Federated learning(FL) is a machine learning paradigm designed to protect data privacy and security among multiple clients. It is widely used in industries such as healthcare, finance, and insurance. However, common personalization techniques such as clustering, data augmentation, and knowledge distillation often increase privacy risks or incur high computational costs with methods like homomorphic encryption. To address these challenges, this paper proposes the Secure Personalized Federated Learning (SPFL) algorithm, combining Clustered Federated Learning (CFL) and Oblivious Transfer (OT). Using the Affinity Propagation (AP) clustering algorithm, SPFL groups clients by model similarity to create personalized global models without additional information sharing. OT has achieved efficient delivery of security models. Experimental results show SPFL enhances security, adapts to Non-Independent and Identically Distributed (Non-IID) data, improves global model accuracy by more than 6.5%, and reduces the overall running time under three alpha values by more than 19.82% compared to Paillier encryption.

Effective detection of surface defects on wind turbine blades contributes to the continuous andrgetble operation of wind power equipment. It also facilitates the management and maintenance of equipment components. Wind farms contain a large number of wind turbines and surface defects vary in type and location, making the inspection process extremely complicated. In response to these issues, this paper proposes a wind turbine blade surface defect detection model named WTBFlaw-YOLO. The model is based on an improved version of YOLOv11. First, we designed a spatial-to-depth convolutional neural network block (SPD-Conv). We replaced every convolution stride and pooling layer in the convolutional neural network with SPD-Conv. This design improves WTBFlaw-YOLO’s ability to detect blade surface defects in low-resolution wind turbine images. Secondly, we designed a high-level screening feature pyramid network (HS-FPN) in the neck section. We achieved multi-level feature fusion with HS-FPN. This fusion enables WTBFlaw-YOLO to filter targets of various sizes and enhances its ability to express features at different scales. Finally, we designed a dedicated detection head for small defect target detection (SDTDH) in the head section’s multi-head detector. We used SDTDH to improve the detection precision of WTBFlaw-YOLO for micro defects on wind turbine blade surfaces. Experimental results demonstrate that WTBFlaw-YOLO achieves 89.7% mean average precision (using mAP50 as an example) on the public dataset (wind turbine-v2). The model outperforms other models. We conducted multiple ablation experiments. The experiments confirm the effectiveness and robustness of the WTBFlaw-YOLO model.

Opportunistic networks can support message delivery flexibly without infrastructure nor end-to-end connectivity, making them suitable for various scenarios, such as, crowd sensing, Internet of Things (IoT), vehicular ad hoc networks, etc. However, the performance of opportunistic networks, especially the delivery rate and network's operational time, is adversely affected by the uncertainty of encounter probabilities and the limited energy of IoT nodes. In this paper, we propose EQ-STAR, an optimal reachable path approach that combines encounter probabilities and energy to enhance network performance. First, the DySAT-pro model provides more accurate encounter probabilities based on the network's periodic patterns. Second, the Energy-Efficient high-Quality Markov Decision Process (EQMDP) model balances energy consumption and encounter probabilities to optimize the high-quality reachable path between the source and destination nodes. Finally, the next-hop relay node is selected based on the reachable path determined by EQMDP. Extensive experimental results show that our approach outperforms classicial and state-of-the-art baselines in both realworld and synthetic datasets.

Modern data centre networks demand ultra-low latency and high throughput, making Remote Direct Memory Access (RDMA) increasingly important. However, RDMA’s performance degrades significantly under congestion due to limited built-in congestion control. Existing approaches, based on Explicit Congestion Notification (ECN) and Round-Trip Time (RTT) suffer from limitations including delayed feedback and noisy signals when used alone. We propose Dynamic Hybrid ECN-RTT Congestion Control (DHERC), a novel algorithm that integrates RTT-based early detection with ECN-based confirmation to achieve balanced congestion control. Through extensive simulations, we demonstrate that DHERC outperforms state-of-the-art schemes across key performance metrics. DHERC achieves better flow completion time performance, improved throughput fairness, and good goodput efficiency while maintaining competitive tail performance. The results establish multi-signal congestion control as a promising approach for RDMA networks requiring predictable performance, efficiency, and fairness.

As 6G networks must support diverse applications with heterogeneous quality-of-service requirements, efficient allocation of limited network resources becomes important. This paper addresses the critical challenge of user admission control in 6G networks enhanced by Reconfigurable Intelligent Surfaces (RIS) and Mobile Edge Computing (MEC). We propose an optimization framework that leverages RIS technology to enhance user admission based on spatial characteristics, priority levels, and resource constraints. Our approach first filters users based on angular alignment with RIS reflection directions, then constructs priority queues considering service requirements and arrival times, and finally performs user grouping to maximize RIS resource utilization. The proposed algorithm incorporates a utility function that balances Quality of Service (QoS) performance, RIS utilization, and MEC efficiency in admission decisions. Simulation results demonstrate that our approach significantly improves system performance with RIS-enhanced configurations. For high-priority eURLLC services, our method maintains over 90% admission rates even at maximum load, ensuring mission-critical applications receive guaranteed service quality.

Named Data Networking (NDN) is an information-centric internet architecture that delivers packets based on the name of the content in the packet. A key component of NDN is the caching strategy designed to reduce total network latency and load on content producers. To improve the speed and reliability of web content delivery, existing caching strategies typically cache content on a large number of intermediate nodes, which incur significant energy consumption and memory overhead. However, in Internet of Things (IoT) scenarios, memory and energy of nodes are scarce resources. Therefore, in NDN-based IoT applications, traditional caching strategies can cause node failures due to energy depletion, which can significantly reduce the network operational lifetime as well as create problems that caching is supposed to solve. In this paper, a caching strategy based on node centrality and energy availability, called Energy-aware Approximate Betweenness Centrality (EABC) is proposed for NDN-based IoT. EABC uses a topology-based heuristic to cache data content on nodes with high centrality and makes caching decisions based on the remaining energy of the nodes. We evaluate EABC using simulations based on ndnSIM in different topologies and compare it with several existing NDN caching strategies. The results show that EABC performs better in different types of network topologies, reduces the average transmission delay of data and balances the energy consumption of highly central nodes, thus extending the network lifetime.

Identifying essential genes in an organism is crucial for bioinformatics and genomics research. In this paper, we propose a new computational method for gene similarity prediction, named GT-KGCN, which combines the advantages of the Graph Transformer (GT), Graph Convolutional Network (GCN), and Kolmogorov-Arnold Network (KAN). Specifically, GT-KGCN utilizes graph transformers for feature embedding and multi-head attention aggregation, GCN for hierarchical aggregation of information from neighboring nodes, and KAN for optimizing feature learning through learnable activation functions. Model inputs include Protein-Protein Interaction (PPI) networks, gene expression profiles, subcellular localization, and orthologs information. We evaluated the performance of GT-KGCN on two different PPI network databases (BioGRID and STRING) using multi-omics datasets of Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens. The experimental results show that GT-KGCN achieves a prediction accuracy of 99.12% for Escherichia coli, 98.47% for Saccharomyces cerevisiae, 92.22% for Drosophila melanogaster, and 92.30% for Homo sapiens. The GT-KGCN model outperforms traditional shallow machine learning methods and other deep learning methods in essential gene prediction. The ablation study verifies the positive effects of multi-omics datasets as well as different convolutional layers on the prediction performance of the model, with gene expression data showing the most significant performance improvement and demonstrating the model’s superiority.

Heralded entanglement generation between nodes of a future quantum internet is a fundamental operation that unlocks the potential for quantum communication. In this paper, we propose a decentralized synchronization protocol that operates at the classical control-plane of the link layer, to navigate the coordination challenges of generating heralded entanglement across few-qubit quantum network nodes. Additionally, with quantum network simulations using NetSquid, we show that our protocol achieves lower entanglement request latencies than a naive distributed queue approach. We observe a sixfold reduction in average request latency growth as the number of quantum network links increases. The Eventual Synchronization Protocol (ESP) allows nodes to coordinate on heralded entanglement generation in a scalable manner within multi-peer quantum networks. To the best of our knowledge, this is the first decentralized synchronization protocol for managing heralded entanglement requests.

Session-based Social Recommendation (SSR) harnesses social relationships within online social networks to improve Session-based Recommendation (SR) performance. However, existing SSR algorithms often face the challenge of "friend data sparsity". Additionally, significant discrepancies may exist between the purchase preferences of social network friends and those of the target user, thereby diminishing the influence of friends relative to the target user’s preferences. To tackle these challenges, this paper introduces the concept of "Like-minded Peers" (LMP), representing users whose preferences are aligned with the target user’s current session in their history sessions. To the best of our knowledge, this is the first work to use LMP as an enhancement for modeling the social influence in SSR. It not only alleviates the problem of friend data sparseness but also effectively incorporates users with similar preferences. Furthermore, we propose a novel model named Transformer Encoder with Graph Attention Aggregator Recommendation. Experimental results on four real-world datasets demonstrates the efficacy and superiority of our proposed model.

The traditional particle swarm optimization (PSO) algorithm suffers from shortcomings like easily falling into local optimum and inadequate sharing of information among particles, To ad-dress these limitations and enhance the search capacity of the particle swarm algorithm, we present a novel particle swarm optimization algorithm known as Exponential Dynamic Inertia Weight for Particle Swarm Optimization (ExDyPSO) in this paper. ExDyPSO is composed of two parts: firstly, by introducing dynamic inertia weight based on exponential distributions and acceleration factors that vary with the number of iterations, harmonizes the global and local search capabilities. Secondly, a stochastic particle-based jump-out strategy is proposed to surmount the case of particles falling into stagnation during the search process, thus effectively addressing the issue that PSO is prone to falling into local optimum. To assess the performance of ExDyPSO, we carried out experiments on eight benchmark functions and compared its performance against four alternative PSO variations. The experimental findings demonstrate that ExDyPSO achieves quicker and more accurate convergence towards the global optimal solution, all the while sustaining population diversity.

Most existing transaction sharding technologies allocate transactions randomly or based on transaction addresses, leading to imbalanced workloads and high ratio of cross-shard transactions (CST). The uneven workload makes it impossible for blockchain to fully utilize resources for transaction processing, which degrades system throughput. CST incur additional overheads in communication, verification, and processing, increasing block confirmation latency. This paper proposes a blockchain sharding transaction allocation algorithm known as correlation and workload-based transaction allocation (CWTA) to address the above issues. CWTA regularly pumps a certain number of pending transactions from the transactions pool to establish a multiple-directed transaction graph (MDTG) and achieves faster allocation efficiency by quickly gathering the out-edges of each account based on MDTG. This process has successfully changed the distribution mode of transactions from one-by-one to group-by-group which depends on the correlation between the groups for current and upcoming workloads of each shard. CWTA has demonstrated its effectiveness through abundant transaction allocation results with 2-16 shards. Compared to similar existing work, CWTA can achieve a much more balanced transaction workload and less allocation time without increasing the CST ratio which denotes better scalability, and achieve better throughput.

The conventional approach of choosing the best route to carry network traffic in wireless multi-hop networks does not maximize the overall network throughput and can lead to short-term instabilities in network state with dire consequences. To date, wireless network route selection considers mainly network or link metrics, always picking the best links, thus channeling all packets through a subset of all available links. This leaves weaker links under-utilized although such links can in fact be used to carry smaller packets or packets with less stringent requirements and free up bandwidth on the better links for larger packets or traffic with higher service requirements. As network traffic volume and heterogeneity increase in future networks, we need to maximize the usage of available network bandwidth and distribute the network traffic load. We combine network link metrics and packet attributes to determine the successful packet transmission probability, and then use this outcome to pick suitable links to forward the packet, which is not necessarily the link with the best metric. To validate the efficacy of our proposed approach in routing performance and energy efficiency, we applied it in routing for wireless multi-hop networks. More importantly, we are able to spread the traffic across nodes in the network, thus achieving better network load-balancing and higher network resource utilization.

Active nodes in opportunistic networks have a greater mobility range and undertake more message-forwarding tasks. This leads to issues of uneven traffic load and delayed cache space release in opportunistic networks. This paper proposes a load distribution model based on forwarding assistance (LDMFA) which integrates the node delivery prediction value and traffic idleness index to mitigate the blind selection of relay nodes and reduce message forwarding delay. To identify a selfish node, the message throughput rate of the node is selected as the evaluation index. The most appropriate relay node is selected by comparing the traffic idle index of neighbour nodes and calculating their forwarding assistance. This paper also introduces a cache optimization mechanism to address network performance degradation due to overloaded node traffic. Simulation results show that, compared with the prevailing opportunistic routing algorithms, the model improves the message delivery success ratio by 20%.

