consensus cycles. Nodes with low stake are forced to leave at the end of current round and may return to a new round when they repurchase the stake tokens, while nodes with sufficient stake may leave the network temporarily at the completion of current round due to mobility. We consider multiple DPoS classes based on node's initial stake and probability of truthful voting, and model their behavior using embedded Markov Chain which corresponds to a Semi Markov Process (SMP). We show that probability of reaching consensus is higher when rounds are shorter and/or there are more nodes in the network. In addition, we find that nodes from higher priority classes are mostly excluded from voting due to their mobility, while those from lower priority classes are excluded more often on account of low stake.

The recommendation of points of interest (POIs) is essential in location-based social networks. It makes it easier for users and locations to share information. Recently, researchers tend to recommend POIs by treating them as large-scale retrieval systems that require a large amount of training data representing query-item relevance. However, gathering user feedback in retrieval systems is an expensive task. Existing POI recommender systems make recommendations based on user and item (location) interactions solely. However, there are numerous sources of feedback to consider. For example, when the user visits a POI, what is the POI is about and such. Integrating all these different types of feedback is essential when developing a POI recommender. In this paper, we propose using user and item information and auxiliary information to improve the recommendation modelling in a retrieval system. We develop a deep neural network architecture to model query-item relevance in the presence of both collaborative and content information. We also improve the quality of the learned representations of queries and items by including the contextual information from the user feedback data. The application of these learned representations to a large-scale dataset resulted in significant improvements.

In this work we integrate Proof of Stake (PoS) consensus technique with multiple entry Practical Byzantine Fault Tolerance voting in a permissioned blockchain network. We introduce several PoS classes based on stake and truthfulness of voting. Each class is modeled using Semi Markov Process (SMP). We derive probability of reaching the two-thirds majority of total number of votes, and highlight the impact of the populations of individual stake/priority classes on achieving consensus. We have also connected stake classes with Enhanced Distribution Coordination Function EDCA for leader selection, which enables nodes from high stake classes to have higher frequency of leader role and gain more revenue for block handling.

Practical Byzantine Fault Tolerance (PBFT) is a widely used consensus protocol which is sensitive to malicious behavior of the designated leader. In this paper we discuss two mechanisms that allow any ordering node on the consensus committee to act as the leader, thus alleviating the dependency on the leader. The selection of the next leader is performed by arbitration, rather than through a predefined sequence or round-robin mechanism. As the result, the proposed mechanisms lead to improved security since a malicious leader cannot stall the consensus and the next leader is not known beforehand. Performance evaluation shows that the proposed mechanisms indeed offer independence of the chosen leader and a reduction of queuing times of client proposals, albeit with some performance degradation.

An implementation of a blockchain-based data storage and Internet of Things (IoT) system is described in this paper. A Practical Byzantine Fault Tolerance (PBFT)-like protocol is used to achieve consensus. The proposed approach consists of two layers, the lower layer with a number of clusters and the upper layer. The upper layer consists of virtual cluster composed of delegate nodes from lower clusters. Each cluster in the lower layer allows its member nodes to initiate simultaneous consensus rounds, implemented using a dedicated overlay network per node. Each overlay network is rooted in one node and connects it with every other node. This allows concurrent multiple entry PBFT consensus sessions in each lower layer cluster. In the upper layer, the virtual cluster members have to contend for linking their accepted blocks into the blockchain ledger. Performance analysis of the proposed approach is performed using a discrete-time Markov Chain (DTMC) and M/G/1 queuing-based analytical model. The efficiency of the proposed model is verified by testing over a wide range of parameter values.

Malignant miners with small computing power can achieve unfair revenue and degrade system throughput through launching Fork after withholding (FAW) attack in a Proof-of-Work (PoW) blockchain system. The existing works about FAW attack have some of the following issues: (i) only studying Bitcoin blockchain, (ii) assuming that the blockchain network is perfect and then ignoring forks due to block propagation delay, and (iii) assuming that there is only one pool under attack. This paper attempts to investigate FAW attack in imperfect Bitcoin and Ethereum networks where malicious miners attack multiple victim pools. We develop a simulator to capture the chain dynamics under FAW attack in a PoW system where the longest-chain protocol is used. Two different computing power allocation strategies for malicious miners, PAS and EAS, are investigated in terms of the profitability of FAW adversaries, the loss of victims, and the blockchain throughput. The results reveal that FAW adversaries can get more revenue under PAS when more victim pools are subjected to attack in both Bitcoin and Ethereum. If FAW adversaries adopt EAS and the number of victims vary from 1 to 12, they can get maximal revenue when attack 7 victims in Bitcoin. The blockchain throughput decreases significantly under PAS while it is almost unchanged under EAS with the increasing number of victims in both Bitcoin and Ethereum. Our work helps the design of countermeasures against FAW attack.

Unmanned aerial vehicle (UAV) communication is easily wiretapped by malignant nodes due to the broadcast nature of line-of-sight (LoS) wireless channels. To tackle this problem, this paper investigates a dual-UAV aided secure dynamic ground-to-UAV (G2U) communication system. By dynamic, we mean UAVs communicate with moving ground devices (GDs). Our objective is maximizing the sum secrecy rate by the joint optimization of UAV trajectory and GDs transmit power. To achieve it, we first formulate this nonconvex optimization problem as a Constrained Markov Decision Process (CMDP) under the constraints of UAV flying speed, initial and final locations, limited energy, and average transmit power. Then, a Deep Deterministic Policy Gradient (DDPG) based deep reinforcement learning algorithm is designed, named SC-TDPC, to learn the optimal transmit power and UAV trajectory. The experiment results demonstrate that, compared to other benchmark schemes, SC-TDPC can efficiently enhance the UAV communication security in terms of sum secrecy rate.

The widespread application of deep neural network (DNN) techniques is being challenged by adversarial examples—the legitimate input added with imperceptible and well-designed perturbation that can fool DNNs easily in the DNN testing/deploying stage. Previous white-box adversarial example generation algorithms used the Jacobian gradient information to add the perturbation. This imprecise and inexplicit information can cause unnecessary perturbation when generating adversarial examples. This paper aims to address this issue. We first propose to apply the more informative and distilled gradient information, namely, integrated gradient, to generate adversarial examples. To further make the perturbation more imperceptible, we propose to employ the restriction combination of L 0 and L 1 / L 2 second, which can restrict the total perturbation and the perturbation points simultaneously. Meanwhile, to address the nondifferentiable problem of L 1 , we explore a proximal operation of L 1 third. On the basis of these three works, we propose two Integrated gradient-based White-box Adversarial example generation algorithms (IWA): Integrated gradient-based Finite Point Attack (IFPA) and Integrated gradient-based Universe Attack (IUA). IFPA is suitable for situations where there are a determined number of points to be perturbed. IUA is suitable for situations where no perturbation point number is preset to obtain more adversarial examples. We verify the effectiveness of the proposed algorithms on both structured and unstructured data sets, and compare them with five baseline generation algorithms. The results show that our proposed algorithms craft adversarial examples with more imperceptible perturbation and satisfactory crafting rate. L 2 restriction is suitable for unstructured data sets and L 1 restriction performs better in the structured data set.

The longest-chain and Greedy Heaviest Observed Subtree (GHOST) protocols are the two most famous chain-selection protocols to address forking in Proof-of-Work (PoW) blockchain systems. Inclusive protocol was proposed to lower the loss of miners who produce stale blocks and increase the blockchain throughput. This paper aims to make an analytical-model-based quantitative comparison of their capabilities against selfish mining attack. Analytical models have been developed for the longest-chain protocol but less to the GHOST protocol. However, the blockchain dynamics and evolution are different when adopting different chain-selection protocols. Therefore, the corresponding analytical models and/or the formulas of calculating metrics (such as miner profitability and system throughput) may be different. To address these challenges, this paper first develops a novel Markov model and the formulas of evaluation metrics, in order to analyze a GHOST-based blockchain system under selfish mining attack. Then extensive experiments are conducted for comparison and we observe that: (i) The GHOST protocol is more resistant to selfish mining attack than the longest-chain protocol from the aspect of relative revenue of selfish miners. (ii) Inclusive protocol can promote the security (evaluated in terms of miner profitability) improvement of the system which has little total computational power or a high forking probability. Additionally, the longest-chain protocol is more sensitive to inclusive protocol than GHOST protocol. (iii) It is hard for each of the two common-used difficulty adjustment algorithms to achieve higher system throughput and security.

The pervasiveness of GPS-enabled mobile devices and the widespread use of location-based services have resulted in the generation of massive amounts of geo-tagged data. In recent times, the data analysis now has access to more sources, including reviews, news, and images, which also raises questions about the reliability of Point-of-Interest (POI) data sources. While previous research attempted to detect fake POI data through various security mechanisms, the current work attempts to capture the fake POI data in a much simpler way. The proposed work is focused on supervised learning methods and their capability to find hidden patterns in location-based data. The ground truth labels are obtained through real-world data, and the fake data is generated using an API, so we get a dataset with both the real and fake labels on the location data. The objective is to predict the truth about a POI using the Multi-Layer Perceptron (MLP) method. In the proposed work, MLP based on data classification technique is used to classify location data accurately. The proposed method is compared with traditional classification and robust and recent deep neural methods. The results show that the proposed method is better than the baseline methods.

The well known Practical Byzantine Fault Tolerance (PBFT) consensus algorithm is not well suited to blockchain-based Internet of Things (IoT) systems which cover large geographical areas. To reduce queuing delays and eliminates a permanent leader as a single point of failure, we use a multiple entry, multi-tier PBFT architecture and investigate the distribution of orderers that will lead to minimization of the total delay from the reception of a block of IoT data to the moment it is linked to the global blockchain. Our results indicate that the total number of orderers for given system coverage and total load are main determinants of the block linking time. We show that, given the dimensions of an area and the number of orderers, partitioning the orderers into a smaller number of tiers with more clusters will lead to lower block linking time. These observations may be used in the process of planning and dimensioning of multi-tier cluster architectures for blockchain-enabled IoT systems.

Practical Byzantine Fault Tolerance (PBFT) is a widely used consensus protocol which is sensitive to malicious behavior of the consensus leader, esp. when that behavior occurs only sporadically which makes it harder to detect. We propose an arbitration scheme suitable for Internet of Things (IoT) applications in which a replicated blockchain ledger is used for storage of IoT data. The scheme removes most of the vulnerabilities at the expense of slight performance degradation. We further describe a modification which adds redundancy to remove the possibility of additional attacks. Experimental results confirm the validity and efficacy of the proposed scheme.

Various Machine Learning (ML) models have been developed for malware detection. But their widespread application is challenged by adversarial attacks using adversarial malware examples. Generative Adversarial Networks (GAN) is one of the effective approaches to help build possible unknown attacks and expose the vulnerability of targeted systems. The existing GAN-based ML models have the weaknesses of unstable training and low-quality adversarial examples. In this paper, we propose a novel Mal-LSGAN model to tackle these weaknesses. By using a Least Square (LS) loss function and new activation function combinations, Mal-LSGAN achieves a higher Attack Success Rate (ASR) and a lower True Positive Rate (TPR) in 6 ML detectors, compared with the existing MalGAN and Imp-MalGAN. In Multi-Layer Perceptron (MLP), Mal-LSGAN can even decrease TPR from 97.81% of original examples to 2.92% of adversarial examples. The experimental results also demonstrate that Mal-Lsgangets the preferable transferability of adversarial malware examples.

Selfish mining is a recognized misbehaving attack in Bitcoin. Selfish miners intentionally delay the release of newly mined blocks with the goal of gaining more revenue. While several studies have been devoted to analyzing the selfish miner behavior, the impact of selfish behavior on Bitcoin network performance has received little to no attention at all. In this work, we focus on that impact using a Markov chain that models selfish behavior both from the aspect of mining and form the aspect of block distribution time. We find that blocks mined by honest miners undergo longer distribution time compared to blocks mined by selfish miners. This delay results in intentional forking and the resulting network inconsistency provides more opportunity for selfish miners to gain unfair revenue.

The use of blockchain for Internet-of-Things (IoT) data recording necessitates an efficient and scalable consensus mechanism. In this paper, we describe a two-tier architecture in which IoT data is packaged in batches or blocks, approved by a low-tier cluster first and a top-tier cluster second, before being added to the replicated blockchain ledger. Both tiers use PBFT-like consensus enhanced with multiple-entry point operation using bandwidth reservation. This approach eliminates the dependence on a single primary leader that is characteristic for PBFT-like protocols, and allows the system to be deployed in geographically wide area. We provide a detailed probabilistic analysis of the proposed architecture using a discrete time Markov chain, and show that system performance depends on the number of ordering nodes in each cluster and the number of low-tier clusters.

Electronic health (e-health) information system relies on cloud computing technologies to provide massive medical data computing and storage services. Especially, the recently proposed Machine Learning as a Service (MLaaS) on these medical data can not only effectively improve the healthcare service quality, but also support the end users with limited computing resources. However, MLaaS on the massive medical data faces the challenge of privacy. Homomorphic encryption technology has been explored to assure the privacy of medical data owners in MLaaS but with the weaknesses of limited homomorphic operations and low efficiency. To alleviate these weaknesses, this paper proposes a novel privacy-preserving non-collusion dualcloud (NCDC) model-based e-health information system using neural network (NN) computing. The system can not only assure medical data privacy through adopting homomorphic encryption technology but also assure NN model privacy by adding fake neurons to the NN. In addition, the proposed e-health information system also has the following advantages: (i) Simple key generation. (ii) No constraint on the size of medical data to be encrypted. (iii) The less loss of prediction accuracy between encrypted and original medical data. (iv) Supporting more homomorphic operations and having better computing efficiency through experiment verification.

Selfish mining, as a serious threat to blockchain, has been attracting attentions from academic and industry. Stochastic modeling has been explored to quantitatively investigate selfish mining in imperfect blockchain networks. However, prior modeling-based analysis approaches have some of the following issues: (1) only focus on Bitcoin or Ethereum, or (2) ignore extended forks and just consider natural forks, or (3) only compute the mining revenue without assessing the performance and security of the blockchain system when the system suffers from selfish mining. In this paper, we aim to address these issues. We build a Markov chain to make quantitative analysis of selfish mining in imperfect Bitcoin and Ethereum networks with natural and extended forks. Formulas are derived to calculate the mining revenue for the selfish pool (comprising selfish miners) and honest miners, respectively. Moreover, we derive the formulas of performance metrics (namely, transactions per second and stale block ratio) and the formula of security metric (namely, the probability of double-spending success) of the system. These quantitative results can help understand the impact of selfish mining on imperfect blockchain networks and then help the detection of selfish mining.

Moving target defense (MTD), emerging as a game-changer in the cyber defense area, has got a lot of attention and development recently. As a proactive defense technique, MTD dynamically changes system attributes in order to create more uncertainties of the system and has been proved to be effective against cyber attacks. Beyond this, there is still a lack of researches with respect to the quantitative analysis of the effect of MTD on system performance. This paper aims to quantitatively investigate how MTD affects system performance while bringing security. We develop Markov process-based models for two different MTD strategies and derive the formulas for metrics of interest. We carry out simulation experiments to validate our proposed models with Mininet. Furthermore, numerical analysis is conducted for comparing these two different strategies in terms of system performance. The numerical results also show how different parameters affect the evaluation metrics. Our models can help defenders conFigure the MTD system in the most suitable way.

Data propagation in the Bitcoin network is inefficient due to its permissionless nature and the lack of multicast/broadcast features. In particular, the number of messages needed to propagate a single transaction is very high which is rather wasteful in terms of bandwidth utilization. In this work we propose a simple modification of Bitcoin software that allows a vast reduction of the number of messages needed for propagating a transaction over the network. The modification consists of deferring the transaction announcements until a certain predefined number of new transactions is collected. We show that the number of messages can be substantially reduced in this manner. The price to pay for this reduction is an increase in transaction propagation delay. However, the tradeoff between the reduction of traffic and transaction delay can be minimized by judicious choice of the threshold number of deferred transactions.

Practical Byzantine Fault Tolerance (PBFT) consensus algorithm is unsuitable for Internet of things (IoT) applications due to the need for a single view leader. In this work we propose to augment PBFT with a contention-based bandwidth reservation phase that allows any ordering node to initiate a new consensus round. We model the operation of the proposed algorithm, and show that system throughput is not significantly affected by the increased communication load when the number of ordering nodes increases, as the load per ordering node actually decreases. In this fashion, the proposed algorithm allows the deployment of wide area IoT networks that use PBFT-based consensus.

Compact block protocol aims to reduce bandwidth usage and, possibly, latency in the Bitcoin network. In this work, we present an analytical model to evaluate the impact of churning nodes on the Bitcoin network when the compact block protocol is used. We use a Continuous Time Markov Chain to model wake-up and sleeping behavior of each node in the network. We also calculate synchronization time, including transaction deficit recovery, when a node rejoins the network. When a node is absent from the network, it misses blocks mined during its absence but also some of the transactions which may be omitted from the subsequent compact blocks. Our results indicate that synchronization time takes more than a minute when node is away from the network for several hours. Moreover, the majority of synchronization time is spent on block verification and transaction deficit recovery.

Compact blocks and compact block protocol are a recent addition to the Bitcoin (BTC) data propagation protocol that aims to reduce bandwidth requirements and, possibly, reduce latency as well. In this work we have evaluated improvement of operation of BTC network under a mix of regular and compact block traffic in low-bandwidth mode. We have performed queuing analysis of the BTC network and obtained performance descriptors of block and transaction delivery times as well as forking probability. Although compact block size is more than an order of magnitude smaller than regular block size, improvement of delivery times is within bounds of 0% to 20%. Forking probability shows highest improvement of 25%. However, further analysis shows that compact block protocol requires high transaction traffic in order to prevent transaction pool deficit which causes further interaction among the peers.

This work deals with problem of deployment of Practical Byzantine Fault Tolerance (PBFT) consensus algorithm in Internet of things (IoT) applications requiring strict order among records linked in blockchains at multiple geographical points. Due to the needs of reliability and coverage of larger geographical areas it becomes necessary to extend current PBFT systems with single entry node towards multiple entry nodes. In this work we model PBFT systems with single and multiple entry points and compare their performance. We have implemented multiple entry system using CSMA/CA algorithm over fully connected P2P networks. We have compared two systems with four orderers against increasing system load and against increasing geographical coverage. Our results indicate that system with four entry points has double capacity over the system with single entry point.

This work proposes and evaluates a Practical Byzantine Fault Tolerance (PBFT)-inspired ordering service for IoT data collection and block formation in a permissioned blockchain environment. We implement an algorithm for atomic insertion of request to ordering service in which each ordering node can initiate insertion and lead the consensus protocol, unlike traditional current implementations of ordering service which rely on a single point of entry. We have modeled record insertion service into the P2P ordering service with constant number of nodes, variable request rate, and known distribution of one-way propagation delays among the ordering peers. Performance results show the behavior of system descriptors and the limits of system capacity expressed in terms of total request rate.

Abstract The rapid and widespread adoption of internet of things-related services advances the development of the cloud-edge framework, including multiple cloud datacenters (CDCs) and edge micro-datacenters (EDCs). This paper aims to apply analytical modeling techniques to assess the effectiveness of cloud-edge computing resource allocation policies from the perspective of improving the performance of cloud-edge service. We focus on two types of physical device (PD)-allocation policies that define how to select a PD from a CDC/EDC for service provision. The first is randomly selecting a PD, denoted as RandAvail. The other is denoted as SEQ, in which an available idle PD is selected to serve client requests only after the waiting queues of all busy PDs are full. We first present the models in the case of an On–Off request arrival process and verify the approximate accuracy of the proposed models through simulations. Then, we apply analytical models for comparing RandAvail and SEQ policies, in terms of request rejection probability and mean response time, under various system parameter settings.

In this work we analyze the blockchain forking events, blockchain partitioning, and duration of inconsistent state of the ledger in a Bitcoin delivery network. Using a comprehensive probabilistic model, we obtain the probability distribution of two- and three-way forks, the forked partition sizes, and the duration of ledger inconsistency until the resolution. We show that the three-way forking probability is substantially lower than that of a two-way forking and that the partition sizes in the case of two-way forking tend to equalize when the number of nodes increases. Finally, we show that the duration of ledger inconsistency state exhibits long tail probability distribution which means that successive forking events can force the ledger to remain inconsistent for long time.

Moving Target Defense (MTD) technology protects a target system by complicating the attacking process of adversaries. It has been gaining more and more attention with the massive growth of vulnerabilities and the widespread deployment of critical network services. This paper aims to analyze service Mean Time To Failure (MTTF) in a vulnerable network system which suffers attacks from adversaries. The system consists of multiple Physical Machines (PM) and each PM can support Docker Containers (DC) to run service. It applies Dynamic Platform Protection Technique (DPT), a kind of MTD techniques, to reduce the impact of attacks on service. A DC can be live migrated among these PMs in order to provision continuous service to users. We propose a model which captures the service behaviors during the service execution in the system. Our model allows both service residency/execution time at a PM and service migration time to be generally distributed. We also derive the formula for calculating MTTF and its approximate accuracy is validated through comparing analytical results with simulation results. Moreover, a formula is proposed to predict the total cost of the system, which helps administrators manage the network system effectively.

Drones are going to be extensively engaged in real life, security and surveillance network. However, a reliable UAV network requires multi-node and multi-hop communication. Designing an efficient UAV network requires designing a network architecture that not only extends the control and operating range but also provides reliable, low latency communication message to all the nodes. In this paper we have proposed an architecture of an UAV control network using IEEE 802.11ah relay scheme.

In this work we provide comprehensive analytical model for Bitcoin distribution network. We apply Jackson network model on the whole Bitcoin network where individual nodes operate as priority M/G/1 queuing systems. Data arrival process to the nodes is modeled as a non-homogeneous Poisson process in which the data arrival rates to the nodes are derived from the analytical model of gossip data delivery protocol. This model considers random probability distribution of node connectivity. Performance results include network distribution time for blocks, node response time for blocks, and populations of data distribution algorithms as functions on network size. Usefulness of this model is demonstrated by efficiently computing the forking probability for the Bitcoin blockchain.

This paper presents a constrained application protocol enabled multithreaded Internet of Things (IoT) gateway that allows reliable and quick access to big data from IoT domains. We consider a proxy caching policy to accelerate the content delivery rate of sensor data while reducing the energy consumption of corresponding nodes. We propose a selective caching policy in which resource validation requests are sent based on user requests, besides updates from IoT nodes. We also describe an analytical model to evaluate the performance of multithreaded IoT gateway under various traffic conditions to meet the quality of service constraints. The proposed model is evaluated over single hop IPv6 over low-power wireless personal area networks/IEEE 802.15.4 cluster. The performance evaluation clearly shows the effectiveness of our model in providing fast response and reduced energy consumption of the resources.