Bufferbloat, or excessive queuing delay under load, is a noticeable quality of service degradation that occurs when latency-sensitive traffic experiences the effects of increased packet buffering delays in network devices. This phenomenon leads to increased latency and reduced network performance, particularly affecting real-time voice and video traffic, online gaming, and other interactive applications that demand low-latency at all times. Bufferbloat also poses serious risks to future ambitions of latency-critical applications such as telesurgery, autonomous vehicles, and virtual reality, as it undermines network consistency and creates significant operational issues for service providers to manage. Bufferbloat is especially detrimental to Wireless Internet Service Providers (WISPs), due to their often ad-hoc and dynamic nature. To better characterise the prevalence of bufferbloat, we analyse a real-world WISP network and propose "Polus", a framework for detecting and characterising adverse network conditions caused by the phenomenon.

With the proliferation of mobile intelligent terminals, opportunistic networks have attracted widespread attention as a complementary technology to multi-network convergence. Different from traditional wireless networks, message delivery in opportunistic networks does not rely on a fixed infrastructure, but rather storing messages in a cache and utilizing the movement and encounters of nodes to relay messages. However, in practical application scenarios, nodes have limited storage space and energy and will easily exhibit selfishness. An increase in the number of selfish nodes will drastically degrade the performance of the network. To solve the problem of significant network performance degradation when the number of selfish nodes is high, this paper proposes an Incentive mechanism of Selfish nodes based on Energy optimization and Game Theory (ISEGT). The mechanism abstracts the process of forwarding messages by nodes into a bargaining game process, and selectively forwards messages based on nodes' remaining energy and other circumstances. The experimental results show that the ISEGT mechanism can motivate selfish nodes to actively participate in message forwarding, which improves the success rate of message delivery and the survival rate of nodes, and optimizes the overall performance of the network.

In opportunistic networks, it is a challenge to find the best relay node instead of blindly selecting from among available nodes to forward messages and effectively transmit them to their destinations. By comparing the similarity between nodes, the next hop node that is most similar to the destination node is found in time. Existing node similarity-based opportunistic networks routing algorithms only calculate similarity between nodes according to nodes' properties that can be directly obtained from the network, such as the historical meeting record between nodes, etc. However, this superficial similarity calculation is obviously inadequate to describe the inherent dynamic nature of opportunistic networks at both spatial and temporal levels, and ignores the salient movement characteristics of nodes, resulting in poor routing performance. Therefore, this paper proposes an opportunistic network routing strategy based on pre-training and fine-tuning (PTFT) model. Firstly, an autoencoder is added to the graph neural network model to encode node movement behavior. Then, a pre-training graph neural network model in large scale opportunistic network scenarios is applied to learn potential features of nodes through fine-tuning. Finally, we calculate the similarity between nodes based on the node potential feature, thereby assisting the nodes to achieve an efficient routing decision. The simulation results show that our PTFT-based algorithm not only has superior performance compared to traditional routing algorithms, but also faster learning speed than other machine learning-based routing algorithms.

We consider the problem of scheduling audio data transmissions in data-intensive audio sensor networks for animal tracking where the sensor nodes must use WiFi duty cycling to reduce power consumption. WiFi duty cycling entails a startup cost due to probing, authentication, association, and host configuration which are significant and can reach more than 10 seconds. As such, transmission scheduling is not trivial because a naïve approach of switching on WiFi whenever there is a data to send would result in excessive energy consumption overhead. We model duty cycling after an M/G/1 queue with removable server, formulate the optimization problem considering both energy and latency overheads, and obtain the optimal$N^{\ast}$by which the interface should be switched on to schedule data transmission. We propose Optimal Threshold-based Transmission Scheduling (OTTS), a low-complexity algorithm for determining the optimal threshold and commencing transmission. Experiments and trace-based simulations show that OTTS can yield substantial reduction in power consumption that is controllable through an energy-latency trade-off parameter. Compared with interval-based scheduling, OTTS provides lower delay which is more significant at tighter power consumption constraints.

This work proposes the utilization of the quantum approximate optimization algorithm (QAOA) for user pairing in non-orthogonal multiple access (NOMA). By exploiting quantum concepts such as the quantum adiabatic theorem, a user pairing solution was derived that approximates a suboptimal sum rate. Moreover, an improved-QAOA is proposed to further enhance the performance by reducing the number of users in the cluster by dividing it into more clusters. Simulation results have demonstrated that the improved-QAOA yields a higher average achievable sum rate when compared to QAOA, and its solution is closer to the theoretical optimal value obtained by a brute-force enumeration method.

The rapid evolution of mobile networks presents challenges for devices with limited computing power. Mobile or multi-access edge computing (MEC) addresses this by providing computing resources in proximity to end devices. However, MEC servers face constraints in resource sharing, necessitating efficient allocation. We propose the Balanced Offload for Multi-type Tasks (BOMT) algorithm. Tasks are prioritized based on type, size, and maximum tolerable delay. Different offloading algorithms are applied for varying priority tasks, considering current server load. Simulation results demonstrate BOMT’s effectiveness in reducing latency, enhancing user coverage, and improving task completion rates.

Decentralised technology backed by blockchain has gained a huge popularity in recent years as it secures autonomous ecosystems without needing a central authority. The origin of the blockchain concept began in the financial domain using cryptocurrency, but over the last few years, blockchain has been applied to a variety of industries. In the era of Industry 4.0, most industries are leveraging automation by using Internet of Things (IoT). Despite numerous applications of blockchain in the industries, due to the significant latency in consensus algorithm in blockchain, businesses using IoT technology are facing performance issues in adopting blockchain. A number of studies address the obstacles for transaction processing performance and system scalability, mostly based on a public blockchain. However, they still involve centralised components, and thus fail to fully utilise decentralisation. Therefore, a private blockchain-based IoT data integration platform is proposed to achieve data integrity and system scalability. Along with the lightweight IoT gateway instead of any other additional middleware, the process and the system configuration are streamlined. By using Hyperledger Fabric, the design is validated and it is shown that the proposed architecture outperforms other conventional model in IoT data processing.

Wireless sensor network (WSN) has many applications, such as, military scenarios, habitat monitoring and home security. In recent years, with the advancement of energy harvesting (EH) technology, nodes can obtain available energy from the surrounding environment for their own use, thus extending their lifetimes. Under these conditions, research aimed at improving the WSN lifecycle has further shifted towards improving the performance of the network, albeit subject to unique energy harvesting constraints. This paper proposes an energy prediction algorithm for the devices and an Energy and Density Adaptive Clustering (EDAC) protocol to improve network throughput and transmission ratio for EH-powered WSNs. Based on the EH characteristics, we first employed Convolutional Neural Network (CNN) and Bidirectional Long-Short Term Memory (Bi-LSTM) algorithm for energy prediction, then we divide the energy of the sensor nodes into three levels: low, medium, and high energy levels. At high energy levels, nodes can be selected as cluster head nodes, while at low energy levels, nodes must sleep and charge. EDAC first uses the K-Means clustering algorithm to dynamically cluster the surviving nodes in each round and sets a threshold to partition the clustering density. On this basis, a new adaptive cluster head election formula is proposed for cluster head election based on the energy levels of nodes, the predicted energy of the next stage, and the density of clusters. In the stable communication stage of the network, we introduce a "backup cluster head" to temporarily forward the remaining data packets within the cluster when the current cluster head expires. Our simulation results show that our algorithm significantly improves throughput and data transfer rate compared to the traditional and improved clustering protocols.

The fifth generation (5G) mobile network is a new generation of broadband mobile communication technology with the potential to address the increasing demands of new user services and applications that have stringent low latency and high bandwidth requirements. Besides the enhanced mobile broadband (eMBB) which is an evolution of broadband services from previous generations, the ultra reliable low latency communication (URLLC) service comes with stringent delay requirements that are needed to support new applications like autonomous vehicles, augmented/virtual reality, etc. Mobile or multiple access edge computing (MEC) emerged to provide services and computing resources for users at the network edge to provide faster access speeds and lower end-to-end delays. To better meet user needs and maximize resource utilization, network resources need to be allocated and managed efficiently. Admission control for user requests is one of the methods used that can effectively prevent network congestion, thereby improving the overall performance of the system. In this paper, we propose a RED-based Admission Control with Latency Considerations (REDAL) algorithm for user admission control that aims to increase throughput, meet users’ delay requirements and reduce packet discard rate. By explicitly accounting for user traffic delay constraints and bandwidth requirements, we are able to meet the strict delay constraints of URLLC traffic while meeting the bandwidth requirements of eMBB traffic. We validate our approach in an MEC scenario to demonstrate high resource utilization and also keeping the request discard rate below 20%.

Nanotechnology permits the manipulation of materials at nanoscale. Contrarily to traditional ad hoc wireless networks, electromagnetic nanonetworks are massively dense networks of highly-constrained nanodevices, connected by a ultra high-capacity terahertz channel (in the order of Tb/s). Like in traditional networks, congestion and collisions can appear in nanonetworks. However, they have a different meaning, and the parameters influencing them are also different. A parameter specific to nanonetworks is beta ($\beta$), which expresses the ratio of time between two consecutive bits and the bit length, and is sometimes called the symbol rate. No paper in the literature has evaluated this parameter, neither congestion and collisions in a dense network. Therefore, in this paper we study the influence of this parameter together with source packet rate to congestion and collisions in the context of a multi-flow nanonetwork. We conclude that lower source rates and lower $\beta$ values result in less congestion and thus a good packet delivery to the destination.

Electromagnetic nanocommunication is a novel paradigm of communication among nanometer-sized devices in the terahertz band, which enables groundbreaking applications. In these multi-hop ultra-dense networks, the massively dense communications among nanodevices remain a challenge and most traditional routing protocols are inefficient. To reduce the number of forwarders and to scale up the routing protocols, we proposed in a previous work a scheme which selects a small set of efficient forwarders in a ring near the communication range at each hop. However, contrary to expectations, it appears in long multi-hops path, that the number of forwarders progressively increases with the distance from the source. In the current work, we improve this scheme by choosing the ring's shape carefully from a multihop perspective. We combine it with two routing protocols and implement them in a dense nanonetwork simulator. Extensive simulations of our scheme show that, compared to the initial approach, it notably reduces the number of forwarders while providing a guaranteed delivery to the intended destinations.

Low latency is critical to applications such as control of unmanned aerial vehicles. Such latency-sensitive services can be hosted closer to the user at the fog layer which can reduce overall latency through the reduction of transmission time and network congestion. To keep the latency low for mobile users connected to services deployed at the fog, these services need to be constantly migrated to follow the users. Unlike the cloud nodes, fog nodes are less reliable and are therefore subject to higher failure rate. In this paper, we propose an enhancement to the post-copy live migration algorithm to make it robust against failure. Simulation results show that robust migration reduces total migration time between 10-26% and downtime between 2-23% compared to non-robust migration. Furthermore, when the bandwidth to the backup node is lower, robust migration provides further improvement in both metrics.

This paper proposes a novel protocol-agnostic approach to optimize the performance of routing protocols in ultra-dense networks through a careful selection of the forwarders in a multi-hop transmission. In an ultra-dense wireless network, nodes have hundreds of neighbours, and existing routing protocols which require neighbourhood information are unable to operate efficiently. Using our method, each node wanting to transmit first selects forwarders that fall in a ring near the border of the communication range of the transmitting node, which makes up a subset of all the node's neighbours. This significantly reduces the number of nodes contending for the wireless channel yet ensures that there are sufficient forwarders to deliver packets successfully. We validate our approach using two routing schemes, one flooding and one unicast, augmented with our forwarder selection method and applied to an electromagnetic nanonetwork scenario as a novel incarnation of ultra-dense networks. Simulations using an enhanced propagation model show that our forwarder selection method drastically reduces the number of forwarders while still allowing packets to reach the intended destinations.

Network data constantly evolves with new network applications and protocols. There is a need for robust techniques to detect anomalous behaviour. Offline models trained with static data lose validity when new variants of traffic emerge. They require retraining but the need for ground truth and lengthy training times make this task challenging. Meanwhile, online models which detect outliers in streaming data are susceptible to the curse of dimensionality and natural variability. Today’s anomalies may be tomorrow’s new traffic and existing methods do not provide a way to differentiate between them. We propose a framework that makes the most of both approaches: an offline deep learning model extracts features of normal traffic and provides a bias for an online outlier detection model to select data for training. The online model retains its previously learnt knowledge and retrains itself with new data. Online thresholds are updated in a drifting manner and the Mann-Whitney U test is incorporated to prevent inaccurate updates. We perform analysis on the scores, develop heuristics to detect new traffic and evaluate using three deep learning models and four outlier detection methods on the UNSW-NB15 and CTU-13 datasets. The framework improves upon any individual offline or online models in isolation.

The challenge of anomaly detection is to obtain an accurate understanding of expected behaviour which is intensified when the data are distributed heterogeneously. Transmitting raw data to a central site incurs high communication overhead and raises privacy issues. The concept of Edge AI allows computation to be performed at the edge site allowing for quick decision making in mission critical scenarios such as self-driving cars. A model is learnt locally and its parameters are transmitted and aggregated. However, existing methods of aggregation do not account for variance and heterogeneous distribution of data. They also do not consider edge constraints such as limited computational, memory and communication capabilities of edge devices. In this work, a fully Bayesian approach is employed by means of a Bayesian Random Vector Functional Link AutoEncoder being incorporated with Expectation Propagation for distributed training. Our anomaly detection system operates without any transmission of raw data, is robust under inhomogeneous network densities and under uneven and biased data distributions. It allows for asynchronous updates to converge in a few iterations and is a relatively simple neural network addressing edge constraints without compromising on performance as compared to existing more complex models.

The provision of rural broadband infrastructure is a challenge for network operators across the globe, irrespective of their size. Wireless Internet Service Providers (WISPs) have shown that the small scale deployment of wireless broadband infrastructure is a viable alternative to relying on cellular network providers for remote coverage. However, out of concern for excessive energy consumption WISPs must often resort to using off-grid renewable energy sources such as solar energy for powering network sites, often resulting in undesirable, low-performance backhaul radios being used between sites. To encourage the development of high-performance, sustainable WISP networks, we present ElasticWISP, a backhaul optimisation architecture. ElasticWISP dynamically controls the configuration of backhaul radios based on bandwidth demands and the network-wide energy consumption of these radios. Through simulations driven by real WISP topology and data traffic, we show that ElasticWISP can offer energy savings of approximately 65% when following our design methodology.

To meet the rising data-offloading demands, IEEE 802.11-based WiFi networks have undergone consistent densification. The unlicensed spectrum has also been harnessed through LTE-WiFi coexistence. However, in dense and ultradense networks (DNs/UDNs), the network capacity is even more adversely impacted by the endemic interference. Yet, the precise nature of Capacity Interference Relationship (CIR) in DNs/UDNs and LTE-WiFi coexistence remains to be studied. Densification also exacerbates the challenges to network optimization. The conventional approaches to simplify the complex SINR-Capacity constraints lead to high convergence times in DN/UDN optimization. We investigate the CIR in dense and ultra-dense WiFi (IEEE 802. 11a) and LTE-WiFi (LTULAA) networks through real-time experiments. We then subject the empirical data to linear and polynomial regression to determine the nature of CIR and demonstrate that strong linear correlations may exist. We also study the impact of predictor variables, topology, and radio access technology on CIR. Most importantly, we propose CIRNO, a CIR-inspired network optimization approach, wherein the empirically determined CIR equation replaces the theoretically assumed SINR-Capacity constraints in optimization formulations. We evaluate CIRNO by implementing three recent works on optimization. We demonstrate the relevance of CIR and CIRNO in DNs/UDNs through a significant reduction in convergence times (by over 50%) while maintaining high accuracy (over 95%). To the best of our knowledge, this is the first work to statistically analyze CIR in DNs/UDNs and LTE-WiFi heterogeneous networks (HetNets) and to use CIR regression equations in network optimization.

With the advancement in technology, normal network traffic is becoming more heterogeneous. In this scenario, the problem of detecting anomalies is intensified. In the literature, offline methods see more data and can be optimised to achieve lower false positive rates. However, they cannot readily adapt to changing network conditions or capture concept-drift. This necessitates an incremental online learning model. On the other hand, online training is easily affected by noise. In this paper, we propose a hybrid Online Offline system in which the Offline model retains general characteristics of network traffic while the Online model continuously learns. The Offline model acts as a bias for the Online model to select new data to learn from. The Online model retains its knowledge and adapts to the changing ground truth. They are put to work together to detect anomalies. We implement this idea with an Online Support Vector Machine (SVM) which retains its support vectors and shifts its decision boundary guided by an Offline Radius Nearest Neighbor (Rad-NN). The method is evaluated on the NSL-KDD 2009 dataset. This relatively simple model achieves over 95% accuracy on known anomalies and over 60% detection rate on most of the unknown anomalies.

Data Centre Networks (DCNs) handle large volumes of data transmission that can consume a lot of bandwidth in short bursts or over prolonged periods of time. One class of traffic that constantly poses a challenge is Heavy-Hitter (HH) flows - large-volume flows that consume considerably more network resources than other flows combined. The identification of such flows is critical to prevent network congestion and overall network performance degradation. Most of the existing methods to identify HHs are based on thresholds, i.e., if the flow exceeds a predefined threshold, it will be marked as a HH; otherwise, it will be classified as a non-HH. However, these approaches present two significant issues. First, there is no consistent and accepted threshold that would reliably classify flows. Second, the existing threshold approaches use counters (duration, packets, and bytes); thus their accuracy depends on how complete the flow information is. In this paper, we address those issues using per-flow packet size distribution which can capture the behaviour and dynamics of network traffic flow more accurately than the counters in the early stage of the flow. We then propose the use of the template matching technique to identify HHs and achieved a classification accuracy of 96% using only the first 14 packets of a flow.

The integration knowledge of wearable sensors, wireless communications, and artificial intelligence have brought forth the smart health systems, which empower the consumer’s to make a difference to their well-being by connecting data to personalized analysis to timely insights. Therefore, the real-time data obtained directly reflects the personal status of interest and can be used in a variety of healthcare applications in the Internet of Things (IoT), from preventive treatment to diagnostics and rehabilitation, as well as in virtual and augmented reality environments.

Localization is a fundamental function in cooperative control of micro unmanned aerial vehicles (UAVs), but is easily affected by flip ambiguities because of measurement errors and flying motions. This study proposes a localization method that can avoid the occurrence of flip ambiguities in bounded distance measurement errors and constrained flying motions; to demonstrate its efficacy, the method is implemented on bilateration and trilateration. For bilateration, an improved bi-boundary model based on the unit disk graph model is created to compensate for the shortage of distance constraints, and two boundaries are estimated as the communication range constraint. The characteristic of the intersections of the communication range and distance constraints is studied to present a unique localization criterion which can avoid the occurrence of flip ambiguities. Similarly, for trilateration, another unique localization criterion for avoiding flip ambiguities is proposed according to the characteristic of the intersections of three distance constraints. The theoretical proof shows that these proposed criteria are correct. A localization algorithm is constructed based on these two criteria. The algorithm is validated using simulations for different scenarios and parameters, and the proposed method is shown to provide excellent localization performance in terms of average estimated error. Our code can be found at: https://github.com/QingbeiGuo/AFALA.git.

The threat of distributed denial of service (DDoS) attacks has worsened recently with the proliferation of unsecured Internet of Things (IoT) devices. Detecting these attacks is often difficult when using a traditional networking paradigm as network information and control are decentralised. We study the effectiveness of using machine learning (ML) to detect DDoS attacks, facilitated by Software-Defined Networking (SDN), a recent paradigm that aims to improve network management by centralising network information and control. In this study, ML algorithms are implemented on nmeta2, an SDN-based traffic classification architecture, and evaluated on a physical network testbed to demonstrate their efficacy during a DDoS attack scenario, especially in accurately classifying non-malicious traffic. This is unlike most approaches that aim to identify/classify malicious traffic but also misclassify non-malicious traffic, inadvertently leading to degraded performance for legitimate network traffic. Furthermore, there is potentially considerable data loss during DDoS attacks that can further degrade classification performance. We examine these issues that arise when using ML to detect DDoS attacks in live network scenarios.

OpenFlow supports internal buffering of data packets in an SDN switch whereby a fraction of data packet header is sent to the controller instead of an entire data packet. This internal buffering increases the robustness and the utilization of the link between SDN switches and controller by absorbing temporary burst of packets which may overwhelm the controller. Existing queuing models for SDN have focused on the switches that immediately send packets to the controller for decisioning, with no existing models investigating the impact of the internal buffer in an SDN switch and the associated trade-offs of having an internal buffer. In this paper, we propose an analytical model for SDN switch with the internal buffer to investigate the potential benefits, drawbacks and trade-off of internal buffering in SDN switches. It was observed that a switch with internal buffer achieves up to 30% lower average packet transfer delay and 7% lower packet loss rate at the cost of requiring up to 50% more queue capacity than one without the internal buffer. The proposed model is validated with discrete event simulation where the difference between simulation and analytical results was between 0.6% and 2.8% for average packet transfer delay and less than 6% for average packet loss rate. With this investigation, we provide some guidelines to SDN switch designers on the merits, demerits and trade-off of internal buffering in an SDN switch.

The need to add intelligence to the existing power grid in order to operate as a cognitive, self-monitoring and selfhealing system has become imminent. The monolithic energy value chain comes with a lot of constraints such as elongated outage, electricity theft and low equipment optimization. The next-generation grid is a data-centric network with heterogeneous hierarchical interconnected layers. Network devices in the emerging grid need a standard platform for proper integration, monitoring and control. In this paper, a holistic smart grid architectural landscape that clearly separates the power and communication domains to enable “evolving smart grid” engineers provide efficient networking solutions is presented. The communication routes and device topologies for the six smart grid applications are described based on the IEEE Guide for Smart Grid Interoperability and National Institute of Standards and Technology frameworks. Also, the deployments of intelligent electronic devices for microgrid control, monitoring and islanding operations are highlighted.

Electromagnetic-based wireless nanosensor networks (EM-WNSNs) working in the terahertz (THz) band are developing fast because of the high sensing resolution that benefits a wide range of applications. In EM-WNSNs, nano-sinks that aggregate data from nanosensors to the Internet of Things (IoT) gateway collectively form a backhaul tier of EM-WNSNs. Unlike the data tier composed of densely deployed nanosensors that are closely connected, the backhaul tier consisting of less dense nano-sinks is highly vulnerable to the dynamic channel states of THz band that is sensitive to the surrounding humidity change. Considering the limited energy storage of nano-devices, an energy-efficient forwarding scheme that adapts to dynamic channel states is needed to ensure both connectivity and efficiency of data transfers over the backhaul. Current channel-aware approaches are based on complex hardware support that cannot be deployed on nano-devices due to the size constraints. To solve this problem, the lightweight time-to-live (TTL)-based efficient forwarding (TEForward) has been proposed for the backhaul tier of multihop polling-based EM-WNSNs. In TEForward, nano-sinks select forwarders and direct packets based on the duplicate counts and TTL values of polling beacons which reflect the latest network topology. In this paper, we develop a TEForward scheme for EM-WNSNs with multiple coordinated IoT gateways. This TEForward scheme has low complexity and resource demand and achieves high energy efficiency that is 1.4% below the optimum.

Wireless sensor networks for rarely occurring critical events must maintain sensing coverage and low-latency network connectivity to ensure event detection and subsequent rapid propagation of notification messages. Few algorithms have been proposed that address both coverage and forwarding and those that do are either unconcerned with rapid propagation or are not optimised to handle the constant changes in topology observed in duty-cycled networks. This article proposes an algorithm for Coverage Preservation with Rapid Forwarding (CPRF). The algorithm is shown to deliver perfect coverage maintenance and low-latency guaranteed message propagation whilst allowing stored-charge conservation via collaborative duty cycling in energy-harvesting networks. Favourable comparisons are made against established and recently proposed algorithms in both sparse planned and dense random distributions. Further, an implementation for commercially available wireless sensing devices is evaluated for detection and notification of damage to highway light poles caused by vortex shedding.

Wireless mesh networks (WMNs) cannot utilize the full capability of these radio technologies without an effective channel assignment algorithm. Existing metrics for characterising the efficacy of channel assignment algorithms poorly reflect the goodput distribution in WMNs. In this paper, we develop a fitness evaluation algorithm that feeds back several indicators of efficacy to a channel assignment algorithm and evaluate the resulting goodput distribution. To the best of our knowledge, our algorithm is the first to evaluate the fitness of channel assignment algorithms with goodput distribution, average goodput, and fairness index. Simulation results show that our evaluation algorithm provides accurate prediction of goodput distribution, average goodput, and fairness index with errors below 8%.