Vehicular fog computing (VFC) complements vehicular cloud computing as a promising solution for accommodating the surge of mobile traffic and reducing latency. This paper considers vehicular fog service (VFS) provided by a vehicular fog (VF), which is formed on-the-fly by integrating computing and storage resources of parked vehicles. VF dynamicity, due to vehicles' random arrivals and departures, poses a number of challenges for reliable and secure VFS provision to client vehicles. We propose a novel mechanism which consists of a VF construction method and a VFS access method to ensure VFS reliability and security without sacrificing performance. The reliability and security of VFS under our mechanism are discussed in detail. Moreover, we investigate the impact of the proposed mechanism on VF throughput and show that the mechanism is lightweight enough to be used in the latency-sensitive VFC.

As modern vehicles and distributed fog services advance apace, vehicular fog services (VFSs) are being expected to span across multiple geo-distributed datacenters, which inevitably leads to cross-datacenter authentication. Traditional cross-datacenter authentication models are not suitable for the scenario of high-speed moving vehicles accessing VFS, because these models either ignored user privacy or ignored the delay requirement of driving vehicles. This paper proposes a blockchain-assisted lightweight anonymous authentication (BLA) mechanism for distributed VFS, which is provisioned to driving vehicles. BLA can achieve the following advantages: 1) realizing a flexible cross-datacenter authentication, in which a vehicle can decide whether to be reauthenticated or not when it enters a new vehicular fog datacenter; 2) achieving anonymity, and granting vehicle users the responsibility of preserving their privacy; 3) it is lightweight by achieving noninteractivity between vehicles and service managers (SMs), and eliminating the communication between SMs in the authentication process, which significantly reduces the communication delay; and 4) resisting the attack that the database governed by one center is tampered with. BLA achieves these advantages by effectively combining modern cryptographical technology and blockchain technology. These security features are demonstrated by carrying out security analysis. Meanwhile, extensive simulations are conducted to validate the efficiency and practicality of BLA.

Mobile devices may offload their applications to a virtual machine running on a cloud host. This application may fork new tasks which require virtual machines of their own on the same physical machine. Achieving satisfactory performance level in such a scenario requires flexible resource allocation mechanisms in the cloud data center. In this paper we present two such mechanisms which use prioritization: one in which forked tasks are given full priority over newly arrived tasks, and another in which a threshold is established to control the priority so that full priority is given to the forked tasks if their number exceeds a predefined threshold. We analyze the performance of both mechanisms using a Markovian multiserver queueing system with two priority levels to model the resource allocation process, and a multi-dimensional Markov system based on a Birth-Death queueing system with finite population, to model virtual machine provisioning. Our performance results indicate that the threshold-based priority scheme not only performs better, but can also be tuned to achieve the desired performance level.

IEEE 802.11ah protocol is specifically designed to provide network connectivity to a large number of energy efficient heterogeneous internet of things (IoT) devices. Restricted access window (RAW) mechanism of the protocol is an innovative feature which aims at reducing medium access contention by slotting the beacon interval and allowing limited number of nodes to contend in a specific slot. In this paper, we evaluate important medium access control (MAC) layer performance metrics of differentiated quality of service (QoS) heterogeneous IoT nodes in IEEE 802.11ah RAW mechanism. Our analysis evaluates the feasibility of coexistence of priority and non-priority traffic in IoT devices without degrading network performance.

In this work we examine the tradeoffs involved in cache operation in an IoT domain controlled by a single Constrained Application Protocol (CoAP) proxy using IEEE 802.15.4 low power wireless personal area network technology. As the requirements for fresh data supplied to the clients and the reduction of traffic in the IoT domain are contradictory, a viable tradeoff is sought to obtain satisfactory performance. To this end, we compare the performance of a recently proposed group-based approach to that of a Bernoulli-scheduled, server-initiated proactive refresh, and show that the latter achieves superior cache hit ratio while offering substantial savings in terms of actual number of IoT domain messages exchanged.

In this paper we develop lightweight algorithms for monitoring and estimating data lifetime and round trip time at CoAP proxy. We deploy these algorithms in CoAP IoT domain with observe feature with random inter-observation times which can be a consequence of parameterized queries. Algorithms are based on kernel estimation of probability density distributions (pdf). As a result proxy maintains approximate pdfs of these parameters which can be used in congestion control and/or anomaly detection in IoT domain. Results show that estimations with 400-500 samples render satisfactory tradeoff between accuracy and computational complexity even under skewed probability distributions such as exponential distribution.

We describe a priority based caching policy for a hybrid IoT proxy which is updated using POST methods from IoT nodes. However, if the user request finds that cached record is outdated, the proxy will send a validation GET request to the IoT node. We model the performance of the multi- staged IoT proxy/gateway, assuming two classes of user requests with different arrival rates. We investigate the effects of caching in maintaining the freshness of cache records while maintaining low delay and low energy communications. A network of queues is used to model the hybrid proxy IoT gateway over a single hop 6LowPAN/IEEE 802.15.4 cluster. The evaluation results show that the proposed model can reduce the probability of outdated records while providing energy saving for both classes of the requests.

The CoAP observe feature is a promising way to reduce energy consumption of IoT nodes and improve communication performance. To make the most benefit of this, the proxy which observes data from multiple nodes in a single IoT domain, must estimate the data lifetime and eliminate, or at least diminish, the impact of variability introduced by random transmission times from IoT nodes to proxy. We have investigated impact of lenient versus more conservative estimations of data freshness on traffic performance in the cluster and on energy consumption at IoT nodes. Results show that for exponentially distributed inter-observation times with mean of 60s, conservative estimations of Max_Age run smoothly on clusters with well over 500 nodes.

The IEEE 802.11ax draft of high efficiency (HE) amendment to the ubiquitous 802.11 standard aims at increasing spectral and power efficiency of the network. However, the performance degradation due to hidden terminal problem had always been a concern especially in uplink transmission. Although the trigger based uplink transmission in IEEE 802.11ax is successful to some extent, the hidden node problem still persists in coexisting network. In particular, the uplink throughput is drastically reduced with the increase of packet arrival rate in presence of hidden node. To combat the hidden terminal problem, we propose to increase the carrier sensing threshold (CSTH) of STAs during association with HE access point (AP). Our analysis shows that this approach allows collision probability in uplink transmission to be reduced significantly.

In this paper we analyze the performance of a CoAP proxy with layered architecture and a built-in cache. The proxy acts as the gateway connected to the sensor nodes through an IEEE 802.15.4 low data rate wireless personal area network (LR WPAN). We investigate the impact of various traffic and proxy parameters on the performance of the proxy, in particular the interplay of the data validity interval defined through the CoAP Max-Age parameter.

The constrained application protocol (CoAP) observe feature has the potential to achieve fast communication and low energy consumption. We have analyzed a CoAP proxy which stores observed data from multiple Internet of Things (IoT) nodes that belong to single IoT domain. To maintain the freshness of cached records under random data arrivals, we have implemented the evaluation of Max-Age parameter at the proxy. We have also implemented congestion control at both IoT nodes and proxy, and show that it interacts with data freshness estimation at the proxy. Our results demonstrate the capacity limits of a single IoT domain against cluster size, interobservation time, proxy re-registration rate, ratio of confirmable/nonconfirmable observations, and presence of congestion control.

In this paper, we consider Internet of Things (IoT) domain running multicasting constrained application protocol (CoAP) over IEEE 802.15.4 network ended by CoAP proxy/cache. We examine the features of CoAP multicasting in order to ensure freshness of data in the cache as a function of the leisure parameter which allows devices to reply in arbitrary (random) time periods after receiving multicast GET request. We also investigate communication delay in the IoT domain and daily energy consumption of devices under several leisure schemes which may be implemented at the application level or at the medium access control layer. The impact of the leisure parameter appears to be critical for congestion avoidance. We show that a combination of proactive and reactive cache update with appropriate multicast leisure scheme can achieve low probability of outdated data while limiting the energy expenditure of nodes to a satisfactory value. Furthermore, best performance with respect to delay is obtained when the leisure period is integrated in the CSMA/CA backoff process.

The IEEE 802.11ac amendment enhances WLAN throughput by exploiting the spatial diversity of the antennas in a multi-user multi-input multi-output downlink transmission. Still, network resources remain under-utilized in uplink transmission due to single-user communication. In this paper, we propose an access point-controlled MAC protocol (A-MAC) that enables simultaneous transmissions from multiple STAs in uplink. The protocol uses the EDCA channel access technique to initiate multi-user transmission and the OFDMA method to transmit multiple RTSs simultaneously. It also introduces the explicit channel sounding technique by using dedicated OFDM subcarrier blocks for each user. Performance measurement shows that network throughput of the A-MAC is 150% higher than that of a single uplink transmission, thanks to the availability of concurrent multiple RTS transmissions in the uplink. The proposed protocol shortens the backoff time by up to 50% for all traffic categories due to concurrent multiple transmissions and thus enhances the battery life of the nodes. We observe that although the smaller backoff window of high-priority traffic category enhances the network throughput, higher intensity of high-priority traffic drives the network faster to saturation. Furthermore, better network stability and fairness among different traffic categories can be achieved when the dominant traffic has low priority.

IEEE 802.11ac amendment enhances WLAN throughput by exploiting spatial diversity of antennas in MU-MIMO downlink transmission. However, in the existing framework ample network resources remain underutilized in uplink transmission due to single user communication. In this paper, we have proposed an efficient Access Point controlled MAC (A-MAC) protocol that enables concurrent multiple uplink transmissions for different priority traffic classes. Performance measurement shows that network throughput of A-MAC is 150% higher than the throughput of single uplink transmission in existing IEEE 802.11ac protocol. We have achieve the highest payload throughput of 64% when the number of nodes Nsum= 8 and = 138 packet/s at uniformly varying packet arrival rates for all STAs. As the number of nodes increases, the network throughput gradually decreases but the packet handling capacity of the network increases. The peak network capacity of 1200 packets/s is achieved with Nsum= 16 and = 75 packets/s for all STAs. Further increase of the number of nodes results in decreasing both network throughput and packet handling capacity of the network. We have also evaluated the performance metrics of our proposed A-MAC protocol under non-uniformly varying packet arrival rates for a fixed number of nodes Nsum= 24. The analysis shows that when the network has higher intensity of low priority traffic, the aggregate packet handling capacity of the network is 1400 packets/s. However, as the intensity of high priority traffic increases, the aggregate packet handling capacity of the network decreases to 1000 packets/s. The results show that the more the higher priority packets arrive, the faster the network becomes unstable.

In this work we have examined aspects of reliable and scalable acquisition of big data from IoT domains. We have modeled the performance of CoAP enabled cross proxy IoT gateway towards the domain of CoAP enabled IoT devices. We focused on maintaining freshness of cache records at the proxy while maintaining low delay and low energy communications. We show that non-confirmable CoAP mode works well with acknowledged mode of 6LoWPAN/IEEE 802.15.4 cluster and that high delay and timeout values proposed in CoAP can be avoided. To support data freshness limit imposed by CoAP we have included sensor replicas which report the same variable. We have shown that addition of sensor replicas together with proactive cache update can ensure low record outdate probability while combination of proactive and reactive cache update can still have low outdate probability while limiting energy expenditure of nodes.

IEEE 802.11ac standard provides an increase in performance by introducing the pioneering downlink multi-user MIMO technique which allows simultaneous transmission to multiple clients in the down-link direction [1]. In this paper we have evaluated MU-TXOP sharing probabilities in IEEE 802.11ac at different traffic intensities using analytical model based on discrete Markov chain and E-limited M/G/1 queuing model. Our results show that the low priority traffic group benefits most from MU-TXOP sharing and achieves 150% throughput gain at low traffic (12 Mbps).

The formation of a Cognitive Personal Area Network (CPAN) involves a group of nodes connecting to a dedicated coordinator. In this paper, we propose a protocol for the formation of a CPAN in which nodes arrive randomly and their times to rendezvous (TTRs) with the CPAN coordinator may overlap, partially or fully, with one another. We develop an analytical model for the group joining time, i.e., the time to form the CPAN by an arbitrary number of nodes. Experimental results show that the time to form smaller size CPANs is mostly equivalent to the sum of all nodes' joining times. As the number of nodes gets larger, the group joining time flattens, which indicates that nodes that arrive later are more likely to join the CPAN before some of the nodes that arrived earlier.

This paper presents inter-piconet communications in the scenario where a shared bridge node connects two Cognitive Personal Area Networks (CPANs). The bridge carries both inter-CPAN traffic and synchronization information. The bridge exchanges synchronization information in a prioritized manner, which ensures that the two CPANs can coexist without operating on the same channel at the same time. We validate the network performance by analyzing the frequency of synchronization information exchanges. The simulation results also provide insight into the impact of various traffic. The results indicate that the proposed method is effective and efficient when the traffic load and the frequency of synchronization information exchanges are low to moderate.

Sensor cloud is an extension of traditional sensor networks that virtualizes sensor nodes in order to achieve improved response time for client queries and reduction of energy consumption of physical sensor nodes in order to prolong their lifetime. In this paper we investigate information sharing in a sensor cloud with two types of sensors with different amount of data to send in each reading, and two types (classes) of client applications with different arrival rates. We show that caching of data readings can give noticeable improvements in performance, esp. at high arrival rate of client queries. Poll sharing amongst classes can also provide benefits but only in case where slower class polls are extended to update obsolete readings from the faster class (which may be done at no extra cost); other ways of sharing, including reading both values each time, are found to be inferior in terms of response time reduction. These findings can be operationalized to devise an efficient sensing policy for the sensor cloud.

In this paper, we have explored the possibility of using IEEE 802.11ac as the MAC layer solution for VANET to meet the new requirements for various types of broadband services for Vehicle to Infrastructure (V2I) wireless communication. We have proposed an efficient RSU controlled MAC protocol that enables uplink MU-MIMO transmission for different priority traffic categories. Performance metrics show that the throughput enhancement of proposed MAC in MU-MIMO uplink transmission is three times the throughput of a single user transmission. We observe that due to smaller contention window size, the mean backoff time for the highest priority messages remain in the range of millisecond even at high packet arrival rate and node density. The saturation throughput analysis clearly shows two desired operating points: highest network throughput and highest network capacity. We achieve a maximum payload throughput of 64% at an aggregate packet arrival rate of 1100 packets/sec whereas 54% network throughput is achieved at a maximum load of 1200 packets/sec. We observe that the stable operating load remains between 1100-1200 packets/sec in our model.

Sensor cloud is an extension of traditional sensor networks that allows information sharing among users, improves response time to user requests, and reduces energy consumption of individual sensors to prolong their lifetime. In this paper we investigate the performance of data caching in a sensor cloud, assuming random selection of sensors to be read and a fixed duration of the time interval in which the sensor reading is valid. We show that significant improvements in performance, both with respect to response time and energy savings, can be achieved, depending on the percentage of sensors in each request, request arrival rate, and the number of concurrent servers issuing sensing requests.

In this paper we model and evaluate power ramping as a mechanism for achieving explicit access priority among traffic classes in LTE/LTE-A, taking into account preamble SINR considerations, preamble collisions, capture effect, and Physical Downlink Control Channel (PDCCH) deficiency. The results show that explicit power ramping can provide reasonable performance differentiation among traffic classes but it still cannot overcome coupling among traffic classes which exists due to preamble collisions and insufficient PDCCH resources.

Radio frequency (RF) recharging can extend maintenance-free operation of wireless sensor networks. However, the period between recharging is limited by the distance between the most distant sensor node and the master which sends out recharging pulses. To increase this period, we propose a scheme in which nodes are logically grouped into circular zones centered at the master, so that nodes in a given zone act as relays for their counterparts in the more distant zones. We describe and analyze a polling MAC protocol that supports zoning and relaying through a probabilistic model of the energy depletion process as well as a queueing model of packet transmission process. Our results indicate that zoning extends the time interval between recharge pulses and leads to equalization of node lifetimes.

Power ramping is one of the mechanisms proposed to help alleviate congestion during random access in LTE cells. We evaluate its performance taking into account preamble SINR considerations as well as Physical Downlink Control Channel (PDCCH) deficiency. Our results show that power ramping can speed up access at small to moderate loads, but that it is actually detrimental at moderate to high loads, with the ability to cause abrupt onset of saturation.

We describe a Vehicular M2M (VM2M) overlay network over random access channel (RACH) in LTE that aims to emulate the control channel (CCH) of vehicular ad hoc networks (VANETs). VM2M overlay is implemented over a dedicated subset of preamble codes, at the physical layer, and uses a medium access control (MAC) layer modeled as IEEE 802.15.4 carrier sense multiple access (CSMA/CA) mechanism. We evaluate the performance and interaction of regular LTE (H2H) traffic and VM2M traffic, in particular the impact of RACH resource configuration and preamble format (PF) in large cells. We have found that the format PF = 2 is capable but not ideal for handling large amount of CCH traffic due to repeated preamble transmissions in H2H layer; better results may be obtained if the frequency of RACH subframe allocation for CCH is increased, or a larger number of preambles is used at the physical layer of CCH.

We investigate the performance of a wireless sensor network composed of a number of IEEE 802.15.4 clusters interconnected in a linear structure. To equalize node lifetime, we utilize randomized sleep at the individual node level and rotation of the bridge role among the nodes located in shared areas between the clusters. Our Markov chain analysis indicates that the proposed mechanism is capable of achieving fair distribution of energy consumption among nodes and the resulting maximization of network lifetime.

In this paper, we evaluate the impact of physical layer on the performance of the Control Channel (CCH) in a Vehicular Access Network (VANET). CCH is implemented as an overlay network over LTE physical random access channel (PRACH). Physical layer of the VM2M overlay uses dedicated Zadoff-Chu preambles while the medium access control (MAC) layer uses a carrier sense multiple access (CSMA/CA) mechanism. We study three deployment scenarios of VM2M overlay-based CCH and evaluate the performance tradeoffs between the VM2M and human to human (H2H) traffic, and show that the overlay is a feasible solution to implement the CCH in a VANET.

Coexistence of H2H and M2M terminals in a LTE/LTE- A cellular network is severely limited by the random access scheme prescribed by the standard. In this paper, we describe an overlay scheme for concurrent M2M and H2H access on PRACH in which M2M terminals use a subset of ZC codes for its PHY layer and a beacon enabled CSMA-CA mechanism similar to IEEE 802.15.4 for its MAC layer. We analyze the performance of the proposed scheme and show that it provides satisfactory capacity for both H2H and M2M traffic.

Wireless sensor networks with radio frequency recharging of node batteries suffer from an imbalance in recharge amount on account of different distance to the master node. We describe an approach in which nodes are logically partitioned into a number of zones so that nodes in a closer zone act as relays for nodes in the next more distant zone. We show that the optimum partitioning can be found through exhaustive search, and propose a simple heuristic algorithm that can improve the performance without undue deterioration of the network bandwidth.

Academic social networks such as ResearchGate and Academia.edu are being increasingly used as a vehicle for publicizing research results and facilitating collaboration among researchers. In this paper we present a preliminary investigation about student/supervisor collaboration in Computer Science using the data available on ResearchGate, and also discuss the relationship between publication views, downloads, and citations that highlight the manner in which researchers use the publication data.

We have developed an analytical model for a non-saturated IEEE 802.15.6 wireless body area network (WBAN) operating under an error-prone channel. The most suitable vehicle for improving network performance was found to be the choice of access phase lengths based on traffic loads for different user priorities (UPs). It was also found that the deployment of exclusive access phase (EAP) is not necessary in a typical WBAN; in fact, short exclusive and random access phases (EAP and RAP, respectively) lead to inefficient use of available bandwidth. We have also found that four user priorities (out of the eight available) typically suffice to achieve even the most stringent requirements for WBAN performance.

Abstract We present, model, and evaluate a novel clustering algorithm running on top of IEEE 802.15.4 wireless sensor networks operating in slotted, beacon‐enabled mode. The adaptive low‐energy clustering algorithm provides randomized sleep and randomized rotation of the cluster‐head role so as to maximize the useful lifetime of the network by improving efficiency and balancing the lifetime of individual nodes. We model the adaptive low‐energy clustering algorithm through probabilistic analysis and show that its parameters can be tuned to extend the network lifetime and reduce the delay and energy overhead imposed by clustering. Copyright © 2014 John Wiley & Sons, Ltd.

ResearchGate.net is an academic social network that aims to facilitate knowledge sharing and academic collaboration through publication upload and download, questions and answers, and other mechanisms. In this paper we have collected data about the collaboration of Canadian Computer Science researchers, first through a quantitative exploration of student/supervisor collaboration on joint publications, and second, through a quantitative exploration of other collaboration mechanisms. Our results indicate that researchgate.net does indeed facilitate collaboration, but also finds some interesting properties of the available facilities of the social network platform.

Radio-frequency (RF) based recharging of sensor nodes is a promising way to reduce maintenance and extend the operational life of wireless sensor networks. However RF attenuation causes the network nodes with largest distance from the access point (master node) to dictate the rate of recharging which imposes unnecessary breaks in the operation of nodes closer to the master. This deteriorates the throughput of the nodes close to the master. To solve this problem we have designed location aided MAC protocol which supports recharging such that all nodes deplete their batteries at approximately the same time so that recharging pulse comes on time for all the nodes. To achieve that we have partitioned network nodes into circular zones around the master and assigned implicit priorities among the zones. Priorities decrease towards the edge of the network and regulate relative throughput among the zones. We have built probabilistic performance model to evaluate the impact of the recharging process on data communication of different zones by varying traffic load and network size.

We investigate the performance of priority-based access in a channel-hopping cognitive network, in particular the interaction between the number of packets each node is allowed to send in a single transmission cycle and the penalty coefficient that determines the amount of sensing to be done upon a successful transmission. Our results indicate that giving higher bandwidth allocation to low priority traffic class improves the performance of the network without affecting the performance for high priority class traffic.

Cognitive radio networks rely on spectrum sensing performed in a collaborative manner by the cognitive nodes themselves. Priority differentiation in such a network can be accomplished through different scheduling policies, differentiated duration of mandatory spectrum sensing, or a combination of the two. This differentiation will affect not only packet delays, but also the accuracy of channel sensing and, by extension, the probability of collisions with primary user transmissions which will critically affect the operation of the network. In this paper we provide a probabilistic analysis of the interplay between priority differentiation and network performance, and investigate the resulting tradeoffs under different prioritization approaches.

In this paper we propose a task admission control scheme in a cloud system. We develop two task admission control algorithms which are based on a random task filtering policy. First, we present a lightweight task admission control algorithm which is appropriate for the cloud systems with smooth changes of task arrival rate. This algorithm is based on long-term estimation of average utilization and offered load. Second, we introduce a task admission algorithm which is efficient for highly dynamic systems and is based on instantaneous utilization. The performance of the proposed schemes is evaluated against varying intensities of offered load. Performance evaluation of the proposed schemes confirms that both of them are able to ensure that the system is kept in the stable operating region.