Electromagnetic-based Wireless Nano Sensor Networks (EM-WNSNs) working in the TeraHertz (THz) band are developing fast because of the high sensing resolution that benefits a wide range of applications. In EM-WNSNs, nano sinks that aggregate data from nano sensors to the Internet of Things (IoT) gateway collectively form a backhaul tier of EM-WNSNs. Unlike the data tier composed of densely deployed nano sensors that are closely connected, the backhaul tier consisting of less dense nano sinks is highly vulnerable to the dynamic channel states of THz band that is sensitive to the surrounding humidity change. Thus, a forwarding scheme that adapts to dynamic channel states is needed to ensure both connectivity and efficiency of data transfers over the backhaul. Current channel-aware approaches are based on complex hardware support that cannot be deployed on nano devices due to the size constraints. To solve this problem, the Time-To-Live (TTL)-based Efficient Forwarding (TEFoward) with low complexity is proposed for the backhaul tier of multi-hop polling-based EM-WNSNs. In TEFoward, the functions carried by polling beacons are diversified besides data extraction. Nano sinks select forwarders and direct packets based on the duplicate counts and TTL values of polling beacons which reflect the latest topology information. Demanding lightweight resource support, TEFoward achieves high end-to-end data delivery ratio and energy efficiency.

The performance of GPUs on mobile devices is generally the bottleneck of multimedia mobile applications (e.g., 3D games and virtual reality). Previous attempts to tackle the issue mainly migrate GPU computation to servers residing in remote cloud centers. However, the costly network delay is especially undesirable for highly-interactive multimedia applications since a fast response time is critical for user experience. In this paper, we propose GBooster, a system that accelerates multimedia mobile applications by transparently offloading GPU tasks onto neighboring multimedia devices such as Smart TVs and Gaming Consoles. Specifically, GBooster intercepts and redirects system graphics calls by utilizing the Dynamic Linker Hooking technique, which requires no modification of the applications and the mobile systems. In addition, a major concern for offloading is the high energy consumption incurred by network transmissions. To address this concern, GBooster is designed to intelligently switch between the low-power Bluetooth and the high-throughput WiFi based on the traffic demand. We implement GBooster on the Android system and evaluate its performance. The results demonstrate that it can boost applications' frame rates by up to 85%. In terms of power consumption, GBooster can preserve up to 70% energy compared with local execution.

Network devices rely on forwarding rules to forward packets. With the latest OpenFlow standard (the de-facto software defined network standard), the rules that make up a forwarding set may be expressed in the form of a single table or multiple linked tables. This raises the question of how to reconcile the forwarding behaviour of different flow tables? In this paper we propose two approaches to convert various forwarding sets into a canonical form called equivalent forwarding set and formalise the process of evaluating equivalence between two forwarding sets in terms of networking function.

Event detection has become a ubiquitous application in the domain of wireless sensor networks. In any distributed event detection system, duplicate event data that can increase the likelihood of network congestion is a primary concern, and in-network data aggregation is a popular approach to alleviate this problem; similarly, duplicate data can be discarded. We propose a novel approach that utilises localisation techniques to quickly identify and discard duplicates among event reports. Using just the locations of nodes reporting events, the proposed classifier is able to make a decision on which event reports are to be discarded as duplicates. We show that our approach has the potential to greatly reduce packet load in a network and thus save energy and increase sensor life.

Software Defined Networking (SDN) is a new network architecture that separates control plane and data plane. SDN switch models in the literature primarily use queueing theory in two distinct categories: (i) single shared buffer for control plane traffic and data plane traffic, and (ii) buffer with two priorities to isolate control plane and data plane traffic. Several research works have independently studied these two buffer sharing mechanism but do not address the tradeoffs between these two buffer sharing mechanisms. The evaluation shows that shared buffer requires approximately up to 85% higher delays to install the flow table entries in the switch and up to 82% extra buffer capacity compared with the priority queueing buffer.

Software Defined Networking (SDN) enables a centralised entity - the controller - to monitor the network's status by collecting traffic statistics such as packets, bytes, etc. Each statistic is associated with a forwarding table entry (FTE) in a switch whose structure and format is specified by the OpenFlow standard (de-facto SDN standard). For a flow with a FTE, its statistic is easily acquired by an inquiry from a controller to the switch on this flow's corresponding FTE. If a flow has no matching FTE, its statistic is not known until a new FTE is installed for the purpose of monitoring it. However, the time to install these FTEs and the potential conflicts between them and the existing FTEs jeopardise the feasibility of this approach. To avoid these drawbacks, this paper proposes a traffic estimation approach based on the existing FTE's statistics. With the help of boolean algebra, the deterministic confidence interval of any given flowset can be estimated. This approach avoids the FTE installation time and also saves the FTE storage space.

Electromagnetic Wireless Nano Sensor Networks (EM-WNSNs) with molecule-level sensing capacity benefit a wide range of applications that demand high resolution. To accommodate the potentially high data volume, small antenna size, and limited energy capacity, the high-speed TeraHertz (THz) pulses sparsely distributed over time are adopted for communications among nano devices. For EM-WNSNs connected to the Internet of Things (IoT), matching the throughput of nanonetworks and the IoT access bandwidth is significant for resource utilization efficiency. On the one hand, high throughput from nano sensors are limited by IoT access bandwidth, which results in low energy efficiency of EM-WNSNs when traffic regulation mechanisms are applied. On the other hand, low throughput from nanonetworks under utilize the allocated access bandwidth. To enable efficient data forwarding towards the access network, the EM-WNSNs throughput must match the IoT access bandwidth. For this purpose, the Adaptive Pulse Interval Scheduling (APIS) scheme, which schedules the arrival pattern of pulses transmitted by nano sinks based on the access bandwidth, is proposed for event-based EM-WNSNs. From performance evaluation and modelling, APIS implements data forwarding with high energy efficiency for nanonetworks and high bandwidth efficiency for IoT access networks. To the best of our knowledge, APIS is the first pulse level access scheduling solution for EM-WNSNs connected to the IoT.

The Internet of Things (IoT) is a fundamental change to the nature of the Internet whereby a massive and diverse range of objects will become network addressable. This may be the networking of previously discrete devices but also the embedding of computers into devices or components that did not previously have it. Not all connected devices have the same access needs nor rights, and network access control must be able to address the diversity effectively. Using a Software Defined Networking (SDN) approach with captive portals and port based network access (IEEE 802.1X) this paper demonstrates the first network access control (NAC) using SDN through judicious use of multiple flow tables in the widely used OpenFlow v1.3 protocol. We show that the 802.1X approach requires up to 72% fewer packets to be exchanged compared to the captive portal approach and up to 80% reduction in terms of authentication delay. Our results also show that access control via DHCP, DNS and HTTP services experience similar authentication delays because the dominant delay is due to the OpenFlow control channel delay. The work presented herein makes significant progress towards empowering network administrators with fine grained control over WiFi networks.

To evaluate a node's cooperativeness/selfishness behaviour in a heterogeneous wireless environment, global reports from other nodes in wireless multihop networks (WMNs) may be required to strengthen the judgment made using local observation. However, it cannot be assumed that a node is always honest in sharing the behaviour information. A group of nodes might collude to falsely accuse/praise a particular node to gain communication benefits at the cost of other nodes' resources. This paper proposes a filtering mechanism named Trust Features-based Evidence (TFE) by formulating evidence that is based on trust features to reduce the number of liar nodes and thus, ensuring the authenticity of the shared information. Assisted by an efficient cross-checking algorithm, the TFE mechanism is able to reduce the number of cheating nodes over time and the rate of illegitimate collusion, by having detected cheaters aware that the only way to survive in the network is by presenting genuine information.

Driven by the high sensing resolution provided by advanced nano technologies, electromagnetic-based wireless nano sensor networks (EM-WNSNs) operating in the terahertz (THz) band is becoming an integral part of the Internet of Things (IoT). Data acquisition for EM-WNSNs faces two challenges: 1) the dynamic IoT backhaul bandwidth and 2) THz channel conditions that jointly impact resource utilization efficiency. Specifically, the mismatch between the demands of the EM-WNSNs and the available bandwidth of the backhaul link reduces the bandwidth efficiency of IoT backhaul or the energy efficiency of EM-WNSNs. To address the new constraints that emerge from the EM-WNSN, an on-demand probabilistic (OP) polling scheme is proposed for the backhaul tier of EM-WNSNs which is composed of nano sinks that aggregate and transport data from nano sensors to the IoT gateway. OP polling adjusts the packet aggregation process of nano sinks according to the up-to-date network conditions. From performance evaluation, OP polling achieves an 18% higher bandwidth efficiency compared to the previously proposed on-demand efficient polling and 69% less energy consumption compared to the greedy polling.

Smart devices are increasingly shrinking in size, which results in new challenges for user-mobile interaction through minuscule touchscreens. Existing works to explore alternative interaction technologies mainly rely on external devices which degrade portability. In this paper, we propose UbiTouch, a novel system that extends smartphones with virtual touchpads on desktops using built-in smartphone sensors. It senses a user's finger movement with a proximity and ambient light sensor whose raw sensory data from underlying hardware are strongly dependent on the finger's locations. UbiTouch maps the raw data into the finger's positions by utilizing Curvilinear Component Analysis and improve tracking accuracy via a particle filter. We have evaluate our system in three scenarios with different lighting conditions by five users. The results show that UbiTouch achieves centimetre-level localization accuracy and poses no significant impact on the battery life. We envisage that UbiTouch could support applications such as text-writing and drawing.

In a large network, it is often important to be able to detect high-volume traffic in near real-time. Existing work on the detection and identification of such high volume traffic (so-called heavy hitters) is typically delegated to individual nodes and often relies on deep packet inspection and/or packet sampling. However, these techniques have well known limitations in terms of its ability to scale with network size. Inspired by the capabilities of Software Defined Networking (SDN), we explore a novel heavy hitter detection solution based on understanding connections between traffic statistics and OpenFlow rules. Our approach relies on mining traffic statistics (e.g. port bitrate) and forwarding table entry (FTE) to improve heavy hitter detection. The rationale behind this approach are (i) the information is readily available with minimal overheads, thus it scales better with increasing network size; and (ii) the FTEs and traffic statistics provide different vantage for detection and identification of heavy hitters. We evaluate the effectiveness and accuracy of our proposed heavy hitter detection algorithm on a test bed as a proof-of-concept. The test results show that our approach to heavy hitter detection simultaneously achieves considerable accuracy and good scalability.

Driven by the high sensing resolution provided by advanced nano technologies, Electromagnetic-based Wireless Nano Sensor Networks (EM-WNSNs) operating in the terahertz (THz) band is becoming an integral part of the Internet of Things (IoT). Data acquisition for EM-WNSNs face two challenges: the dynamic IoT backhaul bandwidth and THz channel conditions that jointly impact resource utilization efficiency. Specifically, the mismatch between the demands of the EM-WNSNs and the available bandwidth of the backhaul link reduces the bandwidth efficiency of IoT backhaul or the energy efficiency of EM-WNSNs. To address the new constraints that emerge from the EM-WNSN, an On-demand Efficient (OE) polling is proposed for the backhaul tier of EM-WNSNs which is composed of nano sinks that aggregate and transport data from nano sensors to the IoT gateway. The OE polling adjusts the packet aggregation process of nano sinks according to the up-to-date network conditions and we show that it achieves both high bandwidth efficiency for IoT backhaul and high energy efficiency for EM-WNSNs. To the best of our knowledge, OE polling is the first data acquisition scheme that connects EM-WNSNs with IoT by taking into consideration the dynamic IoT backhaul bandwidth and THz channel conditions.

Wireless sensor networks for rarely occurring critical events must maintain sensing coverage and low latency network connectivity to ensure event detection and subsequent rapid propagation of notification messages. Existing geographic forwarding algorithms have proved successful in providing energy efficient network connectivity for arbitrary topologies where sensing coverage is not guaranteed. This paper proposes a location aware algorithm for Swift Opportunistic Forwarding of Infrequent Events (SOFIE) that takes advantage of geometric properties common to sensing networks providing perfect area coverage. The algorithm is shown to deliver more rapid message propagation than two established, general purpose geographic forwarding algorithm in optimally and randomly placed networks of varied sensing node density. Further, the algorithm is shown to maintain this advantage when deployed in a coverage preserving, duty-cycled sensing network where nodes may power down whilst the network is actively forwarding event notification messages.

Cooperation in wireless multihop networks remains a significant research problem to be addressed as a node's selfishness can cause communication failure. Incentive mechanisms in the form of reputation or credit rewards have been proposed to stimulate nodes to cooperate. Complementing each other, a hybrid of the two types of schemes has been proposed to improve efficiency in mitigating selfishness, at the cost of high communication overhead. This paper proposes an alternative to reputation level incentive named Recharge-As-Reward (RAR) mechanism, as an explicit reward to strengthen motivation for nodes to cooperate. RAR is also combined with a Credit-As-Reward (CAR) mechanism that is based on centralized credit-based architecture, which makes our scheme a hybrid incentive mechanism. Through a comparison with another hybrid scheme, we show that RAR/CAR is able to increase nodes' forwarding rates while reducing message/communication overhead by being less reliant on a central agent.

Software Defined Networking (SDN) is the latest network architecture that does for networking what virtualisation did for servers in data centres. In SDN, separation of the control plane from the data plane brought about new flexibility in the routing of flows through the network. Closely associated with SDN is OpenFlow, the most widely used protocol governing the information exchange between the data plane (switching devices) and the control plane (controller). The ease of implementing and testing new schemes in SDN has prompted many researchers to adopt the experimental and prototyping approach to validate their ideas. Consequently, there has been very little work done to evaluate the performance of SDN and/or OpenFlow-based networks analytically. While the experimentation approach in validation has merits, analytical modelling provides valuable insights by making explicit the dependence of SDN performance on chosen parameters. In this paper: (i) we propose a queueing model of an OpenFlow-based SDN that takes into account classful treatment of packets arriving at a switch and (ii) derive an exact analysis of the proposed queueing model.

In the Internet of Things(IoT), the size constraint of those small and embedded devices limits the network lifetime because limited energy can be stored on these devices. In recent years, energy harvesting technology has attracted increasing attention, due to its ability to extend the network lifetime significantly. However, the performance of IoT devices powered by energy harvesting sources has not been fully analyzed and understood. In this paper, we model the energy harvesting process in IoT devices using slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA /CA) mechanism of IEEE 802.15.4 standard, and analyze the performance in terms of delay and throughput. Our new model successfully integrates the energy harvesting process and binary backoff process through a unified Markov chain model. Finally, the new model is validated by simulation and the throughput errors between simulation and analytical model are no more than 6%. We demonstrate the application of the model with different energy harvesting rate corresponding to different sources such as solar and vibration energy harvesters.

Software-Defined Networking (SDN) is an emerging networking technology that has attracted intense interest from both the industry and research communities. Thus far, it is primarily applied to datacenters and research network environments. Despite meticulous effort in planning and equipment selection prior to deployment, there remain unknowns that can affect the network's performance after equipment has been deployed and is fully operational. Network administrators and planners would benefit from a tool that is able to monitor the load on various network entities and visualize this in real-time and, even better, predict likely performance changes arising from traffic variation; this allows them to make prompt decisions to prevent seemingly small hotspots from becoming major bottlenecks. In this paper, we present a network visualization and performance prediction tool that enables network planners to examine how their networks' performance will be affected as the traffic loads and network utilization changes. This is a first of its kind where performance prediction is based on queueing analytic models of the network configuration coupled with real-time measurements taken from the network devices.

A handover in wireless networks is the mechanism to maintain quality of service (QoS) by transferring an ongoing call or data connection from one point of access to another. IEEE802.11-based wireless local area networks (WLANs) do not explicitly support handovers, but permit a changeover if two access points (APs) have identical identifiers. This changeover is triggered by the mobility of a mobile host or an AP's inability to support hosts in its vicinity, and such changeovers are not seamless. The changeover is initiated by the mobile host which results in disruptions to ongoing connections. In this paper, we develop a changeover prediction model as an evidence-based tool to help minimise such disruptions. We determined the best set of predictors (i.e. a model) for changeover prediction using variable selection and multiple regression. The results show that our approach yields a consistent, reproducible changeover prediction model across different locations around a campus area WLAN. This finding can provide significant insights to the design and development of future handover algorithms built on top of IEEE 802.11 WLAN.

It is well established that an effective pedagogy for project management requires students to get real-world experience. The challenge in providing this when teaching undergraduate engineers is the dichotomy between achieving realism and maintaining sufficient simplicity to make the course tractable. A real-world group technology project at Victoria University of Wellington (VUW) in New Zealand establishes essential non-technical attributes required by the engineering profession while covering key elements of the project management body of knowledge (PMBOK). This paper first shows how the project covers the knowledge required in project management and then presents the results of two years of data collected from students' reflection on their own learning. We have established a pleasing congruence across the years against the specific learning topics of team working skills, communication skills and personal working skills with an improvement in project management skills. A key finding emerging from our analysis is the importance of reinforcement learning and reflective learning. We show a key link between these two learning mechanisms and the project pedagogy. Further analysis shows the link between the project pedagogy and four skill areas acquired. Finally, our research has identified specific areas for us to focus on for subsequent years.

Current one-to-one wireless communications is reaching the Shannon limit. Previous works have studied space multiplexing schemes, e.g., superposition coding and successive interference cancellation, to overcome the limitation. In this paper, we focus on a network multiple-input multiple- output (MIMO) architecture, which is one such space multiplexing scheme, where multiple access points in the network MIMO system cooperate with each other to improve wireless communications capacity. However, channel sounding overhead for estimating the channel state information of every path between multiple access points and multiple clients is a significant problem. Likewise, the computational overhead for deciding which paths to use for the network MIMO transmission is also high. We propose Throughput-Aware Random Clustering (TARC) of access points to reduce the network MIMO overhead. TARC takes a cross-layer approach for choosing access points to participate in network MIMO transmission on the physical layer based on throughput in the data link layer. From our validation using simulations, we show that the proposed method is able to achieve approximately 2.5 times higher throughput than using all access points in the network and 1.4 times better throughput than adopting static cluster sizes.

Wireless mesh networks (WMNs) deliver network services including Internet connectivity, emergency response communications, and cellular traffic offloading. Network planning plays an increasingly important role in ensuring quality of service for such heterogeneous services. Simplicity, usability and accuracy of a model are three elements common to a successful model for network planning. Whilst research has been undertaken to model the IEEE 802.11 MAC protocol with good accuracy, these models take a packet centric view thus increasing the model complexity and restricting their usefulness in network planning. We present a novel goodput distribution model for IEEE 802.11 WMNs requiring only topology information and derived specifically to be beneficial to network planning. Analysis shows the model provides accurate estimation of goodput distributions and correctly identifies starving links in a variety of topologies.

Wireless sensor networks for rarely occurring geospatial events must minimally maintain constant sensing coverage to ensure detection wherever and whenever such events occur. This paper proposes an Equitable Sleep Coverage Algorithm for Rare Geospatial Occurrences (ESCARGO) for timely event notification where the network must also maintain connectivity at all times regardless of the significant energy cost of doing so. This non time-synchronized, fully distributed duty-cycling algorithm is shown to significantly extend the operational lifetime of battery powered rare event sensing networks with an optimised, low node density, non-random deployment scheme without compromising detection probability or notification delay. The algorithm is further shown to facilitate potentially indefinite coverage maintenance in sensor networks powered by small form factor solar panels without relying on charging efficiencies beyond the capabilities of existing technology.

Software Defined Networking (SDN) is an innovative approach to networking architecture that opens up avenues to create a whole new class of networking functionality. While data centre networks are steadily adopting the SDN approach with considerable success, other areas of networking such as network access control, load balancing and traffic classification remain nascent. Traffic classification in SDN is relatively experimental and attempts for SDN traffic classification to become a viable solution for enterprise networks require additional investigation. This paper reports on the practical experiences and lessons learned while developing an SDN based traffic classification platform for an enterprise network. We use the platform to demonstrate the feasibility of SDN based traffic classifiers by evaluating against a set of desired outcomes. We make note of the design choices using the currently available technologies that may be helpful to networks operators considering deploying their own solution. We conclude the paper with suggested changes to better address limitations for software traffic classification that will remove the need for workarounds with future versions of OpenFlow.

Wireless sensor networks (WSNs) are designed for sensing phenomenon and acquiring data. In structural health monitoring (SHM) of critical infrastructure, increasingly large number of sensor nodes are deployed to acquire data at the spatial density needed for structural integrity assessment. After rare catastrophic events such as earthquakes, a large volume of data related to the event can be produced in an instant and need to be sent (to remote locations) for analysis. When many nodes are trying to transmit their data simultaneously, the contention for the wireless channel increases the probability of packet collisions resulting in packet drops, multiple retransmission attempts and consequently delays; it is also not uncommon to find certain nodes (e.g. closer to the sink) having better chances of successful transmission leading to biased data delivery. While clustering has been extensively used to reduce contention in wireless networks, researchers have not given adequate attention to cluster-level performance, preferring to focus on node-level performance. This paper presents a new perspective on cluster-based WSNs and proposes a cluster centric design that aims to tackle medium access control (MAC) layer congestion associated with burst packet generation in an unbiased manner, making it suitable for applications like SHM.

Ambient energy from solar, vibration, heat and wind provide alternative energy sources to power sensors and extend the lifetime of wireless sensor networks which have traditionally been powered by batteries. This paper aims to enhance the performance of energy harvesting powered wireless sensor networks in three aspects: relaying, scheduling, and medium access control. To better adapt to the characteristics of energy harvesting, an asynchronous receiver-initiated duty-cycling approach is preferred in energy harvesting powered wireless sensor networks. This reduces the duty cycle of senders, and regulates the active and sleep intervals according to the energy levels of sensors. When nodes run out of power and need time to recharge, network holes or voids develop, forcing data packets to be routed via other paths, like detours. The proposed relaying strategy aims to prevent holes by balancing the load across the network according to nodes' energy harvesting characteristics. This is a natural consequence of the asynchronous duty cycling by scheduling transmission based on the receiver's availability. The simulation results show that our scheme outperforms in terms of sender duty cycle, end-to-end delay and delivery ratio, especially in challenged conditions where other protocols fail.

Rare catastrophic events, like earthquakes, can cause substantial damage in a short span of time. Data on the level of stress sustained by buildings and other critical infrastructure acquired during the event can significantly help in post-disaster recovery and assessment of buildings' structural integrity. While installing sensors to acquire such data is not difficult, ensuring that there is power to drive the sensors at the critical moment of the event is a challenge. In this paper, we propose an event-driven energy-harvesting (EDEH) wireless sensor network (WSN) in which the sensors are powered by the energy harvested from the consequence of the event, e.g. buildings shaking during an earthquake. The scarce amount of energy harvested during the short event occurrence time poses great challenges for the medium access control (MAC) design, which is the focus of our research. Furthermore, when all sensors harvest energy from the event, they become active simultaneously leading to serious channel contention problems. As such, we first examine the amount of harvestable energy and then show analytically that our MAC protocol is able to provide higher packet delivery ratio than conventional wireless technology, e.g. IEEE802.15.4.

In wireless multihop networks, cooperation is of utmost importance to ensure the success of communication. However, due to limited resources especially energy, nodes may be compelled to adopt selfish behaviour by not forwarding packets for other nodes. Selfishness is a very subjective element to be measured because it is hard to determine whether or not a particular node's behaviour is intentional or a consequence of the environment. Most, if not all, published work assumed that this behaviour can be assessed but do not explicitly describe how selfishness is measured or quantified. In this paper, we propose a method to quantify a node's behaviour in forwarding packets for other nodes from the perspective of a single observer node (i.e. first-hand observation) and provide quantifiable metrics to represent the node's actual effort. We show that by using the proposed method, we are able to classify several types of selfishness and fairness behaviour.

Network coding is a technique for maximizing the use of available bandwidth capacity. This is achieved by having nodes not just forwarding packets but combining several incoming packets into a single outgoing packet for transmission. Unfortunately, network coding is vulnerable to pollution attacks where a single malicious node can disrupt the operation of the complete network. Several protocols to detect pollution attacks have been proposed in the literature. In this paper we describe a new pollution attack detection protocol that extends the existing SpaceMac protocol. This paper describes how we have modeled the protocol in order to carry out a security analysis and presents the results of that analysis.

In this paper, we consider a two-way relay network (TWRN) consisting of two sources and N amplify-and-forward (AF) relays (N ≥ 1), among which the best relay is chosen to assist the data communication. In a time-varying fading channel, the relay selection may be based on the outdated channel state information (CSI). We study the impact of outdated relay selection on the system performance by deriving a tight lower bound for the outage probability in Rayleigh block-fading channels. We further provide an asymptotic analysis in high signal-to-noise ratio (SNR) region. From the asymptotic results, we can find that the system diversity order degenerates into unity as long as the CSI is outdated. Numerical results demonstrate the tightness of the performance bounds as well as the effects of outdated relay selection on the system performance. Simulation results are also provided to corroborate the theoretical analysis.