We describe a low-overhead two-way bridging scheme for cognitive personal area networks (CPANs) and analyze its performance through probabilistic analysis and renewal theory. We show that the CPANs are indeed decoupled in terms of synchronization, but the performance of both local and non-local traffic in either CPAN depends on the traffic intensity in both CPANs, as well as on the portion of traffic targeting non-local destinations.

We describe a wireless sensor network with a medium access control (MAC) protocol that supports wireless recharging of nodes. The MAC protocol uses round-robin scheduling with 1-limited service for simplicity. Nodes that are running out of energy may request a recharge through a radio-frequency pulse. To reduce the likelihood of energy outage, we propose a collaborative protocol in which nodes that hear another node's recharge request will repeat it until the master node responds with a recharge pulse. Performance of the proposed protocol is evaluated using a probabilistic energy expenditure model. Our results indicate that the collaborative protocol can ensure uninterrupted network operation under a widely varying range of parameter values.

This paper provides a model for task admission control and resource allocation in the cloud infrastructure. We have examined the performance of resource allocation in this solution through different test scenarios. Also, we have presented two algorithms to control the admission of incoming tasks. These algorithms are based upon establishing thresholds for task arrival rate and task blocking probability. Our experiments indicate that these simple admission control algorithms can vastly improve system performance.

In this paper we propose a bridging algorithm for cognitive personal area networks (CPAN). The bridge node transfers inter-CPAN traffic between CPANs, and is given higher priority over local traffic since the bridge alternates between the two CPANs. We evaluate the performance of this algorithm, in particular the impact of bridge transmission on data transmissions in the destination CPAN. Our results indicate useful bounds on traffic intensity per node and amount of inter-CPAN traffic, which can be useful for CPAN design and dimensioning.

Wireless sensor networks that use slotted, beacon-enabled IEEE 802.15.4 standard are often implemented using clustering with randomized sleep of ordinary nodes to extend the network lifetime. To reduce the imbalance of power consumption between cluster-head and ordinary nodes, we propose a fair and efficient cluster-head rotation protocol with virtually zero overhead and no dead time. Through Markov chain modeling and probabilistic analysis, we show that the proposed approach achieves extremely good balance of nodes' lifetime and optimal network lifetime in maximin sense, outperforming other approaches that use TDMA access.

In this paper, we propose a coexistence scheme that allows radio-frequency identification readers or sinks to collect data from radio-frequency identification tags or sensors that use IEEE 802.15.4 wireless personal area network standard and send it to the access point via an IEEE 802.11 wireless local area network. The proposed scheme uses time scheduling and bridging via readers to allow for simultaneous operation of heterogeneous wireless networks that operate in the industrial, scientific, and medical frequency band at 2.4GHz. A simple sleep management approach is developed that allows tags to reduce their power consumption as well as collision rate. To provide timely reaction to sudden changes in tag population or reader availability, we devise a scheme where the scheduling parameters are adjustable and readers are mobile. We evaluate the performance of our solution in four scenarios and confirm the flexibility of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd.

Abstract Spectrum decision—that is, the selection of a channel for the next hop—is one of the most important factors that affect the performance of a channel‐hopping cognitive network. In this paper, we compare the performance of a number of channel selection algorithms through the probability of collisions with primary user transmissions. The results indicate that a simple histogram‐based selection algorithm performs the best, except in the somewhat unrealistic scenario where primary users are homogeneous with respect to mean period and mean duty cycle of their channel activity, in which case selecting the next‐hop channel among those that have most recently turned idle offers the best performance. Furthermore, histogram‐based selection is shown to be quite resilient to errors inherent to channel sensing and is thus a primary candidate for a wide range of applications of channel‐hopping cognitive networks. Copyright © 2014 John Wiley & Sons, Ltd.

Smart grid is expected to improve the efficiency, reliability and economics of current energy systems. Using two-way flow of electricity and information, smart grid builds an automated, highly distributed energy delivery network. In this paper, we present the requirements for intrusion detection systems (IDSs) in neighborhood area network (NAN) as a component of smart grid. We propose an IDS that is implemented in a distributed fashion with respect to NAN's communication and computation needs. An analytical approach is employed for detecting Wormhole attacks. We validate our NAN IDS scheme using OPNET Modeler [1]. The analytical part of the solution is developed in Maple [2] and integrated with OPNET.

Operation of cognitive secondary networks is critically dependent on the activity patterns of primary users. In this paper, we investigate the probability distribution of spectral holes, assuming that active and idle periods of primary users are independent random variables (which need not be identically distributed). We consider black, white, and gray holes, which correspond to time intervals when all channels are busy, all channels are idle, and some channels are busy while others are idle, respectively. We show that the duration of black and white holes may be described using an exponential approximation which holds regardless of the actual probability distribution of channel active and idle times, as long as the number of channels is not too small. The time interval between successive black hole occurrences is shown to be exponentially distributed as well. We also analyze the behavior of gray holes and quantify their impact using a simple proxy measure.

We consider a simple Medium Access Control (MAC) protocol that lends itself well for the operation of wireless sensor networks (WSN) with wireless RF recharging of sensor node energy source. Since individual nodes are equipped with a single antenna, data transmission must be temporarily interrupted when one or more nodes request energy recharging. We develop a probabilistic model of the energy depletion process within the proposed round-robin MAC operating with a 1-limited scheduling policy. We evaluate the impact of recharging period on the MAC operation under varying traffic load and varying bit error rate.

Advanced metering infrastructure AMI is one of the key elements in smart grid, which facilitates the communication of metering data to a substation in one direction and control messages in the reverse direction. Using wireless technologies and communication devices e.g., smart meters, which are located in the physically insecure places, makes the AMI vulnerable to cyber attacks. In order to ensure the reliability and security of AMI, attack prevention techniques and intrusion detection systems IDSs should be in place to protect the AMI communications from malicious attacks and security breaches, respectively. In this paper, we discuss the security requirements and vulnerabilities of AMI and review the existing threat prevention and detection solutions. We propose an IDS for neighborhood area network NAN in AMI, taking into account the NAN-specific requirements. Copyright © 2012 John Wiley & Sons, Ltd.

Rendezvous in cognitive networks refers to the ability of cognitive nodes to find each other and form a network, or to find and join an already operating cognitive network. Two main approached to rendezvous have emerged: sequence-based mechanism that guarantees maximum time-to-rendezvous and blind random hopping resilient to unpredictable primary user activity. In this paper we develop analytical models for time to rendezvous in the presence of primary user activity for the orthogonal sequence-based mechanism and a blind rendezvous mechanism integrated with a transmission tax-based MAC protocol with cooperative sensing. Our analysis shows that the blind mechanism performs better under random primary user activity, the difference being more pronounced when the number of channels is high and/or primary user activity is more intense. In addition, the probabilistic mechanism allows rendezvous with either an emergent or a fully operational CH-CPAN piconet without any interruption, unlike the sequence-based mechanism which precludes any data exchange during the rendezvous process.

When a channel-hopping cognitive network experiences a collision with primary user transmission, it may re-form the network by repeating the network formation (rendezvous) procedure, which is costly and time-consuming, or attempt to recover by re-establishing operation on an idle channel from a predefined backup set. In this paper we analyze a practical recovery mechanism implemented atop the transmission tax-based MAC protocol. The recovery mechanism uses a list of backup channels obtained from sensing data. We investigate the performance of the mechanism and show that a small number of backup channels suffices to ensure speedy and reliable recovery.

Cloud centers often use virtualization which requires live migration of virtual machines to improve performance and availability. In this paper, we describe an analytical performance model that measures the important performance indicators, namely, rejection probability and total delay, of a cloud center whilst taking into account live migration of virtual machines. Using the proposed performance model, we show that live virtual machine migration reduces task rejection probability and renders the super-task delay nearly independent of mean service time of individual tasks in a super-task.

In a channel-hopping Cognitive Personal Area Network (CPAN), the main objective of the working channel selection mechanism is to avoid collisions with unpredictable primary user activity. As the result, the manner in which the working channel for the next hop is selected, is among the most important determinants of CPAN piconet performance. In this paper we investigate the performance of two working channel selection algorithms and compare them with the simple random selection approach. We show that a simple algorithm where the working channel is selected amongst those that have most recently turned idle offer better performance in the homogeneous case (i.e., where the primary user activity on all channels follows a similar pattern), while the case with heterogeneous primary user activity on different channels is better handled by the algorithm in which predicted probability distribution of channel idle times is used to select the next working channel.

In this paper we evaluate efficiency of our new clustering algorithm (ALEC) by modeling the network behavior during set-up and steady-state phases of the algorithm for CH and non CH nodes. We investigate the effects of sensing reliability and clustering period on the network lifetime. We also analyze the power consumption and delay overheads of the clustering algorithm. According to the results, energy consumption and delay overheads of ALEC algorithm is very low and acceptable. The results also show that longer network lifetime can be achieved by higher values of clustering periods and lower values of sensing reliability.

Rendezvous refers to the ability of cognitive nodes to find each other and form a network, or to find an already operating cognitive network and join it. It is a challenging problem, in particular in channel- hopping cognitive networks. In this paper, we discuss the performance of the probabilistic blind rendezvous mechanism based on the transmission tax-based MAC protocol with cooperative sensing at the MAC level. We investigate the performance of the algorithm, with particular focus on the difference in performance in an emergent vs. a fully operational channel hopping cognitive piconet.

In this paper we discuss probabilistic node lifetime properties under our new clustering algorithm. We investigate the effects of event sensing reliability and number of clusters on the network lifetime. Our model compares effects of physical layer and MAC layer through bit error rate and packet collision probability. In our analysis, closed-form expressions are obtained for the probability generating functions of different intervals of the algorithm, from which other relevant statistics, such as mean, coefficient of variation and skewness, can be derived. The results show that higher values of mean lifetime can be achieved by either lower values of sensing reliability or lower values of number of clusters. Our results confirm that all nodes will die almost at the same time.

Accurate performance evaluation of cloud computing resources is a necessary prerequisite for ensuring that quality of service parameters remain within agreed limits. In this paper, we employ both the analytical and simulation modeling to addresses the complexity of cloud computing systems. Analytical model is comprised of distinct functional submodels, the results of which are combined in an iterative manner to obtain the solution with required accuracy. Our models incorporate the important features of cloud centers such as batch arrival of user requests, resource virtualization, and realistic servicing steps, to obtain important performance metrics such as task blocking probability and total waiting time incurred on user requests. Also, our results reveal important insights for capacity planning to control delay of servicing users requests.

In this paper, we evaluate the performance of cloud centers with high degree of virtualization and Poisson batch task arrivals. To this end, we develop an analytical model and validate it with an independent simulation model. Task service times are modeled with a general probability distribution, but the model also accounts for the deterioration of performance due to the workload at each node. The model allows for calculation of important performance indicators such as mean response time, waiting time in the queue, queue length, blocking probability, probability of immediate service, and probability distribution of the number of tasks in the system. Furthermore, we show that the performance of a cloud center may be improved if incoming requests are partitioned on the basis of the coefficient of variation of service time and batch size.

In this paper, we propose an analytical performance model that addresses the complexity of cloud centers through distinct stochastic submodels, the results of which are integrated to obtain the overall solution. Our model incorporates the important aspects of cloud centers such as pool management, compound requests (i.e., a set of requests submitted by one user simultaneously), resource virtualization and realistic servicing steps. In this manner, we obtain not only a detailed assessment of cloud center performance, but also clear insights into equilibrium arrangement and capacity planning that allows servicing delays, task rejection probability, and power consumption to be kept under control.

The articles in this special section focus on the topic of cloud computing, technologies, applications, and new areas of technological innovation.

This paper provides a systematic meta-analysis of 27 studies that investigate the impact of Test-Driven Development (TDD) on external code quality and productivity. The results indicate that, in general, TDD has a small positive effect on quality but little to no discernible effect on productivity. However, subgroup analysis has found both the quality improvement and the productivity drop to be much larger in industrial studies in comparison with academic studies. A larger drop of productivity was found in studies where the difference in test effort between the TDD and the control group's process was significant. A larger improvement in quality was also found in the academic studies when the difference in test effort is substantial; however, no conclusion could be derived regarding the industrial studies due to the lack of data. Finally, the influence of developer experience and task size as moderator variables was investigated, and a statistically significant positive correlation was found between task size and the magnitude of the improvement in quality.

A VANET that uses IEEE 802.11p EDCA mechanism is susceptible to a synchronization-based DDoS attack due to periodicity of transmissions and small contention window sizes. To make things worse, neither the sender nor receivers of periodic broadcasts will be aware of the attack since broadcast communications in VANET do not have acknowledgements. In this paper, we analyze the prospect of a synchronization-based DDoS attacks on vehicular communications and propose mitigation techniques to avoid such an attack.

Accurate availability and performance analysis are important requirements to guarantee quality of services (QoS) for cloud users. In this paper, we integrate an availability model in overall analytical sub-models of cloud system. Each sub-model captures a specific aspect of cloud centers. The key performance metrics such as task blocking probability and total delay incurred on user tasks are obtained. Our results can be used by an admission control to prevent the cloud center from entering unstable regime of operation. The results also reveal practical insights into capacity planning for cloud computing centers.

In this paper we describe a rendezvous protocol that enables nodes to quickly and reliably discover an existing channel-hopping cognitive PAN. The proposed protocol does not rely on the presence of a dedicated controller, the availability of a common control channel, or clock synchronization between the cognitive PAN and the new node. Furthermore, the CPAN need not suspend its operation for the discovery procedure to succeed. The performance of the proposed protocol is evaluated under fixed and adaptive superframe duration.

We discuss the changes needed in several of the key areas of 3GPP Long Term Evolution (LTE) network technology that would enable LTE networks to accommodate Machine-Type Communication traffic and applications, in particular those related to smart metering.

Since the signal transmission in WBANs takes place around or in the human body, the channel fading significantly affects the error performance of the networks. In this paper, we investigate MAC performance of an IEEE 802.15.6-based WBAN operating over a Rician-faded channel. We deploy Bit Error Rate (BER) as a function of channel quality, diversity order, and Signal to Noise Ratio (SNR) values for all User Priorities (UPs) in analytical and simulation models to evaluate the MAC performance. We study how varying Signal to Noise Ratio (SNR) of all UPs affects the MAC performance. Our results indicate that data frame sizes and channel quality between a node and the hub are the most effective parameters on PHY/MAC performance of a WBAN.

We investigate the coexistence of Wi-Fi (WLAN) and RFID/sensor networks that use IEEE 802.15.4 standard in the Industrial, Scientific and Medical (ISM) frequency band. To minimize the interference between the networks, a time scheduling scheme is combined with a sleeping scheme for RFID tags/sensors to further reduce the collision rate and energy consumption. We evaluate the performance of our solution under two different scenarios using discrete event simulation.

In this paper, we investigate the capacity limits in an variable duty cycle VANET that uses the IEEE 802.11p standard. We show that the default value of duty cycle that allocates time equally between control and service channels (CCH and SCH, respectively) leads to unbalanced performance, and that the performance of typical traffic on those channels can be greatly improved by judicious choice of the value for duty cycle. We also find the values of spatial density of vehicular traffic that lead to spatial saturation of network traffic.

The paper describes a simple channel scan protocol through which a station can quickly discover and join an existing piconet following a specified channel-hopping MAC protocol piconet in its vicinity. The proposed MAC protocol is simple and does not need a dedicated control channel. Moreover, the cognitive piconet can operate normally throughout the search, unlike Bluetooth where piconet operation is temporarily suspended during the inquiry/inquiry scan procedure. The performance of the proposed protocol is evaluated through discrete event simulation.

Successful development of cloud computing paradigm necessitates accurate performance evaluation of cloud data centers. As exact modeling of cloud centers is not feasible due to the nature of cloud centers and diversity of user requests, we describe a novel approximate analytical model for performance evaluation of cloud server farms and solve it to obtain accurate estimation of the complete probability distribution of the request response time and other important performance indicators. The model allows cloud operators to determine the relationship between the number of servers and input buffer size, on one side, and the performance indicators such as mean number of tasks in the system, blocking probability, and probability that a task will obtain immediate service, on the other.

In this paper, we investigate the impact of transmission opportunity (TXOP), arbitration interframe space (AIFS), and contention window on the performance of an IEEE 802.11e cluster with four traffic classes under Poisson frame arrivals. We derive an analytical model of the cluster using queuing model of individual nodes, discrete time Markov chain, and probabilistic modeling of the backoff process. The analytical model demonstrates the complex interaction between TXOP, on one side, and AIFS and contention window, on the other. We derive saturation and stability points for all traffic classes and discuss their dependency on TXOP allocations. Our results indicate that use of nonzero TXOP parameter under Poisson frame arrivals improves performance slightly by separating points of saturation and instability. More substantial performance improvements should be expected by deploying TXOP differentiation under bursty traffic. Since all traffic classes need to operate in stable, nonsaturated regime, this work has important implications for the design of congestion control and admission control schemes in IEEE 802.11e clusters.

ABSTRACT In this paper, we investigate the capacity limits in a variable duty cycle VANET that uses the IEEE 802.11p standard. We show that the default value of duty cycle that allocates time equally between control and service channels leads to unbalanced performance and that the performance of typical traffic on those channels can be greatly improved by judicious choice of the value for duty cycle. We find the values of spatial density of vehicular traffic that lead to spatial saturation of network traffic. In addition, to model the distribution of the vehicles on the road segment, we used Poisson distribution, as well as Erlang‐k distribution. Copyright © 2012 John Wiley & Sons, Ltd.

Quality of service, QoS, has a great impact on wider adoption of cloud computing. Maintaining the QoS at an acceptable level for cloud users requires an accurate and well adapted performance analysis approach. In this paper, we describe a new approximate analytical model for performance evaluation of cloud server farms under burst arrivals and solve it to obtain important performance indicators such as mean request response time, blocking probability, probability of immediate service and probability distribution of number of tasks in the system. This model allows cloud operators to tune the parameters such as the number of servers and/or burst size, on one side, and the values of blocking probability and probability that a task request will obtain immediate service, on the other.

We investigate the performance of simple cognitive personal area network (CPAN) with cooperative sensing. Nodes are equipped with small buffers of capacity K each, and each node is allowed to transmit a batch of up to μ packets in one transmission cycle. Upon transmission, each node must support the operation of the CPAN by performing sensing duty in the amount obtained by multiplying the number of packets sent in a batch by a variable penalty coefficient We model this system and show the relationship between packet blocking and carried load with respect to scheduling parameter μ, penalty coefficient, traffic load and piconet size.

IEEE 802.11p is a new communication standard for wireless access in vehicular environment (WAVE), intended to support road safety messages, electronic toll collection, infotainment services, and other as well as a number of other intelligent transportation systems (ITS) applications. In this paper, we focus on the impact of the dwelling time on the control channel (CCH) vs. that on the SCH (i.e., the duty cycle) on the performance of video communication on the service channel (SCH), using a novel analytical model. We show that a range of duty cycles allows for satisfactory performance of video traffic on SCH as well as timely delivery of safety messages on CCH.

In this paper we investigate probability distribution of idle time in IEEE 802.11e networks with an access point. The probability distribution is shown to be sub-geometric and it can be approximated with a sub-exponential Gamma distribution. Secondary network operating within the transmission range of such a WLAN can use these probability distributions to estimate the probability of collisions with the transmissions from WLAN nodes, and make decision about the timing of their own forthcoming transmissions.

Abstract In this paper, we propose and evaluate a cognitive personal area network (CPAN) suitable for wireless monitoring and control outside the Industrial, Scientific and Medical (ISM) band. Network is based on frequency hopping but the channels are selected on the basis of spectrum sensing performed by the nodes. Nodes balance their data collecting activity and spectrum sensing such that the duration of spectrum sensing done by a node is proportional to the number of packets previously transmitted by that node. We show that the operation of a node in this setup can be modeled as a gated exhaustive round‐robin system with vacations, and analyze the performance of the system. The impact of the value of the penalty factor, which determines the duration of sensing activity with respect to transmission, on packet access delay and stability limits of the system is discussed in detail. Copyright © 2009 John Wiley & Sons, Ltd.

ABSTRACT We investigate the performance of simple cognitive personal area networks (CPANs) with cooperative sensing. Nodes are equipped with small buffers of capacity K each, and each node is allowed to transmit a batch of up to μ packets in one transmission cycle. Upon transmission, each node must support the operation of the CPAN by performing sensing duty in the amount obtained by multiplying the number of packets sent in a batch by a variable penalty coefficient. We model this system and show the relationship between values of design parameters and piconet performance. We also show that performance and bandwidth utilization can be improved by a simple technique of dynamically adjusting the duration of the superframe to cater to instantaneous traffic volume. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper we investigate the ratio of dwelling time in control channel (CCH) and service channel (SCH) for single channel devices deploying WAVE (Wireless Access in Vehicular Environments) protocols. Based on analytical model for both channels and four traffic types we present tradeoffs in channel performance when CCH/SCH duty cycle is changing. Our results show that, when video traffic is absent, duty cycles smaller than 0.5 can offer satisfactory performance on CCH while running larger amount of revenue generating traffic on SCH.

Efficient operation of a cognitive personal area network (CPAN) may be achieved if each data transmission is taxed by requiring the transmitting node to participate in cooperative sensing for a prescribed time period. We investigate the data transmission performance of this approach as well as its sensing accuracy. Through adaptive management of both the sensing process and data transmission, we show that the two facets of performance may be adjusted almost independently.

Packet transmission in cognitive personal area networks (CPANs) may be linked to spectrum sensing, in the sense that each node is allowed to transmit a single packet in one transmission cycle, but must then `pay' for it by spectrum sensing; this ensures fairness with respect to transmission as well as the sensing burden. The amount of `payment' effort is dimensioned so as to keep the total sensing error below a pre-defined limit. We model the integrated system and evaluate its performance with respect to packet transmissions and spectrum sensing.

In this paper we investigate the transition between non-saturation and saturation regimes for the EDCA function within the IEEE 802.11e standard. We develop queueing and Markov chain models for node operation, and demonstrate interaction among traffic classes in transition from non-saturation to saturation regime. We derive probability generating functions (PGFs) for the probability distributions of packet service time and buffer occupancy. We also derive Laplace-Stieltjes transform (LST) for the probability distribution of frame response time and show stability limits for each traffic class.

Efficient operation of a cognitive personal area network (CPAN) may be achieved if each data transmission is taxed by requiring the transmitting node to participate in cooperative sensing for a prescribed time period. Extended sensing, however, tends to impair the data transmission performance. In this paper we investigate the performance of G-limited scheduling which ensures satisfactory packet delay for each node, whilst making sure that sufficient sensing is done so as to maintain smooth operation of the CPAN.