Wireless Sensor Networks (WSNs) have traditionally been powered by portable energy sources, viz. batteries, which limit their operational lifetime. To ensure WSN sustainability, researchers have turned to alternative energy sources for power. Harvesting ambient energy to power WSNs is a promising approach but energy harvesting devices of the same footprint as wireless sensors are unable to provide sufficient energy for sustained operation; availability of energy is also highly unpredictable. Harvesting energy from radio waves can provide energy-on-demand using a radio transmitter to broadcast over the sensor network when the sensors need to be powered up to operate. However, only minute amounts of energy can be extracted from radio waves, presenting a monumental constraint in terms of the duty cycle and accomplishable tasks. We present our design of a data delivery scheme which aims to optimize the usage of the minute amounts of harvested energy through a sink synchronized protocol. The design has been implemented and experimentally validated utilizing commercially available RF energy harvesting devices.

This paper presents a performance study on the effects of noise and evaporation in two variants of Particle Swarm Optimization (PSO) on large-scale optimization problems. The variants in consideration are the Synchronous PSO (S-PSO) and the Random Asynchronous PSO (RA-PSO), both of which are evaluated upon the set of benchmark functions presented at the IEEE CEC'2010 Special Session and Competition on Large-Scale Global Optimization. Results show an important detriment to the performance of both variants in the presence of different levels of noise. However, such detriment is significantly mitigated by incorporating an evaporation mechanism into particles to deal with such disruptive effects. Moreover, results show that RA-PSO is significantly better than S-PSO, more tolerant to noise, and better suited for the evaporation mechanism.

This work provides a further study on the difference between synchronous and asynchronous updates in Particle Swarm Optimization with different neighborhood sizes ranging from local best to global best. Ten well-known functions are used as benchmarks on both variants. Statistical tests performed on the results provide strong evidence to claim that synchronous updates yield in general better results with similar or even faster speed of convergence than its asynchronous counterpart, contrary to observations and conclusions of previous studies based solely on descriptive statistics.

Energy harvesting has been studied as a candidate for powering next generation wireless sensor networks. The technologies that can harvest electric power from ambient energy sources include solar, vibration, heat and wind. However, sensor nodes powered by energy harvesting devices cannot always communicate with other nodes because the energy harvesting devices cannot provide a stable supply power. A node cannot know whether its neighboring nodes have enough energy to receive a data packet that it has transmitted. During the process of relaying the packet, each additional hop increases the overall probability of losing the packet. In this paper, we propose two data collection protocols for the energy harvesting wireless sensor networks called Probabilistic ReTransmission protocol (PRT) and PRT with Collision Consideration (PRT-CC). The idea is to derive the number of times to retransmit a packet based on the reception probability and the active intervals computed by the receivers themselves. In PRT-CC, each node computes the reception probability with packet collision consideration. The simulation results show that the proposed protocols achieve higher delivery ratio than the previous works (GR-DD and GR DD-RT).

The Internet of Things (IoT) is a new concept that refers to an Internet connecting not just computer systems but a plethora of systems, devices, and objects, collectively referred to as “Things”, and encompasses technologies for identification and tracking, sensing and actuation, both wired and wireless communications, and also, intelligence and cognition. Wireless communications, which is an integral part of IoT, suffers from radio irregularity - a phenomenon referring to radio waves being selectively absorbed, reflected or scattered by objects in their paths, e.g., human bodies that comprises liquid, bone and flesh. Radio irregularity is often regarded as a problem in wireless communications but, with the envisioned pervasiveness of IoT, we aim to exploit radio irregularity as a means to detect people. We demonstrate how radio signal fluctuations arising from radio irregularity can be used to provide a low-cost alternative to dedicated sensing systems for indoor automated people counting.

Wireless Sensor Networks Powered by Ambient Energy Harvesting (WSN-HEAP) can perform the task of continuous and remote monitoring of the environment without the need for replacement of batteries. We identify three important design considerations for wireless networking protocols in WSN-HEAP: the unpredictable energy supply, the propagation losses in different environments and the suitable power level to use. In this paper, we perform an empirical characterization of commercially available solar and thermal energy harvesting sensor nodes. We deploy a transmitter-receiver pair at different distances and in various environments to conduct link measurements to determine the packet delivery ratio and RSSI values. We also quantified the energy harvesting characteristics of the sensor node. Then, we analyze the collected data to provide insights and guidelines for designing networking protocols for WSN-HEAP. Our analysis shows that the transmission range of the node is highly dependent on the environment in which it is deployed in and the RSSI values can only be used to estimate the transmitter-receiver distance in some environments. Furthermore, the charging time exhibits large variances even in the absence of mobility.

Underwater localization is challenging as its efficacy is affected by propagation delays, motion-induced doppler shift, phase and amplitude fluctuations, multipath interference etc that are inherent in underwater acoustic channels. In this paper, we consider a recently proposed Underwater Positioning Scheme, which offers unique localization only in a finite region. We quantify the conditions for unique localization and propose a variant that offers unique localization with high probability regardless of the reference and unknown node deployment. We demonstrate the trade-offs between both schemes in terms of localizability space, localization latency and energy consumption.

Wireless multihop ad hoc networks face a multitude of challenging problems including highly dynamic multihop topologies, lossy and noisy communication channels, and sporadic connectivity which contribute to frequent link failures. Rapid and accurate link failure detection is therefore important to maintain correct and optimum operation of network routing protocols. In this paper, we propose a unified link failure detection and recovery architecture (ulfra) which uses link layer feedback for rapid failure detection and packet salvaging for packet recovery. While link layer feedback and packet salvaging have been studied in simulations and simple experiments, no thorough experimental study have been undertaken to evaluate their real-world performance. This paper essentially fills this void as we implement and evaluate ulfra in an IEEE 802.11 multihop ad hoc network. Our experimental results show that link layer feedback, as modeled in current network simulators, actually performs worse than hello beaconing as it generates excessive false failure detections. To improve its performance, we implement a veto mechanism to reduce spurious detections. Experimental results show that the veto mechanism dramatically improves the performance of link layer feedback in terms of packet delivery, delay, and routing overhead as it considerably reduces the number of false detections. Compared with hello, it delivers 15–20% more packets at high node failure and 12–20% more at high network traffic.

Traditional networks are built on the assumption that network entities cooperate based on a mandatory network communication semantic to achieve desirable qualities such as efficiency and scalability. Over the years, this assumption has been eroded by the emergence of users that alter network behavior in a way to benefit themselves at the expense of others. At one extreme, a malicious user/node may eavesdrop on sensitive data or deliberately inject packets into the network to disrupt network operations. The solution to this generally lies in encryption and authentication. In contrast, a rational node acts only to achieve an outcome that he desires most. In such a case, cooperation is still achievable if the outcome is to the best interest of the node. The node misbehavior problem would be more pronounced in multihop wireless networks like mobile ad hoc and sensor networks, which are typically made up of wireless battery-powered devices that must cooperate to forward packets for one another. However, cooperation may be hard to maintain as it consumes scarce resources such as bandwidth, computational power, and battery power. This paper applies game theory to achieve collusive networking behavior in such network environments. In this paper, pricing, promiscuous listening, and mass punishments are avoided altogether. Our model builds on recent work in the field of Economics on the theory of imperfect private monitoring for the dynamic Bertrand oligopoly, and adapts it to the wireless multihop network. The model derives conditions for collusive packet forwarding, truthful routing broadcasts, and packet acknowledgments under a lossy wireless multihop environment, thus capturing many important characteristics of the network layer and link layer in one integrated analysis that has not been achieved previously. We also provide a proof of the viability of the model under a theoretical wireless environment. Finally, we show how the model can be applied to design a generic protocol which we call the Selfishness Resilient Resource Reservation protocol, and validate the effectiveness of this protocol in ensuring cooperation using simulations.

Wireless sensor networks (WSNs) are known to be highly energy-constrained and each networkpsilas lifetime has a strong dependence on the nodespsila battery capacity. As such, the network lifetime has been a critical concern in WSN research. While numerous energy-efficient protocols have been proposed to prolong the network lifetime, various definitions of network lifetime have also been used for the different scenarios and protocols. The lifetime of a sensor network is most commonly defined as the time to the first sensor node failure - seemingly over-pessimistic in many envisaged deployment scenarios. While other definitions exist, there has not been any consensus on which quantitative lifetime definition is most useful. In this paper, we aim to provide as objectively as possible, a comparative study of WSN protocols based on various network lifetime definitions. We also discuss the implications of these metrics and their applicability in evaluating the effectiveness of WSN data delivery schemes.

Motion and intrusion detection are often cited among various Wireless Sensor Network (WSN) applications. A typical configuration comprises clusters of wireless nodes equipped with motion sensors to detect human motion. Currently, the performance of WSN is subject to several constraints, mainly the phenomenon of radio irregularity and finite onboard computation/energy resources. In Radio Frequency (RF) propagation, radio irregularity rises to a higher level in the presence of human activity due to the absorption effect of the human body. In this paper, the feasibility of monitoring RF transmission for the purpose of intrusion detection is investigated. With empirical data obtained from the Crossbow TelosB platform in several different environments, the impact of human activity on the signal strength of RF signals in a WSN is evaluated. This paper offers a novel approach to intrusion detection by turning a constraint in WSN, namely radio irregularity, into an advantage for the purpose of intrusion detection. Unlike most related work, the ¿intruders¿ neither transmit nor receive any RF signals. By enabling existing wireless infrastructures to serve as intrusion detectors instead of deploying numerous costly sensors, this approach shows great promise for providing novel solutions.

Energy consumption is an important issue in the design of wireless sensor networks which typically rely on nonrenewable energy sources like batteries for power. Recent advances in ambient energy harvesting technologies have made it a viable alternative source of energy for powering wireless sensor networks perpetually. In this paper, we optimize network performance by finding the optimal routing algorithm and relay node placement scheme for wireless sensor networks powered by ambient energy harvesting. We evaluate the performance of three different variants of geographic routing algorithms and consider two relay node placement schemes, viz. uniform string topology and a cluster string topology. The performance metrics are network throughput (T), goodput (G), source sending rate (SR), efficiency (eta), data delivery ratio (DR) and hop count (H). Simulation results obtained using the Qualnet simulator show that there is an optimal combination of routing algorithm and relay node placement scheme that maximizes the required performance metric. These results aim to provide insights into the impact of routing algorithms and relay node placement schemes on wireless sensor networks that rely solely on ambient energy harvesting for power.

In this paper, a hybrid network consisting of both WiMAX and WiFi links is set up and used as a testbed for Voice-over-IP (VoIP) performance studies. Relevant metrics are defined and used to analyse the performance of this hybrid network and its ability to support VoIP calls. End-to-end delay and packets loss are measured as a function of number of ongoing VoIP calls. Based on these measured results, we propose a procedure to evaluate the maximum number of ongoing calls can be supported by the hybrid network while maintain the call quality at the desired level using the ITU-T specified E-model. The experimental studies show that this hybrid network is capable of supporting real-time VoIP calls, and that the network is able to support up to 12 simultaneous G.711-based VoIP calls and more than 20 simultaneous G.729-based VoIP calls. While it remains a challenge to ascertain the exact reasons behind the low network utilization, the proposed procedure for determining the maximum number of VoIP calls which meet the QoS requirements will be useful and valuable to service providers with the intention to provide VoIP services over such hybrid networks that interoperate these two technologies in the near future.

Wireless sensor networks are expected to be deployed in harsh environments characterised by extremely poor and fluctuating channel conditions. With the commonly adopted single-sink architecture, such conditions are exemplified by contention near the sink as a result of multipath delivery. This may be reduced by deploying multiple sinks spatially- apart e.g., along the edges of the network such that multiple spatially diverse paths that diverge like a starburst from each node towards these sinks can be set-up. Such an architecture opens up new challenges to the data delivery scheme, which determines the performance of the network. Since the compactness of sensors with limited energy resources restrict the use of sophisticated mechanisms, we consider simple data delivery schemes suited for such a multi-sink architecture. We optimise a single-path data delivery scheme with simple ARQ for a spatially-invariant environment, and demonstrate that its optimality over a spatially-diverse multipath scheme extends to spatially-variant environments. We also verify our analysis with simulations obtained using the Qualnet simulator.