In this paper we investigate the performance of a cognitive personal area network (CPAN) with round robin service where each node is allowed to transmit at most one packet in one transmission cycle. Furthermore, upon transmitting a packet, the node has to perform spectrum sensing in order to distribute the sensing load in a fair manner and enable smooth operation of the CPAN. Duration of the sensing period is regulated by a predefined penalty coefficient; however, sensing may be interrupted by reception activity of the node. We present a probabilistic model of this system and investigate the performance limits with respect to the traffic load and value of the penalty coefficient.

Cognitive radio technology necessitates accurate and timely sensing of the primary users' activity on the chosen set of channels. We assume that sensing is performed by a number of nodes in a personal area network (PAN), and that sensing results are collected by the PAN coordinator which combines them to form a coherent channel map. The simplest selection procedure is a simple random choice of channels to be sensed; to reduce the delay in detecting the end of spectral opportunities, different sensing probabilities are assigned to active and inactive channels. To improve the accuracy of the sensing process even further, we propose to modulate the sensing probabilities according to the duration of active/inactive periods, or according to the time of last sensing event. The paper analyzes the performance of these policies and discusses the range of parameters in which they lead to a reduction of sensing error.

In this paper we consider the interconnection of an IEEE 802.15.4 body area network (BAN) in which nodes sense physiological variables such as electrocardiography (EKG), electroencephalography (EEG), pulse oximeter data, blood pressure and cardiac output, with an IEEE 802.11b room/ward WLAN. We model the operation of this two-tier network assuming that 802.15.4 BAN operates in CSMA-CA mode and that the BAN coordinator acts as the bridge which conveys BAN packets to the 802.11b access point. We analyze the two-hop network delay and discuss the mutual interaction of different data streams as well as impact of the number of bridges on packet delay.

Cognitive personal area networks build the available channel map by using cooperative sensing of primary channels by piconet members. Number of primary channels is usually larger than the number of piconet members available for channel sensing. This means that at the given time only a subset of primary channels will be sensed and that information about channel state of the other channels will be inaccurate. In this paper we try to establish the dependency of the sensing error on the number of sensors (piconet members) and the number of primary channels. Results show that a number of sensors which is about one-half of the number of channels allows the sensing error to remain below 1%.

Cognitive radio technology necessitates accurate and timely sensing of primary users' activity on the chosen set of channels. Reduction of sensing error may be accomplished through random selection in which idle and active channels are assigned different probabilities of being selected for sensing. The paper presents a probabilistic analysis of this policy and investigates the range of values in which such, incomplete sensing is capable of maintaining an accurate view of the status of the working channel set.

Cognitive radio technology necessitates accurate and timely sensing of the primary users' activity on the chosen set of channels. We assume incomplete sensing, in which the number of sensing nodes is smaller than the number of channels; the results of sensing are cooperatively combined to form a coherent channel map. As the times when spectral opportunities end are more critical for network performance, idle channels should be sensed more frequently than the active ones. The paper presents a probabilistic analysis of two differential sensing policies, and investigates the range of values in which such, incomplete sensing is capable of maintaining an accurate view of the status of the working channel set.

Cognitive radio technology necessitates accurate and timely sensing of the primary users' activity on the chosen set of channels. If sensing error is unacceptably high, we can undertake differential sensing in which subsets of sensing nodes target idle and active channels, respectively, or even redu

Abstract Patient data privacy, as one of the foremost security concerns in healthcare applications, must be enforced through the use of strong cryptography. However, in the scenario where the patient wears a body network in which lightweight, battery‐operated wireless sensors monitor various health variables of interest, the requirements for strong cryptography must often be balanced against the requirements for energy efficiency. In this paper, we describe two algorithms for key distribution. The first algorithm relies on a central trusted security server (CTSS) to authenticate that participants indeed belong to the patient's group and to generate the session key. In the second algorithm, participants authenticate each other using certificates and are largely independent of the central trusted security server (CTSS); this algorithm uses elliptic curve cryptography (ECC) to reduce energy consumption by cryptographic computations. In both cases, the patient's security processor has a lead role in authenticating group membership and the key generation process. Using the data from commercial devices compliant with the IEEE 802.15.4 low data rate WPAN technology, we show that this approach can be successfully implemented in networks built with low power motes. Copyright © 2008 John Wiley & Sons, Ltd.

We investigate the feasibility of a simple, traffic volume-based intrusion detection for an IEEE 802.15.4 compliant sensor cluster operating in beacon-enabled, slotted CSMA-CA mode. We have used simple exponential averaging to filter out some of the inherent variability in individual device arrival rate, and introduced a small hysteresis in the decision process in order to avoid false alarms due to dithering. Initial results demonstrate that the intrusion detection implemented in this manner may indeed operate quickly and efficiently.

In this paper, we analyze impact of period of key exchanges on the lifetime of interconnected IEEE 802.15.4 beacon enabled clusters. Bridges between the clusters are implemented on cluster coordinators in source clusters. Each cluster coordinator will initiate key exchange with the ordinary node or with the bridge after it receives nkdata packets from the node (or bridge). We assume that all nodes are applying power management technique based on the constant event sensing reliability required by the coordinator. We evaluate node populations which will equalize cluster lifetimes in this environment.

In this paper, we consider interconnected IEEE 802.15.4 beacon enabled clusters with secure and reliable sensing. Each cluster has to contribute constant number of packets per second to the sink. Packets are protected with Message Authentication Code which requires secret key to be shared between each node and coordinator. We consider generic key exchange protocol with arbitrary complexity and periodic key update. Bridges between the clusters are implemented on cluster coordinators and participate in key exchange as regular nodes when they visit foreign cluster. We evaluate impact of complexity of key exchange protocol and level of event sensing reliability on the network lifetime.

Individual 802.15.4 beacon enabled clusters can be interconnected in a master-slave manner where the bridging function is performed by the cluster coordinator of one of the clusters. The bridge can deliver its data to the sink by using the CSMA-CA access just like an ordinary node, or through dedicated GTS access. We compare the performance of the two mechanisms and investigate the impact of bridge residence time on performance. Distinct regions can be identified in which either of the access mechanisms performs better, but in either case, best performance is obtained when the bridge residence time in the sink cluster is as short as possible.

We consider the problem of maintaining the prescribed event sensing reliability while maximizing cluster and network lifetime in a multi-cluster 802.15.4 sensor network. Clusters are connected through bridges which also act as cluster coordinators; both ordinary nodes and bridges resolve contention using the CSMA-CA algorithm. Cluster lifetime is maximized through the use of redundant sensors which are periodically sent to sleep using a simple distributed activity management algorithm. Network lifetime is maximized by equalizing lifetimes of individual clusters through the adjustment of the number of nodes. We model this problem analytically and derive the probability distribution of the network lifetime. We also derive the expression for node count that compensates for the increased load due to contention caused by the bridge. Experiments show that this technique easily equalizes cluster lifetimes.

Performance of collision-based MAC algorithms such as CSMA-CA, as used in networks compliant with IEEE 802.15.4 standard, rapidly deteriorates with the increase of the number of nodes. A promising remedy to this problem is hierarchical partitioning, in which the parent cluster communicates with its child clusters through bridge nodes. The network sink is the coordinator of the parent cluster, while the bridges act as coordinators for their respective child clusters. In this paper, we investigate the performance of the simplest network with two clusters, both of which operate in beacon enabled, slotted CSMA-CA regime, using discrete event simulation. We examine the impact of different traffic and network parameters and identify the conditions that lead to saturation. We show that non-acknowledged transfers offer much better performance in a wide range of traffic and network parameters.

The problem of admission control at the sensor cluster coordinator is considered in a beacon enabled 802.15.4 network. The admission control algorithm is based on analytical modeling of the packet service time, using the number of nodes in a cluster, packet arrival rate, and packet size as independent variables. Simulation experiments confirm the validity of the chosen admission control algorithm, which is shown to provide a slightly conservative estimate with respect to the ideal condition.

The IEEE 802.15.4 specification is a recent low data rate wireless personal area network standard. While basic security services are provided for, there is a lack of more advanced techniques which are indispensable in modern personal area network applications. In addition, performance implications of those services are not known. In this paper, we describe a secure data exchange protocol based on the ZigBee specification and built on top of 802.15.4 link layer. This protocol includes a key exchange mechanism. Then, we evaluate the overhead of this scheme under different application scenarios. Initial results show the range of network and traffic parameters wherein the proposed scheme is feasible to use

We consider the problem of maintaining the prescribed event sensing reliability in a network formed by two 802.15.4 sensor clusters (child and parent). In order to maximize cluster lifetime, both clusters have redundant sensors which are periodically sent to sleep through distributed activity management. The clusters are connected through a bridge which also takes on the role of the coordinator in one of the clusters. In order to maximize the network lifetime, the lifetime of both clusters should be nearly the same, but the increased contention caused by the bridge reduces the lifetime of the parent cluster. We model this problem analytically and derive the probability distribution of the network lifetime. We also derive the expression for node count in the parent cluster that compensates for the interaction with the bridge. The use of this technique ensures that both clusters have nearly the same lifetime, with all the nodes dying within a short time period centered around the mean lifetime.

The performance of an IEEE 802.15.4 compliant network operating in the beacon enabled mode with both downlink and uplink traffic is analyzed through discrete time Markov chains and the theory of M/G/1 queues. The model considers acknowledged transmissions and includes the impact of different network and traffic parameters such as the packet arrival rate, packet size, inactive period between the beacons, and the number of stations. We investigate the nonsaturation region and outline the conditions under which the network abruptly goes to saturation. The analysis of stability of the network queues shows that the stability of the downlink queue at the coordinator is the most critical for network operation. Due to the abruptness with which the switch from nonsaturation to saturation occurs, the network operating point has to be carefully chosen according to the volume of downlink traffic. Furthermore, our model shows that certain features prescribed by the standard actually limit the performance of 802.15.4 networks.

Abstract Recent updates of the Bluetooth specification have introduced significant changes in the Bluetooth protocol stack, including optional flow control. When the Bluetooth piconet is used to carry TCP traffic, complex interactions between TCP congestion control mechanisms and data link layer controls of Bluetooth will occur. In this paper, we model the performance of the piconet with TCP traffic, expressed through segment loss probability, round‐trip time, and goodput, through both probabilistic analysis and discrete‐event simulations. We show that satisfactory performance for TCP traffic may be obtained through proper dimensioning of the Bluetooth architecture parameters. Copyright © 2006 John Wiley & Sons, Ltd.

The activity of individual nodes in a sensor network has to be managed in order to meet the simultaneous goals of maximizing the network lifetime and achieving the desired information throughput at the network sink. In this work we investigate the interaction of activity management with the CSMA-CA-based MAC layer in a beacon enabled IEEE 802.15.4-compliant network cluster. Activity management is performed through Bernoulli scheduling of service and sleep periods, which gives better results than the traditional, exhaustive and I-limited scheduling of the active and sleep periods. We derive the event reliability at the cluster coordinator as the function of physical layer parameters, data link layer parameters, Bernoulli scheduling parameter, and the probability distribution of sleep period. Based on the centralized analytical model, we derive distributed algorithm for calculating sleep parameters and demonstrate that the proposed distributed activity management policy is capable of achieving and maintaining the desired network reliability while maximizing the network lifetime.

The paper discusses security issues of networks compliant with the recent IEEE 802.15.4 standard for low rate WPANs. A number of vulnerabilities at the MAC and PHY layer are identified, and a number of possible attacks at the MAC layer are outlined, some of which can be easily launched with devices that are fully compliant with the 802.15.4 standard. Some remedial measures are proposed to help defend against those attacks or at least alleviate their impact on the performance of the network.