Traditional networks are built on the assumption that network entities cooperate based on a mandatory network communication semantic to achieve desirable qualities such as efficiency and scalability. Over the years, this assumption has been eroded by the emergence of users that alter network behavior in a way to benefit themselves at the expense of others. At one extreme, a malicious user/node may eavesdrop on sensitive data or deliberately inject packets into the network to disrupt network operations. The solution to this generally lies in encryption and authentication. In contrast, a rational node acts only to achieve an outcome that he desires most. In such a case, cooperation is still achievable if the outcome is to the best interest of the node. The node misbehaviour problem would be more pronounced in multihop wireless networks like mobile ad hoc and sensor networks, which are typically made up of wireless battery-powered devices that must cooperate to forward packets for one another. But, cooperation may be hard to maintain as it consumes scarce resources such as bandwidth, computational power and battery power. This paper applies game theory to achieve collusive networking behavior in such network environments. In this work, pricing, promiscuous listening and mass punishments are avoided altogether. Our model builds on recent work in the field of Economics on the theory of imperfect private monitoring for the dynamic Bertrand oligopoly, and adapts it to the wireless multihop network. The model derives conditions for collusive packet forwarding, truthful routing broadcasts and packet acknowledgments under a lossy, wireless, multi-hop environment, thus capturing many important characteristics of the network layer and link layer in one integrated analysis that has not been achieved previously. Finally, we provide a proof of the viability of the model under a theoretical wireless environment.

We research into using Mobile Sensor Networks to harvest physical quantities that emanate from sources and are distributed in space in hazardous environments. Examples are temperature, toxic emissions and pollutions. Mobile sensors are more advantageous than static sensors in many ways such as easy re-deployment and environmental friendliness. However, they are usually deployed at low node densities with equally spaced nodes. As a result, the reconstructed distribution maps are highly distorted. Our approach attacks the problem from the source, by mobilizing the sensors to harvest data with high information content cooperatively and intelligently. As a result, we achieve 3.6 times in error reduction relative to an equally distributed grid. Moreover, in comparison with the Random Waypoint and Broyden-Fletcher-Goldfarb-Shanno methods, we achieve at least 60% more reduction in energy usage by consuming 70% lesser energy. Finally, our approach has a resource utilization efficiency of 50 times that of static sensors.

One of the major challenges for the deployment of underwater acoustic sensor networks (UASN) is the design of a suitable medium access control (MAC) protocol, and CDMA has been earmarked as the most promising candidate. While several works have considered the problems of allocation of code and transmission power separately, in this paper, we propose a receiver-centric interference management approach for joint code/power assignment for MAC in CDMA UASNs. Through extensive numerical simulations, we illustrate its efficacy and demonstrate its superiority to conventional transmitter-centric approaches, for both fixed-power code assignments and joint code/power assignments.

During times of calamity, such as an earthquake or tsunami, rescue and recovery efforts are usually hampered by communications failure as the incumbent communications infrastructure has most likely been damaged or destroyed during the disaster. An ad-hoc communications infrastructure, with support for multimedia traffic such as Voice over IP and video- streaming, must be quickly put in place to support the command, control and communication needs of the rescue and recovery operations. Such applications require relatively fast and robust communications links, and broadband wireless technologies appear to be a viable solution. This paper examines two hierarchical network solutions which allow the delivery of such mission-critical multimedia data between rescue teams and their headquarters over extremely long distances using a combination of wireless network technologies (namely, WiFi, WiMax and GEO Satellite) and multimedia software applications (in particular, Voice-over-IP) to meet the requirements of disaster rescue communication scenarios. The proposed system has been validated experimentally in the field as well as using simulations to demonstrate the scalability of the design.

Underwater sensor networks are typically distributed in nature and the nodes communicate using acoustic waves over a wireless medium. Such networks are characterized by long and variable propagation delays, intermittent connectivity, limited bandwidth and low bit rates. Due to the wireless mode of communication between the sensor nodes, a medium access control (MAC) protocol is required to coordinate access to the shared channel and enable efficient data communication. However, conventional terrestrial wireless network protocols that are based on RF technologies cannot be used underwater. In this paper, we propose PLAN - a MAC Protocol for Long-latency Access Networks that is designed for use in half-duplex underwater acoustic sensor networks. We utilize CDMA as the underlying multiple access technique, due to its resilience to multi-path and Doppler's effects prevalent in underwater environments, coupled with an RTS-CTS handshaking procedure prior to the actual data transmission. Using simulations, we study the performance and efficiency of the proposed MAC protocol in underwater acoustic networks.

Wireless ad-hoc networks is the key technology for community networks and rural regions that lack an established networking infrastructure. On the applications front, IP Telephony is also a killer application among the various with the sought after auto-configuration feature of ad-hoc multimedia services. It is therefore natural that wireless ad-hoc networks are expected to provide IP Telephony services. In this paper, we define strategies for constructing a P2P service control overlay to support IP Telephony services in wireless ad-hoc networks with no mobility, and validate the efficiency of our approach via simulations. This service control overlay provides a critical component to the distributed indexing to provide the required service to all deployment of various session-based multimedia applications in wireless ad-hoc networks.

IP Telephony is a potential killer application among the various multimedia applications and services. It is therefore natural to expect support for these services over new network architectures like wireless ad-hoc networks. Despite the proliferation of IP Telephony services in the Internet, the traditional client/server models that have been used are found to be highly inefficient for wireless ad-hoc networks as compared to peer-to-peer (P2P) models. On the other hand, P2P strategies require some tuning to work well in wireless ad-hoc networks. In this paper, we discuss some undesirable situation that may happen if P2P systems are deployed over wireless ad-hoc networks without adaptation. We then define our strategies for positioning Super Nodes in the physical network underlay as well as P2P ID space according to the constraints of these network technologies. We evaluate the efficiency of our approche in reducing the session establishment time and request failure rate as two important criteria for the performance of IP telephony systems

In wireless sensor networks, accurate and complete sensor data is important to the analysis of a phenomenon. Therefore, designing a robust sensor network is of high importance. We first derive the relationships between transmission power, energy consumption and robustness of the network using analytical modeling. Then, we validate our model by using linear programming to find the optimal routing algorithm that will optimize the robustness of the sensor network. The optimal robust routing algorithm is then compared with other popular routing algorithms used in sensor networks and we find that the robustness achieved varies with different routing algorithms. This demonstrates the need for a cross-layer design approach when designing robust sensor networks. In addition, we also prove that an energy-efficient routing algorithm that optimizes the lifetime of the sensor network does not optimize the robustness of the network. By using correlation analysis on the optimal routing algorithm, we design a robust geographic routing algorithm that only utilizes local neighborhood information to obtain nearly optimal robustness performance. Our algorithm also minimizes data losses in cases when sensor nodes fail.

For large wireless sensor networks, identifying the exact location of every sensor may not be feasible or necessary. A coarse estimate of the sensors' locations is usually sufficient for many applications. An efficient area localization scheme (ALS) has been proposed for large sensor networks. ALS is a range-free localization scheme that tries to estimate the position of a sensor within a certain area rather than its exact location. As the complex calculations are handled by the powerful sinks instead of the sensors, this reduces the energy consumed by the sensors and helps to extend the lifetime of the network. In this paper, we discuss the implementation of ALS using MICAz motes and the localization experiments carried out in both indoor and outdoor environments. We also propose algorithms and techniques to improve the accuracy of ALS in realistic deployment scenarios. We observed that the performance of ALS is comparable or better than other localization schemes that have been implemented while ALS has lower complexity and hardware requirements.

A cross layer approach is important for designing multi-hop wireless network routing protocols. In this paper, we maximize the path reliability by proper scheduling of transmissions at link layer over different paths. In our scheme, each node involved in the routing maintains two paths towards the destination. When forwarding a packet, the transmissions at link layer are optimally scheduled by considering the instantaneous channel reliability, the end-to-end reliability at the parent nodes and the remaining number of transmissions at this hop. We convert the decision process of the transmission scheduling into a Markov decision model and derive the optimal policy using backward induction algorithm. Results show that the end-to-end reliability can be improved significantly with the optimal transmission schedule.

Traffic patterns for mobile communication networks are well known and accurately modelled. However, traffic generation behaviors for sensor networks are often lacking in research and not well understood. We investigate one class of sensor networks for a target tracking application using realistic sensing models and a general target motion model. Results show that the probability of arrivals for a tracking sensor network fits a quadratic curve.

Sensor networks deployed for the purpose of target tracking and intrusion detection have very specific requirements in terms of sensing coverage, timeliness of data and network lifetime. Many existing work focus on wakeup schemes that only attempt to prolong network usage but failed to address issues such as coverage, timeliness of data delivery, target tracking continuity and deployment cost all at the same time. Given any maximum target speed, our solution based on a combinatorics approach, guarantees delay bounds and node lifetime bounds, critical for any practical deployment of a target tracking application. We support our solution further with simulation results.

Abstract Asynchronous half‐duplex Direct‐Sequence Code‐Division Multiple‐Access (DS/CDMA) is a suitable candidate for the MAC protocol design of underwater acoustic (UWA) sensor networks owing to its many attractive features. Our ad‐hoc multi‐hop network is infrastructureless in that it is without centralized base stations or power control. Hence, we develop an asynchronous distributed half‐duplex control protocol to regulate between the transmitting and receiving phases of transmissions. Furthermore, multi‐hop communications are very sensitive to the time variability of the received signal strength in the fading channel and the ambient noise dominated by snapping shrimp in harsh underwater environments, because a broken link in the multi‐hop path is enough to disrupt communications and initiate new route searches. In our configuration, we use the Ad hoc On‐demand Distance Vector (AODV) routing protocol optimized for UWA networks. Empirical studies show that we can model the channel as a slow‐varying frequency non‐selective Rayleigh fading channel. We theoretically analyze the throughput of our configuration by considering three salient features: the ability of the receiver to demodulate the data, the effect of our control protocol and the effect of disconnections on the generation of routing packets. The throughput under various operating conditions is then examined. It is observed that at optimal node separation, the throughput is improved by a factor of 10. Copyright © 2007 John Wiley & Sons, Ltd.

Asynchronous half-duplex direct-sequence code-division multiple-access (DS/CDMA) is a suitable candidate for the MAC protocol design of underwater acoustic (UWA) multi-hop sensor networks due to its many attractive features. However, multi-hop communications is very sensitive to the time variability of fading and the snapping shrimp dominated ambient noise in the harsh underwater environment because a broken link in the multi-hop path is enough to disrupt the communications and initiate new route searches. We analyze the theoretical MAC throughput of the network using the ad hoc on-demand distance vector (AODV) routing protocol with Poisson distributed, constant length packet arrivals at the application layer. We consider horizontal communications of sensors operating in shallow water near coastal regions. Previous empirical studies show that we can model the UWA channel as a slow-varying Rayleigh fading and frequency non-selective channel. The optimal throughput performance of the network under various operating conditions is then examined.

This paper presents an analysis of the control overhead involved in clustering and routing for one-hop clustered mobile ad hoc networks. Previous work on the analysis of control overhead incurred by clustering algorithms focused mainly on the derivation of control overhead in the Knuth big-O notation with respect to network size. However, we observe that the control overhead in a clustered network is closely related to different network parameters, e.g. node mobility, node transmission range, network size, and network density. We present an analysis that captures the effects of different network parameters on the control overhead. The results of our work can provide valuable insights into the amount of overhead that clustering algorithms may incur in different network environments. This facilitates the design of efficient clustering algorithms in order to minimize the control overhead.

This paper aims at performance study of wireless mesh networks (WMNs). It proposes a realistic approach for experiments and simulations on VoIP capacity in 802.11 chain and lattice mesh topologies. Further, it suggests using a relaxed requirement in order to increase VoIP capacity while achieving acceptable QoS. The study aims to preempt any congestion and degradation of network services which will have an adverse effect on the users

Iterative multilateration techniques have been proposed to improve position estimates in localization schemes for sensor networks. However, these techniques are hampered by problems such as propagation of errors, which results in inferior estimates, and high communication overheads leading to poor scalability. In view of this, a novel Selective Iterative Multilateration (SIM) algorithm is described in this paper to improve the accuracy of location estimation in hop count-based localization schemes without incurring unnecessary overhead costs. New anchor nodes are selected judiciously such that their initial position estimates are sufficiently accurate. Also, such new anchor nodes are prevented from appearing in the same regions so that unnecessary overhead is kept to a minimum.