Achieving the desired data rate from a sensor network while simultaneously maximizing the lifetime of individual sensors are somewhat conflicting goals, further complicated by the fact that collisions at the MAC layer will affect both the throughput and power consumption. We address this problem in the context of a beacon enabled 802.15.4 sensor network by analytically modeling the network reliability as the function of the activity management policy and MAC layer parameters. The network coordinator monitors the throughput and adjusts the probability with which individual sensors go to sleep after sending their data. Results show that this activity management policy is able to maintain the desired data rate in a wide range of sensor network sizes and packet arrival rates at the nodes.

Recent updates of the Bluetooth specification to version 1.2 have introduced significant changes in the Bluetooth protocol stack, including optional flow control. When the Bluetooth piconet is used to carry TCP traffic, complex interactions between TCP congestion control mechanisms and Bluetooth data link layer controls will occur. In this paper, we analyze the performance of the piconet with TCP traffic, and show that satisfactory performance for TCP traffic may be obtained through proper dimensioning of the Bluetooth architecture parameters.

Architecture-based development of software product lines requires well established methodologies for both development and assessment of software product line architectures. While several methodologies for the development of such systems have been proposed, their assessment has mostly relied on methods developed for single product architectures. In this paper, we extend the popular ATAM (Architecture Tradeoff Analysis Method) method to cover both the core architecture of the product line and the individual product architectures. In addition, the extended method prescribes a qualitative analytical treatment of variation points using scenarios. We present the main tenets of the new method and illustrate its use through a small case study.

Abstract We analyze the performance of Bluetooth piconets under bursty traffic and finite buffer sizes. We model the joint probability distributions of uplink and downlink queues using theM[x]/G/1/Kmodel with vacations and derive buffer blocking probabilities at slaves and at the master. The results, which are confirmed through simulations, provide some practical guidelines as to the sizing of device buffers. Copyright © 2005 John Wiley & Sons, Ltd.

In this work, we derive the probability distribution of access delay and calculate throughput of a personal area network operating under the IEEE standard 802.15.4 in the beacon enabled mode. We model the network using the theory of discrete time Markov chains and M/G/1/K queues. The model considers acknowledged uplink transmission in non-saturation mode, and includes the impact of different parameters such as packet arrival rate, number of stations, the finite size of individual node buffers, packet size, and inactive period between the beacons. Our model also captures the problem of congestion at the beginning of the superframe due to multiple transmissions delayed from the previous superframe. The results show that average access delays, even for small buffer sizes, may be quite high if the throughput exceeds 50%, which can seriously affect applications with delay bounds. Values of throughput larger than 50% can be achieved at the expense of larger buffer sizes, which imposes implementation problems on devices with small memory resources.

We analyze the performance of E-limited scheduling in Bluetooth piconets under bursty traffic finite buffer case. We model the joint probability distributions of uplink and downlink queues using M/sup [x]//G/1/K model and derive buffer blocking probabilities at slaves and at the master. Wd also derive access delays at slaves' buffers.

Abstract End‐to‐end packet delays in a Bluetooth scatternet with a Slave–Slave (SS) bridge can be minimized by adjusting the bridge residence times in accordance with traffic intensity and locality. This paper presents two algorithms to do so. One of these uses a fixed value of the bridge residence time, while the other adjusts the residence time dynamically according to the instantaneous intensity of inter‐piconet traffic. We discuss the performance of these algorithms and show that the adaptive algorithm offers better performance than the fixed residence time one, provided the parameters are chosen appropriately. We also consider the stability of both algorithms and show that they have comparable stability limits. Copyright © 2004 John Wiley & Sons, Ltd.

The Bluetooth specification allows both asynchronous (ACL) and synchronous (SCO) links to be present in a piconet. However, the performance of ACL traffic rapidly deteriorates when an SCO link is present. This paper investigates the possibility of replacing the Bluetooth SCO connection with a QoS-constrained asynchronous link that uses multi-slot ACL packets. We have analyzed the performance of this scheme, dubbed pseudo-SCO, under limited service and exhaustive service scheduling. It was found that the pseudo-SCO scheme allows asynchronous traffic to experience lower delays than with the regular SCO connection, while supporting the bandwidth requirements of SCO traffic.

Inter-piconet scheduling in a Bluetooth scatternet may be performed in an adaptive manner so as to minimize end-to-end packet delays. Using the theory of queues with vacations, we analyze the impact of different values of scatternet parameters on end-to-end delays, and then investigate the possibility of minimization of the aforementioned delays. We show that such minimization is possible for the scatternet with the master/slave bridge, and that it should be based on inter-piconet traffic. We also describe a simple algorithm that enables the scatternet to achieve near minimum delays in a wide range of traffic parameter values.

In this paper, we propose a polling scheme for intra-piconet scheduling in Bluetooth piconets. The scheme, dubbed adaptive cycle-limited scheduling, limits the maximum piconet service cycle time, while at the same time tries to adapt the time allocated to each slave according to its current traffic, thus providing guaranteed QoS, optimum performance (even in cases of asymmetric traffic), and inherent fairness. Simulations confirm that the performance of the new scheme compares favorably with that of other common polling schemes.

The performance of two Bluetooth piconets linked through a shared device is analyzed using the tools of queueing theory. We analyze both possible topologies: the master/slave (MS) bridge, in which the shared device is the master in one of the piconets and a slave in the other, and the slave/slave (SS) bridge, where the shared device is the slave in both piconets. Two scheduling policies, limited service and exhaustive service, are considered. Analytical results are derived for the probability distribution of access delay (i.e., the time that a packet has to wait before being serviced) and end-to-end delay for both intrapiconet and interpiconet bursty traffic. The SS bridge has been found to offer lower access delays and local end-to-end delay than its MS counterpart, which provides lower end-to-end delay for nonlocal traffic due to the smaller number of hops (three, instead of four) for such traffic. In both topologies, exhaustive service scheduling was found to provide lower delays than the limited service one. All analytical results have been confirmed through simulations.

Abstract This paper analyses the performance of DS‐CDMA networks in the presence of call handoffs. We show that a handoff may violate the SINR requirements for other users, and thus cause an outage in the target cell. We propose to use the probability of such events as a possible metric for quality of service in networks with multiple traffic types, and derive the corresponding QoS parameters. A two‐level admission policy is defined: in tier 1 policy, the network capacity is calculated on the basis of the bound on outage probability. However, this policy does not suffice to prevent outage events upon handoffs for various traffic types, and henceforth, we propose an extension that reserves extra bandwidth for handoff calls, thus ensuring that handoff calls will not violate the outage probability bound. The overhead imposed by the extension is negligible, as the complete two‐tier admission control algorithm is executed only when a call is admitted into the network. Once admitted, calls can freely execute handoffs using the reserved bandwidth. The modified second‐tier bandwidth reservation policy is adaptive with respect to the traffic intensity and user's mobility and we show that it can provide satisfactory call (flow) quality during its lifetime. Analytical results for the QoS have been verified by the simulations. Copyright © 2002 John Wiley & Sons, Ltd.

Abstract Quality of service (QoS) at the call or flow level in Time Division Code Division Multiple Access (TD‐CDMA) networks is an important topic that has not yet received much attention. In this paper, we propose a two‐tier admission and scheduling policy. The scheduling policy is comprised of two procedures, theUser Prioritizer Procedure(UPP) andPacket Allocator Procedure(PAP). TheUPPis used to determine the serving priority of users. The task ofPAPis to allocate packets to a time slot without violating the outage probability by invoking the tier 1 and tier 2 admission control. The tier 1 admission keeps the probability of violating the Bit Error Rate (BER) limits for new call arrivals below a specified threshold. The tier 1 admission allocates bandwidth for newly arrived calls on the basis of the outage probability threshold. The tier 2 admission reserves bandwidth in all surrounding sectors for future handoff events, which minimizes the probability that the handoff call from one sector will cause an outage condition in the target sector. The performance of the proposed policy is evaluated through simulations in the presence of voice and data traffic types. We show that the proposed scheme can provide satisfactory quality for a call or data flow during its lifetime. Copyright © 2003 John Wiley & Sons, Ltd.

We analyze a Bluetooth scatternet with two piconets linked through a master/slave (MS) bridge using a queueing theoretic approach. We derive analytical results for the probability distribution of access delay and end-to-end delay, for both intra- and inter-piconet bursty traffic. We discuss the impact of various traffic parameters on performance, and give some guidelines to help maintain the performance within acceptable limits. All analytical results are confirmed through simulation.

This paper proposes and evaluates two tier call admission control in DS-CDMA networks which reserves bandwidth for handoff events. Under this scheme outage conditions due to handoffs occur only with small and controlled probability. A two-level admission policy is defined: in tier 1 policy, the network capacity is calculated on the basis of the bound on outage probability. However, this policy does not suffice to prevent outage events upon handoffs for various traffic types, and henceforth, we propose an extension which reserves extra bandwidth for handoff calls, thus ensuring that handoff calls will not violate the outage probability bound. The modified second-tier bandwidth reservation policy is adaptive with respect to the traffic intensity, and we show that it can provide satisfactory call (flow) quality during its lifetime.

Using the theory of queues with vacations, we analyze the end-to-end packet delays, for both intra- and inter-piconet traffic, in a Bluetooth scatternet formed by two piconets connected with a slave/slave bridge. We analyze the impact of different values of scatternet parameters and investigate the possibility of choosing some of those, in particular the time interval between bridge exchanges, so as to minimize the aforementioned delays. We show that the minimization of delay should be based on inter-piconet traffic, since the intra-piconet traffic delay is less sensitive to the actual value of the time interval between bridge exchanges.

The performance of a single Bluetooth piconet is analyzed using the theory of M/G/1 queues with vacations. Analytical results for probability distributions of packet access time and service cycle time are derived. Two scheduling policies, limited and exhaustive service, are considered. hi general, exhaustive scheduling was found to perform better than limited service. We also discuss the impact of an simultaneous synchronous connection (SCO) link on piconet performance. All analytical results are confirmed through simulations.

Traditionally, cohesion of a software component is evaluated as the similarity of its constituent parts. It may be more appropriate to measure functional coherence, or coherence of usage of a component, by analyzing external usage patterns of component's clients. We define an appropriate measure, starting from a generic system model and its associated mechanism for calculating object sizes. We show that the new measure satisfies some, but not all, of the properties that a cohesion measure is expected to satisfy. We also provide some intuitive examples to illustrate the concept and its possible uses.

Metamodeling, object-orientation and formal methods are three promising ways to cope with the increasing size and complexity of today's software systems. Furthermore, effective control of development projects requires the use of versatile metrics for software size and complexity. We present a generic formal object-oriented metamodel (GM), specifically tailored for modeling object-oriented software systems, and use it as the foundation for defining two basic measures of structural quality: coupling and cohesion. Even though these two notions are generic, we restrict ourselves to defining them at the class level, which is probably the most useful for object-oriented systems. The metrics have a generic formal definition, which makes them simple, intuitive, and amenable to automated measurement.

A formal definition of the data dictionary for an extended entity-relationship data model is described. Extensions should allow greater semantic expressiveness and more precise modelling, while retaining ease of use, intuitiveness and flexibility. Basic concepts of the model are formally defined using the Z notation, and an enhanced graphical notation is also proposed. Furthermore, a simple transformation of the extended ER schema to a relational one is formally described as well.