Research on wireless sensor networks has often assumed that nodes are homogeneous. In reality, even homogeneous sensors have different capabilities like different levels of initial energy, drain/depletion rate, etc. This leads to the research on heterogeneous networks where at two or more types of nodes are considered with the network divided into clusters where the more powerful sensors act as cluster-heads and handle resource-demanding tasks. By assuming that the more powerful nodes have much more energy as compared to the other normal nodes, it can lead to the network becoming partitioned when the other nodes deplete their energy; the entire network essentially becomes useless thereafter. A key design implication is that the energy supply built into the powerful nodes need not exceed a threshold, as beyond which, the extra provisioning is redundant. In this paper, we consider an event-detection heterogeneous network with two types of nodes, primarily with different maximum (startup) power levels. A multihop clustering topology is assumed and we analyze the lifetime of the network with respect to the maximum size of the cluster, as well as, the ratio of power levels between the two types of nodes

To meet the needs of QoS requirement of multimedia traffic in wireless networks, IEEE 802.11e protocol has been proposed as an upcoming standard. The medium access control (MAC) layer of 802.11e has provided QoS support through priority scheduling for different types of traffic. However, the 802.11e QoS mechanism has an impact on the higher layers i.e. Network layer and create new issues that need to be considered seriously. For example, there exists a path correlation between different data types especially when they share a common link in the network layer. Under high traffic load, the correlation effect will cause many re-route procedures for paths which are shared by different data types as lower priority traffic is likely to be blocked. Such re-route procedure will downgrade the QoS service for the high priority traffic as the common link is broken for all traffic. To break the correlation effect, in this paper, we propose a cross-layer scheme called: Bundled Virtual Circuit to deal with the scheduling and routing in MANET. The key feature of BVC is taking in account of the influence of priority scheduling in MAC layer when designing routing scheme at network layer. The proposed scheme could provide better QoS service for MANET with 802.11e from simulation results.

Simulation is a critical tool for analyzing the performance of multihop routing protocols for mobile wireless networks, be it, autonomous mobile ad hoc networks or multihop extensions to infrastructure- based 3G/4G mobile networks. Node movement and network topology are critical factors determining the performance of routing protocols. Using unrealistic mobility models in simulations, e.g. random waypoint, result in performance results that do not reflect the actual deployment scenarios. In this paper, two typical features of rush hour traffic conditions are considered. Firstly, we focus on modeling high node traffic areas where there is a surge of traffic at specific times. Secondly, we study the choice of destinations which tend to be inversely proportional to the exponential of the distance of the destination from the origin since people tend to travel to locations that are nearer rather than further. We then show that protocols which typically perform well under the random waypoint model perform poorly in the more realistic Rush Hour model.

Simple algorithms have been developed in the area of node localization in a wireless sensor network. For these simple algorithms to work, several assumptions were made. However, the accuracy of such assumptions affects the effectiveness and performance of such algorithms. For example, the irregular propagation pattern of transmission range is often neglected in both simulation and analysis. This paper investigates the accuracy of these assumptions and proposes some modifications to improve the hop count localization algorithm in an actual physical implementation.

Traditional reinforcement learning algorithms learn based on discrete/finite states and actions, thus limit the learned behaviors to discrete/finite space. To address this problem, this paper introduces a distributed reinforcement learning controller that integrates reinforcement learning with behavior based control networks. This learning controller can enable the robot to generate appropriate control policy which combines different elementary behaviors. In addition, to address the problems in concurrent learning, a distributed learning control algorithm is proposed to coordinate concurrent learning processes. The distributed reinforcement learning controller and learning control algorithm are applied to multi-robot tracking of multiple moving targets. The efficacy is demonstrated by simulations.

Localization schemes using hop-counts to reference nodes of known positions have been proposed to localize nodes in a sensor network. However, these schemes usually work well only when the networks have uniform and dense node distribution. A novel density-aware hop-count localization (DHL) algorithm is described to improve the accuracy of location estimation when the node distribution is non-uniform. When the density is low, each hop traversed is not necessarily equivalent to the maximum range distance. A range ratio is used to adjust the accumulated hop-count and this effectively reduces distance overestimation. Simulations show that our algorithm improves the localization accuracy compared to the algorithms without density considerations. The overhead incurred by DHL is found to be lower than that incurred by conventional schemes.

We present a modeling framework for studying header compression performance. For the first time, a source model is developed for header compression. By varying the source and channel models in our framework, different source and deployment scenarios can be modeled. Using this new paradigm, we study the tradeoff dependencies between performance metrics. We also demonstrate how a scheme can be designed to optimize tradeoffs based on the desired level of performance.

A mobile ad hoc network is an autonomous system of infrastructure-less, multihop, wireless mobile nodes. Reactive routing protocols perform well in this environment due to their ability to cope quickly against topological changes. This paper proposes a new routing protocol named CHAMP (caching and multiple path) routing protocol. CHAMP uses cooperative packet caching and shortest multipath routing to reduce packet loss due to frequent route failures. We show through extensive simulation results that these two techniques yield significant improvement in terms of packet delivery, end-to-end delay and routing overhead. We also show that existing protocol optimizations employed to reduce packet loss due to frequent route failures, namely local repair in AODV and packet salvaging in DSR, are not effective at high mobility rates and high network traffic.

Context replication is a new context initialization technique for header compression proposed by the IETF. It increases header compression gains by reducing the size of bulky initialization packets. Context replication compresses header fields based on the redundancy between flows, rather than within flows. For context replication to be performed efficiently, an elaborate understanding of inter-flow field behavior is required. However, inter-flow field behavior is more complicated and relatively unstudied compared to well-known intra-flow field behavior. Also, because context replication is a new technique, much is still unknown about the range of efficiency gains achievable. We present a novel approach to obtain and analyze inter-flow field behavior. Our approach allows the optimization of encoding specifications in context replication. By implementation, we show that our optimized encodings produce leaner initialization packets compared to current proposals. We also obtain from implementation useful insights on the range of compressed header sizes and efficiency gains in context replication.

A priority-based multi-path routing protocol (PRIMP) is proposed for wireless sensor networks to offer extended network lifetime and robust network fault tolerance. Extensive simulations validate that PRIMP exhibits significantly better performance in energy conservation, load-balancing and data delivery than comparable schemes. Moreover, PRIMP addresses the slow startup issue occurring in data-centric routing schemes.

This paper presents a mobility-based d-hop clustering algorithm (MobDHop), which forms variable-diameter clusters based on node mobility pattern in MANETs. We introduce a new metric to measure the variation of distance between nodes over time in order to estimate the relative mobility of two nodes. We also estimate the stability of clusters based on relative mobility of cluster members. Unlike other clustering algorithms, the diameter of clusters is not restricted to two hops. Instead, the diameter of clusters is flexible and determined by the stability of clusters. Nodes which have similar moving pattern are grouped into one cluster. The simulation results show that MobDHop has stable performance in randomly generated scenarios. It forms lesser clusters than Lowest-ID and MOBIC algorithm in the same scenario. In conclusion, MobDHop can be used to provide an underlying hierarchical routing structure to address the scalability of routing protocol in large MANETs.

In this paper, we propose an analytical model for the RTP profile of robust header compression (ROHC) [C. Bormann et al., July 2001]. The model assumes that the RTP compressor and decompressor work in the unidirectional mode (U-Mode). It includes the features such as W-LSB encoding scheme, state-transition, compression slow-start and periodic context refreshment etc. It has been validated with NS-2 [NS-2 network simulator]. The model explains clearly how the robustness is enhanced by the W-LSB encoding scheme. It can also be used to analyze the performance of the header compression in the different wireless network.

A mobile ad hoc network is an autonomous system of infrastructureless, multihop wireless mobile nodes. Reactive routing protocols perform well in such an environment due to their ability to cope quickly against topological changes. In this paper, we propose a new routing protocol called Caching and Multipath (CHAMP) Routing Protocol. CHAMP uses cooperative packet caching and shortest multipath routing to reduce packet loss due to frequent route breakdowns. Simulation results reveal that by using a five-packet data cache, CHAMP exhibits excellent improvement in packet delivery, outperforming AODV and DSR by at most 30% in stressful scenarios. Furthermore, end-to-end delay is significantly reduced while routing overhead is lower at high mobility rates.

In scenarios where wireless ad hoc networks are deployed, sometimes it would be desirable that ad hoc nodes can communicate with servers in wired networks to upload or download data. In these cases TCP connections will span both wireless ad hoc and wired domains. However, TCP often faces severe unfairness in this type of connection scenario, which forces some TCP flows to completely stop transferring any data despite all links being in good states. In this paper, we propose a simple scheduling scheme, which helps competing TCP connections to achieve fairness without much throughput loss. Simulation results show that our scheme successfully eliminates the extreme unfairness existing in above-mentioned scenarios.

A relative bandwidth service differentiation scheme is proposed for mobile ad-hoc networks (MANETs). The service profile for a traffic session is defined as a relative target rate, which is a fraction of the effective link capacity of nodes. To calculate the effective link capacity of nodes in a randomly moving topology MANET, two methods are presented: one is parameter based and the other is measurement based calculations work effectively to estimate the effective link capacity of nodes and the relative service differentiation is consistent. Furthermore, the differentiation is more consistent when nodes use the measurement based calculation of link capacity because it is more accurate.

This paper investigates quality-of-service (QoS) performance of voice services in hybrid fiber/coax (HFC) networks. We focus on the upstream channel and the performance of the system is evaluated in terms of voice packet loss probability. Two different classes of voice services are considered. For voice without speech activity detector (SAD), there is no packet dropping and hence the analysis is trivial. For voice with SAD, we develop an elaborate discrete-time Markov chain model to obtain the voice packet dropping probability. We also describe the voice traffic models and the performance measures, and include an overview of the media access (MAC) protocol. Numerical examples are presented to demonstrate the performance the system.

The aim of service prioritization is to give resource priority to certain traffic types. For example, the voice service can have higher priority than the data service, Web surfing can have higher priority than FTP and e-mail applications, or some users can have higher priority than others. This paper investigates the performance of service prioritization in mobile ad-hoc networks (MANETs). Two schemes, priority buffer management and priority scheduling, are applied to realize service prioritization and three service profiles are discussed as well as simulation results are presented to show that the priority schemes work well with TCP traffic but not with constant bit rate (CBR) traffic. Besides this, the priority scheduling scheme outperforms the priority buffer management scheme in terms of average end-to-end delay of TCP packets and average throughput of TCP sessions.

Wireless networks have become increasingly common and an increasing number of devices are communicating with each other over lossy links. Unfortunately, TCP performs poorly over lossy links as it is unable to differentiate the loss due to packet corruption from that due to congestion. We present an extension to TCP which enables TCP to distinguish packet corruption from congestion in lossy environments resulting in improved performance. We refer to this extension as the HeAder ChecKsum option (HACK). We implemented our algorithm in the Linux kernel and performed various tests to determine its effectiveness. Our results have shown that HACK performs substantially better than both SACK and NewReno in cases where burst corruptions are frequent. We also found that HACK can co-exist very nicely with SACK and performs even better with SACK enabled.

An analytical model of a single TCP session based on a string topology in wireless multihop networks is presented. The model for the communication process also considers buffering, propagation delay and channel access probability. A Markov chain technique is adopted in analyzing the TCP behavior. Full analysis is given for the case where the TCP source and destination are one hop away and the maximum TCP window size is less than the buffer size in each node. Consideration is also given to the case when the maximum TCP window size is larger than the buffer size in each node. The analytical results are validated against results using the NS simulator.

This paper examines the attributes of TCP tunnels which are TCP circuits that carry IP packets and benefit from the congestion control mechanism of TCP/IP. The deployment of TCP tunnels reduces the many flows situation on the Internet to that of a few flows. TCP tunnels eliminate unnecessary packet loss in the core routers of the congested backbones which waste precious bandwidth leading to congestion collapse due to unresponsive UDP flows. We also highlight that the use of TCP tunnels can, in principle, help prevent certain forms of congestion collapse described by Floyd & Fall (see IEEE/ACM Transactions on Networking, vol.7, no.4, p.458-72, 1999). Using a testbed often Intel PCs running the Linux operating system and traffic generators simulating user applications, we explore: the benefits which TCP tunnels confer upon its payload of user IP traffic; the impact on the congestion within network backbones, and the protection that tunnels offer with respect to the various competing classes of traffic in terms of bandwidth allocation and reduced retransmissions. The deployment of TCP tunnels on the Internet and the issues involved are also discussed and we conclude that with the RFC2309 recommendation of using random early drop (RED) as the default packet-drop policy in Internet routers, coupled with the implementation of a pure tunnel environment on backbone networks makes the deployment of TCP tunnels a feasible endeavour worthy of further investigation.

The authors study the performance of an adaptive token-passing bus protocol where only active stations participate in the token passing, and therefore make up the logical ring. A station is active over an interval of time if during that interval data are generated at the station to be sent over the network. The size of the logical ring thus varies randomly according to the traffic intensity of the system. Existing analytical results for token-passing systems assume a fixed number of stations, which will take part in the token passing regardless of whether a station is active or not. An approximate analytical model is provided for a token-passing system where the effective size of the ring changes dynamically according to the network traffic.>